Atrial fibrillation (AF) increases the risk of heart failure (HF); however, little focus is placed on the risk stratification for, and prevention of, incident HF in patients with AF.This study aimed to construct and validate a machine learning (ML) prediction model for HF hospitalization in patients with AF.The Fushimi AF Registry is a community-based prospective survey of patients with AF in Fushimi-ku, Kyoto, Japan. We divided the data set of the registry into derivation  and validation  cohorts. An ML model was built to predict the incidence of HF hospitalization using the derivation cohort, and predictive ability was examined using the validation cohort.P < 0.001). Based on Kaplan-Meier curves, the ML model could stratify the risk of HF hospitalization during the follow-up period .HF hospitalization occurred in 606 patients (14%) during a median follow-up period of 4.4 years in the entire registry. Data of transthoracic echocardiography and biomarkers were frequently nominated as important predictive variables across all 6 ML models. The ML model based on a random forest algorithm using 7 variables  had high prediction performance (area under the receiver operating characteristics curve [AUC]: 0.75) and was significantly superior to the Framingham HF risk model (AUC: 0.67; The ML model revealed important predictors and helped us to stratify the risk of HF, providing opportunities for the prevention of HF in patients with AF.  ML techniques provide a powerful tool for learning complex relationships between the risk predictors and clinical outcomes from a representative sample of patients.Accordingly, the aim of the present study was to construct an ML model for predicting the incidence of HF events and to validate its performance using the data from a large-scale community-based prospective survey of Japanese AF patients, the Fushimi AF Registry.UMIN000005834).,The Fushimi AF Registry is a community-based multicenter prospective observational survey of patients with AF who visited the participating medical institutions in Fushimi-ku, Kyoto, Japan. The detailed study design, patient enrollment, and definition of the measurements of the registry were previously described , natriuretic peptide levels, imaging findings including chest x-ray and echocardiography, cardiac catheterization findings, response to HF therapy, and in-hospital course. We continued follow-up until the death endpoint, and we defined clinical outcomes as the time to first event.For the purpose of creating and validating the ML model, we divided the entire cohort of the registry into a derivation cohort and validation cohort. The data of patients from 1 cardiovascular center and half of the small and medium-sized hospitals and primary care clinics, which were randomly selected, were assigned as the derivation cohort. The data of patients from the other cardiovascular center and the remaining half of hospitals and primary care clinics were assigned as the validation cohort.,,The data included baseline patient characteristics, oral prescription, the results of blood tests, and imaging data derived from chest x-ray and transthoracic echocardiography at registration. A total of 168 baseline variables were included in the data set of the Fushimi AF Registry. For data preprocessing, variables that were not clinically meaningful  were deleted at the investigators’ discretion based on the clinical perspective. In addition, several variables were created using existing variables . Variables with more than 30% missing data in the derivation cohort were deleted.Supervised ML was used. The specific model algorithms used in this study were random forest, light gradient boosting machine, elastic net, linear support vector machine, neural network, and naive Bayes model. All of these 6 ML algorithms are fully established and commonly used for artificial intelligence prediction tasks. For the model derivation, including training and hyperparameter tuning, and internal evaluation, we performed 5-fold cross-validation in the derivation cohort. In the training step, model hyperparameters were optimized with a grid search algorithm. Grid search tunes and optimizes the model hyperparameters using a greedy way  and R statistical software version 4.0.0.Continuous variables are presented as the mean ± SD when normally distributed, and as the median and interquartile range when non-normally distributed. Distribution was assessed using histograms. The Kaplan-Meier method was used to estimate the cumulative incidences of clinical outcomes, and log-rank testing was performed to assess differences among groups. The hazard ratio of the events was calculated using the Cox proportional hazards model. Receiver operating characteristics curves were compared using the Henley and McNeil method.P < 0.05). Oral anticoagulants were less frequently prescribed, and cardiothoracic ratio and LV ejection fraction were lower in patients in the derivation cohort than in those in the validation cohort   were female. Paroxysmal AF accounted for 2,194 (50%) patients, and 1,204 (27%) patients had pre-existing HF. In total, the derivation cohort and the validation cohort included the data for 2,383 and 2,011 patients, respectively. The patients’ characteristics in the derivation and validation cohorts are presented in  < 0.05) .Table 1PDuring a median follow-up period of 4.4 years (IQR: 2.1-7.0 years), a total of 606 (14%) hospitalizations for HF occurred among the entire 4,394 patients at a rate of 3.3% per person-year. The Kaplan-Meier curve for the incidence of HF hospitalization is shown in The algorithm performance  of each ML model using all variables in the derivation cohort are presented in After calculation of the importance of each variable, the top 10 important variables in each ML model are shown in We extracted the top 7 variables in the random forest algorithm based on their clinical validity, feasibility, and applicability  .P < 0.001).The predictive performances of each practical ML model using the 7 variables for the validation cohort are shown in P < 0.001) (P < 0.001).The patients’ characteristics stratified by the tertiles of a random forest algorithm using the 7 variables for the validation cohort are presented in < 0.001) . Cox regP < 0.001) (P < 0.001), and intermediate risk patients had a 3-fold higher risk of HF hospitalization  than did low-risk patients.Of 2,011 patients in the validation cohort, 1,507 patients did not have pre-existing HF. Even among patients without pre-existing HF, the practical ML model had a certain level of predictive ability . The pra< 0.001) . High-riIn the present study, we explored the risk factors and prediction model using ML techniques, and we revealed the following: First, we demonstrated that ML models have a high predictive performance for the incidence of HF hospitalization in patients with AF. Second, transthoracic echocardiographic data and biomarkers were important variables for predicting future HF events. Third, the practical ML model using simple and readily available variables showed a higher predictive ability than did the pre-existing HF risk model and was able to stratify the risk of HF hospitalization among patients with AF .Central ,,,,,AF and HF are closely linked and often develop concurrently, with each disease predisposing patients to the other. HF now represents the most common cardiovascular complication in patients with AF, developing at a rate nearly twice that of stroke.,Several studies have evaluated the significant predictors of incident HF among patients with AF. The ORBIT-AF  reported that significant predictors for incident HF were advanced age, coronary artery disease, valvular heart disease, renal dysfunction, heart rate, and permanent type of AF.,,This study was unique in that it clarified predictors among data comprising >100 variables, including biological data, histories, biomarkers, and imaging data, using ML algorithms. ML provides the opportunity of discovering new predictors that are not hypothesis driven and without prior assumptions. Previous studies mainly included variables related to the patients’ backgrounds and comorbid conditions, and they were unable to address the importance of biomarkers and imaging data.,,Indeed, the Belgrade AF Study reported that mild left atrial dilatation or low-normal LV ejection fraction in structurally normal heart heralds an increased risk of incident HF.Some burdens are specific to the application of ML models in daily practice. In particular, risk stratification using dozens of variables is difficult or almost impossible to implement in clinical practice. Therefore, we ultimately selected several variables for risk prediction with a view to their future practicality. A practical ML model incorporating only 7 variables  has the potential to become an appropriate risk prediction tool for future HF events among patients with AF. Objective data for these 7 variables are easy to obtain, and we believe that our ML model can be readily available in clinical practice. Of note, a random forest model using these 7 variables was shown to have a higher predictive ability than the Framingham HF risk model . Even am,Recently, catheter ablation or pharmacological therapy, including sodium glucose co-transporter 2 inhibitors, was reported to aid in preventing HF development in selected patients with AF.The present study has several limitations. First, this was an observational study and provides only associative evidence, not causative. Second, some potential important variables were excluded because of a large number of missing values. Indeed, two thirds of patients without pre-existing HF had no data for natriuretic peptide levels in the registry. However, we specifically clarified their prognostic significance for future HF events, highlighting the importance of measuring natriuretic peptide levels in all patients with AF.PerspectivesCOMPETENCY IN MEDICAL KNOWLEDGE: ML revealed that the data of transthoracic echocardiography and biomarkers were important predictors for HF hospitalization in patients with AF. The ML model incorporating these several variables can stratify the risk of HF among patients with AF in daily practice.TRANSLATIONAL OUTLOOK: Further studies are needed to create more accurate risk prediction models incorporating additional important variables, and to investigate the efficacy of pharmacological and/or non-pharmacological therapies for preventing HF events in high-risk patients with AF.ML algorithms had a high predictive performance for HF hospitalization in patients with AF. Imaging data and biomarkers were important variables across all ML models, which suggests their utility for risk prediction of HF events. Our ML model using 7 simple and readily available variables was able to stratify the risk of hospitalization for HF in patients with AF, providing opportunities for the implementation of strategies to prevent HF among patients with AF.The Fushimi AF Registry is supported by research funding from Boehringer Ingelheim, Bayer Healthcare, Pfizer, Bristol-Myers Squibb, Astellas Pharma, AstraZeneca, Daiichi Sankyo, Novartis Pharma, MSD, Sanofi-Aventis, and Takeda Pharmaceutical. The sponsors had no role in the design or conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript. This study was partially supported by the Practical Research Project for Life-Style related Diseases including Cardiovascular Diseases and Diabetes Mellitus from Japan Agency for Medical Research and Development, AMED . Dr Akao has received lecture fees from Pfizer, Bristol-Myers Squibb, Boehringer Ingelheim, Bayer Healthcare, and Daiichi-Sankyo. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.


To evaluate the clinical and biochemical effects of (SGLT2) inhibitors as a fourth oral anti-diabetic drug in patients with Type-2 diabetes mellitus (T2DM).In a tertiary hospital in Karachi, Pakistan, a retrospective assessment of patient medical records was conducted from January 1, 2017 to December 31, 2020.A total of one hundred patients (mean age [Standard Deviation]: 53.8 [9.63] years) with poorly controlled T2DM were included. Data was collected before the SGLT-2 inhibitor was added, as well as three and six months after the medicine was started. Weight, BMI, blood pressure (BP), HbA1c, SGPT, and Creatinine were measured at the start and during the studyThere was a significant reduction in HbA1c  with a mean reduction (MR) of 0.81+1.02% at three months and 1.07+1.11% at six months. A mean weight reduction  of 1.83+2.32 kg at three months and 4.02+6.04 kg at 6 months, respectively, was recorded. A mean BMI reduction of 0.69+0.95 kgm-2 at three months and 2.13+3.41 kgm-2 at six months of follow up, respectively were recorded. A systolic blood pressure (SBP) also showed a significant reduction  with a MR of 5.9+15.76 mmHg at three months and 6.37+18.33 mmHg at 6 months, respectively. Non-significant variation in creatinine and SGPT was also noted.SGLT-2 is an effective oral anti-diabetic medicine that can help individuals with diabetes who are currently using glucose-lowering oral anti-diabetic medications. These medications can help diabetic patients stick to their regimen. Diabetes mellitus is a group of related metabolic disorders characterized by hyperglycemia ensuing from defects in insulin production, insulin action, or both. Persistent hyperglycemia is linked to long-term injury, dysfunction, and failure of multiple organs, including the eyes, kidneys, nerves, heart, and blood vessels. Diabetes may be classified into type one diabetes mellitus , Type-2 diabetes mellitus , Gestational DM (GDM)  and Specific forms of diabetes because of alternative causes.1According to the International Diabetes Federation, around 415 million individuals worldwide were diagnosed with diabetes in 2015, with that figure anticipated to rise to 640 million by 2040.4An antidiabetic drug from no less than eight pharmacologic classes is chosen for the treatment of Type-2 diabetes mellitus. Agents that boost insulin secretion, improve insulin action, and delay glucose absorption fall within this category.6Sodium/glucose cotransporter-2 inhibitors (SGLT2i) function independently of insulin, reduce blood glucose by inhibiting its reabsorption in proximal tubules and by promoting urinary glucose excretion, without inducing hypoglycemia or weight gain. The Objective of our study was to evaluate the clinical and biochemical effects of (SGLT2) inhibitors as a fourth oral anti-diabetic drug in patients with Type-2 diabetes mellitus (T2DM).This was a retrospective, real world, single center observational study carried out on patients of Aga Khan University Hospital (AKUH), a tertiary hospital in Karachi, Sindh, Pakistan between January 2017 and December 2020. The study included patients of either sex having Type-2 diabetes. Inclusion criteria was defined as patients with age more than 16, already being treated with three oral anti-diabetics , Glycated Hemoglobin (HBA1C) of more than 7% , and with an estimated Glomerular Filtration Rate (eGFR) more than 45. All Patients that met these criteria were added SGLT-2 inhibitor namely empagliflozin or dapagliflozin as a fourth anti-diabetic drug to bring Fasting Blood Glucose (FBS) to an acceptable level of 90 – 120 mg/dL and to lower the HbA1c. All the patients were enrolled through convenience sampling. Patients with Type-1 diabetes mellitus were excluded from our study. Additionally, Type-2 diabetics who were also being treated with insulin or GLP-1 agonist were excluded from our study. The parameters assessed at baseline before the addition of SGLT-2 inhibitor included physical parameters such as Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Weight and Body Mass Index (BMI) and biochemical parameters such as Glycated Hemoglobin (HbA1c), Serum Glutamic Pyruvic Transaminase (SGPT) and Creatinine (Cr).All the parameters assessed at baseline before the addition of SGLT-2 inhibitor were also assessed at 3-month interval and at 6-month interval. These included clinical parameters recorded at each clinical visit like Weight recoded by a weighing balance, BMI calculated in the clinic, SBP and DBP by a digital sphygmomanometer. Biochemical markers recorded included HbA1c, SGPT and Cr.All statistical analysis was performed using Statistical package for social science SPSS  and MS-Excel 2016. Comparison between baseline and follow up value of various parameters was done using paired t test. Continuous variables were presented as mean (standard deviation [SD]) with 95% CI as applicable and categorical data were presented as proportions. All P values were 2-sided and were considered significant if P < .05. The comparison between baseline and follow up values was done at 3 months and 6 months after the addition of SGLT-2 inhibitor.<0.001) decrease in weight with a mean decrease of 1.83 kg at 3 and 4.02 kg at six months.The BMI of patients has decreased with a decrease of 0.69 kgm-2 at three months and 2.13 kgm-2 at 6 months, both being statistically significant (P < .001).A total of one hundred patients were taken for this study with 49 females and 51 males. All the parameters required for analysis were recorded before addition of SGLT-2 inhibitor. The baseline parameters have been given in The SBP of the patients has shown a statistically significant decrease with the mean decrease being 5.9 mmHg at 3 months (P = .002) and 6.37 mmHg at 6 months (P = .003).The DBP of patients has also decrease however the decrease has not been statistically significant with P-value > .05.The renal and hepatic function over the 6 months’ period were assessed using serum creatinine and SGPT of the patient. The mean of both these variables remained within the normal range over the 6 months follow up with the results being statistically significant (P < .001) as shown in In this study, we observed the effect of addition of SGLT-2 inhibitors, as a fourth anti diabetic drug, on various clinical and biochemical parameters including Weight, BMI, SBP, DBP, HbA1c, SGPT and Creatinine at 3months and 6 months interval for 100 diabetic patients already undergoing treatment with three oral anti diabetic drugs. Studies of similar have been conducted on different populations however local data is still very scarce particularly regarding the addition of SGLT-2 inhibitors as a fourth anti diabetic drug. Furthermore, impediments to adhering to injectable Insulin therapy, such as the patient’s dread of injectable and the discomfort they cause, as well as refusal and noncompliance, can result in poor glycemic control and complications.Systolic Blood Pressure, Diastolic Blood Pressure, body weight, and HbA1c levels have all improved significantly after taking SGLT-2 inhibitors in previous research.Reduction in weight was observed in another meta-analysis when SGLT-2 inhibitor was added to Metformin as dual therapy.-2 and 2.13 kgm-2 at three and six months’ intervals. In previous studies it was shown that addition of SGLT-2 inhibitors with Metformin as a dual therapy, showed significant reduction in SBP as well as DBP.BMI reduction was reported after use of empagliflozin a SGLT-2 inhibitors by several studies including a meta-analysis conducted by Chewcharat A et al. on diabetic patients having undergone kidney transplant.Although earlier research has linked the use of SGLT-2 inhibitors to an increase in Creatinine,17This is a modest, single-center study with a sample size of 100 patients. Future multicenter trials with a larger sample size and conducted over a longer time period will be required to assess the long-term safety profile of SGLT-2 inhibitors. This discovery paves the way for more research into the health benefits of SGLT-2 inhibitors in the general population.Our research has demonstrated that SGLT-2 inhibitors is a safe and effective oral anti-diabetic therapy that can help individuals with diabetes who are currently using glucose-lowering oral anti-diabetic medications. These medications can be used as an alternative to injectable insulin for people who do not want to use it, and they can help diabetic patients stick to their regimen.MS: Conceived, designed, and did statistical analysis.SAK: Did data collection and manuscript writing.ZAS: did data analysis, the editing of the manuscript.NR: Did a review and final approval of the manuscript.


The mechanical and physiological properties of the arterial wall might affect the behavior of spontaneous cervicocephalic arterial dissections (CCAD). We aimed to determine the effects of endothelial function and arterial stiffness on the clinical characteristics and outcomes of CCAD using brachial flow-mediated dilation (FMD) and brachial-ankle pulse wave velocity (PWV).From a single-center database, we identified patients admitted from April 2011 to December 2021 with a diagnosis of CCAD who underwent both FMD and PWV. FMD was classified as normal and decreased according to institutional thresholds. PWV was categorized into tertiles. Comparative and multivariable analyses were performed to determine the effects of FMD and PWV values on major clinical outcomes.A total of 146 patients  were included. The main presentation was ischemic stroke in 76.7% of the patients, while 23.3% presented with headache or other symptoms. Healing of the dissection was observed in 55.8%. In multivariable analysis, Normal FMD levels  were associated with spontaneous healing of the dissection. Highest PWV tertile  was associated with ischemic presentation. There was a higher ischemic stroke recurrence in the 3rd PWV tertile, and more frequent aneurysmal enlargement in the lowest PWV tertile, but their frequency was low, precluding multivariable analysis.In spontaneous CCAD, preserved endothelial function was associated with spontaneous arterial healing. Arterial stiffness is associated with ischemic presentation. The clinical behavior of cervicocephalic arterial dissection (CCAD) is dynamic. It can cause both ischemic and hemorrhagic stroke and is a leading cause of stroke in patients below 45 years of age, accounting for almost one-fourth of all cases of strokes in this population . They maThe arterial wall exhibits physiological and mechanical properties. Its physiological properties are mainly defined by the endothelium, which is arguably the largest and most diffuse endocrine organ of the body . The endAmong the mechanical properties of the arterial wall, arterial stiffness is an important parameter as a biomarker of arteriosclerosis and is also associated with end-organ injury . ArteriaIn this study, we hypothesized that endothelial function and arterial wall stiffness will be associated with behavior of arterial wall in response to vessel injury. In a single center CCAD population, we evaluated the association between FMD, PWV, and major clinicoradiologic outcomes.Data were collected by a retrospective medical record search. The hospital's electronic medical records were first searched for patients diagnosed with “dissection of intracranial, extracranial, or cerebral arteries, ruptured or unruptured.” Next, the neurology admission patient database was reviewed for the diagnosis of CCAD. Third, patients admitted with a diagnosis of subarachnoid hemorrhage were screened based on the term ‘dissection' in the imaging reports of the brain. The brain images of the patients were then reviewed by a researcher for the confirmatory diagnosis of CCAD . In thisThe clinical presentation was classified as ischemic stroke (confirmation by diffusion weighted imaging), hemorrhagic stroke, or headache and other symptoms. Baseline demographics were collected. The primary arterial luminal morphology was described as stenosis pattern, dilatation pattern (including stenosis and dilatation), and occlusion pattern. The stenosis and dilatation pattern was classified as dilatation pattern according to a previous report that showed similarly higher rupture risk compared to steno-occlusive patterns in these two patterns . The locIn all ischemic stroke and cerebrovascular disease patients admitted to the department of neurology in our hospital, brachial FMD and ankle-brachial artery PWV is usually evaluated as a second line routine work-up for recurrent cerebrovascular disease risk stratification and prevention, along with first line work up such as transthoracic echocardiography and 24 h holter monitoring, which is strongly recommended as stroke etiologic evaluation by current guidelines . It is uEndothelial function was measured by FMD in the brachial artery using the post-ischemic forearm hyperemia method. Cross-sectional imaging with M-mode using a fixation device was used to manually measure the arterial diameter . FMD valt-test and Mann–Whitney U-test, and categorical variables were analyzed using the chi-square test or Fisher's exact test. Based on the univariate analysis, the association between FMD and spontaneous arterial healing was evaluated using multiple logistic regression, including clinically significant co-variables and variables identified in univariate analysis. It was performed with FMD values in both dichotomized form and as a continuous variable. The association between PWV tertile and ischemic presentation was also evaluated using multiple logistic regression, including clinically significant co-variables. This analysis was performed with PWV values in both trichotomized form and as a continuous variable (PWV divided by 100 [PWV/100]). All statistical analyses were performed by use of R software, version 4.0.5. A P-value less than 0.05 was considered to be statistically significant.To evaluate the significance of FMD values on the behavior of CCAD, the study population was classified into decreased FMD and normal FMD groups. A comparative analysis was performed between the two groups. To evaluate the significance of PWV findings on the behavior of CCAD, the population was categorized according to the PWV tertile. Comparative analyses were performed for all three groups. Continuous variables were compared using the student's Among 601 CCAD patients, a total of 320 patients presented to the emergency department with a CCAD involving the major cervicocephalic arteries during the study period and was admitted for acute management, excluding those who presented with subarachnoid hemorrhage. Among them, 51 were admitted to the neurosurgery department for concerns of dissecting aneurysmal rupture. Among 266 patients admitted to the neurology department, 146 patients performed both PWV and FMD, and were included in the analysis . Comparin = 53; 36.3%) and normal FMD group  (p = 0.002) and were more frequently male . There were no differences in symptom onset to presentation or symptoms onset to FMD evaluation. There were no differences in the presentation patterns, dissection luminal morphology, or anterior vs. posterior circulation predominance. Intracranial dissections were predominant in the decreased FMD group , and a higher rate of comorbid hypertension was seen . A lower proportion of patients in the decreased FMD group received anticoagulants . The results of clinical follow-up did not show any difference between the two groups, with similar 3-month mRS, similar rates of new ischemic stroke, and no recurrent subarachnoid hemorrhage in each group. However, in the arterial healing analysis, spontaneous arterial healing was more common in the normal FMD group than in the decreased FMD group . A multivariable analysis was performed to identify the association between FMD values and spontaneous arterial healing including clinically significant variables, and variables identified through the univariate analysis  along with comorbid diabetes mellitus , age, luminal morphology, and extracranial  location as co-variables (p = 0.014) along with the same co-variables (When the patients were categorized into the decreased FMD group ( 63.7%%) , the decanalysis . In the ariables . FMD valariables . Aneurysp < 0.001 ). The proportion of patients with ischemic presentation increased as PWV tertiles increased (32/49 [65.3%] vs. 35/50 [70.0%] vs. 45/47 [95.7%]), p = 0.001). While symptom onset to presentation time was shorter in 3rd tertile group compared to 1st tertile , likely due to a higher ischemic stroke presentation, there were no differences in symptom onset to PWV evaluation. With increasing PWV tertiles, dilatation morphology decreased (23/49 [46.9%] vs. 13/50 [26.0%] vs. 12/47 [25.5%]) and occlusion morphology increased (12/49 [24.5%] vs. 26/50 [52.0%] vs. 23/47 [48.9%]) (p = 0.037). There was also a higher prevalence of comorbid hypertension  with the increase in tertiles. In the clinical follow-up, there was a predominance of ischemic stroke recurrence in the 3rd tertile . There were no differences in spontaneous arterial healing, while aneurysmal enlargement more frequently occurred in the lowest PWV tertile (4/44 (9.1%) vs. 0/44 (0.0%) vs. 0/41 (0.0%). P = 0.019). A multivariable analysis was performed to identify the association between PWV values and ischemic stroke presentation including clinically significant variables, and variables identified through the univariate analysis (p = 0.001), along with extracranial dissections , age, sex, and dissection morphology as co-variables (p = 0.002), along with the same co-variables  funded by the Ministry of Education .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.


Hyperkalemia increases the risk of mortality and cardiovascular-related hospitalizations in patients with hemodialysis. Predictors of hyperkalemia are yet to be identified. We aimed at developing a nomogram able to predict hyperkalemia in patients with hemodialysis.+] ≥ 5.5 mmol/L. Data were collected from hemodialysis management system. Least Absolute Shrinkage Selection Operator (LASSO) analysis selected predictors preliminarily. A prediction model was constructed by multivariate logistic regression and presented as a nomogram. The performance of nomogram was measured by the receiver operating characteristic (ROC) curve, calibration diagram, and decision curve analysis (DCA). This model was validated internally by calculating the performance on a validation cohort.We retrospectively screened patients with end-stage renal disease (ESRD) who had regularly received hemodialysis between Jan 1, 2017, and Aug 31, 2021, at Lishui municipal central hospital in China. The outcome for the nomogram was hyperkalemia, defined as serum potassium [Kn = 256) and validation (n = 145) cohorts randomly. Predictors in this nomogram were the number of hemodialysis session, blood urea nitrogen (BUN), serum sodium, serum calcium, serum phosphorus, and diabetes. The ROC curve of the training set was 0.82 . Similar ROC curve was achieved at validation set 0.81 . The calibration curve demonstrated that the prediction outcome was correlated with the observed outcome.A total of 401 patients were enrolled in this study. 159 (39.65%) patients were hyperkalemia. All participants were divided into development (This nomogram helps clinicians in predicting the risk of PEW and managing serum potassium in the patients with hemodialysis. Hemodialysis is the most common kidney replacement treatment (KRT) used in patients with ESRD. It approximately accounts for 89% of all dialysis –3. In 20Hyperkalemia is an electrolyte abnormality, clinically characterized as an elevation of serum potassium concentration >5.5 mmol/L) . This co.5 mmol/LNowadays, a nomogram as a visually predictive tool calculates the risk of outcomes for individuals. This tool provides valuable guidance for clinical decision-making. It has been widely applied to evaluate the prognosis of patients recently –21. We aWe established and validated a nomogram on hyperkalemia prediction in patients with hemodialysis through a retrospective study. All data came from a hemodialysis center in China.We screened patients treated with maintenance hemodialysis between Jan 1, 2017, and Aug 31, 2021, at a hemodialysis center of Lishui municipal central hospital in China. Eligible participants were over 18 years old. They received continuous hemodialysis for at least 3 months and had regular follow-up visits with vital medical data records. The dialysate potassium concentration in this study was 2 mEq/L. All participants were treated with the same dialysate. The exclusion criteria included unavailable medical information, changed dialysis modality, and trauma/infection occurrence unrelated to hemodialysis within one month.+] ≥ 5.5 mmol/L.We randomly divided the whole dataset into training and validation sets with a proportion of 6:4. Thirty-four parameters were collected from the hemodialysis management system. Parameters included basic characteristics, dialysis-related data, and blood laboratory tests. Basic characteristics contained gender, age, height, weight, primary disease, occupation, education, administration history of furosemide or renin-angiotensin-aldosterone system inhibitors (RAASi), and comorbid hypertension or diabetes. Dialysis-related data and blood laboratory tests examined in the last pre-dialysis were collected. Dialysis-related data consisted of blood flow, ultrafiltration volume, hemodialysis vascular access, hemodialysis modality, hemodialysis duration, and the number of hemodialysis sessions. The blood laboratory tests covered serum potassium, hemoglobin, C-reactive protein (CRP), BUN, uric acid, estimated glomerular filtration rate (eGFR), glucose, serum creatinine, serum sodium, serum calcium, albumin, serum phosphorus, triglyceride, total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and parathyroid hormone (PTH). In this study, serum potassium was a single measurement as the endpoint. The serum potassium was measured before the last hemodialysis session of the week. Hyperkalemia was defined as serum potassium [K+ SD. The abnormal distribution was described by median (IQR). Group comparisons of continuous variables were analyzed by T-test or Wilcoxon rank-sum test. Categorical variables were compared by the chi-square test or Fisher’s exact test. Firstly, predictors were preliminarily screened by LASSO regression in the development set. LASSO analysis shrunk the regression coefficient of variables to zero by a penalized coefficient of Lambda. It excluded variables with zero regression coefficients and selected variables without zero regression coefficients. These selected variables were viewed as the most correlated with hyperkalemia. They were presented as odds ratio (OR) with 95% confidence intervals (CIs). Secondly, multivariate logistic regression analysis established the prediction model in the training set. In the model, the score of each predictor was calculated. A nomogram made the model visualized. Thirdly, model discrimination was evaluated by receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) for 0.75 or more indicated good discrimination. The prediction accuracy was assessed by calibration plots. The clinical utility was estimated by decision curve analysis (DCA). The nomogram was validated by the test set. All tests were two-tailed tests and p ≤ 0.05 was statistically significant. We performed statistical analysis by STATA 15.0 .Descriptive statistics contained continuous and categorical variables. The normal distribution of continuous variables was presented by mean + 14.23 years. 60.1% of patients were men. The hyperkalemia incidence was 39.65%. All enrolled patients were randomly divided into development (n = 256) and validation sets (n = 145) with a proportion of 6:4. There was no significant difference in clinical variables between development and validation cohorts   Fig. . DCA exhPrevious studies developed models to predict the risk of hyperkalemia in patients with chronic kidney disease (CKD) , 23. HowIn this study, the incidence of hyperkalemia was 39.65% in patients with hemodialysis. It was consistent with previous studies . This st+ out and K+ into the cell by Na+ − K+ ATPase. Insulin deficiency reduces the cellular intake of potassium and leads to more potassium on the outside of the cell. In patients with ESRD, uremia decreases tissue sensitivity to insulin. Diabetes burdens the accumulation of extracellular potassium. Consequently, diabetes can be a predictor of hyperkalemia.Several studies investigated the relationship between hyperkalemia and mortality, or cardiovascular disease. They showed hyperkalemia is increasingly becoming one of the complications to increase mortality rate in patients with hemodialysis . A serump < 0.01) [+ − K+ ATPase [+ − K+ ATPase exchanges 3 cytoplasmic Na+ for every 2 extracellular K+ that move into the cell. During phase 2 and 3 of the cardiac action, K+ efflux and Na+/Ca2+ influx complete repolarization [+ and Na+/Ca2+.In patients with hemodialysis, a large portion of BUN is cleared from the blood by hemodialysis modality. The adequacy of hemodialysis is evaluated by the removal of BUN clinically . Greater < 0.01) . Therefo+ ATPase . Na+ − Krization . This anIt is difficult for clinicians to precisely calculate the level of potassium intake from diet.Therefore, this study didn’t include dietary potassium intake in the model. Furthermore, it is still controversial whether dietary potassium intake influences serum potassium and induces hyperkalemia in patients with hemodialysis. Some studies reported that potassium intake more than excretion enhances the risk of hyperkalemia. However, in these studies, the doses of potassium supplements largely exceed the recommended level of dietary potassium intake. Although the daily potassium intake may surpass the extracellular content, the daily change in serum potassium level is less than 10% . Noori eRAASi is a kind of medication for hypertension, including angiotensin-converting enzyme inhibitors (ACEI) and angiotensin receptor blockers (ARB). They improve the outcomes for patients by decreasing cardiovascular events . A lot oThere were some limitations to this study. Firstly, we haven’t externally validated the nomogram. We will continually recruit patients to complete the external validation. Secondly, we ignored the impact of metabolic acidosis on hyperkalemia. In the future, we will research the interaction between metabolic acidosis and hyperkalemia in the nomogram. Thirdly, we didn’t analyze the relationship between the dose of dietary potassium intake and hyperkalemia. It is necessary to investigate the daily optimal dose of dietary potassium intake.We established a risk prediction model of hyperkalemia for patients with hemodialysis. The risk model provide valuable insights into the identification of hyperkalemia risk and prevention.


Dear editorAnnals of Intensive Care [2), inspiratory effort, improving consciousness and NPPV tolerance in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). While this trial provided positive results encouraging further trials on this subject, a few reservations could be raised.We read with great interest the article of Luo et al. “Physiological effects of high-intensity versus low-intensity noninvasive positive pressure ventilation in patients with acute exacerbation of chronic obstructive pulmonary disease: a randomised controlled trial” published in ive Care . In thisFirst, as stated in the title of the manuscript, this study focuses on the physiological effects of the intervention. We notice however, that the authors overlooked an important physiological parameter which is the pH level. The mean pH level significantly differs as of 24 h of NPPV. The pH tends to become alkaline in the high-intensity NPPV group while it remains neutral in the low-intensity NPPV. It is important to point out that the increase in pH levels exposes patients to compensatory hypoventilation, which has been well established in the past . An impoSecond, while we value that the authors reported ventilatory settings and physiological parameters in the supplementary material, we believe more data should have been provided in order to better characterize patients’ respiratory mechanics for a more thorough comparability and generalizability. Regarding NPPV settings and for a more comprehensive and physiological approach, we should keep in mind that the tidal volume generated while on NPPV does not solely depend on the pressure support level. In fact, beyond the patient’s effort and pressure support, the inspiratory pressurization slope and expiratory trigger sensitivity also contribute to generating the tidal volume. The authors failed to address these parameters. In order to demonstrate that the levels of pressure support along with the daily hours of NPPV were the only factors significantly different between both groups, it would have been subtler to show the comparability of these patients in terms of inspiratory slope and expiratory trigger sensitivity usually adjusted to offset leaks and prevent patient–ventilator interaction by adjusting the mechanical drive to the neural drive.As for NPPV monitoring, while we appreciate that the authors excluded emphysematous patients, we think it would have been very elegant to appreciate the degree of pulmonary distention in both groups by analyzing intrinsic positive end-expiratory pressure (PEEP) and both slopes of the expiratory flow curve. Intrinsic PEEP can be estimated by noting the first values of pressure displayed while performing an end-expiratory occlusion in volume control mode with a high inspiratory trigger. Visually analyzing expiratory flow curves can also estimate the degree of obstruction as well as gas trapping.Finally, we notice that the NPPV in this study was performed using a hybrid ventilator with a single-limb circuit with intentional air leaks, which is indirectly concluded by the high leakage values displayed in the supplementary material as well as by the type of ventilator used. We wonder to which extent these results are reproducible and generalizable to patients undergoing NPPV in intensive care units using double-limb circuits with a demand valve and an expiratory valve.Using high-intensity NPPV in AECOPD is interesting to further evaluate in clinical randomized controlled trials. After addressing the question of generalizability to all ventilators, we suggest including additional ventilatory settings and monitoring parameters as well as assessing risks of rapid-induced post-hypercapnic alkalosis in upcoming studies.


Unilateral primary aldosteronism (UPA) and bilateral primary aldosteronism (BPA) are the two subtypes of PA. Discriminating UPA from BPA is of great significance. Although adrenal venous sampling (AVS) is the gold standard for diagnosis, it has shortcomings. Thus, improved methods are needed.The original data were extracted from the public database “Dryad”. Ten parameters were included to develop prediction models for PA subtype diagnosis using machine learning technology. Moreover, the optimal model was chose and validated in an external dataset.In the modeling dataset, 165 patients  were included, while in the external dataset, 43 consecutive patients  were included. The ten parameters utilized in the prediction model include age, sex, systolic and diastolic blood pressure, aldosterone to renin ratio (ARR), serum potassium, ARR after 50 mg captopril challenge test (CCT), primary aldosterone concentration (PAC) after saline infusion test (SIT), PAC reduction rate after SIT, and number of types of antihypertensive agents at diagnosis. The accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model using the random forest classifier were 90.0%, 81.8%, 96.4%, 0.878, and 0.938, respectively, in the testing dataset and 81.4%, 90.0%, 73.9%, 0.818 and 0.887, respectively, in the validating external dataset. The most important variables contributing to the prediction model were PAC after SIT, ARR, and ARR after CCT.We developed a machine learning-based predictive model for PA subtype diagnosis based on ten clinical parameters without CT imaging. In the future, artificial intelligence-based prediction models might become a robust prediction tool for PA subtype diagnosis, thereby, might reducing at least some of the requests for CT or AVS and assisting clinical decision-making. Primary aldosteronism (PA) accounts for approximately 4-10% of hypertension; approximately 30% of PA is a unilateral subtype that is associated with a higher risk of cardiovascular complications and can be cured by surgery, whereas the bilateral subtype is best treated with medication . HoweverMeanwhile, supervised machine learning technology has been used widely and is gaining recognition in medical research since it can automatically formulate computational models by processing the variables with complex relationships, and therefore, frequently presents optimal results compared with traditional methods , 9. Due 22.1https://Datadryad.org). Validation data were obtained from XiangYang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, China. Ethics approval was obtained in the original study by the ethics committee of Chiba University Graduate School of Medicine , maintains high-quality research data with the aim of forming an academic exchange for the protection and reuse of research data in scientific publications. The raw data of the present study were offered publicly by Nagano et al. in 2020 . The oriThe models were validated in an independent external PA cohort from XiangYang Central Hospital, an affiliated hospital of Hubei University of Arts and Science, China. Forty-three consecutive patients with PA who underwent AVS and SIT from 1 May 2021 to 30 April 2022 were included retrospectively based on the following criteria: (1) inclusion criteria: patients with PA and who had undergone successful AVS for subtype diagnosis. (n=54) (2) exclusion criteria: (a) patients without accessible data. (n=0) (b) patients who lacked a definite or correct classification of PA in the postoperative follow-up. (n=6) (c) Patients who did not undergo SIT. (n=5). Finally, 43 patients were included (details are shown in 2.22.2.1Data extraction in the modeling process included age (year), sex, systolic blood pressure , diastolic blood pressure , plasma renin activity , PAC (pg/ml), ARR, serum potassium , urine aldosterone (μg/day), ARR after 50 mg CCT, PRA after the furosemide standing test, PAC after SIT, PAC reduction rate after SIT (%), and the number of types of antihypertensive agents at diagnosis. Blood pressure was measured three times consecutively after the patient sat for at least 15 minutes, and the average value was adopted. Blood samples were collected from the patient in the recumbent position after they rested for at least 30 minutes, and commercial radioimmunoassay kits  were applied to these samples for the measurement of PAC and PRA. For the measurement of cortisol concentrations, the IMMULYZE (Siemens K.K.) assay was used. Serum potassium was measured by standard methods. Before and during patient examinations, calcium channel blockers or alpha-l blockers were administered, while patients with a severe condition were also treated with diuretics or other antihypertensive drugs. To make a definitive diagnosis of PA, patients underwent one or more of the following tests: saline infusion test, captopril challenge test, or furosemide upright test; notably, 91.8% of patients underwent more than two of these tests. AVS was performed with 0.25 mg of adrenocorticotropic hormone for stimulation. A selectivity index  above 5 was considered the threshold for successful adrenal vein cannulation. UPA was highly suspected when the lateral index  was above 3 after ACTH infusion and was confirmed in the postoperative follow-up , as described in the original study .Python 3.6.13  was used to develop and validate the machine learning-based algorithm. Patients with PA were stratified by subtype diagnosis and split randomly into either the training set 70%, N=115) or the testing set  (random seed=1). To find an optimal prediction model, five supervised machine learning classifiers were used, including random forest (RF), support vector machines (SVM), gradient boosting decision tree (GBDT), logistic regression (LR), and AdaBoost. First, all possible available variables were used to build models, according to previous research reports 0%, N=115, 11. Two2.3Data extracted for the validation process included age, sex, SBP, DBP, ARR, serum K, ARR after 50 mg CCT, PAC after SIT, PAC reduction rate after SIT (%), and number of types of antihypertensive agents at diagnosis. The measurements of PAC and PRA in the recumbent position were extracted and tested using commercial radioimmunoassay kits , and cortisol concentrations were measured by the same method. The methods used to measure other variables were consistent with the original study . PA was 2.4Python 3.6.13  was used for development and validation of the models. STATA Version 16  was used for statistical analysis. Continuous variables are presented as the mean ± standard deviation or the median with an interquartile range , and categorical variables are reported as counts with proportion (%). The student’s t test was used to analyze continuous variables when they were normally distributed, if not, the Mann-Whitney U test was used. The chi-square test was used for categorical variables. Accuracy was calculated as the primary endpoint to evaluate the performances of the prediction models, and secondary endpoints included sensitivity, specificity, F1 score, and AUC. Little missing data was replaced by the mean value (Details were shown in 33.1P<0.05). Accordingly, PAC reduction after SIT was significantly lower in the UPA subgroup than that in the BPA group (P<0.05). The serum potassium level was lower in patients with UPA than in those with BPA in both the modeling and the external dataset. Comparisons of other clinical and biochemical parameters were not significantly different between the two groups  were included, while in the external dataset, we included 43 consecutive patients  that underwent a successful AVS with accessible data. As expected, those with UPA had higher ARR, higher ARR after CCT, and higher PAC after SIT than those with BPA in both datasets (https://github.com/shaominbaby/PA). A demonstration of how to apply the predictive model is shown in http://misetech.cn:83/xyszxyy.jsp, for Chinese users).Among the classifiers assessed in the testing set, the best performance was observed in the model utilizing the RF classifier , the accuracy was 100%, while when it was equal to or more than 0.7, the accuracy was 95%. Thus if AVS was only performed in those whose subtype predicition probability < 0.7, then 51.2% of the AVS and 23.3% of the CT would have been avoided, with the sensitivity of 0.950 and specificity of 0.957 . The process of using this predictive model is shown in Based on Chinese population, CCT and SIT were reported to be accurate alternatives for each other . Since C44.1PA is associated with a higher risk of cardiovascular and cerebrovascular risk compared with essential hypertension . UPA and4.2Over the last few decades, many methods for the diagnosis of PA subtypes have been proposed. Although CT and magnetic resonance imaging (MRI) are the easiest and most accessible diagnostic tools, they are not reliable in differentiating subtypes of PA, with a pooled sensitivity of 68% and pooled specificity of 57% . Other c4.3Machine learning is based on nonlinear algorithms, and consequently, can compute multidimensional variables simultaneously resulting in consistent levels of increased accuracy. This tool has been gaining increasing attention and recognition in medical decision-making .Currently, the SIT and CCT tests are most commonly used in clinical practice for subtyping PA . In the via AVS. Thus, the proportion of UPA in our external dataset was 46.5%, which is higher than that of the modeling dataset and previous reports ; Science and Technology Research Key Project of Education Department of Hubei Province, China (Grand number D20212602); Sanuo Diabetes Charity Foundation, China.This study uses data from Dryad database. We thank the authors of the original study  for their excellent work.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.


Information on early, guideline discordant referrals in nephrology is limited. Our objective was to investigate trends in referral patterns to nephrology for patients with chronic kidney disease (CKD).2, persistent albuminuria , or progressive and persistent decline in eGFR until index nephrology visit (≥ 5 mL/min/1.73m2).Retrospective cohort study of adults with ≥1 visits to a nephrologist from primary care with ≥1 serum creatinine and/or urine protein measurement <180 days before index nephrology visit, from 2006 and 2019 in Alberta, Canada. Guideline discordant referrals were those that did not meet ≥1 of: Estimated glomerular filtration rate (eGFR) ˂ 30 mL/min/1.73mOf 69,372 patients with CKD, 28,518 (41%) were referred in a guideline concordant manner. The overall rate of first outpatient visits to nephrology increased from 2006 to 2019, although guideline discordant referrals showed a greater increase  versus guideline concordant referrals . The guideline concordant cohort were more likely to be on renin-angiotensin system blockers or beta blockers , and had a higher risk of CKD progression , kidney failure , cardiovascular event  and mortality .A significant proportion nephrology referrals from primary care were not consistent with current guideline-recommended criteria for referral. Further work is needed to identify quality improvement initiatives aimed at enhancing referral patterns of patients with CKD. It is estimated that the number of patients with kidney failure requiring kidney replacement therapy (KRT) will increase worldwide to 5.439 million by 2030 . ManagemKidney Disease: Improving Global Outcomes (KDIGO) has published recommendations based on laboratory and clinical data regarding the timing of specialist referrals for patients with CKD . Timely Late referrals to nephrology have been well-studied, and can lead to increased hospitalization, higher costs, increased mortality, and worse outcomes after initiation of dialysis –11. HoweThe study cohort comprised patients over age 18 in Alberta, Canada who had their first outpatient visit to a nephrologist between April 1, 2005 and March 31, 2019. We obtained data from the Alberta Kidney Disease Network (AKDN) database, which incorporates data from Alberta Health such as provincial health registry status, physician claims, hospital discharge abstracts, and ambulatory care utilization; the Northern and Southern Alberta Renal Programs to capture patients receiving chronic dialysis; and clinical laboratories in Alberta . We obta2, 15–59 mL/min/1.73m2, and ≤ 15 mL/min/1.73m2 [We used the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation to calculate eGFR . We estiWe defined a referral as GC based on eGFR category, decline in eGFR, and albuminuria during the six months prior to the initial nephrology visit. Our criteria reflect KDIGO guidelines, which are based on evidence of the benefits of timely referral, including delaying the need to initiate KRT and improved survival . Our spe2, orA most recent eGFR < 30 mL/min/1.73mPresence of persistent albuminuria on at least two consecutive measurements prior to the index nephrology visit, or2 decrease from the first eGFR measurement that persisted until the index nephology visit).Progressive decline in eGFR  to determine rural versus urban residential location. We assessed material deprivation based on the Pampalon Deprivation Index (1 = least deprived to 5 = most deprived) [We recorded baseline demographic data, including age, sex, and postal code of residence from the Alberta Health administrative data files. We linked postal codes to the Canadian Census using the Postal Code Conversion File (eprived) , 15. We eprived) , 17. We We identified pre-existing co-morbid conditions from hospital discharge records, physician claims, and ambulatory care classification system files based on validated algorithms , 19. We The primary outcome was the annual rate of initial outpatient visits to nephrologists, expressed as a number per million population (pmp). The population count was obtained from the Alberta Health provincial health registry file . We calcSecondary outcomes included annual rates of initial outpatient visits to other internal medicine specialists . We also studied the associations between referral characteristics and time to clinical outcomes, including the first use of use of medications , CKD progression, the incidence of kidney failure treated with KRT , first hospitalization for cardiovascular events, and all-cause mortality. We defined CKD progression as a sustained reduction in eGFR of more than 50%, sustained doubling of ACR or PCR, or a sustained increase (at least one level) in Udip measurements from baseline. We defined increases as “sustained” if all subsequent outpatient measurements  continued to meet the aforementioned criteria during the follow-up period. The incidence of kidney failure was identified using the Northern and Southern Alberta Renal Programs registries, as previously described . We defip < 0.05 as the threshold for statistical significance.We described continuous variables using medians and interquartile ranges, and categorical variables as proportions. To compare differences between GD referrals and GC referrals, we used chi-square tests for categorical variables and Kruskal-Wallis tests for continuous variables. We used least squares regression analysis to identify the secular trend of the annual rate of nephrology and internal medicine visits from 2006 to 2019. To evaluate autocorrelations of the regression residuals, we used the C-H test/Dubin Wastin test  and corrBetween April 1, 2005 and March 31, 2019, 69,495 adults 18 years of age or older residing in Alberta had initial outpatient visits to nephrologists . The stup = 0.03). The overall rate of GC referrals was 584 pmp in 2006 and slightly increased each year to 629 pmp in 2019 (S1 Table in p = 0.002). Both categories of referrals increased over time, with the overall rate of GD referrals increasing each year from 2006 to 2019 and to a greater extent over this period than GC referrals (S1 Table in p = 0.02) although the difference is not statistically significant (p = 0.19). Patients with eGFR ≥ 60 ml/min/1.73 m2 (S2 Table in p < 0.001) and ACR < 30 mg/g (S2 Table in p < 0.001) had the highest referral rates and positive referral trends from 2006 to 2019 Age and sex standardized data show similar trends and are presented in the . The overall rate of increase for visits to internal medicine specialists for the GC group was 128 pmp in 2006 and rose to 286 pmp in 2019 ; for the GD group, this was 195 pmp in 2006 and rose to 461 pmp in 2019 .The overall rate of initial outpatient visits to internal medicine specialists for patients who also saw nephrologists was 387 pmp in 2006 and increased slightly each year to 860 pmp in 2019 , experience CKD progression , develop kidney failure , have a cardiovascular event  or die  compared to the GD cohort . At baseline , patients in the GC group were more likely to be on ACEi, ARB, statin, or beta blocker medications  compared to those with eGFR ≥ 60 mL/min/1.73m2. This pattern was also seen in patients with albuminuria > 30 mg/g and patients over age 40, compared to those without albuminuria and those under age 40. Those with eGFR < 15 mL/min/1.73m2 were less likely to be prescribed ACEi/ARB or beta blocker  than for patients younger than 40.Overall, patients with lower eGFR and higher albuminuria were at increased risk for CKD progression, kidney failure, cardiovascular events, and death. Kidney failure was less likely for older patients  and for patients with eGFR ≥ 60 ml/min/1.73 m, Canada .Prior studies have shown that the introduction of eGFR reporting was associated with an increase in the number of nephrology referrals –27. EvidThere may be several reasons for the high proportion of GD referrals. First, PCPs and nephrologists may have different interpretations of each other’s responsibilities. For PCPs, the focus of collaborative care with nephrologists is on slowing CKD progression and managing hypertension and diabetes, which typically are priorities in early CKD. In contrast, the focus of collaborative care for nephrologists tends to be on preparation for KRT . Second,Patients with GC referrals more often had vascular disease, diabetes, hypertension, pulmonary disease, dementia, and cancer. These specific comorbidities are associated with higher rates of death and hospitalization in the CKD population . The GC Our study shows a need to enhance current methods for referring CKD patients to nephrology. Prolonged waiting times in the Canadian healthcare system limit timely access to specialist care . A signiCentralized systems for managing CKD referrals have been shown to reduce wait times significantly in Canadian cohorts , 40. UseThis study should be interpreted considering its limitations. First, we did not include patients exclusively referred for reasons not based on eGFR and/or proteinuria measures, such as recurrent nephrolithiasis, refractory hypertension, and cystic kidney diseases , as therWe also acknowledge that there may be reasons for referrals which are not explicitly covered in the guidelines but involve practices which may be outside the comfort zone of a PCP. These include initiation of SGLT-2 inhibitors or dealing with polypharmacy in a patient with impaired renal function but eGFR > 30. However, it was not feasible to ascertain all reasons for referrals within our large cohort.In conclusion, a large proportion of referrals to nephrology do not appear to meet the criteria stipulated in current practice guideline recommendations. Our study highlights the need for quality improvement initiatives to enhance referral patterns of patients with CKD to increase appropriateness and efficiency in the delivery of specialist kidney care and to support PCPs in early CKD management.S1 File(DOCX)Click here for additional data file.


Despite being responsible for half of heart failure-related hospitalizations, heart failure with preserved ejection fraction (HFpEF) has limited evidence-based treatment options. Currently, a substantial clinical issue is that the disease etiology is very heterogenous with no patient-specific treatment options. Modeling can provide a framework for evaluating alternative treatment strategies. Counterpulsation strategies have the capacity to improve left ventricular diastolic filling by reducing systolic blood pressure and augmenting the diastolic pressure that drives coronary perfusion. Here, we propose a framework for testing the effectiveness of a soft robotic extra-aortic counterpulsation strategy using a patient-specific closed-loop hemodynamic lumped parameter model of a patient with HFpEF. The soft robotic device prototype was characterized experimentally in a physiologically pressurized (50–150 mmHg) soft silicone vessel and modeled as a combination of a pressure source and a capacitance. The patient-specific model was created using open-source software and validated against hemodynamics obtained by imaging of a patient  with HFpEF. The impact of actuation timing on the flows and pressures as well as systolic function was analyzed. Good agreement between the patient-specific model and patient data was achieved with relative errors below 5% in all categories except for the diastolic aortic root pressure and the end systolic volume. The most effective reduction in systolic pressure compared to baseline (147 vs. 141 mmHg) was achieved when actuating 350 ms before systole. In this case, flow splits were preserved, and cardiac output was increased (5.17 vs. 5.34 L/min), resulting in increased blood flow to the coronaries (0.15 vs. 0.16 L/min). Both arterial elastance (0.77 vs. 0.74 mmHg/mL) and stroke work (11.8 vs. 10.6 kJ) were decreased compared to baseline, however left atrial pressure increased (11.2 vs. 11.5 mmHg). A higher actuation pressure is associated with higher systolic pressure reduction and slightly higher coronary flow. The soft robotic device prototype achieves reduced systolic pressure, reduced stroke work, slightly increased coronary perfusion, but increased left atrial pressures in HFpEF patients. In future work, the framework could include additional physiological mechanisms, a larger patient cohort with HFpEF, and testing against clinically used devices. In heart failure (HF) the heart is unable to appropriately supply the receiving organs with blood, leading to over five million hospitalizations yearly –4. To daRecent efforts have proposed multiple device-based treatment options such as the intra-atrial shunt device , 10, useCounterpulsation is a treatment strategy often applied in HF with reduced ejection fraction (HFrEF), but understudied for its usefulness in HFpEF. Counterpulsation reduces afterload and systolic pressure, and increases coronary perfusion . Intra-aPatient-specific lumped parameter computational models provide a low-cost option for proof-of concept testing of innovative treatment options in interaction with the cardiovascular system. Lumped parameter computational models are efficient  and are We demonstrated and evaluated a framework  for protWe created a patient-specific lumped parameter model of the cardiovascular system tuned to the hemodynamics of a patient with HFpEF. The study was approved by the local IRB board. Aortic blood flows measurements were obtained by 2D phase contrast cardiac magnetic resonance imaging and blood pressures as upper arm cuff pressures within 1 h of acquisition . The patient was selected based on high diastolic filling pressures assessed by echocardiography , a criteria defined by the American Society of Echocardiography and the European Association of Cardiovascular Imaging in . We utilThe computational model consisted of the vessels of the upper and lower bodies, the left heart and the valves  connecteInitial conditions of pressure, flow, and volume for the first cardiac cycle were estimated using a minimum squared error function. The function starts solving for the initial conditions based on values found in literature. A time-periodic state is reached after five cardiac cycles.http://simvascular.github.io)  was created with the following steps. We first constructed an anatomic model using SimVascular (A time-dependent elastance model relating pressure to volume (Point 3 in s(t) of the valve changes between 0 and 1 with 0 being a fully closed valve.The mitral valve (MV) and the aortic valve (AV)  mimicking the soft robot's effect on a vessel (Section 2.2.2).The prototype of the soft robot is based on the McKibben actuator principle and consists of a single non-elastic pneumatically actuated bladder in a mesh which is wrapped around the ascending aorta . When inft robot . The captact, the delay with respect to the beginning of the cardiac cycle δtact (measured as R-wave of the electrocardiogram), the pressure inside the actuator pact, and the pressure inside the aorta paorta. The duration of the actuator contraction was always 300 ms, such that for delays above 700 ms the actuator only deflated at the beginning of the next cycle. The duration of the contraction of the soft robot was ton while toff was the duration of a full actuation cycle including the relaxation. The times ton and toff varied for different pact and were determined experimentally  is an experimental coefficient. Time coefficients k1 and k2 were determined experimentally and are equal to 13.04 and 34.66 respectively. δtact is the delay of the soft robot actuation. Coefficients c and d ensure continuity between the contraction and relaxations functions at moments ton and toff.here a1, a2, a3, a4 and a5 were obtained by minimizing the root mean square error (RMSE = gexp) using the Curve Fitting Tool from MATLAB .where the coefficients psr, a closed-, non-actuated loop filled with a glycerine-water (40–60%) mix mimicking the viscosity of blood  is also reduced compared to baseline. The delay of 650 ms together with 300 ms actuation time mean that the soft robot relaxes shortly after the start of the ventricular contraction. However, the left atrial pressure increases upon actuation (11.5 mmHg compared to 11.2 mmHg). On the contrary, the worst case actuation was found to be at a delay of 0 ms and thus occurring simultaneous to the ventricular contraction creating higher peak systolic pressure (164 vs. 147 mmHg) and stroke work (11.8 vs. 11.1 kJ).An actuation delay of 650 ms results in maximal peak systolic pressure reductions (141 vs. 147 mmHg), allows for the highest cardiac output (5.34 vs. 5.17 L/min) and the lowest stroke work (10.6 kJ compared to 11.8 kJ) compared to baseline . In thispact = 12 psi is seen in the coronary arteries (25 mmHg), followed by the carotid arteries (23 mmHg) and the descending aorta (11 mmHg). While, the overall cardiac output increased, this diastolic support did only lead to a small increase in coronary flow. In the worst case, the pressure and flow created by the soft robot adds up on top of the systolic activity of the heart, further increasing the afterload.When comparing the PV loops of the optimal and baseline settings we see that the diastolic filling volume is increased while the end systolic volume is decreased resulting in a larger stroke volume. Moreover, the peak systolic pressure is decreased . A seconThe flow splits toward the separate parts of the upper body are not known for the patient but their modeled values are presented in psr .We note a higher diastolic pressure increase during diastole with a higher psr . In turnWe created a patient-specific lumped parameter model of a HFpEF patient and a soft robotic extra-aortic counterpulsation device prototype to study the physiological implications of varying operating conditions. Full tuning to the patient-specific flow dynamics was achieved. We found that if operated in the optimal setting, we could achieve lower systolic pressure, a lower stroke work, and slightly improved perfusion of the coronaries at the cost of higher left atrial pressures.This study evaluated a treatment strategy for HFpEF in a patient-specific model tuned to an individual subject. Previous work on modeling device-based treatment strategies in HFpEF have focused on the use of averaged population characteristics and grouping based on hemodynamic phenotypes –14, 37. The device modeling was inspired by previous work on modeling counterpulsation devices in lumped parameter and 1D models , 39. Ourpact = 12 psi, delay = 650 ms), an additional diastolic pressure and flow waveform is observed in all cardiovascular branches, which is inline with previous studies (in vivo studies about IABPs and the C-Pulse System, however the cardiac output is increased to a much lesser extent (Using our simulation, we evaluated the effect of counterpulsation on the hemodynamics in HFpEF at different timings. When actuated during optimal setting actuation ( studies . The effr extent , 39, 40.The worst case actuation points toward the risks of the counterpulsation device with increased peak systolic pressure. A systolic actuation of the device can lead to an increase of peak systolic pressure by 17 mmHg, while the stroke work of the native heart increases by 0.7 kJ compared to baseline. The soft robotic actuator relies on accurate real-time sensing of the ventricular contraction to avoid harming the cardiovascular system, as has been shown in other pulsating soft robotic devices. While, the existing approaches to trigger IABPs might be useful for triggering of a soft robot, our results point toward the risks of pulsating technologies. In contrast to IABPs, the extra-aortic approach mitigates risks associated with infection and thromboembolic considerations, while several other risk factors will need to be addressed such as long-term functioning of the device as well as implications for the aortic valve.pact in tuning the effect of the soft robotic device prototype on the patient's physiology. A higher pact led to lower systolic peak pressures as well as higher cardiac output. The ability to tune the impact allows for scenario-specific adaptation such as rest and exercise but also for gradual increase in support to the heart. Furthermore, a bridge to recovery application can be envisioned through support modulation. Based on the similarity of the flow waveforms between the soft robot and IABPs, no impact on cerebral function is expected (We were also able to show the importance of the chosen expected .Clinical considerations: The usefulness of counterpulsation strategies is highly debated . While Iin-vivo trials with the soft robotic actuator will enable further validation of the findings of the lumped parameter model presented.Limitations: The study constitutes a framework for patient-specific hemodynamic modeling of device based treatment. The cohort of patients will need to be increased to cover a larger variety of patients as well as the heterogeneity in pathophysiology found in HFpEF. Moreover, the simulation currently lacks integration of the autoregulatory control mechanisms such as the baroreflex as well as coronary blood flow regulations. Furthermore, the effect of the silicon vessel on the evaluation of the soft robots was not specifically addressed in this work. Ongoing experimental work and The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by IRB Veteran Association Palo Alto. The patients/participants provided their written informed consent to participate in this study.MA, DE, SD, and IC contributed to conception and design of the study. IC organized the data acquisition and provided clinical context. MA and SD performed the experiments and wrote the first draft of the manuscript. JP and AM provided access to the original code. All authors contributed to manuscript revision, read, and approved the submitted version.SD received funding from the Swiss National Research Foundation P2EZP2_188964. SD, IC, and DE received funding from the NIH grant RO1 HL131823. AM and JP received funding from NIH grants R01EB029362 and R01EB018302.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.


The cut-off point for the NT-proBNP value for postoperative MODS was calculated at 1300 pg/mL. Conclusions: A high preoperative level of NTpro-BNP may be associated with the onset of MODS in the early postoperative period. The results of the study may also suggest that earlier cardiac surgery for significant valvular heart disease may be associated with an improved prognosis in this group of patients.Background: Multiple organ dysfunction syndrome (MODS) is the progressive and potentially reversible dysfunction of at least two organ systems in the course of an acute and life-threatening disorder of systemic homeostasis. MODS is a serious post-cardiac-surgery complication in valvular heart disease that is associated with a high risk of death. This study assessed the predictive ability of selected preoperative and perioperative parameters for the occurrence of MODS in the early postoperative period in a group of patients with severe valvular heart disease. Methods: Subsequent patients with significant symptomatic valvular heart disease who underwent cardiac surgery were recruited in the study. The main end-point was postoperative MODS, defined as a dysfunction of at least two organs—perioperative stroke, heart failure requiring mechanical circulatory support, respiratory failure requiring mechanical ventilation, and postoperative acute kidney injury requiring renal replacement therapy. A logistic regression was used to assess relationships between variables. Results: There were 602 patients recruited for this study. The main end-point occurred in 40 patients. Preoperative NT-proBNP (OR 1.026; 95% CI 1.012–1.041;  Multiple organ dysfunction syndrome (MODS) is a very serious complication that may occur in the early postoperative period in patients undergoing cardiac surgery due to valvular heart disease. It is the leading cause of death in critically ill patients ,2,3. MODThis prospective study was conducted at the National Institute of Cardiology in Warsaw, Poland, on a group of consecutive patients who underwent cardiac surgery for symptomatic severe valvular heart disease. The following exclusion criteria were considered in this study: lack of consent for inclusion in the study, significant atherosclerotic changes in the carotid arteries, aged under 18 years, porcelain aorta, coexistence of autoimmune diseases, inflammatory bowel diseases, and neoplastic diseases in the active stage. Each patient had a blood sample taken for biomarkers the day before surgery. A Sysmex K-4500 electronic counter was used to assess the complete blood count. NT-proBNP levels were assessed using the electrochemiluminescent immunoassays of Elecsys 2010 . In each case, a median sternotomy was performed under normothermia and general anesthesia, and cold-blooded cardioplegia was applied. In the present study, the main end-point was multiple organ dysfunction syndrome, which occurred in the early postoperative period. The definition of MODS includes the dysfunction of at least two organs—the central nervous system , cardiovascular system , respiratory failure , or acute kidney injury . The follow-up period lasted until hospital discharge or death. Consent for this study was obtained from the bioethics committee operating at the National Institute of Cardiology in Warsaw, Poland.2 test, or Student’s t-test were used to compare particular groups of variables. The Shapiro–Wilk normality test was used to study the distribution of the sample. The relationship between variables was assessed using logistic regression analysis. Pre- and intraoperative variables (listed in p < 0.05) obtained in the univariate logistic regression analysis were then subjected to multivariate logistic regression analysis. Using the Youden index, the cut-off point for NT-proBNP values was determined, which met the criterion of maximum sensitivity and specificity for postoperative MODS. The predictive value of NT-proBNP for the occurrence of the main end-point was estimated using the area under the receiver operating characteristic (ROC) curve. In order to investigate possible relationships between the variables, the Spearman rank correlation test was performed.The IBM SPSS software, version 2.0 , was used for statistical analyses. The data presented are described as the mean ± SD and frequency (%). The Mann–Whitney U test, Pearson χp = 0.001) and hemoglobin levels  remained independent predictors of the main end-point. The cut-off point for the NT-proBNP value for postoperative MODS was calculated at 1300 pg/mL. p < 0.001), LVEF , high-sensitivity Troponin T concentration , and GFR . Of the 40 patients who had postoperative MODS, 29 cases were fatal. The mean concentration of NT-proBNP in the group of patients with postoperative MODS who died was 6406 pg/mL (±1209) and was significantly higher compared to patients with postoperative MODS who survived this complication: 4058 pg/mL (±1208) (p < 0.05).A total of 602 patients were recruited in this study and underwent cardiac surgical treatment for valvular heart disease . The main pathophysiological basis of postoperative MODS is the hypoxia of peripheral tissues, which in turn leads to cell damage. Hypoxia can also be caused by three underlying causes that may coexist: reduced cardiac output, reduced hemoglobin levels, or impaired oxygen uptake by target cells . In patiCardiac output (CO), i.e., the amount of blood pumped by the left ventricle in 1 min, next to blood count parameters, is one of the main determinants of the ability of the body to deliver oxygen to the cells. Heart failure, which is manifested, inter alia, by a decreased cardiac output is common among patients admitted to an intensive cardiac care unit after heart valve surgery . In the The results presented above indicate that the occurrence of hypoxia and related tissue damage in the perioperative period is also influenced by the baseline hemoglobin concentration, which also decreases due to the loss of perioperative blood and the use of extracorporeal circulation ,26,27,28The preoperative concentration of NT-proBNP in the blood may be a useful parameter for assessing the risk of developing multiple organ dysfunction syndrome in the early postoperative period in patients undergoing cardiac surgery due to severe valvular heart disease. These are the results of a single-center study with a moderate number of patients recruited for the study. In future studies, increasing the number of research centers and participants may confirm our results. In conclusion, the assessment of the NT-proBNP concentration may be helpful for the care of patients with severe symptomatic valvular disease, both during observation and qualification for cardiac surgery and in the early perioperative period.


To investigate the value of intestinal flora in predicting major adverse cardiovascular and cerebrovascular events (MACCE) in patients with refractory hypertension (RH).  359 patients with RH hospitalized in our hospital from April 2020 to March 2021 were followed up for 1 year and selected for the study. These patients were divided into a MACCE group and no-MACCE group. Results were analyzed by comparing general information, the abundance of intestinal flora at the phylum level, and the abundance of intestinal flora at the species level between the two groups. The influence factors related to MACCE were evaluated using multifactor logistic regression analysis, and the value of intestinal flora in predicting MACCE was determined using receiver operating characteristic (ROC) and the area under ROC (AUC). P < 0.05). The abundances of Actinomycetes and Verrucomicrobia were higher in the MACCE group than in the no-MACCE group, while unnamed viruses were the opposite (P < 0.05). The abundances of Eubacterium eligens, Akkermansia muciniphila, Prevotella stercorea, and Eubacterium rectale were lower in the MACCE group than in the no-MACCE group, while Escherichia coli, Clostridium hathewayi, and Ruminococcus gnavus were opposite (P < 0.05). Systolic blood pressure, Actinomycetes, unnamed viruses, Verrucomicrobia, Eubacterium eligens, Akkermansia muciniphila, Prevotella stercorea, Eubacterium rectale, Escherichia coli, Clostridium hathewayi, and Ruminococcus gnavus were closely associated with MACCE in RH patients (P < 0.05). In addition, Akkermansia muciniphila had the highest AUC among the single indicator but was still lower than the AUC of the combined detection.  Systolic blood pressure was higher in the MACCE group than in the no-MACCE group ( The increases of Actinomycetes, Verrucomicrobia, Escherichia coli, Clostridium hathewayi, and Ruminococcus gnavus and the decreases of unnamed viruses, Eubacterium eligens, Akkermansia muciniphila, Prevotella stercorea, and Eubacterium rectale were associated with MACCE in RH patients, and the combined detection may provide a method and idea for predicting and preventing MACCE. In China, the morbidity of hypertension is 18.8% in people aged ≥18, while the blood pressure control rate is only 6.1%, which is not only related to medication compliance and treatment timeliness but also related to the number of patients with refractory hypertension (RH) . IntestiThe study recruited 359 patients with RH hospitalized in our hospital from April 2020 to March 2021. During the 1-year follow-up, 4 patients were lost to follow-up and 355 patients were visited, including 179 females and 176 males, aged from 38 to 86 years, with an average of 56.64 ± 13.90 years. All patients signed the written informed consent. The study was permitted by the Ethics Committee of our hospital. All patients were divided into 2 groups, including a MACCE group and no-MACCE group.Inclusion criteria are listed as follows: (i) patients meeting the diagnostic criteria of RH , (ii) paGeneral information questionnaire was performed to collect RH patient information, including age, gender, body mass index, diastolic blood pressure, systolic blood pressure, duration of disease, diabetes, hyperlipidemia, smoking, drinking, chronic renal insufficiency, and medication history.Reagents used in this study included Stool DNA Kit  and 2 × Taq PCR MasterMix . Primer synthesis was performed by Sangon Biotech . Experiment instruments included an ultraviolet spectrophotometer , polymerase chain reaction (PCR) thermocycler , automatic PCR analysis system , and ultraviolet transmission and reflection analyzer .2 g of midstream stools was collected from RH patients using a special stool collector, and DNA was extracted using a stool DNA Kit. DNA purification and concentration were analyzed by an ultraviolet spectrophotometer, and DNA integrity was determined by agarose gel electrophoresis. The OD260/OD280 of purified DNA with high purity is between 1.6 and 1.8. Then, V3-V4 variable regions of microbial 16 SrDNA were amplified by PCR with primers  with the reaction at 94°C for 3 min, 27 cycles at 94°C for 30 s, 72°C for 30 s, and 72°C for 10 min and then identified by high-throughput sequencing using an Illumina HiSeq sequencer, followed by bioinformatics analysis. The DNA was quantified by quantitative real-time PCR with a PCR thermocycler. Double barcode was introduced into the primer area for PCR amplification to prevent amplification bias and the occurrence of chimeric sequences to ensure the satisfactory concentration of amplification products in the minimum cycle number of samples.When investigating microbial diversity, operational taxa are introduced to facilitate analysis and improve efficiency. Representative sequences of the operational taxonomic unit were selected and compared with ribosomal RNA data of Greengenes Database 13-8 version based on 99% sequence similarity clustering to obtain the annotation information of species. Single sequences and chimeric sequences without duplication were taken out, and the confidence threshold was set to 70% to obtain the composition of the tested samples at phylum and species classification levels. The species abundance spectrum was evaluated according to the proportion of phylum and species in the total number of sequences.The observation indexer were as follows: (1) general information was compared between the two groups. (2) The abundance of intestinal flora at the phylum level was compared between the two groups. (3) The abundance of intestinal flora at the species level was compared between the two groups. (4) The influence factors of RH complicated with MACCE were analyzed between the two groups. (5) The predictive value of intestinal microbiota-related indexes for MACCE was assessed between the two groups.χ2 test and expressed as n (%), while variables data were compared with Student's t-tests and expressed as means ± standard deviations (SD). The factors related to MACCE were evaluated using multifactor logistic regression analysis, and the value of intestinal flora in predicting MACCE was determined using receiver operating characteristic (ROC) and the area under ROC (AUC). P < 0.05 indicated statistical significance.All data were analyzed using SPSS24.0. Attribute data were compared using the P < 0.05) , including 55 cases with cerebral hemorrhage, 62 cases with cerebral infarction, 44 cases with acute myocardial infarction, and 8 cases with cardiovascular and cerebrovascular event-related death. 186 cases (52.39%) did not occur with MACCE. The results showed that there was no significant difference in age, gender, body mass index, diastolic blood pressure, duration of disease, diabetes, hyperlipidemia, smoking, drinking, chronic renal insufficiency, and medication history when comparing the two groups . But, sy < 0.05) .P < 0.05)  .P < 0.05)  .P < 0.05) was taken as the independent variable. As shown in P < 0.05).MACCE occurrence was taken as the dependent variable, and the comparison index between the two groups (According to the ROC curve of intestinal microbiota-related indexes to predict MACCE, we found that the combined detection had the highest AUC .Diastolic blood pressure and/or systolic blood pressure still cannot be effectively controlled after RH patients sufficiently take more than 3 types of antihypertensive drugs including diuretics. It is difficult to control blood pressure in such patients, and the risk of MACCE increases accordingly . Thus, iAt the level of phylum classification, Bacteroidetes and phylum Firmicutes are the two most dominant phyla in the intestinal flora of healthy people and hypertensive people . The preAt the level of species classification, the number of Escherichia coli, Clostridium hathewayi, and Ruminococcus gnavus is higher in hypertensive patients than in healthy people . Our datIn the present study, according to the results of ROC analysis, we found that the AUC of each intestinal flora in predicting MACCE was >0.7, showing their predictive value in MACCE. In particular, the AUC of Akkermansia muciniphila was the highest compared with other intestinal flora, suggesting that Akkermansia muciniphila had the highest predictive value in MACCE among these intestinal florae. However, the AUC of combined detection of the intestinal flora was 0.926, which was significantly higher than that of other single intestinal flora, suggesting that the combined detection of the intestinal flora could improve the reliability of predicting MACCE. The reason may be that these intestinal florae are associated with the occurrence of MACCE, and the combined detection covers more mechanisms of MACCE occurrence. However, intestinal flora detection has certain requirements on hospital hardware and software configuration, which may limit its application in primary medical centers. Thus, further studies are needed to study more convenient intestinal flora detection methods.Taken together, the increased number of Actinomycetes, Verrucomicrobia, Escherichia coli, Clostridium hathewayi, and Ruminococcus gnavus and the decreased number of unnamed virus, Eubacterium eligens, Akkermansia muciniphila, Prevotella stercorea, and Eubacterium rectale were associated with MACCE in RH patients. The combined detection can provide a method and idea for predicting and preventing MACCE.