Acidosis frequently occurs in severe acute kidney injury (AKI), and continuous renal replacement therapy (CRRT) can control this pathologic condition. Nevertheless, acidosis may be aggravated; thus, monitoring is essential after starting CRRT. Herein, we addressed the longitudinal trajectory of acidosis on CRRT and its relationship with worse outcomes.n = 1815). Cox proportional hazard models were used to calculate hazard ratios of all-cause mortality after adjusting multiple variables or matching their propensity scores.The latent growth mixture model was applied to classify the trajectories of pH during the first 24 hours and those of C-reactive protein (CRP) after 24 hours on CRRT due to AKI , recovering group , aggravating group , and ill-being group . The pH clusters had different trends of C-reactive protein (CRP) after 24 hours; the 1st and 2nd pH clusters had lower levels, but the 3rd to 5th pH clusters had an increasing trend of CRP. The 1st pH cluster had the best survival rates, and the 3rd to 5th pH clusters had the worst survival rates. This survival difference was significant despite adjusting for other variables or matching propensity scores.Initial trajectories of acidosis determine subsequent worse outcomes, such as mortality and inflammation, in patients undergoing CRRT due to AKI.The online version contains supplementary material available at 10.1186/s12882-022-03047-4. Acute kidney injury (AKI) is a critical factor in increasing the mortality of critically ill patients admitted to the intensive care unit (ICU) –5. ContiMetabolic acidosis is an important feature in severe AKI , 18. ThiTo date, no studies have considered the acidosis trend and its relationship with outcomes after starting CRRT. Herein, we aimed to address this issue by clustering the first 24-hour trajectories of pH on CRRT, and identified that certain trajectory groups had high mortality outcomes despite adjusting multiple variables or matching propensity scores. Furthermore, the pH trends determined the subsequent trend of systemic inflammation evaluated with high sensitivity C-reactive protein (CRP) levels, which might contribute to mortality differences.n = 24) and who had end-stage kidney disease at the time of initiating CRRT (n = 91) were excluded. Patients who underwent arterial blood gas analysis less than 5 times in the first 24 hours after the initiation of CRRT (n = 467) were also excluded. Accordingly, 1815 patients were included in the analyses , ICU division, use of inotropes, application of mechanical ventilation, type of central catheter, setting of CRRT , use of bicarbonate ampoules, and anuria status. The severity of illness was evaluated using the Charlson comorbidity index (CCI) , sequentP value less than 0.05 was considered statistically significant. All statistical analyses were performed using R software .Baseline characteristics are described as proportions and means ± standard deviations when categorical and continuous variables were normally distributed and as medians with interquartile ranges when they were not normally distributed. The normality of the distribution was analyzed using the Kolmogorov–Smirnov test. A chi-square test or Fisher’s exact test was used to compare categorical variables. Student’s t test or the Mann–Whitney U test was used for continuous variables with or without a normal distribution, respectively. ANOVA with post hoc analysis was used to evaluate the difference in baseline characteristics. The latent growth mixture model was applied to classify the trajectory of change in pH and CRP. To reflect the goodness of fit of the linear-linear latent growth mixture model, the Akaike information criterion, Bayesian information criterion, and sample-adjusted Bayesian information criterion were used. Cox proportional hazard models with and without stepwise adjustment of multiple variables were used to calculate the hazard ratio (HR) of mortality outcomes. We tested the proportional hazard assumption using the Schoenfeld test. Kaplan–Meier survival curves were drawn, and differences in the curves were determined using a log-rank test. Because many baseline parameters differed between groups, propensity score-based matching with inverse probability treatment weighting was additionally performed. All baseline variables were used to calculate propensity scores. A two-tailed 2 was 35.6 ± 12.5 mmHg. Partial pressure of carbon dioxide (pCO2) was significantly higher in the 5th cluster compared to the 1st cluster . Fig. P < 0.001). The mortality risk increased from the 1st cluster to the 5th cluster, irrespective of adjusting for multiple variables , 1193 patients (65.7%) died. The mortality incidence was 26.7 deaths per 1000 patient-days. The all-cause mortality rates were 44.3, 55.7, 74.2, 78.2 and 82.2% from the 1st cluster to the 5th cluster, respectively (To evaluate the subsequent inflammatory status, clustering was performed using the CRP values between 1 and 3 days on CRRT. A total of 3 clusters were identified by distinctive trends of CRP Fig. . The 1stSince AKI patients who require CRRT are in critical condition, clinicians need to consider the patient status, including vital signs, biochemical results, imaging tests, and medical history. Among these, acidosis is one that reflects hemodynamics, respiration, and tissue oxygenation at once , 24. HerThe detrimental effect of acidosis on patient outcome has been documented, particularly in chronic kidney disease –27. TherSimilar to chronic kidney disease, metabolic acidosis may be associated with poorer outcomes in AKI patients –39. AcidThe process of acidosis begins with the formation of free radicals, which leads to oxidative stress and results in endothelial dysfunction; finally, cytokines are released that make it difficult to maintain an appropriate blood pressure level –44. Furt2, anion gap, and lactic acid, were not traced, making it difficult to interpret the independent relationship with outcomes. Nevertheless, we collected information such as baseline pCO2, anion gap, and lactate to differentiate the early causes of acidosis in each pH cluster. Finally, time-varying ICU cares, including shifts in catheter site, changes in CRRT settings, and inotropic dosage were not considered in the analysis.The present study has strengths, such as no missing values and concrete statistical analyses. Nonetheless, there are certain limitations to be discussed. First, because the study design was retrospective in nature, the results could not determine causality between acidosis trajectories and outcomes. Selection bias and residual confounding factors might exist, although we used matching methods to overcome them. Second, other pH-related biochemical parameters, such as pCOIn conclusion, acidosis trajectories determine subsequent worse outcomes, such as high mortality and systemic inflammatory response in patients starting CRRT due to AKI. Accordingly, precise monitoring of acidosis on CRRT may be helpful to predict patient outcomes. However, randomized clinical trial is needed to determine whether pH correction during CRRT improves the survival rate in patients with specific pH groups and to overcome the limitation of retrospective nature. Future trials will address other clinical outcomes, such as renal recovery and ventilator weaning. Hopefully, the present results will be a conceptual rationale for clinical trials with acidosis correction.Additional file 1:Table S1. Baseline patient characteristics after extreme gradient boosting model with inverse probability treatment weighting-based propensity scores matching.Additional file 2:Table S2. Baseline serum pH determinants according to the pH clusters.


Chronic kidney disease (CKD) is a progressive disease with high incidence but early imperceptible symptoms. Since China’s rural areas are subject to inadequate medical check-ups and single disease screening programme, it could easily translate into end-stage renal failure. This study aimed to construct an early warning model for CKD tailored to impoverished areas by employing machine learning (ML) algorithms with easily accessible parameters from ten rural areas in Shanxi Province, thereby, promoting a forward shift of treatment time and improving patients’ quality of life.From April to November 2019, CKD opportunistic screening was carried out in 10 rural areas in Shanxi Province. First, general information, physical examination data, blood and urine specimens were collected from 13,550 subjects. Afterward, feature selection of explanatory variables was performed using LASSO regression, and target datasets were balanced using the SMOTE (synthetic minority over-sampling technique) algorithm, i.e., albuminuria-to-creatinine ratio (ACR) and α1-microglobulin-to-creatinine ratio (MCR). Next, Bagging, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were employed for classification of ACR outcomes and MCR outcomes, respectively.12,330 rural residents were included in this study, with 20 explanatory variables. The cases with increased ACR and increased MCR represented 1,587 (12.8%) and 1,456 (11.8%), respectively. After conducting LASSO, 14 and 15 explanatory variables remained in these two datasets, respectively. Bagging, RF, and XGBoost performed well in classification, with the AUC reaching 0.74, 0.87, 0.87, 0.89 for ACR outcomes and 0.75, 0.88, 0.89, 0.90 for MCR outcomes. The five variables contributing most to the classification of ACR outcomes and MCR outcomes constituted SBP, TG, TC, and Hcy, DBP and age, TG, SBP, Hcy and FPG, respectively. Overall, the machine learning algorithms could emerge as a warning model for CKD.ML algorithms in conjunction with rural accessible indexes boast good performance in classification, which allows for an early warning model for CKD. This model could help achieve large-scale population screening for CKD in poverty-stricken areas and should be promoted to improve the quality of life and reduce the mortality rate. Chronic kidney disease (CKD) is defined as renal structural or functional abnormalities for 3 months, with a prevalence of 13.4% worldwide . More seAccurate diagnosis is closely related to the detection of CKD. Undoubtedly, albuminuria and α1-microglobulin serve as a good approach for early CKD screening in tertiary hospitals. Yet, when it comes to the rural areas in China, health care workers, critical care units, emergency facilities, health services and medical examinations are not necessarily guaranteed –6. In suPrevious studies demonstrated that some risk factors, such as demographic, lifestyle, and blood biochemical parameters could be used to predict disease occurrence , 9. AlsoIn this study, we aimed to employ machine learning algorithms for early screening of CKD and developed a warning model targeted at poverty-stricken areas using demographic, blood biochemical and physical examinations data from ten rural areas in Shanxi Province, intending to achieve a larger-scale CKD screening programme at a lower cost  where possible, thus shifting forward its treatment window and improving quality of life.From April 2019 to November 2019, Shanxi Provincial People’s Hospital carried out opportunistic screening for chronic kidney disease for residents over 40 years old in Ningwu County, Lu County, Yangqu County, Linxian County, Shouyang County, Zezhou County, Huozhou City, Hejin City, Linyi County and Ruicheng County in Shanxi Province. Up to 13,550 residents participated in this screening and 12,285 eventually enrolled in the study, including 5,206 men and 7,079 women aged 41–91 years. All study subjects signed informed consent forms and it was approved by the Shanxi Provincial Hospital Ethics Committee (No. 2021213).Inclusion criteria: (1) Residents aged 40 years and above; (2) Participants without communication barriers; (3) Participants understanding the study significance and willing to sign a written informed consent; (4) participants without cognitive impairment or mental illness. Exclusion criteria: (1) incomplete information recorded; (2) poor compliance; (3) pregnant women or those with a history of drug abuse.Data is collected through questionnaires, physical examinations, and laboratory analysis. The questionnaire included demographic information , lifestyle . Questionnaires were conducted online and were completed by the subjects themselves or their family members. Physical examination comprised height, weight and systolic blood pressure (SBP), diastolic blood pressure (DBP), measured twice and averaged. All data were measured by medical professionals. Body mass index (BMI) was calculated by weight in kilograms divided by the square of height in meters.1-microglobulin (α1MG), urine creatinine (UCr) and microalbuminuria (MAU), respectively.Fasting venous blood was collected from subjects for fasting blood glucose (FPG), glycated haemoglobin (GHb), homocysteine (Hcy), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein (HDL-C). Urine specimens were collected from subjects. After centrifugation of 3,000 r/min for 10 min, the supernatant (low-speed centrifuge Anhui Zhongke Zhongke A SC3616) was extracted, and latex turbidity, sarcosine oxidase and immunoturbidimetry were employed for detection of α2), normal (18.5–24.0 kg/m2), overweight (24.0–28.0 kg/m2), obesity (≥28 kg/m2). ACR was defined as MAU divided by UCr multiplied by 8.84; MCR was defined as α1MG divided by UCr multiplied by 8.84.Study participants’ annual income, educational levels, health history, and lifestyle information was obtained from the questionnaire. Annual income was defined as <5K yuan, 5–10K yuan, 10–20K yuan, >20K yuan; educational attainment was defined as ≤ primary school, ≤ middle school, ≤ high school, ≥ college; smoking was classified as yes or no; alcohol consumption was classified as always (>100 g/time and 3 times/week), sometimes (<3 times/week or < 100 g/time) and rarely; exercise was classified as “none or a little” or “regular” . BMI was defined as underweight , lifestyle , blood biochemistry , physical examination indexes , a total of 20 variables. The response variables were defined as ACR outcomes or MCR outcomes with two classes  ACR ≥ 30 mg/g was defined as increased ACR; MCR > 23 mg/g was defined as increased MCR. The increased ones were assigned 1, and the normal ones were assigned as 0.Since missing data is not an issue of great severity in this study, we excluded those samples with missing values, without making a data imputation. Then, Absolute Shrinkage and Selection Operator (LASSO) was used to select features that are more relevant to the response variables, the increased ACR and the increased MCR. Afterward, Synthetic Minority Over-Sampling Technique (SMOTE) was utilized to balance the classes to enable the machine learning models to better learn the data features, thus making the best classification. The workflow was shown in LASSO represents one of the commonly used feature selection methods. It is characterized by adding L1 regularization penalty term when fitting generalized linear regression, so that the sum of absolute values of regression coefficients is less than a certain value. Its purpose is to minimize the sum of squares of residuals, and force the regression coefficients of variables that contribute less to the model to compress to zero, enabling a feature sparsity process , 12. It Imbalanced datasets are not unusual in medical research, because the number of non-patients is extremely larger than that of patients, which serves as an obstacle to predictive performance . The SynThree models, i.e., RF , 19, BagEvaluation parameters include Accuracy (1), Specificity (2), Sensitivity (3) and Area under the receiver operating curve (AUC). When patients with kidney disease are classified as patients, the prediction is defined as True Positive (TP), and when a healthy person is classified as healthy, the prediction is defined as True Negative (TN). In addition, if healthy subjects are considered patients, the prediction is False Positive (FP); Similarly, False Negative (FN) if patients are considered healthy subjects. Accuracy is to evaluate how accurate the machine learning algorithms are to detect what it is supposed to measure. Specificity is the ability to correctly exclude those without renal conditions and Sensitivity is to correctly identify those with renal conditions.25, P75), as appropriate. Model construction: The datasets were divided into training set (80%) and testing set (20%). The former is used for model training, i.e., e, RF, Bagging, and XGBoost, while the latter is used for model performance evaluation. The comparisons between training set and testing set were conducted using t-test or non-parameter test for quantitative variables, and Chi-square test for qualitative variables. All analyses were implemented in R software (version 4.0.3).Qualitative data are described as percentages (%), and quantitative data are expressed as mean standard deviation (M±SD) or median ± interquartile (PP < 0.05), as shown in A total of 12,330 rural residents participated in this study, of whom 5,230 were men and 7,100 were women aged 40–91 years. There were 1,587 (12.8%) cases with increased ACR and 1,456 (11.8%) cases with increased MCR, as shown in As shown in When constructing models for ACR outcomes, the number of increased ACR and normal ACR in the training set were both 4,932, and 1,233 in the testing set, respectively. When constructing model for MCR, the number of increased MCR and normal MCR in the training set were 4,973 and 4,891, respectively, and 1,273 and 1,192 in the testing set. The Accuracy, Sensitivity and Specificity of Bagging, RF and XGBoost reached over 99.00% in the training sets, and the AUC reached 0.99, as shown in Since XGBoost performs best in the classification, we indicated the contribution of the explanatory variables to the model by Gain, and the larger the Gain, the more important the variables were for the XGBoost model. The five variables that contributed most to the classification of ACR outcomes in the XGBoost model represented SBP, TG, TC, and Hcy, DBP. The five variables that were most important for the classification of MCR outcomes constituted age, TG, SBP, Hcy and FPG .To our knowledge, this study was the first one to employ machine learning algorithms in conjunction with existing indicators readily available in rural areas to construct an early warning model for CKD. In constructing the model, demographic information, physical examination and blood biochemical were taken as the explanatory variables; ACR  and MCR In 2012, Logistic Regression (LR) was employed for CKD prediction, but the model is accompanied by some defects , tumors In our previous work , we alsoThis study demonstrates that XGBoost represents the best classifier because the algorithm is a serial integrated learning algorithm based on Gradient Boosting Decision Tree that builds boosting trees in parallel by dependency generation . The objIn hypertension, the early glomerular filtration rate could remain normal, but when arterial pressure is constantly rising, exceeding the kidney’s ability to self-regulate, it leads to glomerular hyperperfusion, which leads to damage to the visceral epithelial cells of the renal tubules, increasing the permeability of the glomerular basement membrane and thus causing proteinuria , while lWe think our early warning model is practical and down-to-earth in rural China. As the largest developing country confronted with underdeveloped healthcare systems and aging population , China iSome limitations also stand out in this manuscript. First, this study was based on a cross-sectional survey, and we did not conduct a follow-up for patients with proteinuria. Second, we constructed the models with data from Shanxi Province, and there is no other data for verification. Our next step is to collect more data from other regions to test the models’ generalization. Additionally, cost-effectiveness was initially considered in this study, and other indicators reflecting CKD, such as blood creatinine, were not collected, which is our next focus. Besides, we did not collect a detailed history of smoking, alcohol consumption and dietary intake. More detailed information would allow for a more accurate and valid model.In short, CKD has emerged as a global public health issue and its early diagnosis is of great importance. In rural China where primary health care service system and health education remain to be improved and strengthened, how to construct a warning model for CKD targeted at poverty-stricken areas is of great necessity and significance. In this study, we proposed a warning model using the available and accessible demographical, blood biochemical and lifestyle data in conjunction with machine learning approaches for rural areas. This model offers a leg-up for early CKD-assisted diagnosis in rural areas, which facilitates tailoring precise management and therapy for patients, thus, improving their quality of life and slowing the mortality rate.Data supporting the conclusions in this manuscript would be made available from the corresponding author upon reasonable request.The studies involving human participants were reviewed and approved by Ethics Committee of Shanxi Provincial People’s Hospital. The patients/participants provided their written informed consent to participate in this study.WS and YLu were responsible for the data analysis and the writing of the manuscript. LQ and JQ helped polish the manuscript. AL, YZ, and YLi gave precious advice on the statistical methods. RL and XZ were responsible for the conception and design of the research. All authors read and approved the final draft.


P < 0.001), sensitivity was 0.90, specificity was 0.93, positive predictive value was 0.83 and negative predictive value was 0.96. Smartwatch can accurately estimate cfPWV to evaluate arterial stiffness. This method is simple and feasible and is suitable for people to actively and early monitor vascular elasticity.To evaluate the accuracy of the smartwatch in estimating carotid-femoral pulse wave velocity (cfPWV). A cohort of gender-matched volunteers aged 18–80 years were recruited. At the sitting and supine positions, cfPWV was measured alternately by smartwatch and CompliorAnalyse, for each participant, and nine sets of data were collected from each participant with a 60 s interval between measurements. The accuracy of cfPWV measurement for smartwatches was assessed using mean error (ME) and mean absolute error (MAE), while the consistency of the two methods was assessed using the Bland-Altman analysis and concordance class correlation. A total of 347 participants were enrolled. The mean cfPWV was 9.01 ± 2.29 m/s measured by CompliorAnalyse and 9.06 ± 1.94 m/s by smartwatch. The consistency correlation coefficient (CCC) was 0.9045 (95% CI 0.8853–0.9206), the ME was 0.046 ± 0.92, and the MAE was 0.66 (95% CI 0.59–0.73). Bland-Altman analysis showed that the error of 95% samples was in the range between −1.77 m/s and 1.86 m/s. The Kappa value of cfPWV greater than 10 m/s was 0.79, the area under the ROC curve was 0.97 ( Arteriosclerotic disease is a sneaky, progressive condition. Patients will experience vascular functional disease first, followed by structural vascular disease. Clinically, patients may be asymptomatic and have increased arterial stiffness. Arteriosclerosis and atherosclerosis will worsen over time if the patient does not pay attention, leading to cardiovascular and cerebrovascular diseases –4. At prHuawei Smart Terminal has developed a mobile wearable smartwatch that can assess health status and has successfully launched it on the market. Multi-function sensors on the smartwatch collect the wearer’s ECG, pulse, and other data . The pulAccording to statistical requirements, at least 320 participants were recruited from the social population, with at least 100 participants from each of the three age groups, namely < 30 years old, 30–60 years old, and > 60 years old. This study was approved by the Ethics Committee of Peking University People’s Hospital, with the ethics approval number: 2019 PHB-196-01, and all participants filled in the informed consent form before participating in the study.a.>18 years old, and gender is not limited;b.Healthy participants with hypertension, diabetes, or dyslipidemia considered;c.Participants with normal heart rhythm (no arrhythmia/pulse disturbance during measurement);d.Participants who can cooperate with medical staff to conduct arteriosclerosis detection;e.Participants who can voluntarily join the group and sign the informed consent.a.Patients with a definite diagnosis of cardiovascular and cerebrovascular diseases;b.People with mood disorders  and conditions that are not suitable for continuing arterial elasticity measurement;c.People with peripheral arterial occlusive lesions of the left and right carotid arteries, upper extremity arteries, iliac arteries, and femoral arteries are unable to perform the PWV test;d.Participants may be rejected for any reason;e.The researcher believes that the candidate is ineligible for consideration.Gender, age, height, weight, and medical history were all recorded as part of the patient’s general clinical information . Each participant sat in the clinic for 15 min and had their heart auscultated for 2 min, according to the 2018 Chinese Guidelines for the Prevention and Treatment of Hypertension , to exclvia the Bluetooth protocol. Next, they installed and opened the matching APP on the phone.Following rigorous training, cardiovascular specialists and technicians at Peking University People’s Hospital measured nine sets of data for each subject. The specific operations are as follows: The researchers turned on the terminal smartwatch (model: HUAWEI WATCH GT 2 Pro ECG) from 8:00 to 10:00 in the morning to match the mobile operating system (Android or Hongmeng) After 10 min of rest, the participant’s left hand wears a smart terminal watch, and the right hand gently touches the collector on the watch. The participant’s electrocardiogram and photoplethysmography (ECG + PPG) data are collected to calculate the cfPWV . Each measurement time is 30 s, with each interval of 60 s. The data can be viewed and recorded using the corresponding mobile phone APP. The data is presented in HW-cfPWV units. If the difference between the three measurements is greater than 0.5 m/s, a measurement is added, and an average is calculated.The participant takes a supine position, and the professional tester measures the distance between the left carotid artery and the femoral artery using the French Comparality Automatic pulse wave velocity measurement system, model number: CompliorAnalyse . Set the pressure receiver to two arteries and fine-tune it until the screen displays the correct pulse waveform. When measuring the participant’s 10 heart cycles, it is considered effective if the error range fluctuates within 5%. The measurement was carried out three times. Complior-cfPWV is used to represent the obtained data. If the difference between the three measurements is greater than 0.5 m/s, add one measurement and calculate the average value to complete the measurement.In the lying position, the participant collects ECG + PPG information (measurement for 30 s) by wearing (left hand) the Huawei smart watch (measurement for 30 s). Simultaneously, the cfPWV is measured, and two measurements are alternately performed , each time the interval is 60 s.In this study, we used the measurement results of Complioranalyse as a gold standard for cfPWV and compared the same research object using a cfPWV result measured by an intelligent terminal watch algorithm shown in Statistical analysis were conducted in RStudio  and NCSS . Continuous variables are described by mean ± standard deviation, and categorical variables are expressed as numbers (percentage).Concordance Class Correlation (CCC) is used to evaluate the overall agreement between the testing device and the gold standard device.ME (Mean Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) are used to measure the specific error between the testing device and the gold standard device.Bland-Altman analysis: Bland-Altman plot was used to visualize the characteristics of relative difference (testing device minus gold standard device). 95% LoA (limits of agreement) was used to determine the acceptable error range.Generalized linear models are used to analyze the factors influencing the measurement error.Kappa, ROC curve analysis are used to analyze the agreement and detection capability of the testing device in determining atherosclerosis (cfPWV > 10 m/s) with the gold standard device.2, no hypertension, diabetes, hyperlipidemia, etc.). Further, 104 patients suffering from hypertension, diabetes, and blood lipids were included. The average age of the participant was 51.88 ± 18.50 years old, with an average systolic pressure of 118.52 ± 15.46 mmHg, with an average diastolic pressure of 74.52 ± 9.15 mmHg, as shown in A total of 347 participants , including 243 healthy people , discrete trend indicators , and other indicators are similar shown in The results showed that the Concordance Correlation Coefficient (CCC) is 0.9045, with a 95 percent confidence interval (CI) of 0.8853–0.9206 and a Pearson Correlation Coefficient (Pearson) of 0.9182, indicating that the two sets of data consistency are better .P-value < 0.0001 when age, gender, blood pressure, heart rate, hypertension, whether the participant takes medicine, and other layers are considered , and the B-A standard error (Me) was 0.046, the B-A standard deviation (STD) was 0.92, with the 95% sample measurement error distribution ranging from −1.77 m/s (95% CI −1.9395 to −1.6044) to 1.86 m/s (95% CI 1.6966 to 2.0316). The error is not a random distribution in the figure, and there is a linear relationship between the actual value and the error .t-test or non-parametric method, Wilcoxon Signed-Rank Test was used, and the statistical power is 0.99. The results show that the cfPWV value calculated from the HW smartwatch is equivalent to the Complior-cfPWV equivalent (the mean error is within ± 1 m/s), as shown in The deviation of the cfPWV level measured by both HW-cfPWV and Complior-cfPWV in 347 people was 0.046. The cfPWV measurements have passed the equivalent test of 1 m/s, regardless of whether the paired According to the International Artery Association Guide , the folP < 0.001, the sensitivity is 0.899, the specificity is 0.927, indicating that the smartwatch measurement has a good ability to identify arteriosclerosis (cfPWV > 10 m/s), as shown in The ROC curve of HW-cfPWV and Complior-cfPWV is 0.968, Using cfPWV = 10 m/s as a CUT-OFF value, the consistency of HW-cfPWV and Complior-cfPWV in detecting arteriosclerosis (cfPWV > 10 m/s) is examined. The KAPPA value is 0.792, indicating that the two methods have good consistency in judging arteriosclerosis (cfPWV > 10 m/s), as shown in The MAE detected by HW-cfPWV was 0.657 (95% CI 0.587–0.726), RMSE was 0.927 (95% CI 0.806–1.035), and MAPE was 7.385% (95% CI 6.615% –8.154%). Although the error between the HW-cfPWV and the Complior-cfPWV measurement value is small, the error distribution is not normal, indicating that the error is affected by some non-random factors. From an individual perspective, 95% of the individual measurement error B-A is between −1.78 m/s to 1.87 m/s . The layArteriosclerosis is a vascular disease that worsens with age and increased risk factors (blood pressure and lipid levels) . NowadayThe elasticity and stiffness of the elastic aorta, which are less affected by vasoactive substances in the blood, can be measured using cfPWV, and the results are repeatable. cfPWV is an important index for evaluating arterial stiffness that is currently accepted and recommended by domestic and international hypertension guidelines and early assessment content for arteriosclerosis caused by hypertension , 9. Incrvia the smart terminal and developed the parameter of arterial elasticity estimation, namely cfPWV, using the actuarial calculation of machine learning. In this study, the Complior arteriosclerosis analyzer was used as the “gold standard” for cfPWV measurement, and the cfPWV estimated by smartwatches was compared with that of arteriosclerosis measurement instruments. The results of HW-cfPWV and Complior-cfPWV have high consistency (Kappa = 0.79) and diagnostic performance in judging arteriosclerosis (cfPWV > 10 m/s) area under the ROC curve 0.968, with sensitivity 0.899, specificity 0.927, positive predictive value 0.83, negative predictive value 0.96, macroscopic F1 value 86%. The results of multiple measurements and different body positions show that the smartwatch has good accuracy in measuring cfPWV, with a consistency correlation coefficient (CCC) of 0.9 and a mean absolute error of 0.66 m/s. The evaluation standard for the accuracy of non-invasive equipment for pulse wave velocity measurement is given based on the mean error (0.046 m/s) of two Bland-Altman analyses, according to the guidelines of the International Arterial Association (Using mobile wearable devices has begun to develop active health models in the digital age. Mobile smartwatches can detect many physiological parameters, including heart rate, respiration, sleep, blood oxygen, and other parameters. These features are crucial in guiding active health for watch wearers. Many smartwatches can now measure ECG function and detect arrhythmia in real-time, allowing for atrial fibrillation screening , 22–25.  cfPWV >  m/s areaHowever, a generalized linear model also showed a statistically significant association between age and systolic blood pressure. Bland-Altman stratification showed that the LoA was wider in those aged > 60 years and SBP ≥ 140 mmHg, indicating that age and blood pressure were the factors affecting the absolute value error. It is speculated that the reason may be due to age-related changes in arterial blood vessels. Peripheral arteriosclerosis is more common in the elderly and people with high blood pressure , 27. RadSmartwatches have a specific value in assessing vascular stiffness in young and middle-aged people. Our previous study  found thLimitations: The study found that the cfPWV obtained by current smartwatches has certain errors in older patients with elevated blood pressure. The current HW smartwatch for cfPWV determination is more suitable for self-monitoring of people aged < 60 years and SBP < 140 mmHg due to the limited population, particularly the number of patients older than 70 years old with hypertension and diabetes. However, it has some limitations as a medical diagnostic tool. Thus, it is recommended to go to the hospital for corresponding examinations if the smartwatch has repeatedly measured cfPWV for the same age or people with comorbidities. The study of using smartwatches to assess the accuracy of cfPWV in elderly and hypertensive patients is presently underway.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.NS participated in the research design, guided the experiment process, and wrote the full text. LW participated in the research design, research test, and revision of the article. YX, HW, YYC, and JL participated in the revision of the article. FY and YXC participated in the study’s testing. ZZ was responsible for the statistical analysis of the data and the revision of the article. All authors contributed to the article and approved the submitted version.The 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.


Apart from modulating systemic hemodynamics, NE may also affect regional blood flow, such as the hepatic circulation, which contains a wide variety of adrenergic receptors. It may alter regional vascular tonus and hepatic blood flow (HBF) by reducing portal vein flow (PVF) or hepatic arterial flow (HAF). The aim of this study was to assess the effects of NE on HBF.Norepinephrine (NE) is a αPatients scheduled for pancreaticoduodenectomy were included. All patients received standardized anesthetic care using propofol and remifentanil and were hemodynamically stabilized using a goal-directed hemodynamic strategy guided by Pulsioflex™. On surgical indication, somatostatin (SOMATO) was given to reduce pancreatic secretion. HBF measurements were performed using transit-time ultrasound (Medistim™). Baseline hemodynamic and HBF measurements were made after pancreatectomy, at T1. Afterwards, NE infusion was initiated to increase mean arterial pressure (MAP) by 10 – 20% of baseline MAP (T2) and by 20 – 30% of baseline MAP (T3). HBF and hemodynamic measurements were performed simultaneously at these three time-points.p < 0.01) by a reduction HAF (p < 0.01), while the effect on PVF remained unclear. Post-hoc analysis showed that SOMATO-treated patients had a significant lower PVF at baseline (p < 0.05), which did not change during NE infusion. In these patients, reduction of total HBF was primarily related to a reduction of HAF (p < 0.01). In untreated patients, NE infusion reduced total HBF both by a reduction HAF (p < 0.01) and PVF (p < 0.05).A total of 28 patients were analyzed. Administration of NE significantly increased MAP but had no effect on cardiac index. NE infusion reduced total HBF in all patients .NCT03965117 (28 – 05 – 2019).Clin.trail.gov:  The association between maintaining adequate perioperative blood pressure and postoperative preservation of organ function is well documented , 2. Use Norepinephrine (NE) is a commonly used vasopressor during goal-directed hemodynamic therapy (GDHT) to increase blood pressure . HoweverThe splanchnic system is an important blood reservoir and redistribution of blood from the splanchnic circulation by venoconstriction, highly contributes to venous return, cardiac output and systemic hemodynamics . In addiHepatic blood flow contributes to the splanchnic circulation. Indeed, regulation of splanchnic blood flow is determined by splanchnic vascular resistance sites, which are located mainly in the pre-portal arterial vasculature, by hepatic arterial vasculature, and by portal venous vasculature –11. ThesThe aim of the study was to assess the effect of NE infusion on HBF and systemic hemodynamic variables in hemodynamically stabilized patients.−1), severe heart failure (ejection fraction < 25%), hemodynamic instability, atrial fibrillation, sepsis, body mass index > 40 kg m−2, severe coagulopathy (INR > 2), thrombocytopenia (< 80 × 103 μL−1), end stage liver disease or pregnancy and breastfeeding women.The study was approved by the ethics committee of the Ghent University Hospital (EC: 2019/0395) and registered under EudraCT number: 2018–004,139-66 (25 – 03 – 2019) and at Clin.trail.gov number NCT03965117 (28 – 05 – 2019). After written informed consent, patients of both genders aged between 18 and 80 years, scheduled for pancreaticoduodenectomy (Whipple`s procedure) and with an American Society of Anesthesiologist (ASA) physical status I to III were included. Exclusion criteria were allergy to norepinephrine, renal insufficiency  and portal pressure (PPorta). The secondary objective of the study was to evaluate the effects of NE infusion on systemic hemodynamic variables such as systemic vascular resistance indexed (SVRi), portal venous resistance indexed (PVRi), cardiac index (CI), blood loss and total fluid administration. Post-hoc analysis was performed on patients who received somatostatin (SOMATO) to evaluate its effect on HBF and the interaction with NE.The primary objective was to assess – in hemodynamically stabilized patients – the effect of NE on hepatic blood flow (HBF) and hepatic vascular pressures such as caval pressure  and propofol was titrated to remain a BIS value between 40 and 60. For intraoperative analgesia, TCI remifentanil (Minto model) was used. TCI remifentanil was started at an effect site concentration of 5 ng ml−1 and titrated according to heart rate and blood pressure. Neuromuscular blockade was achieved using rocuronium, 1 mg kg−1 ideal body weight at induction and intermittent boluses during surgery. An additional bolus of rocuronium 10 mg was given before the experimental measurements. After tracheal intubation and lung recruitment, mechanical ventilation was started with a tidal volume 8 – 10 ml kg−1 ideal body weight, respiratory rate 14 – 16 min−1 and a positive end-expiratory pressure of 5 cmH2O. Arterial blood gas analysis was used to adjust ventilatory settings.All patients received standard anesthesia care according to the departmental written protocol. Before induction of anesthesia, an epidural catheter was placed for postoperative analgesia but only used at the end of surgery, after all experimental measurements were performed. Induction and maintenance of general anesthesia was obtained using target-controlled infusion (TCI) of propofol (Schnider model), starting at an effect site concentration of 5.0 mcg ml−1 h−1 was administered. The hemodynamic goal was a CI > 2.0 L min−1 m−2 with a MAP > 60 mmHg and a pulse pressure variation (PPV) < 12%. When PPV was > 12% a bolus of 200 ml crystalloids  was administered. In case of bleeding, 200 ml colloid , was administered. Intraoperative hypotension was defined at MAP < 60 mmHg. When CI was > 2.0 L min−1 m−2 in the presence of a MAP < 60 mmHg, a NE infusion was started at 0.1 mcg kg−1 min−1 and titrated according to the MAP. To temporarily bridge the latency of effect the NE infusion, boluses of ephedrine 3 mg were administered when heart rate (HR) was less than 60 beats per minute or phenylephrine 100 mcg, if HR > 60 beats min−1. At the discretion of the surgeon, some patients received SOMATO, prior to the pancreatic resection, to reduce pancreatic secretion. In that case, SOMATO 240 mcg bolus was administered, followed by a continuous infusion of SOMATO of 6 mg per 24 h. For postoperative analgesia, all patients received clonidine 150 mcg and magnesium 2 g intravenously after opening of the abdominal cavity. At the end of surgery, all patients received 1 g paracetamol and 10 – 15 ml ropivacaine 0.15% epidurally. For prevention of postoperative nausea and vomiting, dexamethasone 5 mg was administered before induction of anesthesia and ondansetron 4 mg was given at the end of surgery. A nerve stimulator was used to assess evoked muscular response with train-of-four ratio. Reversal of neuromuscular block was achieved using sugammadex according to train-of-four ratio.Hemodynamic monitoring was performed using a PiCCO catheter  which was placed in the left femoral artery and a central venous catheter which was placed in the right jugular vein. All patients received a standardized goal-directed hemodynamic therapy (GDHT). A baseline crystalloid infusion  of 3 ml kgHemodynamic variables were measured and recorded using Pulsioflex™ . After placement of the 4 or 5-Fr arterial catheter in the femoral artery, the pulse contour analysis was calibrated using 3 boluses of 20 ml of cold saline. The hemodynamic variables used were HR, central venous pressure (CVP), MAP, CI and PPV. Calibration was repeated before experimental measurements were initiated.−1. At the same time, the pulsatility index (PI) was calculated. It quantifies pulsatility of a blood flow wave which represents vascular resistance of the blood vessel downstream.TTFM was used to measure HAF and PVF. Different probe sizes were used according to the type and size of the vessel (range 2 – 12 mm) and blood flow was expressed in ml min−1 solution) was started at 0,1 mcg kg−1 min−1 to increase MAP by 10 – 20% of baseline MAP (T2) and 20 – 30% of baseline MAP (T3). NE infusion was given through a dedicated lumina of the central line and a continuous infusion of saline at 50 ml.h−1 was used as drive line. NE was titrated according to MAP with 0.5 ml.h−1. Before each measurement, a stabilization period of 5 min was allowed, to ensure steady state hemodynamic conditions. At each specific timepoint hepatic blood flows and pressures were measured together with the systemic hemodynamic data. Simultaneously with flow measurements, pressure measurements were performed in the portal and caval vein. A 25-gauge needle was placed directly in the vein and connected to a pressure transducer. All measurements were performed during apnea to minimize the effect of ventilation.After pancreatectomy, baseline MAP was recorded at T1. NE infusion  was calculated and indexed to body surface area:To address the patient fluid status, mean circulatory filling pressure (Pms) and heart performance (Eh) were calculated :Pms is determined by CVP, MAP and CO and expressed in mmHg:Factor c is depending on length, weight and age:The heart performance (Eh) can be calculated according to following formula:Vascular tone of the hepatic artery in the different experimental conditions, was quantified by calculating the vascular conductance :\documenFor sample size calculation, a previous pilot trial with similar methodology was used . Based on = 20) or absence  of SOMATO administration. The statistical model was updated by adding these groups as interaction factor to the fixed effect. All statistical tests were performed using R (version 3.3.3) [To assess a potential effect of the concomitant administration of SOMATO on the effect of NE, a post hoc analysis was performed. Patients were divided based on presence , inability to place a PiCCO catheter due to atheromatous plaques in the femoral artery (n = 4), intraoperative arrythmia (n = 1), intraoperative anaphylactic shock with hemodynamic instability (n = 1), vasospasm of the hepatic artery requiring papaverine (n = 3), failure of the technical device (TTFM) (n = 1), patient withdrawal prior to inclusion (n = 2) and target MAP not reached (n = 1). Figure n = 20) and those who were not treated with SOMATO . Patient characteristics are listed in Table Between 28/05/2019 and 19/10/2020 a total of 113 patients were screened for eligibility. Registration of the first patient (number 01) was done on 29/05/2019. A total of 49 patients were included of which 21 dropped out, due to inoperability (Intraoperative characteristics are summarized in Table −1 min−1 (SD 0.03 mcg kg−1 min−1) at T2 and 0.101 mcg kg−1 min−1 (SD 0.05 mcg kg−1 min−1) at T3. All patients met the pre-defined hemodynamic targets and none of the patients necessitated NE before the experimental measurements. Both ephedrine and phenylephrine were used to counteract post-induction hypotension (respectively 10.5 mg (SD 7.9 mg) and 270 mcg (SD 310 mcg). All patients were hemodynamically stabilized by the time of the first measurement. The pre-defined hemodynamic targets were met at baseline T1 which can be found in Table Data are summarized in Table −1 m−2 (SD 182 ml min−1 m−2) to 465 ml min−1 m−2 (SD 149 ml min−1 m−2) at T2 (p < 0.01) and 458 ml min−1 m−2 (144 ml min−1 m−2) at T3 (p < 0.01). This reduction is the result of a significant reduction in HAFi from 215 ml min−1 m−2 (SD 118 ml min−1 m−2) at T1 to respectively 163 ml min−1 m−2 (SD 83 ml min−1 m−2) at T2 (p < 0.01) and 142 ml min−1 m−2 (SD 70 ml min−1 m−2) at T3 (p < 0.01). The effect of NE on PVFi is less clear and no significant alternations are seen.Data are summarized in Table −1 m−2 mmHg−1 (SD 1.8 ml min−1 m−2 mmHg−1) at T1 to respectively 1.9 min−1 m−2 mmHg−1 (SD 1.0 ml min−1 m−2 mmHg−1) at T2 (p < 0.01) and 1.6 min−1 m−2 mmHg−1 (SD 0.8 ml min−1 m−2 mmHg−1) at T3 (p < 0.01), indicating its effect on the vascular tone of the hepatic artery. The PVRi, however, remained similar over time. Additionally, the PI remained identical.Caval and portal pressures remained unchanged with NE infusion. NE significantly decreased conductance from 3.0 ml mini in both groups. For PVFi however, effects of NE differed between group S and group NS. Patients receiving SOMATO (group S) had a significantly lower PVFi of 295 ml min−1 m−2 (SD 109 ml min−1 m−2) versus 429 ml min−1 m−2 (SD 161 ml min−1 m−2) at T1 (p < 0.05), which remained unchanged with NE. In untreated patients (group NS), NE decreased PVFi and the reduction of total HBFi is the result of a reduction in HAFi and PVFi. Data are summarized in Table Post-hoc analysis showed that SOMATO interacts with this underlying mechanism. NE dose-dependently reduced HAFThe results of the current study show that in patients under a strict GDHT protocol, administration of NE resulted in significantly reduced total HBF which was mediated by a reduction in HAF while the effect on PVF remained unclear. A post-hoc analysis showed that this response seemed to be influenced by the administration of SOMATO. SOMATO-treated patients had a lower PVF at baseline and administration of NE in these patients, lead to a reduction of total HBF primarily by a reduction of HAF while PVF remained low and unchanged. In SOMATO-untreated patients, total HBF reduction was mediated by both a reduction in HAF and PVF.We choose pancreaticoduodenectomy as experimental model for this study because – in contrast to liver surgery – this type of surgery is standardized without potential anticipated hemodynamic disturbances. Additionally, there is an easy access to hepatic vasculature during the procedure, which allowed us to perform direct flow measurements of HBF. No previous human studies have used direct flow measurements in assessing the effect of NE on HBF. TTFM was used, which is very reliable and considered to be the ¨gold standard¨ for measuring blood flow .Recent studies emphasize the importance of maintaining adequate blood pressure to prevent postoperative organ failure , 2. HearNE is frequently used as vasopressor to counteract hypotension during high-risk surgery or critically ill patients , 22. It NE could play a dual role, on the one hand by modulating macro-hemodynamic variables such as blood pressure and cardiac function and on the other hand by affecting regional HBF. Previous animal studies have shown that NE improves cardiac output and venous return by recruitment of unstressed volume into stressed volume , 23, 24.1-, α2- and β2-adrenorecptor in the splanchnic vasculature, the dosage of NE, the pre-existing sympathetic tonus of the splanchnic vasculature and the blood volume in the splanchnic circulation all may play a crucial role to predict the effect of NE [The effect of NE on splanchnic circulation is puzzling and not yet fully understood. NE affects both systemic and regional hemodynamic variables but the clinical effect of NE on splanchnic vasculature depends on different factors. Relative density of αct of NE .NE affects the splanchnic vascular resistance sites, which are scattered all over the pre-portal arterial, hepatic arterial and portal venous vasculature , 13. ChaThe effects of NE on hepatic circulation seem to be influenced by the concomitant administration of SOMATO. A total of 20 patients received—on surgical indication – SOMATO, to reduce pancreatic secretions, and protect pancreaticoduodenal anastomosis. Besides reducing pancreatic secretions, SOMATO also induces a mesenterial vasoconstriction . PVF is Interestingly, although PVF is lower at baseline, these patients did not have higher HAF as would be expected with the hepatic artery buffer response. This effect can buffer up to 70% of the reduced PVF but ultimately this buffer response seems to become exhausted and HAF returns to normal values , 33–35.As SOMATO was given before pancreatectomy at the start of the operation, a possible explanation would indeed be that the buffer response to SOMATO-induced decrease in PVF had already faded away by the time of the experimental protocol, ultimately resulting in the normalization of HAF. Of note, in the SOMATO-treated patients, NE administration did not further reduce PVF, contrary to what was observed in patients not receiving SOMATO. In these patients, the reduction of total HBF was primarily related to a reduction in HAF. The precise underlying reasons for this different response are not clear and the present study does not allow to further elucidate this issue.Our study has some limitations. First, our measurements were only performed on the HBF, which is only a part of the splanchnic circulation. As PVF is the sum of outflow of blood from the mesenteric organs, alternations in PVF are indirectly caused by the effect of NE on pre-portal arterial and portal vein vasculature.Secondly, our measurements were only performed for a short duration of NE infusion, and it remains unclear, how prolonged infusion of NE may alter HBF over time. Further research is therefore needed to evaluate, whether prolonged NE infusion affects hepatic function and clinical outcomes.Thirdly, our study cohort consisted of a small group of patients. The sample size was determined for the primary objective, HBF. Although NE significantly reduced HBF, the effect on vascular resistance is less clear. According to Ohm`s law, when flow decreases with unchanged portal and caval pressures, the resistance in the portal vein should be increased. However, we did not find this effect. A possible explanation could be that the measurement techniques such as PI and PVRi, have a low sensitivity . The comThe results of the current study show that modulating blood pressure with vasopressors such as NE may substantially alter HBF. These findings underscore the importance of considering also potential effects on regional tissue blood flow. Although NE improves blood pressure, it may profoundly affect regional blood flow.Administration of NE reduced total HBF, by decreasing HAF, while the effect on PVF remained unclear. SOMATO-treated patients had a lower PVF at baseline, which remained unaffected during NE infusion. In these patients the decrease in total HBF with NE was entirely related to the decrease in HAF. In SOMATO-untreated patients PVF also significantly decreased with NE.


In Korea, the prevalence rate of hypertension among people aged over 30 is on an upward trend, which increased from 26.2% in 2008 to 28.3% in 2018. This hovers above the global morbidity rate of hypertension. As hypertension is the cause of cerebrovascular or cardiovascular diseases, early treatment and management are critical. Also, while there has been numerous research conducted on exercise intervention, the number of studies done on hypertension and physical fitness falls far below. To identify the relationship between health and physical fitness depending on the blood pressure levels of Korean seniors, the physical fitness test results of the National Fitness Award 100 conducted in 2017 were used, and blood pressure level, physique, and physical fitness data of 17,110 elderly population aged above 65 were analyzed. IBM Statistics SPSS 25.0 was used to process the collected data. Due to the gender difference in blood pressure levels, physique, and physical strength, the analysis was conducted by each gender, and the analysis of variance (ANOVA) was performed to identify the differences in physique and physical strength depending on the blood pressure level. Also, Tukey’s HDS test was conducted if such differences were found. All analyzes were tested at the significance level (α) of 0.05. As a result of comparing the physique factors by blood pressure level, only males showed a significant difference between groups in terms of height(p = .019), and higher blood pressure implied greater height. Also, for both genders, those with high blood pressure appeared to have greater body weight(p < .001), body fat percentage(p < .001), BMI(p < .001), waist circumference(p < .001), and waist-to-height ratio(p < .001),. In addition, as the result of comparing health-related physical strength and skill-related physical strength by blood pressure level, males showed a significant difference only in muscular strength(p = .026) and flexibility(p < .001), while females showed a significant difference between groups in terms of cardiovascular endurance(p < .001), muscular strength(p = .025), muscular endurance(p < .001), balance, and motor coordination except for flexibility. Holistically, males only showed a significant difference in muscular strength and flexibility while females appeared to have a significant difference between groups in all categories except flexibility, which can be considered that physical fitness factors influence the blood pressure of females more than males. The improvement and changes in lifestyle with the growing economy have also transformed the lifestyle and eating habits of people. These changes have led to medical problems linked to metabolic syndromes such as high blood pressure, dyslipidemia, hyperglycemia and obesity .Hypertension, in particular, is one of the major causes of death along with diseases related to our brain, heart, kidney, and others ; as hypeGenetic characteristics, gender, age, smoking, drinking, overweight, and lack of physical activity  were fouThe Korean Society of Hypertension and American Heart Association provide a guideline that healthy lifestyle must be combined with taking medication to effectively manage hypertension , 14. In Physical activity, one of the ways to improve hypertension, not only lowers blood pressure directly  but alsoAlso, as an overall integrated measure of physical function related to daily physical activity or exercise performance, physical fitness is a predictor of causes and mortality of cardiovascular disease –31 that As physical activity and physical strength are interlinked like a chain, more engagement in physical activity leads to better physical strength. In other words, regular aerobic exercise helps improve cardiovascular endurance while lowering blood pressure –38, and While many international and national organizations have been recommending seniors to carry out the physical activity at least for a minimal amount to maintain health, let alone numerous studies about exercise intervention conducted to identify the importance of physical activity and preventing hypertension, there have not been enough in-depth studies on blood pressure and physical strength.Therefore, based on the data from the “National Fitness Award 100” of the Korea Sports Promotion Foundation (KSPO), which has been leading the measurement of physical strength by life cycle, this study aimed to identify physical strength factors that can be helpful for the improvement of senior blood pressure and look at the relationship between Korean seniors’ blood pressure level and physical strength. It also aimed to provide primary data for the establishment of exercise programs for seniors by blood pressure, accustomed to different situations depending on necessary physical strength factors.The study utilized the physical fitness test results of the “2017 National Fitness Award 100” provided by the Korea Sports Promotion Foundation (KSPO). The National Fitness Award of KSPO is an official certification authority designated by the government, which officially performs physical fitness measurement, physical fitness evaluation, exercise prescription, and physical fitness certification. The subjects were seniors aged 65 or above. The total number of subjects was 17,110 , and the average age was 71.8 for females and 72.7 for males. This experimental protocol was approved by the Institutional Review Committee of Sungshin Women’s University (SSWUIRB-2021-036) and informed to and received the consent of all participants. The characteristics of the subjects are as follows.The blood pressure levels were divided into four groups based on the standards of the Korean Society of Hypertension: below 120/88mmHg as Normal, 120-129/80mmHg as Elevated, 130-139mmHg systolic pressure or 80-89mmHg diastolic pressure as Prehypertension, and 140mmHg or above systolic pressure or 90mmHg or above diastolic pressure as Hypertension .2). Waist circumference (cm) was measured with a tape measure, and body fat percentage was measured with a body composition analyzer  [For physique, an automatic weight/height scale  was used to measure the height (cm), body weight (kg), and body mass index  .Health-related physical strength  and skill-related physical strength  were measured for physical fitness .The minimum height to which the subject has to raise their knees was set for each subject. Then, the distance between the middle of the knee to the ilium (iliac crest) was measured with a tape measure, after which a tape was used to mark the middle of the femur. With the starting signal, the subject began with their right leg, tagging one knee after another to the rubber string. It was measured as “one time” when both of the legs fully walked for two minutes starting with their right foot. A chance was given for the subject to practice before the measurement, and there were no second measures.The subject was ordered to hold a hand gripper with second knuckles of their hand, stretch out their arm forward keeping their body and arm distanced by 15 degrees and grip it as hardest as they could for five seconds with the posture unchanged. The left and right arms were measured twice one after another, and the best score of each was recorded by 0.1kg unit. Then, relative handgrip strength (handgrip strength ÷ body weight ×100) was calculated.The subject was instructed to sit in the center of the chair with their back straight and both arms crossed at the wrists and gathered in front of the chest. Then on the mark, the subject was told to fully stand up, and return to a fully seated position. They were encouraged to repeat it as many as they could in 30 seconds, and the total count of full sit-and-stands performed for 30 seconds was recorded. If the subject was in a half-stood position when 30 seconds were over, it was counted as a full stand-up. The measurement was only allowed once.The subject was instructed to take off their shoes and sit with knees stretched forward until the soles were placed flat against the measurement device. Then, the subject was told to keep their knees pressed flat to the floor with palms touching the box facing downwards, and bend the upper body and extend it as far forward as possible. Measurement was performed twice, and the better record was selected and recorded in units of 0.1cm.A cone was installed at exactly 3m-point from the back of the cone to the frontal edge of the chair (one spot on the floor). The chair was placed against the wall so that it faces the cone, and the subject was instructed to stand up, walk around the cone, then return to the chair to the seated position as fast as they could with the starting signal. The subject was ordered to repeat it twice and was given only one chance for the measurement. The fastest time was recorded in units of 0.1 seconds.A 3.9m (width) × 1.6m (length) rectangle was drawn, and cones were fixed within the side edges, where a chair was placed at the 2.4m-point from them. The subject was told to stand by, sitting on the chair at the center of the frontal edge of the rectangle, after which was instructed to stand, go around the cone on the right, return to the seated position, stand up right after again, go around the cone of the left, and return to the seated position, with the “start” signal. This course was repeated twice, and the record was measured in units of 0.1 seconds.IBM Statistics SPSS 25.0 was used to process the collected data. Due to the gender difference in blood pressure levels, physique, and physical strength, the analysis was conducted by each gender, and the analysis of variance (ANOVA) was performed to identify the differences in physique and physical strength depending on the blood pressure level. Also, Tukey’s HDS test was conducted if such differences were found. All analyzes were tested at the significance level (α) of 0.05.In terms of blood pressure, 41.6% of female subjects were classified into the Normal group, 37.2% in the Elevated group, 16.9% in the Prehypertension group, and 4.4% in the Hypertension group.For males, 38.5% were classified into the Normal group, 36.4% in the Elevated group, 19.4% in the Prehypertension group, and 5.7% in the Hypertension group. Males showed slightly higher figures in the Prehypertension and Hypertension groups compared to their female counterparts .The study looked at the differences in height, body weight, BMI, waist circumference, waist-to-height ratio, and body fat percentage by blood pressure level. It was found that male seniors showed a statistically significant difference in all physique factors, while female seniors also showed a statistically significant difference in all factors except height.In terms of males, for body height, the Hypertension group (165.95±6.00) was the tallest, while the Normal group (164.97±5.77) was the shortest(p = .019). Meanwhile, body weight increased with higher blood pressure(p < .001). In short, males with higher blood pressure had higher height with greater body weight. For obesity rate measured with waist circumference and waist-to-height ratio, the Elevated group, Prehypertension group, and Hypertension group showed a high figure. For waist circumference, it was 85.35±7.84 for the Elevated group, 86.34±7.59 for the Prehypertension group, and 86.77±9.76 for the Hypertension group. Meanwhile, for the waist-to-height ratio, the Elevated group recorded 0.54±0.05, the Prehypertension group 0.55±0.05, and the Hypertension group 0.55±0.06 . For bodFor females, the Prehypertension group (58.59±7.73) and Hypertension group (58.59±8.88) appeared to have greater body weight(p < .001). For body fat percentage, the Hypertension group recorded (36.11±6.68), and for BMI, the Prehypertension group recorded (25.10±3.06) while the Hypertension group recorded (25.25±3.63). In short, the Hypertension group was found out to have high figures in terms of body fat percentage(p < .001) and BMI(p < .001). In regards to obesity rate measured with waist circumference and waist-to-height ratio, the Prehypertension group and Hypertension group showed a high figure(p < .001). For waist circumference, the Prehypertension group recorded 83.82±7.76, while the Hypertension group recorded 83.60±9.52. For waist-to-height circumference, the two groups recorded 0.55±0.05 and 0.55±0.06, respectively (p < .001) .Male seniors showed a statistically significant difference in muscular strength(p = .026) and flexibility(p < .001), and the Normal group showed a high figure (50.71±10.09) in muscular strength. For flexibility, the Normal group recorded low (3.55±10.01), but the Prehypertension group recorded high (5.45±9.32) .Female seniors showed a statistically significant difference in cardiorespiratory endurance(p < .001), muscular strength(p = .025), and muscular endurance(p < .001). The Normal group recorded high cardiorespiratory endurance (103.87±24.62), followed with the elevated group (102±24.81). For muscular endurance, the Normal group showed a high record (18.90±5.99), while the figure was low in the Prehypertension group (18.20±6.20) .Male seniors did not show a statistically significant difference in skill-related physical strength by blood pressure levels , but femWith With an aim to identify the relationship between physique and physical fitness by blood pressure levels of Korean seniors, this study utilized the physical fitness test results of the National Fitness Award 100 conducted in 2017, analyzing the data of 17,110 subjects aged 65 or above .Essential (primary) hypertension is mainly caused by living a problematic lifestyle, including lack of physical activity and a wrong diet , 48, whiAs a result, both males and females showed a significant difference in body weight, body fat percentage, BMI, waist circumference, and waist-to-height ratio, while only males showed a significant difference in height. Such results are in line with the research that taller height leads to increased blood pressure for males more than females , 51. AlsAmong the factors for improving life habits, the relationship with health conditions such as physical activity and physical strength, and blood pressure and diabetes have been proven to be highly correlated , 54. As A decrease in mean arterial blood pressure during the recovery period after maximum intensity exercise is greater in women, and it was suggested that the decrease in arterial blood pressure of women was due to a greater vasoconstrictor response . TherefoPreceding studies have highlighted muscular strength as a physical strength factor to reduce blood pressure as the weakened grip strength among the oldest old leads to increased blood pressure . Also, oThe ACSM recommends hypertensive patients to carry out flexibility exercises 2 to 3 times a week along with aerobic and resistance exercises in its guidelines . Also, iAge-related increase in systolic blood pressure and pulse pressure accelerates after middle age . SimilarAs in this study, it is not easy to compare preliminary studies with this study because the research on various physical factors according to blood pressure level is very insufficient. However, in a similar preliminary study , musculaIn a study conducted by Kim, Bong Ju et al in 2022 on men and women over 65 years of age that measured muscular strength, muscular endurance, cardiovascular endurance, balance, coordination, and flexibility and gave level 1 if above 70th quintile, level 2 for above 50th quintile, level 3 for above 30th quintile, and the rest level 4, the result turned out to be the opposite of what is generally expected that the blood pressure would be lower when fitness level is hgher. It is believed that follow-up studies are needed for clearer identification because there are still various opinions in the studies on flexibility.According to the results of analyzing skill-related physical fitness by blood pressure, males did not show differences between groups, while females showed differences in motor coordination and balance. Females’ motor coordination and balance were all higher in the Elevated, Prehypertension, and Hypertension groups than the Normal group. In other words, females with higher blood pressure can be regarded to have poor motor coordination and balance. For balance, those aged between 65–75 showed a significant positive correlation between systolic pressure and balance , which wIn sum, males only showed a significant difference in muscular strength and flexibility, while aged females showed a significant difference between groups in all categories except flexibility. It is necessary to develop various gender-based exercise programs that take other physical factors of women and men into account because women have multiple body functions that decrease faster than men , 62–64 aLastly, the 8th report of the Joint National Committee, which suggests guidelines for the treatment and management of hypertension, recommends patients to consistently carry out lifestyle interventions across their overall efforts for managing health. It is important to create a healthy lifestyle for the management of hypertension . In partThis study intended to investigate the association with various physical strength factors that can help to improve blood pressure of the elderly by analyzing health, technology-related physical strength, and physique by blood pressure level of the elderly in Korea, As a result of the study, it was identified that the blood pressure level of the elderly differed in physical strength factors according to gender.As the results of the study, the blood pressure levels of seniors differed greatly by physical strength factors by gender. While males showed a significant difference in only muscular strength and flexibility, females showed a significant difference between groups in health and skill-related and physical fitness  except flexibility, which implied that they are valid indicators for the management of blood pressure. Hence, the results of the study are expected to be used as primary data for the development of exercise programs for the prevention and improvement of hypertension considering the gender difference and various physical strength factors. However, there were limitations as the study was cross-sectional research, and comorbidities and treatment history could not be identified. There is a need for an epidemiological study to examine the association.


Desflurane is known to have a larger vasodilatory effect than that of sevoflurane. However, its generalizability and effect size in actual clinical practice are yet to be proven. Patients aged ≥ 18 years who underwent noncardiac surgery under general anesthesia using inhalation anesthetics (desflurane or sevoflurane) were matched 1:1 by propensity score. The mean intraoperative perfusion index (PI) of each patient were compared between the two groups. Propensity score matching of 1680 patients in the study cohort identified 230 pairs of patients. PI was significantly higher in the desflurane group . PI durations below 1.0 and 1.5 were significantly longer in the sevoflurane group. Mean arterial pressure (MAP) and durations of low MAP did not differ significantly between the two groups. Generalized linear mixed models revealed that the use of sevoflurane, mean MAP, mean heart rate, age, and duration of anesthesia had significant negative effects (lower PI), whereas mean age-adjusted minimum alveolar concentration of inhalation agent had a positive effect on PI . Intraoperative PI was significantly higher in patients administered desflurane than sevoflurane. However, the impact of the choice between desflurane and sevoflurane on intraoperative PI in this clinical setting was minimal. Because organ perfusion does not depend solely on blood pressure, it is crucial to consider other parameters, such as vascular tone and regional perfusion4. Peripheral vascular tone and perfusion may be assessed by measuring the peripheral perfusion index , a parameter derived from the pulse oximetry signal6. It represents the ratio of pulsatile to non-pulsatile components of the signal.Vascular tone is an essential factor for understanding organ perfusion8 and can improve microcirculation9. A recent randomized trial6 found that desflurane exerts a more potent vasodilatory effect than sevoflurane, as shown by higher PI and lower blood pressure. Although these findings were supported by the careful control of many possible confounders, such as comorbidities and stimulation intensity, the findings of that study may have limited generalizability to real-world clinical practice, in which such confounders cannot be controlled. Therefore, the present study retrospectively compared intraoperative PI in patients administered desflurane and sevoflurane as inhalation anesthetics.Inhalation anesthetics have vasodilatory effectsThe study was conducted in accordance with the principles of the Declaration of Helsinki and the study protocol was approved by the Institutional Review Board of Chungnam National University Hospital (CNUH 2021-11-008) on 12 November 2021 with waiver for informed consent and registered at the Clinical Research Information Service, a clinical trial registry in South Korea (KCT0006766).10.This retrospective cohort study included patients aged ≥ 18 years who underwent noncardiac surgery, including general, gynecological, otolaryngological, plastic, and urological surgery, under general anesthesia using inhalation anesthetics (desflurane or sevoflurane) and PI monitoring from February to August 2021 in a university hospital. Patients were excluded if clinical or vital data were missing, their vital records had interruptions, or if information was lacking regarding the inhalation agent, PI, or blood pressure . To account for the differences in their clinical characteristics, patients administered desflurane and sevoflurane were matched 1:1 by propensity score. This manuscript adheres to the applicable STROBE  guidelines11 version 1.8, accessed at https://vitaldb.net, Seoul, Republic of Korea).All vital data were obtained from the prospective registry of the vital signs for surgical patients at Chungnam National University Hospital (CNUH IRB 2019-08-039), which uses a free data collection program .Other data collected from patient medical records included age, sex, body mass index (BMI), comorbidities , Charlson comorbidity index, American Society of Anesthesiologists (ASA) physical status, type of surgery , emergency surgery, duration of anesthesia, intraoperative infusion of vasopressor (norepinephrine), intraoperative transfusion (red blood cells or fresh frozen plasma), intraoperative fluid input, and intraoperative opioid dose  and a patient monitor  and recorded at a frequency of 1 Hz. Oximeter sensor was routinely attached to the index or third finger of the patient unless contraindicated or inaccessible. Blood pressure was measured continuously with an arterial catheter or intermittently at 5-min intervals using a noninvasive blood pressure cuff and recorded at a frequency of 1 Hz. Intra-arterial pressure was primarily used for analysis, if available. Data regarding inhalation anesthetics  were obtained from the anesthesia machines and recorded at a frequency of 0.2–0.25 Hz. All vital signs and records of inhalation agents were extracted as 10 s interval mean values. These data were filtered for errors in blood pressure, so that the mean arterial pressure (MAP) was > 20 mmHg and < 150 mmHg. To include periods only with proper administration of inhalation anesthetics in the analysis, a cut-off value of end-tidal concentration  of the inhalation agent was determined for the vital records of each individual and filtered accordingly. For example, if the median intraoperative end-tidal concentration of sevoflurane was 1.3 volume % , then the cut-off was set at 1.2 volume % and only periods with end-tidal sevoflurane concentrations above this value were included in the analysis. Mean of PI values acquired for three minutes immediately before the initiation of the administration of inhalation anesthetics was considered baseline.Intraoperative PI and heart rate (HR) were monitored continuously using a disposable oximeter sensor , were considered explanatory variables. Patients were matched by 1:1 nearest neighbor matching using a caliper width of 0.09 . Propensity scores were calculated using a logistic regression model, in which the type of inhalation agent was considered a dependent variable and the clinical characteristics of patients, including age, sex, BMI, Charlson comorbidity index, ASA physical status (1–2 or > 2), comorbidities, type of surgery, and intraoperative variables  and compared using chi-squared or Fisher’s exact tests. Statistical significance was set at a two-tailed p-value of < 0.05.Continuous variables were reported as the mean ± SD or median (interquartile range [IQR]), depending on the results of the Shapiro–Wilk test. After the matching process, continuous variables were compared using paired Wilcoxon signed-rank tests15.To identify factors affecting mean PI other than the inhalation agent, especially mean MAP and HR, a negative-binomial generalized linear mixed model was fitted. In that model, mean PI was considered a dependent variable; mean MAP, mean HR, inhalation agent, and the variables involved in the matching process were considered independent variables, and the matching identifier was considered a random effect. Because the dependent variable in the model could be reported only as a non-negative integer, the mean PIs were transformed as rounded values, expressed as 100 Because the results can be influenced by the selection of specific matched data sets, the primary outcomes were analyzed and model fitting was performed using two additional matched datasets.A total of 4210 patients underwent noncardiac surgery under general anesthesia, including inhalation of desflurane or sevoflurane and PI monitoring from February to August 2021. Of these patients, 2530 were excluded due to insufficient data. The remaining 1680 patients were subjected to 1:1 propensity score matching, yielding a total of 230 pairs  had significant negative effects on mean PI , whereas chronic obstructive pulmonary disease and higher mean age-adjusted MAC of inhalation agent had significant positive effects on mean PI , rather than the original values, were included in the final analysis, except for the durations of low PI and MAP. Second, a large number of patients could not be included in the analysis because of the lack of vital recordings or detailed clinical information . Although selection bias due to this issue cannot be excluded, these omissions were more likely to be random (missing at random) rather than systematic. Third, more detailed information, such as bolus use of a vasopressor (e.g. phenylephrine) and the total dose of infused norepinephrine, was not included in the analysis due to unreliable quality. Instead, the use of norepinephrine infusion was included in the analysis as a surrogate marker of considerable requirement of vasopressor because it is commonly adopted after repeated doses of phenylephrine. Fifth, subtle discrepancies in intraoperative management may exist among clinicians, some of whom may prefer either of the inhalation agents. This might have affected the results of the current study.In conclusion, despite the lack of different effect of inhalation agents on MAP, the mean PI was significantly higher in patients administered desflurane than sevoflurane. However, the impact of the choice between desflurane and sevoflurane on intraoperative PI in this clinical setting was minimal which has questionable clinical significance.Supplementary Information 1.Supplementary Information 2.