Electronic healthcare records and external outcome data for hospitalized patients with heart failure

Admission prioritization of heart failure patients with multiple comorbidities

The primary objective of this study was to enhance the operational efficiency of the current healthcare system by proposing a quicker and more effective approach for healthcare providers to deliver services to individuals facing acute heart failure (HF) and concurrent medical conditions. The aim was to support healthcare staff in providing urgent services more efficiently by developing an automated decision-support Patient Prioritization (PP) Tool that utilizes a tailored machine learning (ML) model to prioritize HF patients with chronic heart conditions and concurrent comorbidities during Urgent Care Unit admission. The study applies key ML models to the PhysioNet dataset, encompassing hospital admissions and mortality records of heart failure patients at Zigong Fourth People's Hospital in Sichuan, China, between 2016 and 2019. In addition, the model outcomes for the PhysioNet dataset are compared with the Healthcare Cost and Utilization Project (HCUP) Maryland (MD) State Inpatient Data (SID) for 2014, a secondary dataset containing heart failure patients, to assess the generalizability of results across diverse healthcare settings and patient demographics. The ML models in this project demonstrate efficiencies surpassing 97.8% and specificities exceeding 95% in identifying HF patients at a higher risk and ranking them based on their mortality risk level. Utilizing this machine learning for the PP approach underscores risk assessment, supporting healthcare professionals in managing HF patients more effectively and allocating resources to those in immediate need, whether in hospital or telehealth settings.

Association of serum albumin with heart failure mortality with NYHA class IV in Chinese patients: Insights from PhysioNet database (version 1.3)

BACKGROUND

Studies have proved that low albumin level is associated with increased mortality in most diseases, such as chronic kidney disease and hepatic cirrhosis. However, the relationship between albumin and all-cause death in heart failure patients in China is still unclear.

OBJECTIVES

We aimed to investigate the association between albumin level and 28-day mortality in Chinese hospitalized patients with NYHA IV heart failure.

METHODS

A total of 2008 Chinese patients were included. The correlation between serum albumin level and mortality was tested using a cox proportional hazards regression model. The smooth curve fitting was used to identify non-linear relationships between serum albumin and mortality. The Forest plot analysis was used to assess the association between albumin and 28-day mortality in different groups.

RESULTS

Compared with patients with NYHA II-III, patients with NYHA IV had lower albumin level and higher mortality within 28 days. The albumin on admission was independently and inversely associated with the endpoint risk, which remained significant (hazard ratio: 0.80; 95 % confidence interval: 0.66 to 0.96; p = 0.0196) after multivariable adjustment. The smooth curve fitting showed with the increase of albumin, the mortality within 28 days would decrease. A subgroup analysis found that the inverse association between the albumin level and risk of the mortality was consistent across the subgroup stratified by possible influence factors.

CONCLUSION

Serum albumin level is negatively associated with 28-day mortality in hospitalized heart failure patients within NYHA IV in China, which can be used as an independent predictor.

A Study on Risk Factors for Readmission of Elderly Patients with Pulmonary Tuberculosis Within One Month Using Propensity Score Matching Method

Objective

Exploring the risk factors for readmission of elderly patients with pulmonary tuberculosis (PTB) within one month using the propensity score matching(PSM).

Methods

A retrospective analysis was conducted on the clinical data of elderly patients with PTB who were admitted to the Tuberculosis Department of Lishui Hospital of Traditional Chinese Medicine from January 2020 to October 2023. The patients were divided into two groups: non-readmission group and readmission group based on whether they were readmitted within one month after discharge. The PSM method was used to match the baseline data of the two groups of patients, and multivariate logistic regression analysis was conducted to explore the risk factors for readmission of elderly patients with PTB within one month after discharge.

Results

A total of 1268 hospitalized elderly patients with PTB were included in the study, comprising 977 readmitted patients and 291 newly admitted patients (22.95%). Using the PSM, 288 pairs of patients were successfully matched. Following matching, there were no statistically significant differences between the two groups in terms of gender, age, occupation, body mass index(BMI), past medical history, etc. (all P>0.05). Multivariate logistic regression analysis indicated that infection, drug-induced liver injury(DILI), acute heart failure(AHF), chronic kidney disease(CKD), and extrapulmonary tuberculosis(EPTB) were all identified as risk factors for readmission of elderly patients with PTB.

Conclusion

After controlling for confounding factors through PSM, the study revealed that infection, DILI, AHF, CKD, and EPTB are risk factors for readmission among elderly patients with PTB, highlighting the need for early intervention.

Association between red blood cell distribution width-to-platelet ratio and post-discharge readmission rate in patients with heart failure: A retrospective cohort study

Background

To date, no studies have investigated the association between red blood cell distribution width (RDW)-to-platelet ratio (RPR) and readmission rates among patients with heart failure (HF). As such, the present study aimed to examine the relationship between RPR and readmission rates in patients with HF.

Methods

Data for this study were obtained from the Fourth People's Hospital (Zigong, Sichuan Province, China). Patients were diagnosed with HF in accordance with European Society of Cardiology criteria. The primary outcome was the 28-day readmission rate. Various logistic regression models were constructed to explore the association between RPR and the 28-day readmission rate.

Results

The study comprised 1978 patients with HF, with a 28-day readmission rate of 6.98%. RPR emerged as an independent risk factor for 28-day readmission, evidenced by consistent results across the various regression-adjusted models. The covariate-adjusted propensity score model demonstrated that every 0.1 increase in RPR was associated with an 8.2% increase in 28-day readmission rate (odds ratio [OR] 1.082 [95% confidence interval (CI) 1.012-1.158]; P = 0.0212). Similarly, each 0.1 change in RPR was associated with a 9.8% (OR 1.098 [95% CI 1.014-1.188]) and 7.3% (OR 1.073 [95% CI 0.991-1.161]) increase in 3- and 6-month readmission rates, respectively. However, RPR was not statistically associated with the 6-month readmission rate. Curve fit plots illustrated a nonlinear positive correlation between RPR and 28-day, and 3- and 6-month readmissions. Moreover, the effects of RPR on 28-day, and 3- and 6-month readmission rates remained robust across subgroup variables in stratified analysis. Finally, the effect sizes of pooled multiply imputed data were consistent with the original data, suggesting robust results.

Conclusion

RPR was an independent risk factor for 28-day readmission among patients with HF and also demonstrated modest predictive value for readmissions at 3 and 6 months, despite being non-significant for the 6-month readmission rate. Early identification of patients with HF with elevated RPR would facilitate management and may confer favorable effects on prognosis.

Relationship Between Fasting Blood Glucose and Readmission Within 1 Year in Elderly Patients with Heart Failure

INTRODUCTION

Elevated blood glucose has been linked to unfavorable outcomes among individuals with heart failure (HF). Nevertheless, evidence is scarce regarding the association between fasting blood glucose (FBG) levels and the likelihood of readmission within one year for elderly patients. To address this gap, a retrospective cohort study was conducted, integrating electronic health records of restricted health data from PhysioNet.

METHODS

The study focused on HF patients aged 60 years and older, utilizing baseline data, comorbidities, and laboratory test results as covariates. A total of 374 patients were included in the study. The relationship between 1-year readmission rates and various glucose levels was assessed using Kaplan-Meier plots. The analysis employed three multivariate Cox regression models to examine patients with varying glucose levels.

RESULTS

Following adjustments for relevant factors, an association was observed between FBG levels and the rate of readmission in elderly patients with HF (HR=1.0264 [95% CI 0.9994-1.0541]). The diabetes group faced a higher risk of readmission compared to the normal group. However, this difference in outcome events was not statistically significant, with hazard ratios and their corresponding 95% confidence intervals of 1.2134 (0.9811~1.5007), 1.2393 (0.9993~1.5371), and 1.1905 (0.9570~1.4809), respectively. The robustness of the model was further demonstrated through risk models with subgroup analysis, revealing that FBG levels consistently exerted a stable effect on outcome events, unaffected by covariates such as age, gender, body mass index, glomerular filtration rate, and brain natriuretic peptide.

CONCLUSION

These findings suggest a notable association between elevated FBG at the time of initial hospitalization and the likelihood of readmission within one year among elderly patients with HF.

Chinese experts' consensus on the application of intensive care big data

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

The association of blood urea nitrogen to serum albumin ratio with short-term outcomes in Chinese patients with congestive heart failure: A retrospective cohort study

BACKGROUND AND AIMS

Limited evidence exists on the prognostic outcomes of the blood urea nitrogen to serum albumin ratio (B/A ratio) in congestive heart failure (CHF), particularly in developing countries with scarce heart failure epidemiological data. We aimed to investigate the association between B/A ratio and short-term outcomes in Chinese patients with CHF.

METHODS AND RESULTS

We included 1761 CHF patients with available B/A ratio data from a cohort of 2008 patients. Patients were categorized into three groups based on B/A ratio (low to high). The primary endpoint was death or readmission within 28 days, and the secondary endpoint was death or readmission within 90 days. We employed restricted cubic spline analysis, Cox proportional hazards regression, and Kaplan-Meier curves to evaluate the relationship between B/A ratio at admission and the endpoints. Even after adjusting for other variables, higher B/A ratios were associated with increased rates of 28 days and 90 days mortality or readmission (HR: 2.4, 95% CI: 1.81-3.18 and HR: 1.74, 95% CI: 1.48-2.05). Significant differences in the risks of both primary and secondary endpoints were observed among the three B/A ratio groups. The association between B/A ratio and CHF was stable in the different subgroups (all P for interaction＞0.05).

CONCLUSION

Higher B/A ratios are associated with an increased risk of short-term mortality or readmission in Chinese patients with CHF. The B/A ratio shows promise as a prognostic indicator for short-term outcomes in CHF patients.

Mean arterial pressure and mortality in patients with heart failure: a retrospective analysis of Zigong heart failure database

BACKGROUND

It is commonly observed that a higher target of mean arterial pressure (MAP) is in previous studies. This study assessed the association of MAP with short-term mortality in heart failure (HF) patients.

METHODS

A retrospective cohort study was conducted by using data from Hospitalized patients with heart failure: integrating electronic healthcare records and external outcome database (v1.2 ). The characteristic of patients was described by 3 groups of MAP: below 80 mmHg, 80-100 mmHg, and above 100 mmHg. Univariate and multivariate logistic regression analyses were used to assess the relevance between MAP and all-cause mortality within 28 days and 6 months. For assessing the effect of multiple variables on patient survival time, 28-day and 6-month, Kaplan-Meier survival analysis and Forest plot were performed.

RESULTS

The overall cohort comprised 2008 patients divided by MAP into 3 groups, each group had 344 (17.1%), 938 (46.7%), and 726 (36.2%) patients. Patients in MAP < 80 mmHg group had higher mortality than MAP 80-100 mmHg and MAP ≥ 100 mmHg in 28 days(3.8% versus 1.6% versus 1.2%) and in 6 months (4.9% versus 2.5% versus 2.3%). Univariate analysis showed that MAP as a continuous variate was associated with 28-day (OR was 0.98, 95% CIs: 0.96-0.99, P  = 0.011) and 6-month mortality (OR was 0.98, 95% CIs: 0.97-1, P  = 0.021) in HF patients. Model 4 put into multivariate logistic regression analyses showed MAP 80-100 mmHg (OR was 0.13, 95% CIs: 0.02-0.8, P  = 0.027) stably associated with 28-day and 6-month mortality after adjusted covariable. Kaplan-Meier survival curves revealed a higher survival rate in the MAP ≥ 80 mmHg group than in the MAP < 80 mmHg group. The forest plot showed the stable effect of MAP ≥ 80 mmHg compared with MAP < 80 mmHg, the interaction analysis had no statistical significance effect between the two groups of MAP and multi-variable.

CONCLUSION

It is indicated that MAP was independently associated with 28-day, 6-month all-cause mortality of HF patients, and compared with MAP < 80 mmHg, MAP ≥ 80 mmHg had a lower risk of 28-day, 6-month all-cause mortality of patients with HF.

Relation of serum uric acid levels to readmission and mortality in patients with heart failure

Data on the association between uric acid (UA) levels and clinical outcomes, such as readmission and mortality, in patients with heart failure are scarce. This study explores whether UA exhibits an independent association with the composite endpoint (clinical outcome during 6 months after discharge, including mortality and 6-month readmission) in patients with chronic heart failure while controlling for other covariates. This study was an observational retrospective study. A cohort of 1943 consecutive patients diagnosed with chronic heart failure, who were admitted between December 2016 and June 2019, was included in the study. Data were sourced from PhysioNet. The independent variable analyzed was the UA level, and the dependent variable was a composite endpoint comprising mortality and 6-month readmission. The study had 1943 participants, of which 91.04% were aged more than 60 years and 58.05% were female. The fully-adjusted model yielded a positive correlation between UA levels (per 10 µmol/L) and the composite endpoint as well as readmission, following adjustment for confounding variables (HR = 1.01, 95% CI 1.00-1.01). Notably, a non-linear relationship was observed between UA levels and the composite endpoint, particularly readmission, with a J-shaped correlation observed between UA levels and both the composite endpoint and readmission. Overall, we found that the serum UA levels at admission were independently and positively associated with the risk of the composite endpoint (clinical outcomes during 6 months after discharge), especially readmission after adjusting other covariates. A J-shaped relationship was observed between UA levels and the composite endpoint and readmission.

Association between admission prothrombin time activity and hospital readmission in heart failure: A retrospective study

BACKGROUND

Coagulopathy is a common complication of heart failure (HF), but the prognostic significance of coagulation abnormalities for HF is still poorly understood. This investigation sought to elucidate the association between admission prothrombin time activity (PTA) and short-term readmission in HF.

METHODS

In this retrospective study, we extracted data from a publicly accessible database for hospitalized HF patients in China. The admission laboratory findings were screened by the least absolute shrinkage and selection operator (LASSO) regression. Afterward, the study population was stratified according to the admission PTA level. In univariate and multivariate analysis, we employed logistics regression model to evaluate the association of admission PTA level with short-term readmission. Subgroup analysis was preformed to examine the interaction effect between admission PTA level and covariates, including age, sex, and systolic blood pressure (SBP).

RESULTS

A total of 1505 HF patients were included, of whom 58.7% were female and 35.6% were between 70 and 79 y. In LASSO procedure, admission PTA level was included in optimized models for short-term readmission, and readmitted patients tended to have a lower admission PTA level. Multivariate analysis suggested that the low admission PTA level (admission PTA ≤ 62.3%) was associated with increased risk of 90-day readmission (odds ratio 1.63 [95% CI, 1.09 to 2.46]; P = 0.02) and 180-day readmission (odds ratio 1.65 [95% CI, 1.18 to 2.33]; P = 0.01) compared with patients with the highest admission PTA level (admission PTA ≥ 76.8%) after full adjustment. Moreover, no significant interaction effect was observed in the subgroup analysis, except for admission SBP.

CONCLUSION

Low admission PTA level is associated with an increased risk of 90-day and 180-day hospital readmission in patients with HF.

Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model

Heart failure is a devastating disease that has high mortality rates and a negative impact on quality of life. Heart failure patients often experience emergency readmission after an initial episode, often due to inadequate management. A timely diagnosis and treatment of underlying issues can significantly reduce the risk of emergency readmissions. The purpose of this project was to predict emergency readmissions of discharged heart failure patients using classical machine learning (ML) models based on Electronic Health Record (EHR) data. The dataset used for this study consisted of 166 clinical biomarkers from 2008 patient records. Three feature selection techniques were studied along with 13 classical ML models using five-fold cross-validation. A stacking ML model was trained using the predictions of the three best-performing models for final classification. The stacking ML model provided an accuracy, precision, recall, specificity, F1-score, and area under the curve (AUC) of 89.41%, 90.10%, 89.41%, 87.83%, 89.28%, and 0.881, respectively. This indicates the effectiveness of the proposed model in predicting emergency readmissions. The healthcare providers can intervene pro-actively to reduce emergency hospital readmission risk and improve patient outcomes and decrease healthcare costs using the proposed model.

Increasing efficiency of SVMp+ for handling missing values in healthcare prediction

Missing data presents a challenge for machine learning applications specifically when utilizing electronic health records to develop clinical decision support systems. The lack of these values is due in part to the complex nature of clinical data in which the content is personalized to each patient. Several methods have been developed to handle this issue, such as imputation or complete case analysis, but their limitations restrict the solidity of findings. However, recent studies have explored how using some features as fully available privileged information can increase model performance including in SVM. Building on this insight, we propose a computationally efficient kernel SVM-based framework (l2-SVMp+) that leverages partially available privileged information to guide model construction. Our experiments validated the superiority of l2-SVMp+ over common approaches for handling missingness and previous implementations of SVMp+ in both digit recognition, disease classification and patient readmission prediction tasks. The performance improves as the percentage of available privileged information increases. Our results showcase the capability of l2-SVMp+ to handle incomplete but important features in real-world medical applications, surpassing traditional SVMs that lack privileged information. Additionally, l2-SVMp+ achieves comparable or superior model performance compared to imputed privileged features.

Association between mean arterial pressure and clinical outcomes among patients with heart failure

AIMS

Mean arterial pressure (MAP) is widely used for evaluating organ perfusion, but its impact on clinical outcomes in patients with heart failure (HF) remains poorly understood. The aim of this study is to investigate the relationship between MAP and all-cause mortality and readmission in patients with HF.

METHODS AND RESULTS

We retrospectively analysed data from PhysioNet, involving 2005 patients with HF admitted to Zigong Fourth People's Hospital between 2016 and 2019. The primary outcomes were composite outcomes of all-cause mortality and readmission at 3 and 6 months. The secondary outcomes were readmission at 3 and 6 months. Multivariate-adjusted Cox regression models, restricted cubic spline curves (RCS), and propensity score matching (PSM) were used to explore the relationship between MAP and clinical outcomes. Among 2005 patients with HF [≥70 years, 1460 (72.8%); male, 843 (42.0%)], the incidence of primary outcome at 3 months was 33.4% (223/668), 24.4% (163/668), and 22.7% (152/669), and at 6 months, it was 47.5% (317/668), 38.5% (257/668), and 38.0% (254/669) across MAP tertiles [from Tertile 1 (T1) to Tertile 3 (T3)], respectively. The RCS showed an 'L-shaped' relationship between MAP and primary or secondary endpoints. Multivariate-adjusted Cox models showed that a higher MAP was significantly associated with a lower risk of composite endpoints at 3 months [adjusted hazard ratio (aHR) 0.75, 95% confidence interval (CI) 0.61-0.92, P = 0.006, Tertile 2 (T2); aHR 0.69, 95% CI 0.56-0.86, P = 0.001, T3] and 6 months (aHR 0.79, 95% CI 0.67-0.93, P = 0.005, T2; aHR 0.77, 95% CI 0.64-0.91, P = 0.003, T3) compared with T1. After 1:1 PSM, the effect of maintaining a relatively higher MAP was slightly attenuated. Threshold analyses indicated that per 10 mmHg increase in MAP, there was a 21% and 14% decrease in composite endpoints at 3 and 6 months, respectively (aHR 0.79, 95% CI 0.69-0.91, P = 0.001), and 6 months (aHR 0.86, 95% CI 0.77-0.97, P = 0.013) in patients with MAP ≤ 93 mmHg. The associations were consistent in readmission (secondary outcomes), various subgroups, and sensitivity analysis.

CONCLUSIONS

A higher MAP was associated with a lower risk of a composite of all-cause mortality and readmission. Maintaining a relatively higher MAP could potentially improve the clinical prognosis for patients with HF.

Comparison of linear and non-linear machine learning models for time-dependent readmission or mortality prediction among hospitalized heart failure patients

Although many models are available to predict prognosis of heart failure patients, most tools combining survival analysis are based on proportional hazard model. Non-linear machine learning algorithms would overcome the limitation of the time-independent hazard ratio assumption and provide more information in readmission or mortality prediction among heart failure patients. The present study collected the clinical information of 1796 hospitalized heart failure patients surviving during hospitalization in a Chinese clinical center from December 2016 to June 2019. A traditional multivariate Cox regression model and three machine learning survival models were developed in derivation cohort. Uno's concordance index and integrated Brier score in validation cohort were calculated to evaluate the discrimination and calibration of different models. Time-dependent AUC and Brier score curves were plotted to assess the performance of models at different time phases.

Serum potassium in elderly heart failure patients as a predictor of readmission within 1 year

Blood potassium levels are associated with adverse outcomes in patients with congestive heart failure (HF). However, it is unclear whether there are differences in outcome events in elderly patients with different blood potassium levels at the time of emergency readmission within 1 year. This study used data from patients hospitalized with HF, integrating electronic medical records from the PhysioNet restricted health data database and external outcome data. We conducted a retrospective study of HF patients aged 60 years and older, using baseline data, comorbidities, laboratory tests, and medication use as covariates to analyze the effect of serum potassium levels on outcome events, with the primary outcome being readmission within 1 year. A priori was used to calculate the sample size, and this retrospective cohort study included a total of 788 elderly HF patients, of whom 20.3% had hypokalaemia (K⁺  < 3.5 mmol/L) and 14.7% had hyperkalemia (K⁺  > 4.7 mmol/L). According to a multivariate Cox regression model, patients with hyperkalemia had a shorter time interval between readmissions within 1 year, with a hazard ratio (HR) and its 95% CI of 1.134 (1.006-1.279). Three models were used to analyze patients with different blood potassium levels and, after correction, the high potassium group was at high risk relative to the low and normal groups, with significant differences in outcome events, with HRs and their 95% CI of 1.266 (1.03-1.557), 1.245 (1.01-1.534), and 1.439 (1.142-1.812), respectively. The robustness of the model was also demonstrated by competing risk models with subgroup analysis, showing that blood potassium levels had a stable effect on outcome events and were not altered by covariates (age, sex, diabetes, chronic kidney disease, NT-proBNP, high-sensitivity troponin, and glomerular filtration rate). The results show that high blood potassium levels are associated with the outcome event of readmission within 1 year in elderly patients with HF. Blood potassium levels at the time of the first hospitalization may therefore be a valuable predictor.

Charlson Comorbidity Index is correlated with all-cause readmission within six months in patients with heart failure: a retrospective cohort study in China

BACKGROUND

Charlson Comorbidity Index (CCI) is positively associated with all-cause readmission in patients with heart failure (HF) in western countries. However, there is a scarcity of strong scientific evidence supporting the correlation in China. This study aimed at testing this hypothesis in Chinese.   METHODS: We conducted a secondary analysis of 1,946 patients with HF in Zigong Fourth People's Hospital in China between December 2016 to June 2019. Logistic regression models were used to study the hypotheses, with adjustments for the four regression models. We also explore the linear trend and possible nonlinear relationship between CCI and readmission within six months. We further conducted subgroup analysis and tests for interaction to examine the possible interaction between CCI and the endpoint. Additionally, CCI alone and several combinations of variables based on CCI were used to predict the endpoint. Under the curve (AUC), sensitivity and specificity were reported to evaluate the performance of the predicted model.

RESULTS

In the adjusted II model, CCI was an independent prognostic factor for readmission within six months in patients with HF (OR = 1.14, 95% CI: 1.03-1.26, P = 0.011). Trend tests revealed that there was a significant linear trend for the association. A nonlinear association was identified between them and the inflection point of CCI was 1. Subgroup analyses and tests for interaction indicated that cystatin played an interactive role in the association. ROC analysis indicated neither CCI alone nor combinations of variables based on CCI were adequate for prediction.

CONCLUSION

CCI was independently positively correlated with readmission within six months in patients with HF in Chinese population. However, CCI has limited value on predicting readmission within six months in patients with HF.

The Charlson comorbidity index and short-term readmission in patients with heart failure: A retrospective cohort study

The relationship between the Charlson comorbidity index (CCI) and short-term readmission is as yet unknown. Therefore, we aimed to investigate whether the CCI was independently related to short-term readmission in patients with heart failure (HF) after adjusting for other covariates. From December 2016 to June 2019, 2008 patients who underwent HF were enrolled in the study to determine the relationship between CCI and short-term readmission. Patients with HF were divided into 2 categories based on the predefined CCI (low < 3 and high > =3). The relationships between CCI and short-term readmission were analyzed in multivariable logistic regression models and a 2-piece linear regression model. In the high CCI group, the risk of short-term readmission was higher than that in the low CCI group. A curvilinear association was found between CCI and short-term readmission, with a saturation effect predicted at 2.97. In patients with HF who had CCI scores above 2.97, the risk of short-term readmission increased significantly (OR, 2.66; 95% confidence interval, 1.566-4.537). A high CCI was associated with increased short-term readmission in patients with HF, indicating that the CCI could be useful in estimating the readmission rate and has significant predictive value for clinical outcomes in patients with HF.

Performance of the heart failure risk scores in predicting 1 year mortality and short-term readmission of patients

AIMS

The aim of this study was to assess the performance of these main scores in predicting prognosis in patients with heart failure (HF).

METHODS AND RESULTS

A total of 2008 patients who were admitted to the Fourth People's Hospital of Zigong, Sichuan, from December 2016 to June 2019 and diagnosed with HF were included in the study. We compared the prognostic predictive performance of Seattle Heart Failure Model (SHFM), Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC-HF) risk score, Get With the Guidelines-Heart Failure programme (GWTG-HF), Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure (ASCEND) risk scores, the Acute Decompensated Heart Failure National Registry (ADHERE) model, Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator, and Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure (GISSI-HF) for the endpoints. The primary endpoint was 1 year all-cause mortality and the secondary endpoint was the incidence of 28 day readmission post-discharge. At 1 year follow-up, 44 (2.21%) patients with HF died. Discrimination analyses showed that all risk scores performed reasonably well in predicting 1 year mortality, with areas under the receiver operating characteristic curve (AUCs) fluctuating between 0.757 and 0.822. GISSI-HF showed the best discrimination with the AUC of 0.822 (0.768-0.876), followed by MAGGIC-HF, BCN-Bio-HF, ASCEND, SHFM, GWTG-HF, and ADHERE with AUCs of 0.819 (0.756-0.883), 0.812 (0.758-0.865), 0.802 (0.742-0.862), 0.787 (0.725-0.849), 0.762 (0.684-0.840), and 0.757 (0.681-0.833), respectively. All risk scores were similarly predictive of 28 day emergency readmissions, with AUCs fluctuating between 0.609 and 0.680. Overestimation of mortality occurred in all scores except the ASCEND. The risk scores remained with good prognostic discrimination in patients with biventricular HF and in the subgroup of patients taking angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker.

CONCLUSIONS

Currently assessed risk scores have limited clinical utility, with fair accuracy and calibration in assessing patients' 1 year risk of death and poor accuracy in assessing patients' risk of readmission. There is a need to incorporate more patient-level information, use more advanced technologies, and develop models for different subgroups of patients to achieve more practical, innovative, and accurate risk assessment tools.

Association of Hemoglobin to Red Blood Cell Distribution Width-Standard Deviation (RDW-SD) Ratio and 3-Month Readmission in Elderly Chinese Patients with Heart Failure: A Retrospective Cohort Study

Purpose

Hemoglobin (Hb) and red blood cell distribution width-standard deviation (RDW-SD) have clinical significance in the prognosis of heart failure (HF). Little is known regarding the prognostic value of the Hb/RDW-SD ratio in patients with HF. This study sought to investigate the association between Hb/RDW-SD ratio and HF 3-month readmission in Chinese elderly patients.

Patients and Methods

The present study was a retrospective cohort study. A total of 1816 HF patients were extracted from the Chinese HF database. A generalized linear model was used to explore the association between Hb/RDW-SD and 3-month readmission in HF. The generalized additive model was used to explore the nonlinear relationship, and a two-piecewise linear regression model was used to find the inflection point. Subgroup analysis explored interactions and whether each subgroup was consistent with the primary outcome direction.

Results

Result showed Hb/RDW-SD was negatively associated with HF 3-month readmission (OR = 0.70, 95% CI: 0.55 to 0.89, P = 0.0031). A non-linear relationship was detected between Hb/RDW-SD and HF 3-month readmission with two inflection points (1.78 and 2.17). Both Hb/RDW-SD < 1.78 and Hb/RDW-SD > 2.17 showed a significant correlation between them, with corresponding effect values of (OR = 0.38, 95% CI: 0.17 to 0.87, P = 0.0209) and (OR = 0.44, 95% CI: 0.27 to 0.71, P = 0.0007), respectively.

Conclusion

Hb/RDW-SD is negatively associated with HF 3-month readmission. The relationship between Hb/RDW-SD and HF 3-month readmission is also non-linear. Both Hb/RDW-SD < 1.78 and Hb/RDW-SD > 2.17 were strong negatively associated with HF 3-month readmission.

Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database

Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599-0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.

Association of red cell distribution width with the risk of 3-month readmission in patients with heart failure: A retrospective cohort study

Background

In recent years, red cell distribution width (RDW) has been found to be associated with the prognosis of patients with heart failure (HF) in Western countries. However, evidence from Asia is limited. We aimed to investigate the relationship between RDW and the risk of 3-month readmission in hospitalized Chinese HF patients.

Methods

We retrospectively analyzed HF data from the Fourth Hospital of Zigong, Sichuan, China, involving 1,978 patients admitted for HF between December 2016 and June 2019. The independent variable in our study was RDW, and the endpoint was the risk of readmission within 3 months. This study mainly used a multivariable Cox proportional hazards regression analysis. Smoothed curve fitting was then used to assess the dose-response relationship between RDW and the risk of 3-month readmission.

Results

In the original cohort of 1,978 patients with HF (42% male and 73.1% aged ≥70 years), 495 patients (25.0%) were readmitted within 3 months after discharge. Smoothed curve fitting showed a linear correlation between RDW and the risk of readmission within 3 months. In the multivariable-adjusted model, every 1% increase in RDW was associated with a 9% increased risk of readmission within 3 months (hazard ratio = 1.09, 95% confidence interval: 1.00-1.15; P < 0.005).

Conclusions

A higher RDW value was significantly associated with a greater risk of 3-months readmission in hospitalized patients with HF.

The combined impact of hyponatremia and hematocrit on the risk for 90-day readmission and death in patients with heart failure: dilutional hyponatremia versus depletional hyponatremia

BACKGROUND

Hyponatremia is common in hospitalized patients with heart failure (HF) and predicts a poor prognosis after discharge. In general, hyponatremia can be divided into two types: dilutional or depletional.

OBJECTIVE

Assess the impact of hyponatremia type on short-term outcomes.

DESIGN

Retrospective cohort SETTINGS: Single center in China PATIENTS AND METHODS: We sorted patients by hyponatremia into two types: dilutional hyponatremia (DiH, with hematocrit <35%) and depletional hyponatremia (DeH, with hematocrit ≥35%). The Kaplan-Meier method and Cox regression analysis were used to identify the impact of hyponatremia types on the risk for 90-day readmission and death.

MAIN OUTCOME MEASURES

90-day readmission and death combined.

SAMPLE SIZE

1770 patients.

RESULTS

Hyponatremia was present in 324/1770 patients with 182 cases classified as DiH versus 142 as DeH. Kaplan-Meier analyses showed a higher incidence of poor short-term outcomes in hyponatremia compared with normonatremia (log-rank P<.001), and the risk was higher in DiH than DeH although the difference was not statistically significant (log-rank P=.656). Multivariate Cox regression analyses showed that only DiH was independently associated with short-term outcomes (HR=1.34, 95%CI: 1.02-1.77, P=.038), but not DeH (HR=1.32, 95%CI: 0.97-1.80, P=.081). Analysis of the secondary endpoints showed that DiH increased the risk of readmission but not death (HR=1.36, P=.035 for readmission; HR=1.13, P=.831 for all-cause death).

CONCLUSIONS

Low hematocrit, rather than high hematocrit, with hyponatremia was associated with a risk of 90-day readmission in patients with HF.

LIMITATIONS

Single center, nonrandomized.

CONFLICT OF INTEREST

None.

Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure

Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients' conditions by healthcare professionals but allow for a more optimal use of healthcare resources.

U-Shaped Relationship of Sodium-to-chloride Ratio on admission and Mortality in Elderly Patients with Heart Failure

AIMS

Serum sodium and chloride have clinical significance in the prognosis of heart failure. Little is known regarding the prognostic value of sodium-to-chloride (Na/Cl) ratio in patients with heart failure. This study sought to investigate the association between Na/Cl ratio on admission and mortality risk of elderly patients with acute heart failure in a retrospective cohort.

METHODS AND RESULTS

We included 1819 patients (aged over 60) from the Zigong Heart Failure Study. Patients were grouped according to Na/Cl ratio and followed up for all-cause mortality at 3 months. Restricted cubic spline, cox proportional hazard regression and Kaplan-Meier curve were used to examine the correlation between serum Na/Cl ratio on admission and mortality risk. Restricted cubic spline analysis suggested a U-shaped association between Na/Cl ratio on admission and 3 months mortality risk (p _nonlinearity <0.001), with the nadir of risk at 1.34. After adjustment for multivariate, patients with Na/Cl ratio <1.3 or ≥ 1.4 had hazard ratios for mortality of 3.58 (95% CI, 1.63-7.84) and 2.66 (95% CI, 1.23-5.72) compared with those with Na/Cl ratio of 1.3-1.4. The cumulative hazard of mortality estimates significantly differed across Na/Cl ratio groups (log-rank p<0.001). Subgroup analysis showed there were no interactions with absent or present of hyponatremia and hypochloremia (p for interaction all >0.05).

CONCLUSIONS

Both low and high Na/Cl ratios were associated with an increased mortality risk in elderly patients with acute heart failure. Further studies need to verify these two biochemical phenotypes and develop corresponding treatment strategies.

Non-linear association of cystatin C and all-cause mortality of heart failure: A secondary analysis based on a published database

Background

Prior reports have revealed that basal Cystatin-C (CysC) is positively associated with all-cause death in patients with heart failure (HF). Yet, this positive association is not necessarily generalizable to Chinese HF patients due to methodological limitations and lack of data from Chinese patients.

Materials and methods

We performed secondary data mining based on a retrospective cohort dataset published on the internet. This dataset contains 2008 patients with HF who were admitted to a tertiary hospital in Sichuan Province, China from 2016 to 2019. The exposure variable was baseline CysC and the outcome variable was all-cause death on day 28, day 90, and month 6. Covariates were baseline measurements, including demographic data, drug use, comorbidity score, organ function status (heart, kidney), and severity of heart failure.

Results

Among 1966 selected participants, the mortality rates at 28 days, 90 days and 6 months were 1.83% (36/1966), 2.09% (41/1966) and 2.85% (56/1966) respectively. After adjustment for confounders, the non-linear associations between CysC and all-cause deaths were observed. We calculated the inflection points were about 2.5 mg/L of CysC. On the right of inflection point, each increase of 1 mg/L in CysC was associated with an increase in the risk of 28-day mortality (Relative risk [RR], 2.07; 95% confidence interval [CI], 1.09 to 3.93; P = 0.0266), 90-day mortality (RR, 2.51; 95% CI, 1.38 to 4.57; P = 0.003), and 6-month mortality (RR,2.25; 95% CI, 1.37 to 3.70; P < 0.001).

Conclusion

Our findings suggest that values about 2.5 mg/l of cystatin could be a danger threshold for the short-term risk of death in heart failure. Exceeding this threshold, for every 1 mg/L increase in CysC, the risk of all-cause mortality increased by more than one time.

Clinical Network for Big Data and Personalized Health: Study Protocol and Preliminary Results

The use of secondary hospital-based clinical data and electronical health records (EHR) represent a cost-efficient alternative to investigate chronic conditions. We present the Clinical Network Big Data and Personalised Health project, which collects EHRs for patients accessing hospitals in Central-Southern Italy, through an integrated digital platform to create a digital hub for the collection, management and analysis of personal, clinical and environmental information for patients, associated with a biobank to perform multi-omic analyses. A total of 12,864 participants (61.7% women, mean age 52.6 ± 17.6 years) signed a written informed consent to allow access to their EHRs. The majority of hospital access was in obstetrics and gynaecology (36.3%), while the main reason for hospitalization was represented by diseases of the circulatory system (21.2%). Participants had a secondary education (63.5%), were mostly retired (25.45%), reported low levels of physical activity (59.6%), had low adherence to the Mediterranean diet and were smokers (30.2%). A large percentage (35.8%) were overweight and the prevalence of hypertension, diabetes and hyperlipidemia was 36.4%, 11.1% and 19.6%, respectively. Blood samples were retrieved for 8686 patients (67.5%). This project is aimed at creating a digital hub for the collection, management and analysis of personal, clinical, diagnostic and environmental information for patients, and is associated with a biobank to perform multi-omic analyses.

Serum Chloride Is Inversely Associated With 3 Months Outcomes in Chinese Patients With Heart Failure, a Retrospective Cohort Study

Background

Serum chloride was recently found to be associated with prognosis of heart failure in western countries. However, the evidence was scarce in Asia. We aimed to investigated the relationship between serum chloride and clinical outcomes in a Chinese cohort with hospitalized heart failure.

Methods

We retrospectively analyzed the data from PhysioNet, involving 1996 patients who were admitted with heart failure between December 2016 and June 2019. Outcome was a composite endpoint of all-cause death or rehospitalization at 3 months.

Results

The incidence of the composite endpoint was 26.8% (535/1,996); it was 32.2% (213/662), 25.0% (165/661), and 23.3% (157/673) by chloride tertiles (from the lowest to the highest), respectively. The serum chloride at admission was independently and inversely associated with the composite endpoint risk (hazard ratio: 0.967; 95% confidence interval: 0.939 to 0.996; p = 0.026) in contrast to sodium, which was no longer significant (p > 0.05) after multivariable adjustment. Pearson correlation between serum chloride and sodium was 0.747 (p < 0.001). However, an increased AUC was not observed by adding sodium to model composed of age, sex, NYHA class, diabetes, log BNP and chloride (0.620 vs. 0.612, p = 0.132). Subgroup analysis showed the presence or absence of hyponatremia did not affect the association between chloride and composite endpoint risk.

Conclusions

Low serum chloride at admission was associated with poor outcomes in Chinese hospitalized patients with heart failure. These findings warrant future studies for tackling the potential pathophysiological mechanisms and correction methods of hypochloremia in heart failure.

The Relationship Between the Utilization of Arterial Blood Gas Analysis and Rehospitalization in Heart Failure: A Retrospective Cohort Study

Background

The most common presentation of decompensated HF is dyspnea, and arterial blood gas analysis is an excellent tool for the decision-making process for most dyspneic patients. However, data on the prognostic value of ABG in HF patients are limited. Herein, a retrospective cohort study was conducted to investigate whether the utilization of arterial blood gas analysis was independently associated with re-hospitalization in patients with heart failure.

Methods

As a retrospective cohort study, the relevant clinical data of hospitalized patients admitted to Zigong Fourth People's Hospital, Sichuan, China from December 2016 to June 2019 with a diagnosis of HF were analyzed. The re-hospitalization within 6 months and the use of intravenous diuretic, nitrates, inotropes, or vasopressors were compared between patients with and without arterial blood gas analysis. We used a multivariable logistic regression model, propensity score analysis, and an inverse probability-weighting model to ensure the robustness of our findings.

Results

We included 1,605 patients with heart failure. The overall re-hospitalization rate within 6 months was 38.2%; it was 34.8% and 41.8% for heart failure patients with or without arterial blood gas analysis, respectively. In the inverse probability-weighting model, the use of arterial blood gas analysis was associated with a 26% lower re-hospitalization rate within 6 months.

Conclusion

The performance of arterial blood gas analysis is associated with a 6-month rehospitalization rate benefit in a general population of heart failure patients. This association warrants further investigation.

Triglyceride Glucose Index Was a Predictor of 6-Month Readmission Caused by Pulmonary Infection of Heart Failure Patients

OBJECTIVES

Insulin resistance is associated with the prognosis of heart failure (HF) patients. The triglyceride glucose (TyG) index is a simple marker of insulin resistance. However, it remains unclear whether the TyG index is associated with the incidence of readmission in patients with HF.

METHODS

We enrolled 901 patients with completed records on serum triglyceride and glucose in our study. The TyG index was calculated as log (fasting triglycerides (mg/dL) x fasting glucose (mg/dL)/2). There were 310 cases of readmission and the average TyG index was 7.8 ± 0.7. Restricted cubic spline was fitted to explore the linearity of TyG index associating with 6-month readmission of HF patients. Logistic regression analysis was performed to explore the association between TyG index quartile and the incidence of 6-month readmission.

RESULTS

Only the 6-month readmission was significantly different among TyG quartiles, and it was the highest (41.9%) in the lowest quartile (ranging 6.17∼7.36). the TyG index was nonlinearly associated with 6-month readmission (p for nonlinearity = 0.009), with the lower level of TyG index increasing the risk of 6-month readmission. Besides, multivariable logistic analysis showed that the lowest TyG quartile was associated with a higher incidence of 6-month readmission in the unadjusted model (odds ratio [OR] 1.74, 95% confidence interval [CI] 1.18-2.57; p=0.005), partially adjusted model (OR 1.82, 95%CI 1.22-2.72; p=0.004), and fully-adjusted model (OR 1.65, 95%CI 1.09-2.45; p=0.024). The association was consistent across gender and diabetes group.

CONCLUSION

A lower TyG index independently increased the risk of 6-month readmission in HF patients, which could be a prognostic factor in heart failure.

Exploring risk factors of short-term readmission in heart failure patients: A cohort study

BACKGROUND

The risk of all-cause mortality in patients with heart failure (HF) has been studied previously. Readmission risk of HF patients was rarely explored. Thus, we aimed to explore early warning factors that may influence short-term readmission of HF patients.

METHODS

The data of this study came from an HF database in China. It was a retrospective single-center observational study that collected characteristic data on Chinese HF patients by integrating electronic medical records and follow-up outcome data. Eventually, 1,727 patients with HF were finally included in our study.

RESULTS

In our study, the proportion of HF patients with New York Heart Association (NYHA) class II, III, and IV HF were 17.20%, 52.69%, and 30.11%, respectively. The proportion of patients with readmission within 6 months and readmission within 3 months was 38.33% and 24.20%, respectively. Multivariate logistic regression showed that NYHA class (p _III = 0.028, p _IV < 0.001), diabetes (p = 0.002), Cr (p = 0.003), and RDW-SD (p = 0.039) were risk factors for readmission within 6 months of HF patients. NYHA class (p _III = 0.038, p _IV < 0.001), CCI (p = 0.033), Cr (p = 0.012), UA (p = 0.042), and Na (p = 0.026) were risk factors for readmission within 3 months of HF patients.

CONCLUSIONS

Our study implied risk factors of short-term readmission risk in patients with HF, which may provide policy guidance for the prognosis of patients with HF.

Relationship Between the Eosinophil/Monocyte Ratio and Prognosis in Decompensated Heart Failure: A Retrospective Study

Purpose

The aim of this study was to assess the value of the eosinophil/monocyte ratio (EMR) for predicting the prognosis of decompensated heart failure (HF).

Patients and Methods

This was a retrospective cohort study. We included adults (≥18 years old) diagnosed with decompensated HF for whom EMR data were available. The patients were divided into three groups according to EMR tertiles (T1 [EMR≤0.15], T2 [0.15<EMR≤0.32], and T3 [EMR>0.32]). The primary endpoint was the composite outcome of cardiovascular death or HF rehospitalization.

Results

Initially, the records of 2264 patients with decompensated HF were screened; 1883 of these patients had EMR data and were therefore included in the study. There were 627 patients in the T1 group, 628 in the T2 group, and 628 in the T3 group. The risk of cardiovascular death or HF rehospitalization was significantly different among the three groups (Log rank test, P=0.007). Compared with the T3 group, both the T1 group (hazard ratio [HR]: 1.50, 95% confidence interval [CI]: 1.16–1.94, P=0.002) and the T2 group (HR: 1.34, 95% CI: 1.03–1.74, P=0.030) had significantly higher rates of cardiovascular death or HF rehospitalization. A Cochran-Armitage test for trend showed a positive correlation between the EMR and the composite outcome of cardiovascular death or HF. There was a significant difference between the three groups in terms of cardiovascular death (Log rank test, P<0.001) and HF rehospitalization (Log rank test, P=0.03).

Conclusion

The EMR is positively correlated with the risk of cardiovascular death or HF rehospitalization in patients with decompensated HF. Specifically, the lower the EMR, the higher the risk of cardiovascular death or HF rehospitalization.

Risk factors and management of pulmonary infection in elderly patients with heart failure: A retrospective analysis

ABSTRACT

Pulmonary infection is common in patients with heart failure, yet the risk factors remain unclear. We aimed to evaluate the clinical characteristics and risk factors of pulmonary infection in elderly patients with heart failure, to provide reference to the prevention of pulmonary infection.This study was a retrospective study design. We included elderly heart failure patient admitted to our hospital from April 1, 2018 to August 31, 2020. The characteristics and clinical data of pulmonary infection and no infection patients were assessed. Logistic regression analyses were conducted to identify the risk factors of pulmonary infections in patients with heart failure.A total of 201 patients were included. The incidence of pulmonary infection in patients with heart failure was 23.88%. There were significant differences in the age, diabetes, New York Heart Association (NYHA) grade, left ventricular ejection fraction (LVEF), C-reactive protein (CRP) between infection and no infection group (all P < .05), and there were not differences in the sex, body mass index, alcohol drinking, smoking, hypertension, hyperlipidemia, length of hospital stay between 2 groups (all P > .05). Logistic regression analyses indicated that age ≥70 years, diabetes, NYHA grade III, LVEF ≤55%, and CRP ≥10 mg/L were the independent risk factors of pulmonary infections in patients with heart failure (all P < .05). Pseudomonas aeruginosa (34.48%), Staphylococcus aureus (19.57%), and Klebsiella pneumoniae (15.22%) were the most common 3 pathogens in patients with pulmonary infection.Heart failure patients with age ≥70 years, diabetes, NYHA grade III, LVEF ≤55%, and CRP ≥10 mg/L have higher risks of pulmonary infections, preventive measures targeted on those risk factors are needed to reduce pulmonary infections.

Electronic healthcare records and external outcome data for hospitalized patients with heart failure

Heart failure is one of the most important reasons for hospitalization among elderly individuals and is associated with significant mortality and morbidity. Epidemiological studies require the establishment of high-quality databases. Several datasets that primarily involve heart failure populations have been established in Western countries and have generated many high-quality studies. However, no such dataset is available from China. Due to differences in genetic background and healthcare systems between China and Western countries, the establishment of a heart failure database for the Chinese population is urgently needed. We performed a retrospective single-center observational study to collect data regarding the characteristics of heart failure patients in China by integrating electronic healthcare records and follow-up outcome data. The study collected information for a total of 2,008 patients with heart failure, containing 166 attributes.