This review highlights research behind smartphone and video camera methods for measuring BP. These methods may in future be used at home or in clinics, but must be tested over a larger range of BP and lighting conditions. The review concludes with a discussion of the advantages of the various techniques, their potential clinical applications, and future directions and challenges. Video cameras may potentially measure multiple cardiovascular metrics including and beyond BP, reducing the risk of cardiovascular disease.Regular blood pressure (BP) monitoring enables earlier detection of hypertension and reduces cardiovascular disease. Cuff-based BP measurements require equipment that is inconvenient for some individuals and deters regular home-based monitoring. Since smartphones contain sensors such as video cameras that detect arterial pulsations, they could also be used to assess cardiovascular health. Researchers have developed a variety of image processing and machine learning techniques for predicting BP  Blood pressure (BP) measurement is necessary in determining an individual's risk for cardiovascular disease and the need for early treatment. Early detection and treatment of BP may delay or prevent conditions related to high BP, such as stroke. This is particularly important in the Covid era, where there has been an increase in the number of virtual consultations with patients . DigitalMethods for measuring BP at home or in the clinic are commonly cuff-based. Cuff-based systems are automated; however, they present difficulty in portability outside the home. Many individuals find application of the cuff awkward, inconvenient, and uncomfortable. This limits the number of daily BP measurements. Since BP varies according to time, season, amount of sleep, and activity, a single measurement over the course of a day, or every few days, does not provide an accurate assessment of cardiovascular changes and BP variation in an individual .Smartphones could serve as alternatives to the cuff. Many individuals possess smartphones and operate their features with ease. Phones are embedded with cameras, microphones, light emitters, and force sensors that can be used to obtain a cardiovascular pulse signal, and ultimately predict BP. Due to their size, they overcome issues of portability, discomfort, or inconvenience.Methods that utilize video cameras to predict BP are continually undergoing research and development for improved accuracy. Most smartphone techniques utilize the video camera to extract the photoplethysmography (PPG) signal from light reflected from the skin. Due to complexity in relating the PPG signal to BP, methodologies have been developed to determine BP from video PPG. This includes image processing to extract blood flow, machine learning algorithms for calculating BP, and incorporation of smartphone features such as the microphone or force sensors. Mathematical models may be applied to separate hemoglobin signals from melanin and light, and the shape of the pulse or different arrival times of the pulse used to predict BP .This paper focuses on currently published video camera and smartphone methods for BP measurement, highlighting research efforts and experiments from a variety of groups. The two categories of smartphone/video-camera BP measurements, contact and non-contact, are covered. Present use of these techniques is discussed, together with their clinical applicability. We conclude by discussing the role of video cameras in health and BP monitoring. This paper provides an in-depth review of BP-video camera measurement technologies, including their accuracy, image processing methodologies, and machine learning algorithms used for predicting BP. This will provide a resource for researchers in this field to compare the advantages/disadvantages and technical details of various published methods.Video cameras and smartphones could measure BP non-invasively. Through simultaneously measuring additional cardiovascular properties such as heart rate or blood oxygenation, video camera technologies may provide continuous monitoring of multiple cardiovascular properties from an individual's home.Contact methods typically involve pressing the fingertip against the rear camera to acquire a PPG signal. The theory of reflection mode PPG in video cameras is initially discussed. This is followed by a description of the three categories of contact-based BP measurements see : (1) OscThe heart generates pulsatile flow, causing blood vessels in the skin to expand and contract. Light absorption by hemoglobin in the blood is maximized when the vessel is fully expanded during systole and minimized during diastole. In reflection mode PPG, light reflected from the skin is detected by a sensor or camera. The PPG waveform has an oscillating “AC” component largely due to arterial pulsation, which is superimposed on a DC component corresponding to fat and blood volume.via video camera methods. This is likely due to green light possessing higher absorption by hemoglobin than red, while penetrating deeper into tissue than blue , only one sensor is needed, and it is less sensitive to motion artifacts because the finger is pressed against the camera. This also produces a stronger signal than with non-contact video PPG methods (section Non-contact Video Camera Measurement of Blood Pressure).via pulse oximeter  have potential for predicting BP and diagnosing conditions such as hypertension.PPG waveform analysis faces challenges, several of which are outlined in . BrieflyLamonaca et al.  trained Visvanathan et al.  analyzedTo reduce noise, Banerjee et al.  approximGao et al.  applied Datta et al.  analyzedDey et al.  incorporThese studies used feature extraction to predict BP, which may be influenced by sensor and signal quality, and vary between studies. It is possible to eliminate feature extraction, as demonstrated by Baek et al. . In thisLarge-scale studies in patients with a range of BP values are necessary to assess the accuracy of an app or technique, since inaccuracy may be induced at higher or lower BP ranges. This could be attributable to utilizing training data largely from normotensive populations, healthy/younger individuals, or using invasive BP measures (instead of cuffs) as reference data . The PreWaveform analysis extracts multiple pulse features, inputting them into machine learning algorithms to calculate BP. Prediction accuracy is dependent on selection of correct features based on the waveform shape, which may depend on characteristics of the sensor. Consequently, algorithms developed on one smartphone may not be transferable to a different phone, since each uses a different set of sensors. Algorithms may require further development to automatically detect subtle features in the pulses relatable to pressure, along with acquisition of large datasets for machine learning. These techniques are often limited to the fingertip (one region), whereas pulse information from multiple regions could increase accuracy of BP estimation algorithms .PTT overcomes some obstacles of waveform analysis, requiring only measurement of relative arrival times of two pulses at different points in the body. PTT is inversely proportional to pulse wave velocity, which increases with BP. It is used as an indirect measure of BP, with a reduced PTT indicating elevated pressure.PTT and pulse arrival time (PAT) are often used interchangeably. Technically, PTT is the time difference between two points in the PPG waveforms measured at different arterial sites. PAT represents the time difference between the R-peak of the electrocardiogram and a characteristic point in the PPG waveform, such as the foot. Since PTT is not in pressure units, methods using PTT to estimate pressure require calibration to relate the quantities. Accuracy of PTT-based BP measurements therefore depends on calibration quality, possibly requiring recalibration after several months. Calibration for each individual is performed by acquiring multiple pressure and PTT measurements and performing regression analysis to relate the two quantities. Multiple pressure and PTT measurements may be acquired through pressure perturbations such as exercise or changing posture .via smooth muscle and negligible viscous effects, which induce PTT variations without affecting blood pressure; and minimal changes to arterial elasticity in response to disease or aging . This was demonstrated by Jeong and Finkelstein,  who founBH) between minimum values in a band-pass filtered waveform from video of the palm, and the raw waveform from the palm. They demonstrated smoothing the PPG waveform causes a phase change indicating heart-rate variability, with TBH indicating the degree of distortion of the PPG waveform. TBH showed a similarly strong correlation with SP as the difference between arrival times of waveforms in the palm and forehead. This suggests TBH has similar accuracy as PTT in calculating BP, but should be more applicable to situations where body movement occurs, such as exercise.A disadvantage to requiring two pulse measurement locations, as in Jeong and Finkelstein , is PTT Single-channel  data contains hemoglobin information. The overall channel signal, however, is affected by melanin content (skin tone), lighting, and subject movement. This may be controlled in experimental situations through consistent and bright lighting, and limiting subject movement. In “real world” environments, there are a variety of skin tones, background light, and subject motion. In these situations, analysis of raw or filtered green channel data risks acquisition of a distorted or altered waveform not accounted for in algorithms relating waveform shape to BP. As detailed below, video frames may be processed using machine learning or mathematical algorithms to extract hemoglobin-dependent features of the signal that are then used to estimate BP.A study by Luo et al.  with oveTo calculate BP, transdermal blood flow data was acquired from 17 facial regions with an Apple iPhone 6. The subject was seated, with back straight and feet on the ground. Data acquisition occurred over 2-min. One hundred and twenty six features were extracted from the videos relating to pulse characteristics, such as shape, amplitude, and heart rate. An additional 29 “meta features” were selected to normalize for variation in imaging conditions across the three channels, and to account for ambient room temperature and demographic characteristics. Principal component analysis (PCA)  reduced Adachi et al.  developeFukunishi et al.  applied The CHROM model reduces the effect of motion and light reflected from the skin that does not possess a pulsatile or blood flow component. Fan et al.  and ChenIndependent component analysis (ICA) assumes a signal is a linear mixture of underlying sources and mathematically extracts them . Poh et In a study of three healthy individuals, Secerbegovic et al.  applied Most of the video signal is not attributed to blood flow fluctuations. PCA  calculatThis review highlighted smartphone and video camera techniques for measuring BP. Wearables, such as watches or similar devices , 77, areSmartphone BP monitors are potentially applicable to ambulatory BP monitoring (examining BP continuously during the day). Measurements in a doctor's office are affected by “white coat” syndrome, where patients are recorded as possessing a higher BP recording when measured clinically. Since BP varies throughout the day, improved understanding of how and why these changes occur could assist physicians in prescribing medication. Smartphones may measure pressure easily, with no additional equipment required beyond the phone or case. In contrast, cuffs may be awkward and bulky, inducing arm soreness or rashes after multiple daily uses . AlthougContact methods have higher signal-to-noise-ratio due to the proximity of skin to the sensor and usage of light beyond ambient light . Additional features in the pulse wave are distinguishable in contact vs. non-contact methods, such as secondary peaks. In using machine learning algorithms to relate waveform shape to BP, these additional features are helpful in increasing the accuracy of the prediction as they may be affected by BP. Contact methods are also less dependent on motion since the finger remains pressed against the camera.Non-contact methods possess other advantages. Contact methods are influenced by the force with which the finger is pressed against the camera. This could vary between individuals, and between trials conducted in the same individual. For waveform analysis, data from only a single region (fingertip) is acquired. Non-contact methods acquire images of multiple regions simultaneously. This enables simultaneous analysis of waveform shape and PTT. Different regions of the body could be affected differently by the sympathetic and parasympathetic nervous system, which is not accounted for through analysis of a single region. Contact methods that incorporate PTT may require additional sensors such as the microphone which produce a weak signal, or a case containing multiple sensors such as an electrocardiogram and PPG , 80. OscSeveral published methods possess a mean error ± standard deviation within the clinically acceptable 5 ± 8 mmHg. This is not necessarily translatable to the clinic, as indicated in the trial of the AuraLife app . Small sMany studies do not meet criteria for validating BP devices. In addition to the AAMI criteria of MAE 5 ± 8 mmHg, several other criteria are listed , such asTOI possesses advantages to other techniques for clinical translation. The sample size of Luo et al.  is over Luo et al.  acquiredVideo-camera processing techniques other than TOI may be successful if applied to a larger population, or through optimized analysis of PPG waveform features. For example, Adachi et al.  only useFuture experiments may forego traditional image processing techniques. Chen and McDuff  developeAdditional possibilities are outlined in . This inThis paper highlighted and compared the variety of methods available for measuring BP with smartphones/video cameras. It emphasizes variations in experimental design and the relevancy of these variations in developing methods for BP measurement that are efficient, easy to use, and non-invasive. This permits regular BP monitoring, contributing to early hypertension or cardiovascular disease risk detection. Possibilities for successful BP monitoring was demonstrated in early studies, such as Lamonaca et al. , that usSmartphone and video BP measurements will likely become more common. Compared to cuff-based techniques, they are cost-effective and convenient. Using a single video, BP may be combined with heart rate detection and stress assessment , blood ovia algorithms whether further treatment is necessary. Ambulatory BP monitoring  may detect abnormal variations in BP not detectable with a single BP measurement session at a doctor's office. Furthermore, ambulatory BP has been demonstrated to correlate more strongly with organ damage caused by hypertension than BP measurements conducted in a clinical setting  and relate this information to stroke risk or susceptibility to cardiovascular disease. This information would be provided in real-time and reduce the need for manual measurements by a medical professional. In addition, through continuous and regular daily monitoring of their own vitals privately with a smartphone, patients can be alerted  setting . Data acmoglobin . Video tOverall, smartphones and video cameras will provide a more complete and earlier assessment of cardiovascular physiology, helping to prevent stroke and blood vessel-related disorders.via video camera. This was achieved through an imaging technology, transdermal optical imaging (TOI), that uses machine learning to extract a cardiovascular pulse signal from facial videos. As discussed in our review, there are numerous additional smartphone technologies beyond TOI that can also be used. Our review compares the different techniques and their technical aspects such as image processing and data analysis. The review concludes with the future of video camera blood pressure measurement, and how it can be combined with measurement of other metrics for a more complete assessment of cardiovascular health.This review discusses video camera methods for measuring blood pressure. During Covid, there has been an increase in the number of virtual consultations with patients. Thus, there is increased interest in developing technologies that will allow patients to monitor vital signs from home. This prevents unnecessary additional trips to doctors, provides daily information on changes in cardiovascular health, and may help detect signs leading to stroke or disease. In 2019, our lab published a paper in Circulation: Cardiovascular Imaging: “Smartphone-based blood pressure measurement using transdermal optical imaging technology” by Luo et al. In a study of over 1300 subjects, we demonstrated accurate blood pressure prediction All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.This work was supported by Natural Sciences and Engineering Research Council of Canada to Z-PF (RGPIN-2014-0671).KL reports a patent, system, and method for contactless blood pressure determination , licensed to Nuralogix Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Symptom constructs included in the Evaluating Respiratory Symptoms in Chronic Obstructive Pulmonary Disease (E-RS®: COPD) tool may be relevant to patients with asthma. The purpose of this study was to evaluate content validity and psychometric performance of the E-RS: COPD in moderate/severe asthma patients.Content validity of the E-RS: COPD was evaluated in patients with moderate/severe asthma using concept elicitation and cognitive debriefing interviews. Secondary analyses using data from two clinical trials in patients with moderate/severe asthma evaluated the factor structure of the E-RS: COPD plus two supplementary items  and assessed psychometric properties of the tool, which will be referred to as E-RS®: Asthma when used in asthma populations.N = 25) achieved concept saturation for asthma respiratory symptoms. Concepts in the E-RS: COPD were relevant to patients and instructions were understood. Most patients  reported experiencing all concepts in the E-RS: COPD; no patients indicated missing symptoms. Secondary analyses of clinical trial data supported the original factor structure . The two supplemental items did not fit with this factor structure and were not retained. RS-Total and subscale score reliability was high . Validity was demonstrated through significant (P < 0.0001) relationships with the St George’s Respiratory Questionnaire (SGRQ) and Asthma Symptom Severity scale. E-RS: Asthma was responsive to change when evaluated using SGRQ, Patient Global Impression of Change and Asthma Quality of Life Questionnaire as anchors (P < 0.0001). Clinically meaningful change thresholds were also identified .Qualitative interviews (The E-RS: Asthma is reliable and responsive for evaluating respiratory symptoms in patients with moderate/severe asthma.The online version contains supplementary material available at 10.1186/s41687-021-00338-6. Asthma is a chronic inflammatory airway disease characterized by respiratory symptoms such as wheeze, shortness of breath (SOB), chest tightness, and cough , which iThe Evaluating Respiratory Symptoms in Chronic Obstructive Pulmonary Disease (E-RS®: COPD) measure is an 11-item patient-reported diary derived from the 14-item The EXAcerbations of Chronic Pulmonary Disease Tool (EXACT®) . The EXALiterature reviews , 8 and qThe current analysis comprised a two-phase approach utilizing both qualitative and quantitative data. First, qualitative interviews were conducted to determine the suitability and content validity of the E-RS: COPD in a moderate/severe asthma population. Second, data from two clinical studies in moderate/severe asthma patients , 12 wereClinicalTrials.gov: NCT03344406) involving one-on-one semi-structured concept elicitation and cognitive debriefing telephone interviews was conducted between December 2017 and September 2018 with patients attending five US asthma and allergy clinics. Quorum Review Institutional Review Board (IRB) approval was obtained prior to initiation of the study (Quorum Review File # 32810). Interviews were conducted in English or Spanish language, lasted 60–90 min and were recorded and translated into English (if required). Eligibility criteria are summarized in Supplementary Table A cross-sectional, qualitative study . A coding dictionary was developed iteratively based on concept themes related to participants’ symptom experience. Words and phrases reported by interview participants were selected using the coding dictionary and grouped into key themes, attributes, concepts, and relationships. Saturation was defined as the point at which no major new themes, descriptions of a concept, or terms were introduced as subsequent interviews were conducted . ConceptClinicalTrials.gov: NCT03012061 [ClinicalTrials.gov: NCT02924688 [Utilizing data from two randomized controlled trials (RCTs)  therapy with the primary outcome of the study (clinic trough forced expiratory volume in 1 s [FEV1]) completed at Week 24.GSK 205832 was a Phase IIb, randomized, double-blind, placebo-controlled, three-arm, parallel-group study conducted between January 2017 and May 2018. The study evaluated the efficacy, safety, and tolerability of two doses of umeclidinium bromide (UMEC) administered once daily (QD) via the ELLIPTA dry powder inhaler (DPI) over 24 weeks in patients with moderate uncontrolled asthma receiving fluticasone furoate (FF) 100 mcg QD. GSK 205715 was a Phase IIIa, randomized, double-blind, active-controlled, six-arm, parallel-group study conducted between December 2016 and August 2018. This study compared the efficacy, safety and tolerability of four fixed-dose triple combinations of FF/UMEC/vilanterol (VI) with two fixed-dose dual combinations of FF/VI, administered QD via the ELLIPTA DPI for 24–52 weeks in patients with moderate/severe asthma uncontrolled on inhaled corticosteroids (ICS) ± a long-acting β1 of ≥12% and ≥ 200 mL following salbutamol inhalation), and evidence of airflow obstruction at screening, although this differed between studies at screening . In 205715, patients were required to meet additional entry criteria at the end of the 3-week run-in period before entering the 2-week stabilization period and at the end of the stabilization period prior to randomization  in both RCTs is summarized in Supplementary Table The E-RS: COPD consists of 11 items that measure respiratory symptoms recalled by patients over the previous 24 h, rather than a change in symptoms over this timeframe, capturing information related to breathlessness, cough, sputum production, chest congestion, and chest tightness. Daily recording provides an assessment of underlying day-to-day variability of symptoms. Items 1–8 are scored on a 5-point scale of not at all to extreme, and items 9–11 are scored on a 6-point scale of not at all to too breathless to do these. Thus, the RS-Total score has a range of 0–40, comprising three subscales: RS-Breathlessness ; RS-Cough and Sputum ; and RS-Chest Symptoms  [Alongside the E-RS: COPD, two supplemental items were included that were rooted in patient feedback from previously reported qualitative work . First, The PGI-S is a single-item questionnaire to evaluate disease severity; patients rated asthma symptoms they are currently experiencing at each study visit using a 5-point scale . The PGIC is a single question used to evaluate response to treatment since the start of the study using a 7-point scale .The SGRQ is a measure of health status in patients with chronic airway obstruction , and incThe AQLQ includes 32 items that measure functional impairment related to asthma. The questions are designed to be self-completed by the patient, with a recall period of the past 2 weeks. The response scale ranges from 1  to 7 . A change of ≥0.5 is considered clinically important .The ACQ measures various attributes of asthma control . ACQ-5 iQuantitative psychometric analyses used a statistical analysis plan informed by previous validation work on the E-RS: COPD in COPD  and in aAnalyses were conducted on blinded data (205832) and interim blinded data (205715) from a PRO dataset, defined as patients included in the intent-to-treat populations with a minimum of 4 days of data for the week prior to baseline, using SAS statistical software version 9.4  or STATA 15 . All statistical tests were two-sided and used a significance level of 0.05. No imputation of missing data was performed.Confirmatory factor analysis (CFA) was conducted using structural equation modeling to evaluate the fit of the factor structure of the E-RS: COPD in patients with moderate/severe asthma. The hypothesis that the E-RS: COPD has three factors and second order unidimensionality  was tested. First, the comparative fit index (CFI) evaluated the proportionate improvement in a model by comparing a hypothesized model against a less restricted baseline model ; values 2,1) [t-tests.Reliability was assessed using internal consistency reliability and test–retest reliability. Cronbach’s alpha coefficient was estimated for the mean weekly RS-total and subscale scores at Week − 2 and at Weeks 0, 4, 12, and 24 (205832) and at Weeks − 5 and − 2 and at Weeks 0, 4, 12, 24, 36, and 52 (205715). Reproducibility of total and subscale E-RS: Asthma daily and mean weekly scores was assessed to evaluate test–retest reliability, utilizing intra-class correlation (ICC) coefficients with a two-way random effects regression model based on absolute agreement   and pairFor daily scores, reproducibility of scores (test–retest) over consecutive days  and over a 7-day interval (Days 1–7) from screen run-in indicated patients were stable during this period if randomized. For weekly scores, reproducibility of scores from the first to the second week of screen run-in were assessed. Additionally, reproducibility of scores for patients with no change in ACQ score at the Week − 2 visit (Visit 1 in 205832 and Visit 2 in 205715) to randomization (Visit 2 in 205832 and Visit 3 in 205715) were assessed.r > 0.40) with conceptually similar measures. A lower correlation (r < 0.40) was required to support discriminant validity. Spearman’s rank correlation coefficients were calculated between the mean weekly E-RS: Asthma score and mean weekly rescue medication use, expected peak expiratory flow (AM), expected FEV1 (AM), nighttime awakenings due to asthma symptoms, and asthma symptom severity scores. In addition, the mean weekly E-RS: Asthma score was compared with FEV1, FEV1% predicted, SGRQ , AQLQ, and ACQ scores from baseline or final visit.Construct validity of the E-RS: Asthma total and subscale scores were assessed using convergent and discriminant validity and known-groups validity at Week 24. Convergent validity was considered supported if E-RS: Asthma scores showed moderate correlation  by examining score differences during the baseline week in patients grouped according to FEVAnalysis of covariance (ANCOVA) models were used to examine differences in change in total and subscale E-RS: Asthma daily scores from baseline week to Weeks 4, 12, and 24, among patients in various responder groups. Baseline scores (mean of Day − 7 to − 1) were controlled for in the models. Cohen’s effect size was calculated for E-RS: Asthma total and subscale scores among patients defined as “responders” using PGIC, SGRQ, or AQLQ.Anchor and distribution-based methods were used to define a responder threshold for E-RS: Asthma. Using anchor-based methods, mean change in mean weekly E-RS: Asthma total and subscale scores from Weeks 0 to 24 were determined by PGIC level and assessed by plotting cumulative distribution function (CDF) graphs. The minimally important change threshold was the mean change in RS-Total and subscale scores of patients who were mildly improved or mildly worse on the PGIC. Exploratory analyses were conducted utilizing the SGRQ and AQLQ minimally important difference thresholds as anchors . Distribution-based methods were conducted as supportive information for development of a responder threshold and included an assessment of the standard error of measurement (SEM) and half standard deviation (SD). SEM was estimated by multiplying the baseline SD of the measure by the square root of one minus its reliability coefficient (ICC from the test–retest assessment) , 30. Haln = 15; 60%) and Caucasian , with a mean (SD) age of 48 (16.5) years (range 23–83 years). Mean (SD) age of first asthma diagnosis was 21.3 (17.8) years , identified from five asthma and allergy clinical sites around the USA, participated in concept elicitation interviews. Most participants were female  of patients indicated a clear understanding of the PRO (one patient was not asked). Most patients  reported that all items were applicable to their personal experience, and 21/25 (84%) thought the items were relevant to all patients with asthma. No key respiratory symptoms were missing.n = 297, 70.7%; 205715: n = 1403, 61.8%), with a mean (SD) age of 48.8 (14.7) years for the 205832 study, and 53.3 (13.1) years for the 205715 study  and in 205715   were observed for RS-Total score in 205715, ranging from 0.957 at screen run-in, 0.955 at Week 0, 0.954 at Week 24, and 0.951 at Week 52. Alpha coefficients were similarly high for RS subscale scores at all timepoints, including RS-Breathlessness (0.939–0.956), RS-Cough and Sputum (0.837–0.881), and RS-Chest Symptoms (0.935–0.950). Test–retest reliability data indicated stability of RS-Total and subscale scores in both trials, with ICC coefficient values > 0.70 for all assessment timepoints.r > 0.40) were observed between RS-Total score and SGRQ-Total, SGRQ Symptoms and SGRQ Activity domains . Similar relationships were observed at Week 24, but correlations were higher . RS-Total and subscale scores showed evidence of convergent validity with asthma symptom severity in both studies, with high correlations at baseline (0.64–0.88) and at Week 24 . In support of known-groups validity, when PGI-S was used as an anchor, mean scores on the E-RS: Asthma increased linearly with increasing global severity in both trials , moderate relationships (ls Table .Table 3P < 0.0001)    mean RS-Total and subscale scores from baseline to Week 4, baseline to Week 12, and baseline to Week 24  Fig. A. Observ01) Fig. B, with m01) Fig. C, with mAmong patients “mildly improved” on the PGIC, mean (SD) changes of − 1.5 5.70) and − 1.9 (3.46) were seen for RS-Total score in 205832 and 205715, with respective changes of − 0.8 (2.84) and − 0.9 (1.72), − 0.3 (1.76) and − 0.5 (1.27), and − 0.4 (1.73) and − 0.5 (1.18) for the RS-Breathlessness, RS-Cough and Sputum, and RS-Chest Symptom scores. CDFs are presented in the online supplement  was reasonably large for a qualitative study, and was sufficient to reach saturation; however, a more diverse sample may have captured additional concepts. Additionally, no conclusions regarding use of the tool in Spanish-speaking asthma populations can be drawn on the basis of this study, although substantial testing of the Spanish EXACT translation (14 items which include the E-RS: COPD) has been completed amongst native Spanish-speakers with COPD in the US, Spain, Mexico, Chile, Argentina, Peru, Colombia and Guatemala, indicating that the respiratory terms used are likely applicable to a broad range of Spanish speakers with respiratory illness.Some limitations should be considered in the interpretation of our findings, primarily in relation to generalizability. First, the mean (SD) age of study populations  may limit the generalizability of the findings to younger patients, particularly adolescents where further qualitative research may be required. However, it should be noted that there was no upper age limit for enrollment in both trials, indicating that our findings are potentially relevant to a broad age range of adults. Second, patients in both phases of the analysis came from clinical studies that studied either moderate or moderate/severe asthma, thus our findings may not be applicable to patients with milder disease. Finally, in the qualitative study, patients were based in the USA, and were predominantly Caucasian, so findings may not be applicable to other countries, cultures, and ethnicities. Whilst Spanish-speaking patients were eligible and substantial effort was made to recruit Spanish-speaking participants, only one such person was interviewed. Nevertheless, the sample size  for use in a new disease, asthma, in line with FDA guidance  and the The E-RS: COPD was found to be valid and reliable for use in this asthma population. The tool will be referred to as E-RS: Asthma when used in asthma populations. The instrument was found to be comprehensive, relevant, acceptable, and understood by patients with moderate/severe asthma. Psychometric analyses support the use of the E-RS: COPD scoring structure in this patient population and the E-RS: Asthma tool showed reliability, validity, and responsiveness, in patients with moderate/severe asthma.Additional file 1: Figure S1. Cumulative distribution function of RS-Total Change score by PGIC in (A) 205832 and (B) 205715 studies. Table S1. Eligibility criteria for the qualitative study (N = 25). Table S2. Eligibility criteria for GSK 205832 included in the psychometric evaluation. Table S3. Eligibility criteria for GSK 205715 for inclusion in the psychometric evaluation. Table S4. Study visit schedule for PRO collection in 205832 and 205715. Table S5. Summary of clinical trials 205832 and 205715. Table S6. Confirmatory factor analysis of E-RS: COPD at Week 0 in 205832 and 205715 studies. Table S7. Distribution-based approaches for the E-RS: Asthma.


In order to improve firefighter health and protect against sudden incapacitation in this public safety occupational group, increased efforts are necessary to screen for and manage high blood pressure.Hypertension is a major risk factor for atherosclerotic cardiovascular disease and cardiac remodeling and is associated with an increased risk of sudden cardiac events, the leading cause of duty-related death in the fire service. We assessed systemic blood pressures and prevalence of hypertension among US firefighters by decade of life. Medical records of career firefighters  from four geographically diverse occupational health clinics were assessed. Hypertension was defined as systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥80 mmHg, or taking antihypertensive medication. Results from the firefighter sample were compared to the US general population . Among the total sample, 69% of firefighters met the criteria for hypertension and 17% were taking antihypertensive medications. Percentages of hypertensive male and female firefighters were 45% and 11% among 20–29 years old, respectively, and increased to 78% and 79% among 50–59 years old, respectively. Compared to the general population, male firefighters had a higher prevalence of hypertension ( Sudden cardiac events have consistently accounted for approximately 50% of duty-related deaths among firefighters over the last decade ,2. FurthHypertension is among the most powerful modifiable risk factors for cardiovascular disease (CVD), mortality, and disability in all populations ,7. FurthTo date, studies on hypertension and other CVD risk factors in firefighters have generally reported on small sample sizes, limited geographical regions, a select group of firefighters, and mainly males ,21,22,23n = 29) and Southwest cohorts (n = 15) were not included in the analysis sample due to inadequate sample sizes to produce reliable estimates for these cohorts within age categories. Antihypertensive medication usage was not available for the male fighters from the Southeast cohort (n = 337); therefore, they were only included in analyses involving the blood pressure measurements. During data quality checks, systolic blood pressure (SBP) values were excluded if ≥182 mmHg (n = 4) or ≤86 mmHg (n = 8); diastolic blood pressure (DBP) values were excluded if >110 mmHg (n = 2) or <50 mmHg (n = 3). The sample included 5063 males and 274 females. The occupational medical evaluations reviewed in this study occurred between 2015 and 2018 and included data from questionnaires and medical evaluations. Each of the 4 clinics shared a de-identified digital database with researchers from the First Responder Health and Safety Laboratory at Skidmore College. The study protocol was reviewed and approved by the Skidmore College Institutional Review Board. Data from the publicly available 2015–2016 and 2017–2018 National Health and Nutrition Examination Surveys (NHANES) included 3004 male and 3322 female adults (20–59 years old) from the general population as a comparison group to the firefighter cohort [Medical records from occupational health clinics that perform periodic medical evaluations for firefighters based in southern Arizona “Southwest cohort”, northern Virginia “Mid-Atlantic cohort”, central Florida “Southeast cohort”, and the capital region of Indiana “Midwest cohort” were assembled to create a geographically diverse cohort of career firefighters. While the cohorts were pooled, due to large differences in sample sizes, each cohort was given equal weight during the statistical analyses. Female firefighters from the Southeast , overweight (≥25 and <30 kg·m−2), and obese (≥30 kg·m2) based on their body mass index (BMI) [2.Firefighters and the general population sample were grouped into age groups of 20–29 years, 30–39 years, 40–49 years, and 50–59 years. Hypertension was defined as having one or more of the following criteria: SBP ≥ 130 mmHg, DBP ≥80 mmHg, or if participants reported taking antihypertensive medications . Among fex (BMI) . Three mp < 0.05 with the assumptions that the variances were equal and estimates were independent [p < 0.05 and was two-sided for all tests. All analyses were conducted using Stata 15.1 .Descriptive statistics are reported as a percent or mean ± standard deviation in ependent . The levDescriptive statistics for the combined cohort are provided in (p < 0.05) among male and female firefighters. For male firefighters, mean SBP increased an average of 4.3 mmHg from the 20–29 year old group to the 50–59 year old group (120.8 ± 0.5 mmHg to 125.1 ± 0.5 mmHg) and mean DBP increased by 3.4 mmHg . For female firefighters, mean SBP increased an average of 13.7 mmHg from the 20–29 year old group to the 50–59 year old group (113.4 ± 1.3 mmHg to 127.1 ± 2.6 mmHg) and mean DBP increased by 8.3 mmHg (72.7 ± 0.8 mmHg to 81.0 ± 1.5 mmHg). Compared to the general population, SBP was slightly higher  only among 20–29 years old male firefighters, but DBP was higher (p < 0.05) across all age groups for both male and female firefighters.Mean blood pressure values among males and female firefighters across age groups for the combined firefighter sample and the general population are presented in p < 0.001) among both male and female firefighters. Percentages of hypertensive male firefighters and female firefighters were 44.6 ± 3.2% and 10.5 ± 5.5% among 20–29 years old, and increased to 77.2 ± 1.6% and 78.8 ± 7.3% among 50–59 years old, respectively. Compared to the general population, the prevalence of hypertension was higher (p < 0.05) among males across all age groups (range of 10.6%–15.9% higher). For female firefighters, the prevalence of hypertension was not different from the general population except for a higher prevalence of hypertension among firefighters aged 50–59 years.Percentages of male and female firefighters with controlled hypertension, uncontrolled hypertension, and unmedicated hypertension are reported in This study is the first large-scale study (>5000 firefighters) to report blood pressure measurements and hypertension prevalence in US male and female firefighters across age groups by decade of life. The primary findings of this study are that percentage of hypertensive firefighters was 45% and 11%  among 20–29 years old, and increased to 77% and 79% among 50–59 years old; male firefighters consistently had a prevalence >40% of untreated hypertensive blood pressure values across age groups; diastolic blood pressure was significantly higher among both male and female firefighters as compared to the general population across all age groups; and male firefighters had a significantly higher prevalence of hypertension than the general population across all ages and female firefighters had higher prevalence than the general population for ages 50–59 years.In this large group of firefighters, we found a mean SBP of 124.1 ± 10.6 mmHg and mean DBP of 80.9 ± 7.0 mmHg. Previous studies have reported similar blood pressure values in smaller cohorts of firefighters, with mean SBP values ranging from 112 to 133 mmHg and mean DBP values ranging from 76 to 85 mmHg ,32,33,34Both male and female firefighters had a significantly higher prevalence of hypertension with increasing age. Hypertension prevalence has been shown to increase with age in the general population , and in Studies have also reported that a low proportion of hypertensive firefighters receive treatment and have controlled blood pressure ,20,21,33SBP in firefighters did not differ from the general population for either males or females except in male firefighters 20–29 years old, and the difference of those statistically significant was less than 3 mmHg, which is of questionable clinical significance. However, DBP was significantly higher in both male and female firefighters than the general population across all age groups . To date, no known studies have reported a difference in DBP between firefighters and the general population. In a study on the effect of working conditions on blood pressure in firefighters, Choi et al. reported that the number of 24-h shifts worked per month was related to DBP, and firefighters who performed 14 and 16 24-h shifts per month had a higher DBP  compared to those working 8–11 shifts (reference group) . The autThe clinical significance of higher DBP among firefighters as compared to the general population is unclear. The higher prevalence of hypertension in the current study may be attributed to the significantly higher DBP in firefighters, given that approximately 50% of firefighters in the sample who were hypertensive were defined as such based solely on elevated DBP (isolated diastolic hypertension (IDH)) classified as SBP < 130 mmHg and DBP ≥ 80 mmHg. In the general population, IDH  was not associated with increased risk of atherosclerotic cardiovascular events ,40. A reHypertension exacerbates atherosclerotic CVD and can lead to structural changes to the heart including LVH and cardiomegaly ,7. A strThe cause of the high prevalence of hypertension in the fire service was not specifically investigated in this study, but it may be related to occupational factors including high psychological stress , exposurThis is the first study to report on blood pressure and hypertension among a large, geographically diverse group of male and female firefighters in the US. The blood pressure values were obtained by health care providers as part of a mandatory occupational health examination, so this study did not suffer from selection bias as some earlier studies may have. Despite these strengths, there are some limitations. While the sample was large and geographically diverse, it was not a random national sample. However, the general findings across the different cohorts consistently demonstrated a need for aggressive prevention measures to reduce the risk for hypertension and to aggressively manage high blood pressure throughout a firefighter’s career.We reviewed occupational health records and found that a large percentage of firefighter were hypertensive based on blood pressure measurements or medication usage. The prevalence of hypertension increased with age, and was higher than the general population among males. Physicians who evaluate firefighters as part of routine occupational screening and those that serve as primary care providers should aggressively counsel firefighters on the importance of managing high blood pressure with lifestyle changes or medication.


Elderly patients with traumatic brain injury increase. Current targets and secondary insult definitions during neurointensive care (NIC) are mostly based on younger patients. The aim was therefore to study the occurrence of predefined secondary insults and the impact on outcome in different ages with particular focus on elderly.ICP, CPP, MAP, and SBP above-/below-predefined thresholds was calculated.Patients admitted to Uppsala 2008–2014 were included. Patient characteristics, NIC management, monitoring data, and outcome were analyzed. The percentage of monitoring time for CPP > 100, MAP > 120, and SBP > 180 and age 16–64 had higher percentage of ICP ≥ 20, CPP ≤ 60, and MAP ≤ 80. Age ≥ 65 contributed independently to the different secondary insult patterens. When patients in all ages were analyzed, low percentage of CPP > 100 and SBP > 180, respectively, was significant predictors of favorable outcome and high percentage of ICP ≥ 20, CPP > 100, SBP ≤ 100, and SBP > 180, respectively, was predictors of death. Analysis of age interaction showed that patients ≥ 65 differed and had a higher odds for favorable outcome with large proportion of good monitoring time with SBP > 180.Five hundred seventy patients were included, 151 elderly ≥ 65 years and 419 younger 16–64 years. Age ≥ 65 had significantly higher percentage of Elderly ≥ 65 have different patterns of secondary insults/physiological variables, which is independently associated to age. The finding that SBP > 180 increased the odds of favorable outcome in the elderly but decreased the odds in younger patients may indicate that blood pressure should be treated differently depending on age.The online version contains supplementary material available at 10.1007/s00701-021-05047-z. The introduction of neurointensive care (NIC), with focused efforts of avoiding secondary insults, has contributed to an increase of favorable outcome for traumatic brain injury (TBI) patients , 23, 27.The NIC of patients with TBI in general is mostly based on data from younger patients and there is insufficient research in the elderly despite the change in population structure . For exaThe aim of this investigation was therefore to study the occurrence of predefined secondary insults and the impact of outcome in different ages with particular focus on the elderly.All TBI patients ≥ 16 years old receiving NIC at Uppsala University Hospital between 2008 and 2014 were retrieved from the Uppsala TBI registry . In totaDemographic data and information about NIC management were obtained from the Uppsala TBI registry . The folTrended minute-by-minute data  was collected in real time from the Philips monitors in our ICU using the Odin software . The PhiCPP; mean arterial pressure, MAP; and systolic blood pressure, SBP), GMT data from the start of monitoring to the end of the seventh monitoring day was studied.For ICP and CPP analyses, at least 12 h of ICP data was required. Using the Odin software, the proportions of good monitoring time (%GMT) spent above-/below-predefined threshold levels were calculated for ICP ≥ 20, CPP ≤ 60, CPP > 100, MAP ≤ 80, MAP > 120, SBP ≤ 100, and SBP > 180. The thresholds originated mainly from our protocol treatment goals [For the purpose of evaluating physiological NIC monitoring data  with the aim of normoventilation as soon as ICP allowed (ICP < 20 mmHg). Wake-up tests were performed regularly  to assess neurological function. All unconscious patients (GCS M ≤ 5), regardless of age, had also ICP monitoring, except in the case of coagulopathy. An external ventricular drainage system (EVD)  was the first choice and an intraparenchymal pressure device was chosen if the ventricles were compressed. Arterial blood pressure was measured with the pressure dome at heart level. Prophylactic anticonvulsants was not used. The treatment goals according to the standardized management protocol were as follows: ICP < 20 mmHg, SBP > 100 mmHg, central venous pressure (CVP) 0–5 mmH2O, CPP > 60 mmHg, blood glucose 5–10 mmol/L, normovolemia, Pa02 > 12 kPa, electrolytes within normal ranges, and body temperature < 38 °C.All patients were treated according to the same standardized treatment protocol . UnconscMass lesions in unconscious patients were evacuated.Raised ICP was treated in a stepwise fashion. If ICP increased ≥ 20 mmHg without mass lesions, cerebrospinal fluid (CSF) was drained from the EVD. Initially small volumes (1–2 ml) were drained intermittently, when there were risk of expanding hematomas and brain swelling. Later CSF was drained using an open system against a pressure level of 15–20 mmHg if needed. If raised ICP persisted, the treatment was escalated with no wake-up tests, continuous sedation with propofol, and stress reduction with ß1-antagonist metoprolol  (0.2–0.3 mg/kg/24 h as an infusion) and α2-agonist clonidin  (0.5–1.0 μg/kg × 8 or the same dose as an infusion).Thiopental coma treatment and/or decompressive craniectomy were last tier treatment option but were initiated more restrictively in the elderly.The NIC mortality was assessed. Follow-up was done after 6 months, using the extended Glasgow outcome scale (GOSE), by structured telephone interviews done by a few selected persons, 39.Differences in the characteristics between age groups were analyzed with Pearsons Chi 2 test.U test was used to compare occurrence of secondary insults between the age groups.Mann-Withney GMT above/below secondary insult thresholds for the physiological variables.Multiple linear regression analysis was done to examine if age ≥ 65 years and admission variables as gender, GCS M, other injuries, extracerebral hematoma, and contusions contributed to the %To evaluate if the %GMT above/below secondary insult thresholds for the physiological variables was associated with outcome, univariate logistic regression analysises were made with favorable outcome (GOSE 5–8) and survival (GOSE 2–8) as dependent variables. To evaluate whether associations differed by age (age 16–64 vs age ≥ 65), multiple logistic regression models were fitted including age, a physiological variable and age by physiological variable interaction as independent variables. The odds ratios (ORs) for physiological variables are reported for each age-group, regardless of the significance of interaction.p < 0.05 was considered statistically significant. All statistical analyses were carried out in IBM SPSS Statistics for Windows except for Pearsons Chi 2 which was done with Microsoft Excel 365.For all patients, the mean age was 49.7 years range 16–94). The age distribution showed one peak at around 20 years of age and another peak around 60–65 years of age (Supplementary Information 6–94. TheThere were no significant differences between the age groups ≥ 65 years and 16–64 years regarding ICP monitoring (55.0% vs 62.5%) and mechanical ventilation (82.1% vs 77.3%) . Follow-up was made at 7 months in median . For all ages, favorable outcome (GOSE 5–8) was observed in 62% (69% in 16–64 years and 42% in elderly) and 13% had died (6% in 16–64 years and 31% in elderly) . The OR (per unit increase in %GMT with SBP > 180) was 2.07 (0.22–1731.66) in patients ≥ 65 years and − 0.03(0.00–0.57) in patients 16–64 years  spent a higher proportion of GMT with high  years spCPP > 100, MAP > 120, and SBP > 180  for all patients indicated that high %SBP on arrival at hospital in the range of 131–150 mmHg, compared to patients with SBP of < 130 mmHg[Hence, blood pressure should probably be treated differently in younger and older patients. The finding that high blood pressures may be advantageous in elderly is supported by Utomo and coll. who found higher odds of independent living at 6 months for patients ≥ 65 years with a  130 mmHg.ICP did not prove to be a significant predictor of outcome in the elderly. This finding should not be interpreted as if ICP is unimportant for outcome and does not need to be monitored in the elderly. Instead, this is probably an effect of the low burden of ICP insults thanks to effective detection and treatment. We have examined our material for events with %GMT ICP ≥ 25 and there was very few events in the elderly . Monitoring of ICP in elderly with TBI is of importance and this has also been shown by You and coll. in a randomized trial of elderly with severe TBI who found lower in-hospital mortality and improved 6-month outcomes for the patients randomized to ICP monitoring [nitoring . We beliThis is a single-center study and the results may therefore be influenced by the local management applied. Thus, the results may not be completely generalizable. There was a selection bias since only patients judged to have a reasonable chance for favorable outcome were accepted for NIC. Treatment bias also needs to be considered since all patients were treated to avoid secondary insults and the % GMT at insult level was in low general.Furthermore, complete multiple logistic regression analyses for assessing the influence of secondary insults on outcome could not be done  due to the relative small number of patients. It was however possible to study the age interaction.CPP > 100 and SBP > 180 were significant predictors of favorable outcome and high %GMT with ICP ≥ 20, CPP > 100, SBP ≤ 100, and SBP > 180 were positive predictors of death. The analysis of age interaction showed that patients ≥ 65 years differed and had a higher odds for favorable outcome and without a significant decrease in survival with large proportion of good monitoring time with SBP > 180.Elderly ≥ 65 years have different patterns of secondary insults/physiological variables, which to some extent is independently associated to age. When patients in all ages were analyzed, low %GMT with This finding may indicate that blood pressure should be treated differently in younger and older patients. More TBI studies in the elderly are warrented to define specific guidelines regarding secondary insult definitions and optimal levels to target. Studies of pressure autoregulation and CPPopt are also desirable.Supplementary file1 (DOCX 32 KB)Supplementary file2 (DOCX 32 KB)Below is the link to the electronic supplementary material.


Emerging studies have revealed a strong link between the gut microbiome and several human diseases. Since human gut microbiome mirrors variations in lifestyle and environment, whether associations between disease conditions and gut microbiome are consistent across populations—particularly in communities practicing traditional subsistence strategies whose microbiomes differ markedly from industrialists—remains unknown. Cardiovascular diseases are the leading cause of mortality in India affecting 55 million people, and high blood pressure is one of the primary risk factors for cardiovascular diseases. We examined associations between gut microbiome and blood pressure along with 14 other variables associated with lifestyle, dietary habits, disease conditions, and clinical blood markers in the three Assamese populations. Our analysis reveals a robust link between the gut microbiome diversity and composition and systolic blood pressure. Moreover, several genera previously associated with hypertension in non-Indian populations were also associated with systolic blood pressure in this cohort and these genera were predictors of elevated blood pressure in these populations. These findings confer opportunities to design personalized, preventative, and targeted interventions harnessing the gut microbiome to tackle the burden of cardiovascular diseases in India. The human gut comprises diverse microbial community collectively known as the gut microbiome (GM), which plays a crucial role in human health and diseases . GM is sLactobacilli has been reported to lower blood pressure in certain human populations [dahi/doi (yogurt), which contains high dosages of Lactobacilli [Hypertension is a major risk factor for cardiovascular, cerebrovascular, and kidney diseases , 11. Theulations , 15, 19.obacilli , hyperteobacilli  and are obacilli . TherefoHere, we evaluated the relationship between the gut microbiome and 22 host factors, including blood pressure in 71 healthy Assamese individuals from three rural villages in Assam, a Northeastern Indian state with high prevalence of hypertension , 24. TwoN = 24), Jagiroad (N = 24), and Kamalabari, Majuli (N = 23). Fresh fecal samples were collected in sterile stool collection tubes containing RNAlater™  solution to maintain the integrity of DNA. The tubes were shaken hard in a back and forth motions until the samples were homogenized. Blood samples were collected using K3 EDTA (tripotassium ethylenediaminetetraacetic acid) vials, and serum was separated immediately by centrifugation at 3000 rpm for 5 min at room temperature. Additionally, anthropometric measures including height, weight, body mass index, and blood pressure measures were also determined from each participant, while sample collection on-site participants also filled out a basic survey questionnaire that assessed their age, gender, diet , consumption of dairy products, alcohol, health status, and use of medication.Fecal and blood samples were collected with written informed consent from a total of 71 individuals within Assam, India. Participants were from three geographic locations, namely, Aanthmile  according to the manufacturer's protocol. This extraction protocol involved chemical lysis followed by heating at 95°C for 15 mins for recovery of Gram-positive bacteria as well. Quantification of double-stranded DNA was determined using the QuantiFluor dsDNA System , and an estimate of sample purity was determined via spectrophotometry by measuring the A260/A280 absorbance ratio. The V3+V4 region of the 16S rDNA gene was PCR amplified and sequenced at Macrogen Inc.  using the 341F-805R primer pairs following the standard Illumina protocols. The amplified DNA fragments were subjected to paired-end (2 × 300 bp) sequencing using the MiSeq platform (Illumina). Gut microbiome sequencing data were obtained in the form of FASTQ files.Microbial DNA was extracted from 200 Read counts for amplicon sequence variants (ASVs) were calculated using the phyloseq pipeline  and the Serum samples were analyzed using standard biochemical assay kits from Coral Clinical Systems  following the manufacturer's protocol. The tests included 6 blood parameters, namely, albumin, globulin, total protein, glucose, cholesterol, and triglycerides levels.All statistical analyses were conducted using R version 3.5.1 . The surP < 0.05 after multiple testing corrections using the Benjamini–Hochberg false discovery rate adjustment were considered statistically significant.Three separate generalized linear mixed models were used to evaluate associations between the blood parameters, dietary factors, location, and different features of the gut microbiome, namely, species richness, Shannon's diversity index, and PCo1 (using Bray–Curtis distance). In all the three models, the gut microbiome features were treated as the response variable, variables linked to diet, lifestyle, and blood were treated as fixed effects, and samples were treated as random effects. A total of 15 factors were treated as explanatory variables with fixed effects, and they were as follows: location, age, sex, milk drinking pattern, gastric condition, medicinal use, systolic blood pressure, diastolic blood pressure, hypertension, BMI, albumin, globulin, triglycerides, cholesterol, and RBS glucose. All statistical tests with Individuals were binned into two groups based on their systolic blood pressure. Individuals with systolic blood pressure <120 mmHg and ≥ 120 mmHg were considered normal and elevated, respectively. All of the 1,935 ASVs were used, and the microbiome of each sample was represented via the feature vector of their relative abundances. As a preprocessing step, the hierarchical feature engineering (HFE) method  was usedLocated in the foothills of the Himalaya, Assam is home to various indigenous ethnic groups that continue to practice traditional subsistence strategies. We collected samples from 71 individuals from three rural villages on the banks of the Brahmaputra River, namely, Aanthmile, Jagiroad, and Kamalabari . The res2. The 51 males (71.8%) were 35.9 ± 11.4 years old with an average BMI of 19.00 ± 7.9 kg/m2 (doi (yogurt) on a daily basis, although the residents of Aanthmile and Jagiroad consumed doi made by propagating cultures of boiled cow milk, whereas the residents of Kamalabari consumed doi prepared by natural fermentation of raw cow milk. Few phenotypes and biochemical parameters, including BMI, blood albumin, globulin, and total protein, varied between locations. Albumin and total protein serve as markers of nutrition, while the higher concentration of globulin is indicative of higher pathogen load [P=1.708e − 05) and it was significantly associated with clinical blood markers including globulin , albumin , total protein , and triglyceride . We did not find correlation between systolic blood pressure and BMI in this cohort.We assessed the impact of lifestyle and dietary habits on these populations using a survey questionnaire that included 22 questions pertaining to current dietary practices such as primary carbohydrate and protein source, consumption of animal products (frequency of meat and dairy products), medicinal usage, and medical conditions such as presence or absence of gastritis. Participants included 20 females (28.2%) with average age of 35.4 ± 11.6 years and an average BMI of 24.5 ± 6.1 kg/m.9 kg/m2 . Partici.9 kg/m2 . For allgen load , 37. FurP=0.0074 and 0.017, respectively, Kruskal–Wallis test, P < 0.05; Dunn's post hoc test).To evaluate the effect of subtle dietary and lifestyle differences between the three populations on their GM, we compared their gut microbiome diversity and composition . RarefacP=1e − 04, PERMANOVA). Visualization of the principal coordinates (PCos) revealed subtle shifts in GM composition in Kamalabari participants. However, no difference was detectable between individuals from Aanthmile and Jagiroad  at the ASV level using Bray–Curtis also showed variations in gut microbiome composition between the three locations . The overall accuracy of the classifier was low (46%), and classification error rates were 37.5%, 58.8%, and 41.2% for Kamalabari, Jagiroad, and Aanthmile, respectively . Of the 15 factors, systolic blood pressure was the only variable that was significantly associated with both measures of alpha diversity, species richness , but no differences were observed between Kamalabari and Aanthmile or Jagiroad and Aanthmile . After accounting for GM variation explained by location, systolic blood pressure was the only other variable associated with PCo1 , BMI, as well as levels of albumin, globulin, triglycerides, cholesterol, and glucose in the blood. Three multivariate generalized linear mixed models were constructed for three GM measures—species richness, Shannon's diversity index, and PCo1 calculated using Bray–Curtis—where each of these 15 variables was considered to have fixed effects and random effect was assigned to each individual. In these multivariate analyses, the location was no longer significantly associated with alpha diversity  that distinguish individuals with normal blood pressure (systolic <120 mmHg) from those elevated (high) blood pressure (systolic ≥ 120 mmHg), three machine learning (ML)-based classifiers were tested. Hierarchical feature engineering (HFE) [ g-UCG04 . The 7 Ave Bayes . Naïve Bve Bayes , 2 of whP=0.008). However, a multivariate model including the 15 variables showed that F/B ratio was not significantly associated with systolic blood pressure .Previous studies have suggested that the relative abundance of Firmicutes-to-Bacteroidetes ratio (F/B) is associated with different measures of health and diseases, including hypertension . Firmicudoi (yogurt). While doi is prepared by natural fermentation of raw milk in the native Assamese community in Kamalabari, it is prepared by propagation of cultures in boiled milk in the Nepali-speaking villages in Aanthmile and Jagiroad. Similarly, the consumption of milk was lower in Kamalabari and their primary source of animal protein was fish while meat was the primary source of animal protein in the other two villages. We also observed differences in a few blood biomarkers between the native Assamese and participants from the other two locations. Serum albumin and total protein levels were lower in the Nepali speakers relative to the Assamese population, but serum globulin level was lower in the Assamese. These findings are consistent with a previous study [Human gut microbiome mirrors lifestyle , 39 and us study . Higher us study . On the us study . These fPrevious studies comparing populations with distinct lifestyles have identified large differences in gut microbiomes , 42, butPrevotella, which has also been shown to be overrepresented in individuals with prehypertension and hypertension in non-Indian populations [Prevotella may play an essential role in hypertension by triggering inflammatory response [Lachnoclostridium that had higher relative abundance in individuals with elevated blood pressure, which is consistent with previous findings where Lachnoclostridium was reported to be positively associated with systolic blood pressure [Megasphaera ASVs were elevated in the high blood pressure group. Megasphaera is a common commensal found in the Indian gut [Faecalibacterium ASV was higher in individuals with elevated systolic blood pressure. Faecalibacterium is an important carbohydrate fermenting bacteria involved in butyrate production via starch fermentation [In addition to lifestyle, we found a robust link between gut bacteria and blood pressure in these participants as both measures of alpha diversity and beta diversity measured using the primary axis in a principal coordinate analysis PCo1) were associated with systolic blood pressure. Furthermore, integration of a machine learning approach identified 19 ASVs that were predictive of elevated blood pressure in this cohort. Importantly, many of these 19 ASVs have been associated with hypertension in previous studies. Also, the direction of change in relative abundance of these ASVs in individuals with high blood pressure in this study is consistent with hypertension patients. For example, four of the ASVs elevated in individuals with high blood pressure belonged to the genus ulations . Prevoteresponse . Similarpressure . Two Megdian gut , and it dian gut . Finallydian gut , 43, whi were assentation , and lowentation .Our study has revealed that systolic blood pressure is associated with gut microbiome in an Indian population. This finding is important for two reasons. First, it demonstrates that gut bacteria can be linked to chronic diseases in traditional populations. Traditional populations have been historically underrepresented in genomics studies , 49, andMicrobiome has become increasingly important biomarker in framing novel strategies to address etiology of several diseases. Lack of microbiome-disease connections in traditional populations has the potential to augment the already existing health disparities in genomic medicine. In India, Assam is home to several ethnic tribes who still practice traditional subsistence while hypertension is the leading cause of cardiovascular diseases in this region. The result presented herein is the first report to address the association of systolic blood pressure with gut microbiome of two different agropastoralist ethnic communities. Compositional profiling revealed that gut microbiome of normal and elevated hypertensive individuals has significantly different amplicon sequence variants. These findings confer opportunities to design personalized, preventative, and targeted interventions harnessing the gut microbiome to tackle the burden of cardiovascular diseases.


Beat-to-beat blood pressure variability is associated with increased stroke risk but its importance at different ages is unclear.To determine the age-sex distribution of blood pressure variability in patients with transient ischemic stroke or minor stroke.In consecutive patients within six weeks of transient ischemic stroke or non-disabling stroke (Oxford Vascular Study), non-invasive blood pressure was measured beat-to-beat over five minutes (Finometer). The age-sex distribution of blood pressure variability  was determined for systolic blood pressure and diastolic blood pressure. The risk of top-decile blood pressure variability was estimated (logistic regression), unadjusted, and adjusted for age, sex, and cardiovascular risk factors.In 908 of 1013 patients, excluding 54 in atrial fibrillation and 51 with low quality recordings, residual coefficient of variation was positively skewed with a median systolic residual coefficient of variation of 4.2% (IQR 3.2–5.5) and diastolic residual coefficient of variation of 3.9% (3.0–5.5), with 90th centile thresholds of 7.2 and 7.3%. Median systolic residual coefficient of variation was higher in patients under 50 years (4.5 and 3.0–5.3) compared to 60–70 years (4.1 and 3.2–5.2), but rose to 4.5% (3.5–6.9) above 80 years, with an increasingly positive skew. The proportion of patients with markedly elevated blood pressure variability in the top-decile increased significantly per decade , after adjustment for sex and risk factors.Median beat-to-beat blood pressure variability fell in midlife, reflecting loss of physiological, organized blood pressure variability. However, rates of markedly elevated blood pressure variability significantly increased with greater age, suggesting that blood pressure variability may be particularly important in older patients. As such, they are of limited use in acute assessment.Patients with episodic hypertension have a high risk of stroke,12 and at least similar predictive value.11 However, normal values for beat-to-beat BPV in at-risk individuals and their distribution across age and sex groups are unknown. Furthermore, beat-to-beat BPV is itself composed of multiple components from physiological rhythms reflecting breathing and intact autonomic function to increased BPV associated with stiff arteries and impaired baroreceptor function in older patients with impaired compensatory mechanisms.12 To assess the potential clinical utility of beat-to-beat BPV, it is necessary to understand the distribution of beat-to-beat BPV in at-risk populations and whether potentially pathological, markedly increased BPV is increased in specific groups.Variability in BP from one beat to the next (beat-to-beat BPV) enables BPV assessment at a single visit and is associated with an increased risk of recurrent stroke and cardiovascular events in patients with a transient ischemic attack (TIA) or minor stroke, with a similar physiological profile to home day-to-day BPVTherefore, we determined the normative age-sex distribution of beat-to-beat BPV variability in patients with a TIA or minor stroke.12 Participants were recruited at the daily emergency clinic, following a referral after attendance at the Emergency Department or from primary care, usually within 24 h. The OXVASC population consists of >92,000 individuals registered with 100 primary-care physicians in Oxfordshire. All consenting patients underwent a standardized medical history and examination, ECG, blood tests, and a stroke protocol MRI brain and contrast-enhanced MRA (or CT-brain and carotid Doppler ultrasound or CT-angiogram). All patients were assessed by a study physician and reviewed by the senior study neurologist (PMR) and are followed-up face-to-face at 1, 3, 6, and 12 months and 2, 5, and 10 years. Access to the data will be openly considered on application to the chief investigator (PMR).Consecutive, consenting patients with TIA or minor stroke were recruited between September 2010 and October 2019 to the Oxford Vascular Study (OXVASC) Phenotyped Cohort.As part of the OXVASC Phenotyped Cohort, a routine prospective cardiovascular physiological assessment is performed at the one-month follow-up visit. Participants were excluded if they were under 18 years, had severe cognitive impairment, were pregnant, had autonomic failure, or had a recent myocardial infarction, unstable angina, heart failure (NYHA 3–4 or ejection fraction <40%), or untreated bilateral carotid stenosis (>70%). OXVASC is approved by the Oxfordshire Research Ethics Committee.After 15–20 min supine rest, beat-to-beat BPV was measured over five minutes in a quiet, dimly lit, temperature-controlled room (21–23℃). Continuous ECG and non-invasive BP were acquired at 200 Hz  via a Powerlab 8/30 . Waveforms were preferentially recorded from the middle phalanx of the middle finger. Automated calibration  was performed until the recording was stable, but turned off during each test. Estimated brachial waveforms (Finometer) were calibrated offline by linear regression to 2–3 supine, oscillometric brachial readings, performed immediately prior to the monitoring period on the contralateral arm, with manual exclusion of artifacts. Measurements were not adjusted for differences in BP between arms. If necessary, the finger cuff was moved to an adjacent finger or the proximal phalanx of the same finger or the hand was warmed. Two sitting clinic BPs, five-minutes apart, were measured at ascertainment and one month in the non-dominant arm by trained personnel.BPV on beat-to-beat monitoring was calculated over five minutes. Ectopic beats and artifacts were automatically detected, visually reviewed, and removed by linear interpolation of R–R interval. BP artifacts were automatically detected and manually reviewed and removed by linear interpolation to adjacent normal beats, with in-house software. Patients in atrial fibrillation during the recording were excluded. Systolic and diastolic BPV were calculated as the standard deviation (SD) and the coefficient of variation (CV = SD/mean), before and after detrending of the recording about a linear regression (rCV). All recordings were reviewed blinded to clinical data to assess for the quality of recording .Distributions of BP indices were described by histograms for all patients, stratified by sex and age. Normality of distributions was assessed by the Shapiro–Wilk test. Summary values for non-normal distributions (p < 0.05) were summarized as the median, skewness, inter-quartile range, and 90th centile.Models were performed for univariate associations; adjusted for age and sex and for age, sex, and cardiovascular risk factors .1031 Assessments were performed in 1013 eligible, consecutive, consenting patients between September 2010 and October 2019, with 18 patients assessed twice. Of 1013 patients, 54 (5.3%) were in atrial fibrillation during the recording, whilst 51 (5%) patients had inadequate recordings. Patients with atrial fibrillation or poor recording quality were older, had higher BP, and were more likely to have a history of hypertension .Table 1Mean SBP was strongly correlated with SD of SBP  but with no correlation with CV–SBP or CV–DBP, before or after detrending. However, there was correlation between mean–DBP and SD–DBP, with an inverse correlation between CV–DBP and mean DBP before and after detrending .Mean SBP and DBP were normally distributed across the population, but all indices of BPV were positively skewed , with a The median value of rCV–SBP and rCV–DBP increased across age groups. However, there was a greater median rCV in patients under 50 compared to patients aged between 50 and 60, although the distribution of CV was more “normal.” However, rCV was more positively skewed with increasing age, with a progressive increase in the top decile of rCV across groups, particularly in patients over the age of 80, suggestive of a non-physiological increase in BPV.In contrast to the greater linear increase in mean SBP with age in women than men, the non-linear change in rCV was similar for both men and women but was more marked for DBP, with a nadir of rCV–DBP occurring at a greater age in women than men . HoweverThe risk of being in the top decile was associated with increased age and sex in univariate analyses for rCV–SBP but not for rCV–DBP, with a similar pattern for mean SBP and an inverse association for mean DBP (Table 3). After adjustment for age, the association with sex was not significant. The same pattern of association was seen for general linear models for the association between demographics measures and rCV (Supplementary Table 3).In this high-risk population, beat-to-beat BP variability over five minutes was positively skewed with a median rCV of 4.2% for systolic BP and 3.9% for diastolic BP. However, there was a non-linear relationship between rCV and age with a fall in BPV in younger patients, likely reflecting loss of intact physiological mechanisms, followed by a high rate of markedly increased BPV in older age-groups, implying that these patients may be at particular risk from excess BPV.11315 few studies have determined the prognostic significance of beat-to-beat blood variability16 despite its widespread use in the assessment of autonomic function.17 We previously demonstrated that beat-to-beat BP variability was associated with a 47% increased risk of stroke and 37% increased risk of cardiovascular events per standard deviation increase in beat-to-beat BPV11 compared to 24 and 33% for day-to-day BP variability. One other study demonstrated that beat-to-beat BPV was increased in acute stroke and associated with poor outcome,16 albeit with SD as the principle index of BPV. Beat-to-beat BPV was also associated with markers of end-organ injury in this population,12 in limited studies in other populations,18 and in limited studies using intra-arterial BP.19 This is consistent with the similar associations of beat-to-beat, visit-to-visit, and day-to-day BPV with physiological mechanisms,12 the similar prognostic value of beat-to-beat and day-to-day BPV in this population,11 and their similarity to the prognostic value of visit-to-visit BPV.2 However, beat-to-beat variability reflects short-term physiological processes, such as those driven by the autonomic nervous system, and does not reflect intra-individual BPV due to longer-term factors such as time of day, day-to-day influences such as weekday versus weekend or seasonal factors, which will only impact upon inter-individual differences in beat-to-beat BPV. As such, its prognostic significance may be driven by different underlying processes and have different relevance in different patient groups.Despite studies demonstrating that visit-to-visit and day-to-day BP variability is associated with an increased risk of cardiovascular events,3 this is a population-specific measurement that cannot be applied in an individual or be used to derive normative values.There is still no agreement as to the optimal measure of BPV or a description of its distribution in at-risk populations. In this study, we confirmed that SD was strongly correlated with mean BP, whilst CV–SBP was not significantly associated with mean–SBP despite a weak negative correlation between mean DBP and CV–DBP. However, due to the baseline drift in BP during recordings, due either to measurement artefact or a physiological reduction in baseline BP, detrending about a linear regression significantly reduced the artifactual component of the positive skew of the population. Although previous studies have also derived variation independent of the mean,11 with a particularly high risk in the top decile of BPV in analyses of visit-to-visit BPV,2 75% and 90% population thresholds for rCV–SBP are likely to be clinically applicable. Furthermore, the association between age and beat-to-beat BPV was markedly non-linear, consistent with reports from the ASCOT–BPLA and UK–TIA trials,2 with similar associations between age and other outcomes.21 As such, simple linear adjustment for age in epidemiological models of BPV and similar variables, as is the norm, will consistently fail to adequately adjust for age in prognostic models, resulting in systematic bias. Either stratified models or non-linear approaches22 are therefore essential to develop age-specific prognostic estimates for key predictors of future cardiovascular risk.BPV was very positively skewed, and this increased with age. As such, dichotomized thresholds for markedly elevated BPV are likely to have prognostic significant. Given an increased risk of recurrent events in the top quartile of BPV in this population,Despite the positive skew of the distribution of BPV, BPV was greater in the youngest patients compared to patients over 50 years old, with a more normal distribution. However, as age increased, there was a progressively greater skew of the population with an increasing threshold for the top decile of rCV–SBP across age strata. This likely reflects different components of BPV, with a fall in physiological, ordered fluctuations in younger patients reflecting an intact autonomic nervous system, followed by later-life development of increased, more randomly distributed BPV in patients with failure of compensatory mechanisms.23 associated with increased beat-to-beat BPV.11 Second, 5% of patients did not have adequate recordings despite methods to improve quality, particularly in elderly patients who may be at a particularly increased risk of stroke. As such, the prevalence of elevated BPV and associated cardiovascular risks may be underestimated. Third, we measured beat-to-beat BPV in a highly controlled environment, using expensive equipment (Finometer). Development of more cost-effective methods would be essential to apply beat-to-beat BPV to routine clinical practice. Fourth, patients with atrial fibrillation were excluded from the analysis due to confounding by the randomly distributed variation in the R–R interval and the increased stroke risk due to embolism, while the technique used has not been validated in patients with AF. Additional research is required to validate or develop measurement techniques in AF although more patients will be required to investigate the determinants and significance of beat-to-beat BPV in patients with AF. Finally, we extensively cleaned and de-trended the data, improving precision of measurement but also limiting its applicability to clinical practice. As such, further development is required to standardize methods of acquisition, data cleaning, and analysis of beat-to-beat BPV in a cost-effective and practical method.There are limitations to our study. First, all patients were assessed for a cerebrovascular event, limiting generalizability to other groups. However, this population is at an increased risk of recurrent strokeOverall, we determined the distribution of beat-to-beat BPV in a relevant, high-risk population with TIA or minor stroke, providing normative values and characterization of the distribution by age and demographic group. This lays the foundation for further research to characterize the physiological determinants of BPV and to differentiate BPV reflecting intact, protective physiological processes from abnormal BPV associated with an increased risk of stroke. Furthermore, this study will enable us to investigate the difference in BPV between different stroke etiologies and vascular anatomy, including the effect of vascular stenosis proximal to the site of measurement. This will allow further analysis of the prognostic significance of beat-to-beat BPV in large populations and trials to determine its validity as a target for treatment in specific patient groups. Finally, it will be critical to develop practical methods to measure BPV in a cost-effective manner in clinical environments. More user-friendly devices have recently become available, but their prognostic significance and utility in measuring BPV need to be validated.Beat-to-beat BP variability is a promising method to characterize BP variability at the time of a single patient assessment. We have characterized the distribution of BPV in a high-risk population, identifying a high rate of markedly elevated BP in older patients. As well as providing evidence of different forms of BPV, this provides a robust foundation for future studies to better characterize the prognostic significance of beat-to-beat BPV and its utility as a treatment target.


This study aimed to construct a 5-year survival prediction model of coronary heart disease (CHD) induced chronic heart failure (CHF), which is supported by the traditional Chinese medicine (TCM) factor, and to verify the model.  Inpatients from January 1, 2012, to December 31, 2017, in seven hospitals in Shandong Province were studied. The random number table was used to randomly divide the seven hospitals into two groups (training set and verification set). In the training set, the least absolute shrinkage selection operator regression was first used to screen the independent variables. Logistic regression was then applied to construct a survival prediction model. The following nomogram visualizes the prediction model results. Finally, C-indices, calibration curves, and decision curves were used to discriminate and calibrate the established model and evaluate its practicability in the clinic. Bootstrap resampling and the verification set were used for internal and external verification, respectively.  A total of 424 eligible patients were included in the model construction and verification. In this 5-year survival prediction model of patients with CHF induced by CHD, eight independent predictors were included. The series of C-indices for the training set, bootstrap resamples, and verification set was 0.885, 0.867, and 0.835, respectively, demonstrating the credibility of our model. Additionally, the receiver operating characteristic curve, calibration curve, and clinical decision curve analysis of the training and verification sets showed that this 5-year survival prediction model was good in discrimination, calibration, and clinical practicability.  This work highlights eight independent factors affecting 5-year mortality in patients with CHF induced by CHD after discharge and further helps reallocate medical resources rationally by precisely identifying high-risk groups. The constructed prediction model not only plays a credible role in prediction but also demonstrates TCM intervention as a protective factor for the 5-year death of patients with CHF induced by CHD, thereby advancing the use of TCM in CHF. Heart failure (HF) is a multi-incentive disease characterized by a structural or functional disorder of the myocardium, which results in decreased pumping power or filling function, a common end of most cardiovascular diseases. Chronic heart failure (CHF) is persistent HF that can stabilize, worsen, or decompensate. As the proportion of ageing society increases globally, the capacity for diagnosis and treatment of cardiovascular diseases, especially acute myocardial infarction, has greatly improved. Followed by the prevalence and incidence of patients with CHF exhibit an upward trend . AccordiSince accurate assessment of prognosis is a prerequisite for later rational treatment strategies, it is of great significance to develop credible models to select prognostic factors. To date, a variety of CHF prognostic prediction models have been established clinically , 5, of wHerein, a 5-year survival prediction model of CHF patients induced by coronary heart disease (CHD) was developed to compensate for the clinical demand for long-lasting prediction. Previous studies have shown that traditional Chinese medicine (TCM) has accumulated rich experience in the treatment of CHF because of its significant increase in patients' quality of life and decrement in readmission rate . HoweverSeven Grade III Class A Chinese Medicine Hospitals in Shandong Province were surveyed. These were the Shandong University of TCM Affiliated Hospital, Weifang Chinese Medicine Hospital, Rizhao Chinese Medicine Hospital, Jinan Chinese Medicine Hospital, Qingdao Chinese Medicine Hospital, Zibo Chinese Medicine Hospital, and Weihai Chinese Medicine Hospital. Inpatients from January 1, 2012, to December 31, 2017, were investigated. The HIS was used to extract information on patients with CHF according to the International Disease Classification ICD-10 code.The inclusion criteria were as follows: (1) consistent with the Chinese Medical Association Cardiovascular Branch “Chinese Heart Failure Diagnosis and Treatment Guidelines” 2014 CHF diagnosis; (2) age between 45 and 75 years; (3) New York Heart Association functional classification (NYHA classification) II, III, IV; (4) CHF induced by CHD; (5) ejection fraction (EF) ＜50%; (6) follow-up time ≥5 years.The exclusion criteria were as follows: (1) acute HF; (2) patients who underwent coronary artery bypass surgery or cardiac resynchronization therapy; (3) associated with noncardiovascular events, such as malignant tumours and psychosis; (4) severe liver and kidney dysfunction; (5) missing data.Characteristics: age, sex, weight, days in the hospital, and course of CHF.Comorbid diseases: hypertension, diabetes, hyperlipidemia, arrhythmia, cerebrovascular disease, respiratory disease, digestive system disease, kidney disease, peripheral vascular disease, and thyroid disease.Treatment: cedilanid, digoxin, diuretics, spironolactone, nitrates, ACE inhibitor/angiotensin II receptor antagonist (ACEI/ARB), beta-blockers, aspirin, clopidogrel, anticoagulant drugs, calcium antagonists, trimetazidine, statins, and TCM intervention .+), sodium (Na+), creatinine (CR), alanine transferase (ALT), total bilirubin (TBIL), triglyceride (TG), high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), blood glucose (GLU), activated partial prothrombin time (APTT), and hemoglobin (HB).Physiological function and laboratory indices were as follows: heart rate, systolic blood pressure, diastolic blood pressure, NYHA classification, EF, left ventricular end-diastolic diameter (LVEDD), N-terminal probrain natriuretic peptide (NT-proBNP), serum potassium  Choice of follow-up time: avoiding the patient's rest time. (2) Training of follow-up researchers, providing language training to improve the communication skills of researchers; conducting professional knowledge training so that follow-up researchers can answer patients' consultations about their diseases and provide relevant suggestions. (3) Preparation for follow-up: the standard follow-up questionnaires should be prepared in advance. Researchers should review the cases, familiarize themselves with each patient's condition in advance, and set up a standardized questioning mode according to the patient's condition.R 3.6.3 software was used for model construction and verification. The Lasso method was used to filter the best predictors from 47 features for its ability in high-dimensional data compression [P value of independent predictors, a nomogram was drawn to visualize the results of the predicted model. The C-indices were used to evaluate the discriminative ability of the model, and a receiver operating characteristic (ROC) curve was plotted to visualize the area under the curve (AUC) value. A calibration curve was applied to visualize the results of Hosmer–Lemeshow and calibrate this model. The clinical decision curve analysis (DCA) evaluates the clinical utility of the model [P value of less than 0.05 was considered statistically significant.The random number table was used to randomly divide the seven hospitals into two groups (training set and verification set). Categorical variables were classified according to clinical standards. Continuous variables were converted into categorical variables according to clinical routine critical points. pression , 10. Mulhe model , 12. 100n = 308), and the remaining two hospitals were assigned to the verification set (n = 116). The flow chart of participant enrollment is shown in The study included 2961 patients with CHF from seven hospitals. Among them, there were 847 patients whose inducements were not CHD. Additionally, 1321 patients with EF ≥ 50% were excluded. Therefore, a total of 793 patients with CHF induced by CHD with EF ＜50% were selected. 17 patients had serious outcome events  during hospitalization, 194 patients were followed up for less than 5 years, 86 patients lost contact, and 72 patients had incomplete data. Finally, a total of 424 participants were qualified from seven hospitals that were randomly divided into two groups. Five hospitals were assigned to the training set  was better than that of single-drug treatment [In this study, we first investigated TCM intervention as a factor and successfully developed a 5-year survival prediction model for patients with CHF induced by CHD. Eight independent predictors, age, course of CHF, ACEI/ARB, aspirin, TCM intervention, NYHA classification, NT-proBNP, and K, were confirmed when building this model. A previous study in the United States showed that as age increased, the mortality rate of CHF increased. Specifically, the one-year mortality rates of patients aged set 65−74, 75−84, and ≥85 years were 22%, 30.3%, and 42.7%, respectively, revealing a growing trend . Accordireatment . Taking reatment . Althougreatment .The NYHA classification is a predictor of readmission and death of patients with CHF. A study by the European Society of Cardiology in Poland showed that the NYHA classification was an independent predictor of one-year death in patients hospitalized for CHF . A multiTCM is a historical tradition with over 2000-year development and has evolved unique theories and technical methods. It is featured in Chinese herbs and plays a key role in the treatment of malignant tumours, chronic diseases, and acute infectious diseases. In particular, the treatment of COVID-19 has played an important role worldwide. TCM has accumulated rich experience in the treatment of CHF because of its significant increase in myocardial contractility, cardiac hemodynamics, and patients' quality of life, leading to reduced readmission rate and mortality and improved long-term prognosis . Moreove+ 5.0, and who received conventional Western treatment but did not apply TCM. Using the model, we not only choose the predictors that have a greater impact on the 5-year mortality of patients with CHF induced by CHD but also automatically assigned quantitative weights to the predictors. The intensity of each factor's effect on the 5-year death was then determined to formulate individualized interventions. During verification, the constructed model showed good performance in terms of identification ability, calibration degree, and clinical benefit. Thus, this 5-year survival prediction model can not only guide doctors for the prevention and treatment of CHD patients induced by CHD but also provide evidence for TCM intervention for patients with CHF.The use of predictive models in patients with CHF has become a standard for routine clinical practice. It helps to better identify high-risk groups that require more rigorous treatment and guide the rational allocation of medical resources. The nomogram is commonly used in cancer and other diseases because it can be applied to individual patients and consider several clinical variables to diagnose a disease or predict prognosis. In recent years, a large number of HF prediction models have been established to provide good support for the prevention and treatment of HF , 26. ForThis study offers the following advantages. (1) HIS is used to extract clinical data, which is more complete and reliable, providing good data support for the real-world study. (2) most CHF prediction models paid more attention to the survival status for 1−3 years, while this study carried out a 5-year follow-up, which provides a direction for improving the treatment strategy of CHF patients with a more long-term prognosis. (3) TCM intervention factors were taken into consideration and successfully proved. Strikingly, a protective effect on the 5-year survival of CHF was demonstrated by TCM, indicating a reliable intervention method for treating CHF. (4) unlike previous models in CHF prediction, one of the most common causes, CHD, was applied as an impetus when conducting this 5-year prediction model. Meanwhile, we considered the possible impact of different EF on the outcome; patients with ejection fraction (HFrEF) and midrange ejection fraction (HFpEF) were selected. (5) The existing CHF prediction models cannot be fully used because they are complicated or contain unconventional clinical indicators. Therefore, this model contains eight factors to achieve a good prediction effect, namely, objective, sensitive, economical, and easily accessible.However, there is some insufficiency to be further explored. (1) We excluded those patients whose data were missed, which may lead to selection bias. (2) To improve the accuracy of the model, this study restricts many conditions when screening the participants, resulting in a limited sample size, which may affect the accuracy of the results. (3) The long follow-up time of this study may cause lost to follow-up bias. (4) Due to the wide variety of TCM, we summed Chinese medicine injection, proprietary Chinese medicine, and Chinese medicine decoction as a comprehensive factor, which may affect the accuracy of the results. (5) Only eight out of 47 predictors were screened in this study based on Lasso regression in which the other 39 factors may be multicollinearity factors on the prognosis of HF.In summary, the model was verified and proved to exhibit good performance. This study clarifies the 5-year prognostic factors of patients with CHF induced by CHD, provides evidence for TCM intervention on CHF under real-world research, and is likely to predict 5-year mortality. Because this study used retrospective data, the current electronic medical records of TCM have problems, such as confusing and complicated symptom terminology, untrue and irregular tongue and pulse diagnosis information, and a large amount of missing data. In addition, the problem of unstructured information extraction in electronic medical records has not yet been addressed. TCM syndrome research still lacks the support of clinical “real data,” so there are fewer TCM syndrome factors included in this study. To solve this problem, our research team explored an objective collection method for TCM syndromes. Furthermore, we will use the mature four-diagnosis instrument to collect TCM syndrome information, hoping to provide high-quality TCM syndrome data to construct models with TCM characteristics and then constantly improve and update the model.