Development and Validation of a Prognostic Model for Patients with Heart Failure with reduced Ejection Fraction

Development and Validation of a Prognostic Model for Patients with Heart Failure with reduced Ejection Fraction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and Validation of a Prognostic Model for Patients with Heart Failure with reduced Ejection Fraction Jia-Lin Yuan, JingYan Huang, SangYu Liang, HuaTong Liu, ChuangXiong Hong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4459657/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Prognostic predictive model for patients with heart failure with reduced ejection fraction (HFrEF) is scarce. This study aimed to develop a prognostic model for HFrEF patients. This retrospective cohort enrolled 211 eligible patients with HFrEF, the median follow-up of this cohort was 16 months, the one-year mortality rate was 26.1%(55/211). Cox regression showed that age, history of coronary artery disease (CAD), glucose (Glu), and the use of beta-blockers were independent predictors of the occurrence of all-cause mortality in patients with HFrEF after discharge. The cohort was divided into the development set (N = 120) and the validation set (N = 91) in a ratio of 6:4. Least absolute shrinkage and selection operator (LASSO) regression and cox regression screened out 4 variables for the development of the model, including age, sex, serum creatinine (sCr) and use of beta-blockers). A nomogram was constructed, it has a C indexes 0.76 (95%CI 0.67–0.84), AUC of ROC curve of 1 year mortality of the model was 0.79 (95%CI 0.68–0.89), indicating a good differentiation of the model. Calibration plot of the model was drawn and revealed a good calibration. The DCA plots showed that when the threshold probability was between 3 ~ 77% (development group), the model adds benefit to the patients. This model is sure to cast some light on clinical medicine for HFrEF. Prognostic Factor Mortality Heart failure with reduced ejection fraction Nomogram Validation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Heart failure (HF) is characterized by cardinal symptoms and signs (e.g. breathlessness, ankle swelling, fatigue, elevated jugular venous pressure, pulmonary crackles, and peripheral oedema). It’s caused by structural and/or functional abnormality of the heart that results in elevated intracardiac pressures and/or inadequate cardiac output at rest and/or during exercise [ 1 ] . Since the industrial revolution and rapid development of modern society, the population of HF patients is skyrocketing [ 2 ] . The prevalence in 2014 in the UK was reported as 1.4%, slowly increasing by 23% from 2002 to 2014 [ 3 ] . In a long-term cohort [ 4 ] , HF patients suffered a high mortality (67.4%), and patients with heart failure with reduced ejection fraction (HFrEF) were worse [ 5 ] . Thus, there is a need to verify specific predictive factors for HFrEF for the purpose of reducing long-term mortality. Although risk scores constructed for prognostic prediction are fruitful, studies specifically focus on HFrEF are scarce. Previous studies [ 6 , 7 ] dug into the development of a risk model for the prognosis of patients with HFrEF produced different results and had a moderate accuracy. The pressing challenge is to identify particular predictive factors of mortality for patients with HFrEF and develop an accurate model for clinical application. Therefore, the aim of this study is to investigate the independent factors that determined the prognosis of patients with HFrEF, and to develop a prognosis model based on a retrospective cohort. Methods Study Design All cases in this retrospective cohort study were selected from hospitalized patients diagnosed with chronic heart failure at the First Affiliated Hospital of Guangzhou University of Traditional Medicine, Guangzhou, GuangDong from January 2015 to June 2022. Patients were included in the study based on the inclusion criteria. Data were retrieved from the hospital's electronic medical record, including patients' general conditions, NYHA classification, length of hospitalization, comorbidities, medicinal prescription, laboratory test and transthoracic echocardiography. Patients with HFrEF who met the inclusion criteria and were discharged uneventfully, were included in this cohort study. Outcomes were ascertained by reviewing medical records during hospitalization or death certificates. Those died in our hospital were excluded and only those who had been discharged uneventfully were included. The survival time was marked on the date of death announcement. Inclusion Criterion The inclusion criterion are as follows: (1) Meet the diagnostic criteria for heart failure in the Framingham study [ 8 ] ; (2) Inpatients diagnosed with chronic heart failure between January 2015 and June 2022 at the First Affiliated Hospital of Guangzhou University of Traditional Medicine; (iii) Age ≥ 18 years; (4) Left ventricular ejection fraction (LVEF) ≤ 40% after or before admission. Exclusion Criterion The exclusion criterion are as follows: (1) Acute heart failure; (2) Patients who were to undergo coronary artery bypass grafting, valve replacement, or cardiac resynchronization therapy; (3) Complicated with acute myocardial infarction, intrinsic fatal arrhythmia, obstructive cardiomyopathy, constrictive pericarditis, or pulmonary embolism; (4) Complicated with congenital heart disease; (5) Complicated with severe liver disease or kidney disease requiring maintenance dialysis; (6) Complicated with severe psychosomatic diseases, malignant blood diseases, malignant tumors, or other uncontrolled systemic diseases; (7) Other conditions that were unsuitable for participation in this study. Those who died (of any cause) during the follow-up period from January 1, 2015 to June 30, 2022, were recorded as an all-cause mortality outcome. Diagnosis Standard The diagnostic criteria for heart failure in this study were based on the Framingham study criteria [ 8 ] . Coronary atherosclerotic heart disease was defined as > 50% stenosis diagnosed by coronary CTA or coronary angiography [ 9 ] . Hypertension was defined as resting SBP ≥ 140 mmHg with or without DBP ≥ 90 mmHg [ 10 ] . Diabetes mellitus (DM) was defined as a group of metabolic disorders characterized by chronic elevation of blood glucose due to impaired insulin secretion or utilization [ 11 ] . Based on the 1995 World Health Organization criteria, cardiomyopathy was defined as myocardial diseases that cause cardiac dysfunction, including hypertrophic cardiomyopathy, dilated cardiomyopathy, restrictive cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy [ 12 ] . Valvular heart disease is defined as a cardiac disorder characterized by structural or functional abnormalities of the heart valves from various causes, resulting in stenosis with (or without) incomplete closure of the orifice [ 13 ] . Atrial fibrillation was defined as a supraventricular arrhythmia characterized by uncoordinated atrial excitation accompanied by decreased cardiac function [ 14 ] . Rheumatic heart disease was defined as myocarditis or heart valve damage secondary to rheumatic fever [ 15 ] . Pulmonary heart disease was defined as heart disease caused by lesions in the lung tissue or pulmonary artery and its branches resulting in increased resistance in the pulmonary circulation and pulmonary hypertension, followed by hypertrophy and enlargement of the right ventricle and even right heart failure [ 16 ] . Anemia was defined as hemoglobin ≤ 120 g/L in men or ≤ 110 g/L in non-pregnant women [ 17 ] . Ethical Approval and Statement The study followed the ethical guidelines of the Declaration of Helsinki on human medical research and the principle of Good Clinical Practice (GCP). It was approved by the Medical Ethics Committee of Guangzhou University of Chinese Medicine (JY2022-148). Due to the retrospective nature of the study, the Medical Ethics Committee of Guangzhou University of Chinese Medicine waived the need of obtaining informed consent. Statistic Analysis Cox regression was used to analyze the correlation between different variables and the occurrence of adverse outcomes (primary outcome indicators). The extent of correlation was expressed as odds ratio (OR), hazard ratio (HR) and 95% Confidence Interval (CI). Least absolute shrinkage and selection operator (LASSO) method was used to identify the optimal variables with non-zero coefficients as independent risk factors. LASSO cross validation (LASSO-CV) was used to find the best combination for the model. Thereafter, a stepwise backward Wald Cox regression was employed to screen for variables for the development of the predictive model, based on the result of LASSO regression. The model with the least Akaike information criterion (AIC) was chosen. Receiver operating characteristic (ROC) curves were plotted and then, C's Index, area under curve (AUC), sensitivity and specificity were calculated. The highest adding-up number of sensitivity and specificity was selected as the best cut-off Point. AUC value was used to evaluate the discrimination of the model, and the model was considered to have good discrimination when AUC > 0.7. Calibration of the model was evaluated by the calibration plot. Nomogram was used to demonstrate the parameters in the final prediction model, and decision curve analysis (DCA) was used to test the clinical validity of the model. Our data was processed by SPSS 26.0 (IBM SPP, Chiacago, IL, USA), Stata 15.0 (StataCorp, College Station, Texas, USA) as well as R software 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria). P < 0.05 (two-sided) was considered statistically significant in all of the above tests. Prior to statistical analysis, all variables were retrieved to detect missing values. Cases with missing values would be firstly traced back to the electronic medical record in order to fill in the missing values. If it was not traceable, then the missing values would be filled in by Multiple Imputation by Chain Equations (MICE) using SPSS 26.0, with a default of k = 5. Results Baseline Characteristics of the Cohort During January 2015 to June 2022, a total of 806 CHF patients were enrolled in this study in the First Affiliated Hospital of Guangzhou University of Chinese Medicine. There were 211 patients with HFrEF included eventually (see details in Fig. 1 ). A total of 89(42%, 89/211) patients with HFrEF in this cohort experienced all-cause death after discharge during January 2015 to June 30, 2022, the median follow-up was 16 months, the one-year mortality rate was 26.1%(55/211) in the study population. The cohort was divided into group D (those who survived) and group S (those who died) by the state of survival at the end of follow-up (see details in Supplementary Table S1 ). Establishment of the Predictive Model A predictive model for the occurrence of all-cause mortality outcomes after hospital discharge in patients with HFrEF was established on the basis of this cohort. All cases were randomly divided and assigned to the training set and validation set in a ratio of 6:4. The training set was used to construct the predictive model, and the validation set was used to verify the model. Comparison of baseline data between two data sets is depicted in Table 1 . We used Lasso Regression and LASSO-CV to help the selection of variables for the development of a model (see Fig. 2 ). Based on the results of lambda.min and lambda.1se (see Supplementary Table S2 ), a total of 10 variables, including sex, age, heart rate, duration of hospitalization, admission times, CAD, hemoglobin (Hb), platelet (PLT), sCr, beta-blockers (BB), were selected for further analysis. Subsequently, backward Wald Cox regression revealed that the independent predictors of all-cause mortality were age, sex, sCr and β-blockers, as shown in Table 2 . All four variables met the proportional hazards assumption. The parameters of the model are shown in Table 3 . As shown below, the predictive model’s equation was obtained as follows: Table 1 Comparison of Basic Characteristics between two Data Sets Training Set N = 120 Validation Set N = 91 P Value Sex, Male 84 (70.0) 67 (73.6) 0.644 Age (year) 72 (62, 80) 72 (61, 81) 0.928 BMI (kg/m 2 ) 22.5 (19.7, 25.2) 23.0 ± 3.5 0.617 Hospitalization days 10 (7, 12) 10 (7, 13) 0.634 Hospitalization Frequency 2 (1, 4) 2 (1, 4) 0.878 SBP (mmHg) 124 (113, 142) 128 (119, 142) 0.382 HR (bpm) 80 (71, 94) 85 (75, 100) 0.109 Survival Time (month) 16 (9, 37) 16 (5, 35) 0.340 LVEF (%) 33 (26, 37) 30 (25, 35) 0.178 LVEDd (mm) 61 (55, 67) 62 ± 10 0.554 BNP (pg/mL) 1131.3 (415.9, 3079.1) 1532.7 (659.6, 3242.6) 0.136 Abbreviations: BMI, body mass index; BNP, B-type natriuretic peptide; HR, heart rate; IQR, interquartile range; LVEDd, left ventricular end-diastolic diameter; LVEF, left ventricular ejection fraction; SBP, systolic blood pressure. Table 2 Cox regression of variables selection for establishment of predictive model Variables HR for One-way Cox Regression (95% CI) P Value HR for Multivariate Cox Regression (95% CI) P Value Age 1.037 (1.013–1.061) 0.002 1.055 (1.027–1.083) < 0.001 Sex 0.576 (0.302–1.101) 0.095 0.370 (0.185–0.740) 0.005 HR 0.984 (0.968–1.001) 0.062 - - Hospitalization Duration 1.034 (0.995–1.075) 0.090 - - Admission Times 1.123 (1.052–1.199) 0.001 - - CAD 1.930 (1.091–3.417) 0.024 - - Hb 0.990 (0.979-1.000) 0.049 - - PLT 0.997 (0.993–1.002) 0.221 - - sCr 1.002 (1.000-1.005) 0.080 1.004 (1.001–1.006) 0.007 BB 0.303 (0.166–0.552) < 0.001 0.245 (0.132–0.455) < 0.001 Abbreviations: BB, beta-blockers; CAD, coronary atherosclerotic disease; Hb, hemoglobin; HR, heart rate; PLT, platelet; sCr, serum creatinine. Table 3 Parameters for the development of the predictive model Variables β HR SE Wald P Value 95%CI for Exp(B) Age 0.053 1.055 0.014 15.388 < 0.001 (1.027–1.083) Sex -0.993 0.370 0.353 7.921 0.005 (0.185–0.740) sCr 0.004 1.004 0.001 7.287 0.007 (1.001–1.006) BB -1.406 0.245 0.315 19.866 < 0.001 (0.132–0.455) Abbreviations: BB, beta-blockers; CAD, coronary atherosclerotic disease; sCr, serum creatinine. Table 4 Parameters of the Predictive Model Area Standard Error P 95%CI Sensitivity Specificity Youden’s index Accuracy Positive Predictive Value Negative Predictive Value Cut-Off Value 0.759 0.033 < 0.001 0.695–0.824 67.4% 75.4% 43% 76.8% 0.674 0.754 0.210 In[P/(1-P)] = 0.053 × (Age) − 0.993×(Sex) + 0.004 × (sCr) − 1.406 × (BB) By employing the above variables, we built a predictive model for measurement of survival rate of patients with HFrEF (see Fig. 3 ). The C indexes were 0.7567 (95%CI 0.6743–0.8392) and 0.7520 (95%CI 0.6647–0.8393) for the training set and the validation set, respectively. Parameters of the Predictive Model By calculating the predicted outcomes and the ROC curve of the model, parameters of the predictive model was evaluated. In the cut-point value of 0.210, the sensitivity, specificity, Youden’s index and accuracy of this model were 67.4%, 75.4%, 43% and 76.8%, respectively (see Table 4 ). Evaluation of the Predictive Model ROC curves of 1 year mortality of the training set and the validation set were draw. The AUC for the training set and the validation set were 0.7869 (95%CI 0.68018–0.89361) and 0.7023 (95%CI 0.5885–0.8160), respectively (see Fig. 4 , A, B). The calibration plots are shown in Fig. 4 , C, D. Good calibration was observed for the probability of mortality in the timing of 24 and 36 months, indicating well accordance with the actual prediction for the long-term mortality. DCA curves were employed to evaluate the clinical utility of the model. As indicated in Fig. 4 , E, F, the blue lines represent treating all patients as dead and the red lines (threshold) represent treating all patients as alive, while the green lines represent the probability of survival calculated by the model. The net benefit is calculated by subtracting the proportion of all patients who are false negative from the proportion who are true positive, weighting by the relative harm of forgoing treatment compared with the negative consequences of an unnecessary treatment [ 18 ] . The DCA plots show that if the threshold probability is between 3 ~ 77% (development group) and 9 ~ 75% (validation group), using this model adds greater benefit than either treating patients as all survived or all dead. This suggests that the model is clinically efficacious. Discussion On the basis of a retrospective setting, have developed a predictive model for prognosis of patients with HFrEF by four easily-attained factors which are patients’ age, sex, serum creatinine and the application of beta-blockers. It has a good discrimination and calibration, with a good clinical utility. A total of 43 potential variables were screen by Lasso regression and were further examined in the use of cox regression. Although the results of lambda.1se were two variables (Age and BB), cox regression analysis proved its AIC was not the best combination. The application of beta-blockers can make a life or death in terms of prognosis for patients with HF. In our cohort, nearly a quarter of all the patients (45/211, 22%) fail to receive the medicine. There are many reasons that beta-blockers are not prescribed for heart failure patients, like consistent hypotension and hypoperfusion or severe bradycardia, in the case of HFrEF. The paucity of beta-blockers could lead to progression of heart failure and frequent hospital admission, which also increases the likelihood of getting infection [ 19 ] . On one side, the unused beta-blocker may represent the perilous condition that the patients situated in due to end-stage heart failure [ 20 ] . Patients with SBP < 85mmHg were excluded in the COPERNICUS trial [ 21 ] . In this cohort, we believed the main reason of paucity of beta-blockers in the prescription was the incapacity of sustaining a normal circulation, which was reflected by the patients’ blood pressure and other signs. To keep a stable state, many physicians tended to prescribe hemodynamic drugs for the patients. Beta-blockers are commonly, contradicting to these medicine. Therefore, beta-blockers were often off the table. On the other side, it also indicates the protective effect of beta-blockers in terms of reducing mortality rate of HF patients while it has been proven long time ago [ 22 – 25 ] . Previous studies with similar models provide valuable information for this study. We were not surprised to observed that namely all our developed schemes for the final model contained the factors of patients’ age and the application of beta-blockers, since the majority of past developed models contained one or two of them [ 6 , 7 , 26 – 29 ] . Kurtulus et al. [ 7 ] constructed a discharge risk model for HFrEF patients, 5 variables were screened in (age ≥ 75, sodium < 130mEq/L, hepatomegaly at admission, not using beta-blocker at discharge and LEF ≤ 20%). The model was performing well (C-statistic 0.74). Similar results were seen in research from Imen et al. [ 6 ] , age and BB were enlisted in the risk model, while the most influential factor was uric acid. Fang et al. [ 26 ] had developed a model based on 4150 ICU patients with HF. Its C-index was 0.70 (95CI 0.67–0.73), including 13 variables like patients’ HF subtype (HFpEF, HFmrEF, HFrEF), age, gender, weight, ICU stay time, ICU type (CSRU, CCU, SICU, TSICU, MICU), atrial fibrillation (AF), COPD, Spironolactone, CABG, Hypertension, GFR, Hb. Among other studies [ 27 – 29 ] that focused on all subtype HF, age was nonetheless a significant factor. This conveys a message that mortality rate increases along side with age increases for all patients with HF. Gender has long been an associated factor for HF patients. The female are predisposed to HFpEF as the male are tend to have HFrEF because of difference in etiology and pathophysiology [ 30 ] . In addition, women are more likely to have a higher blood concentration of angiotensin converting enzyme inhibitor (ACEI)/ angiotensin receptor blockers (ARB)/BB, and the reason behind this is they have smaller body composition, which leads to reduction of glomerular and hepatic filtration rate [ 31 – 34 ] . It in turn, decelerates chemicals clearance and prolongs duration. This explains why female patients with HF lived longer than men [ 35 , 36 ] , along with fewer hospitalization times when taking ARNI/ARB [ 37 ] . Our study discovered that sex was a crucial predictive factor for the prognosis of patients with HFrEF. In the long run, women have a lower mortality compared to men. A previous study [ 38 ] investigated obstructive coronary disease event in HFrEF patients, the researchers discovered that male gender had a significantly higher odds ratio (OR) (5.34, 95%CI 1.87–15.2) comparing to other variables. On the contrary, Imen et al. [ 6 ] reached a different conclusion with our result. In the study, female gender served to be a hazardous factor rather than a protective one. Our cohort showed that the median mortality for men and women was 14 and 21 month (Mann-Whitney U test, P = 0.035), respectively, indicating that female did have a better survival rate. To reach a conclusion, further research with larger population for analysis is needed. Renal function has also been a pivotal factor of mortality for HF patients. Since 2006 people found that the HR of chronic kidney disease was ranging from 1.56–2.34, depending on the study [ 39 ] . A meta-analysis [ 40 ] suggested that renal impairment played a key role in adverse outcomes of HF patients. Additionally, a risk model [ 41 ] developed from machine learning showed the combination of ejection fraction and serum creatinine was sufficient to predict the prognosis of patients with HF. Unfortunately, the dataset didn’t include beta-blockers or other mortality-improving medicine. In the case of severe kidney failure or even dialysis, patients may suffer from serious complications, like frequent acute decompensated heart failure (ADHF) or acute pulmonary oedema, concomitant hyperkaliemia and fatal arrhythmia. In a word, HF patients suffered form chronic kidney disease not only have higher mortality, but are also life-saving therapies greatly limited in these cases, forming a vicious cycle [ 42 ] . Thus, examining serum creatinine and electrolytes is an important step for evaluation of medical treatment and long-term mortality for HFrEF. It’s surprising that factors like left ventricular ejection fraction (LVEF) and B-type natriuretic peptide (BNP) were excluded as independent predictors of mortality rate in our study. After all, the association between LVEF and mortality of HF patients has been long established [ 43 ] . LVEF was one of the predictive factors of long-term mortality for HF patients, especially for HFrEF [ 44 ] . Compared to heart failure with middle reduced ejection fraction (HFmrEF) and heart failure with preserved ejection fraction (HFpEF), patients with HFrEF evidently has a much higher mortality [ 45 ] . However, LVEF and BNP was not one of the hazardous factors of long-term mortality in our study. One cause of this may be the paucity of large samples in our cohort. Insufficient cases lead to the imbalance of data distribution, as most of our variables ranged in a non-normal distribution, which included LVEF. It was reasonable to speculate that a portion of HFrEF patients with lower level of LVEF was not included in our cohort. Likewise, both BNP and N-terminal pro-B-type natriuretic peptide (NT-proBNP) are useful tools for diagnosis of HF [ 1 ] . Previous studies also confirmed they are prognostic markers for HF patients [ 46 , 47 ] . Exclusion of BNP in our model may be due to the same reason that LVEF had. According to the literature, the best model for evaluating prognosis of HF is Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator so far [ 48 ] , with a studied population of 864 HFrEF patients. This study eventually developed a prognosis calculator with an average C-statistic of 0.79. It has 14 predictive factors, including patients’ age, sex, LVEF, serum sodium, estimated glomerular filtration rate, hemoglobin, use of loop diuretics, use of BB, use of ACEI/ARB, and use of statins [ 49 ] . The BCN-Bio-HF model was externally validated by the PROTECT cohort and the PARADIGM-HF cohort, proving its clinical utility. This model has all the four variables our model owned, it apparently takes more factors into account. Yet, more factors does not guarantee greater accuracy. Future comparison between these models and external validation is needed. Conclusion We investigated the risk factors and mortality of patients with HFrEF. A risk model was developed and internally validated, it has a good accuracy and clinical utility. Having only four variables enables convenient usage of this nomogram for the prediction of prognosis of patients with HFrEF. To note, our study reflects a problem that HFrEF patients without taking beta-blockers cast a shadow on their prognosis. Attention should be Limitations Our study has several limitations. First and foremost, our study was a single-center, small-sampled, retrospective cohort inspecting the prognosis of patients with HFrEF. Its small population and study design may inevitably contribute to systemic bias and selection bias. Using the large cohort from accessable online database may be the key. What’s more, we didn’t perform comparison with similar models due to a lack of comprehensive dataset which contains all the variables that previous models had dived into. Finally, external validation from other centers and other countries in the future is needed. Declarations Competing of Interest : The author(s) declare no competing interests. Declarations of Interest : none. 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Characteristics, Prognosis, and Prediction Model of Heart Failure Patients in Intensive Care Units Based on Preserved, Mildly Reduced, and Reduced Ejection Fraction. REV CARDIOVASC MED , 24(6), 165. https://doi.org/10.31083/j.rcm2406165 (2023). Gaziano L, et al. Risk factors and prediction models for incident heart failure with reduced and preserved ejection fraction. ESC HEART FAIL , 8(6), 4893-903. https://doi.org/10.1002/ehf2.13429 (2021). Herman R, et al. Utilizing longitudinal data in assessing all‐cause mortality in patients hospitalized with heart failure. ESC HEART FAIL , 9(5), 3575-84. https://doi.org/10.1002/ehf2.14011 (2022). Chen Y, et al. A heart failure phenotype stratified model for predicting 1-year mortality in patients admitted with acute heart failure: results from an individual participant data meta-analysis of four prospective European cohorts. BMC MED , 19(1), 21. https://doi.org/10.1186/s12916-020-01894-2 (2021). Lam CSP, et al. Sex differences in heart failure. EUR HEART J , 40(47), 3859-68. (2019). Soldin OP, Mattison DR. Sex Differences in Pharmacokinetics and Pharmacodynamics. CLIN PHARMACOKINET , 48(3), 143-57. https://doi.org/10.2165/00003088-200948030-00001 (2009). Israili ZH. Clinical pharmacokinetics of angiotensin II (AT1) receptor blockers in hypertension. J HUM HYPERTENS , 14 Suppl 1(S1):S73-86. https://doi.org/10.1038/sj.jhh.1000991 (2000). Eugene AR. Gender based Dosing of Metoprolol in the Elderly using Population Pharmacokinetic Modeling and Simulations. Int J Clin Pharmacol Toxicol , 5(3), 209-15. (2016) Jochmann N, Stangl K, Garbe E, Baumann G, Stangl V. Female-specific aspects in the pharmacotherapy of chronic cardiovascular diseases. EUR HEART J , 26(16), 1585-95. https://doi.org/10.1093/eurheartj/ehi397 (2005). Levy D, et al. Long-Term Trends in the Incidence of and Survival with Heart Failure. NEW ENGL J MED , 347(18), 1397-402. https://doi.org/10.1056/NEJMoa020265 (2002). Motiejunaite J, et al. The association of long-term outcome and biological sex in patients with acute heart failure from different geographic regions. EUR HEART J , 41(13), 1357-64. https://doi.org/10.1093/eurheartj/ehaa071 (2020). McMurray JJV, et al. Effects of Sacubitril-Valsartan Versus Valsartan in Women Compared With Men With Heart Failure and Preserved Ejection Fraction. CIRCULATION , 141(5), 338-51. https://doi.org/10.1161/CIRCULATIONAHA.119.044491 (2020). Albuquerque F, et al. Predicting obstructive coronary artery disease in heart failure with reduced ejection fraction: A practical clinical score. REV PORT CARDIOL , 42(1), 21-8. https://doi.org/10.1016/j.repc.2021.09.020 (2023). Damman K, Testani JM. The kidney in heart failure: an update. EUR HEART J , 36(23), 1437-44. https://doi.org/10.1093/eurheartj/ehv010 (2015). Smith GL, et al. Renal Impairment and Outcomes in Heart Failure. J AM COLL CARDIOL , 47(10), 1987-96. https://doi.org/10.1016/j.jacc.2005.11.084 (2006). Chicco D, Jurman G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC MED INFORM DECIS , 20(1), 16. https://doi.org/10.1186/s12911-020-1023-5 (2020). Beldhuis IE, et al. Evidence-Based Medical Therapy in Patients With Heart Failure With Reduced Ejection Fraction and Chronic Kidney Disease. CIRCULATION , 145(9), 693-712. https://doi.org/10.1161/CIRCULATIONAHA.121.052792 (2022). Berry, C, et al. The survival of patients with heart failure with preserved or reduced left ventricular ejection fraction: an individual patient data meta-analysis. EUR HEART J , 33(14), 1750-7. https://doi.org/10.1093/eurheartj/ehr254 (2012). Pocock SJ, et al. Meta-Analysis GGIC. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. EUR HEART J , 34(19), 1404-13. https://doi.org/10.1093/eurheartj/ehs337 (2013). Cheng RK, et al. Outcomes in patients with heart failure with preserved, borderline, and reduced ejection fraction in the Medicare population. AM HEART J , 168(5), 721-30. https://doi.org/10.1016/j.ahj.2014.07.008 (2014). Zile MR, et al. Prognostic Implications of Changes in N-Terminal Pro-B-Type Natriuretic Peptide in Patients With Heart Failure. J AM COLL CARDIOL , 68(22), 2425-36. https://doi.org/10.1016/j.jacc.2016.09.931 (2016). Anand IS, et al. Changes in Brain Natriuretic Peptide and Norepinephrine Over Time and Mortality and Morbidity in the Valsartan Heart Failure Trial (Val-HeFT). CIRCULATION , 107(9), 1278-83. https://doi.org/10.1161/01.CIR.0000054164.99881.00 (2003). Codina P, et al. Head‐to‐head comparison of contemporary heart failure risk scores. EUR J HEART FAIL , 23(12), 2035-44. https://doi.org/10.1002/ejhf.2352 (2021). Lupon J, et al. A. Development of a novel heart failure risk tool: the barcelona bio-heart failure risk calculator (BCN bio-HF calculator). PLOS ONE , 9(1), e85466. https://doi.org/10.1371/journal.pone.0085466 (2014). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1S2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4459657","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310385321,"identity":"97c27de1-eb58-4e02-8277-53e82a98e175","order_by":0,"name":"Jia-Lin Yuan","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jia-Lin","middleName":"","lastName":"Yuan","suffix":""},{"id":310385322,"identity":"78c9e117-a8a3-4547-82c5-1ca926481117","order_by":1,"name":"JingYan Huang","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"JingYan","middleName":"","lastName":"Huang","suffix":""},{"id":310385324,"identity":"7ac6247b-cd6e-4407-a0b5-fcaab2bb12d7","order_by":2,"name":"SangYu Liang","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"SangYu","middleName":"","lastName":"Liang","suffix":""},{"id":310385325,"identity":"5fdecdda-1758-498d-9466-cd0cdbb36d86","order_by":3,"name":"HuaTong Liu","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"HuaTong","middleName":"","lastName":"Liu","suffix":""},{"id":310385326,"identity":"a0f72004-2c65-4f86-ae87-6216916f4373","order_by":4,"name":"ChuangXiong Hong","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"ChuangXiong","middleName":"","lastName":"Hong","suffix":""},{"id":310385327,"identity":"6e40ef90-a7a1-46bf-a800-08ddca4c9f7f","order_by":5,"name":"HuiBing Chen","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"HuiBing","middleName":"","lastName":"Chen","suffix":""},{"id":310385329,"identity":"243a678f-63e5-465f-9213-5d81e30df647","order_by":6,"name":"Le Sun","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Sun","suffix":""},{"id":310385331,"identity":"349de791-0391-4504-8675-b1ae41ee102d","order_by":7,"name":"QiuXiong Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYFCCBDDJA+fzMzMffkCaFsl2tjQDYrQggMF5HgUJfBrk3dMff+ZtOyxjLpFj+PBrm12e8WEeBgOGGptoXFoMz7wxMOZtS+Ox7DljbCzbllxsdpj3wAOGY2m5Dbi0zMhhSOZts+ExON5jJi3Zxpy47TBfggFjw2E8WtIfHOZtk+AxOMxj/luyrT5xczOPgQQ+LfISCYbNMFsYP7YdTtzATECLAc8bY8Y559J4DM4cK5ZmOHc8ccZhYCAn4PGLfHv64w9vyg7bG9xI3vjxR1l1Yn//4cMPPtTY4LblAAMDEy8bhMMMj9AEHMrBtgDNYvzxB8Jh/IFH5SgYBaNgFIxcAAARuFsb0YUh+wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"QiuXiong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-05-22 09:09:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4459657/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4459657/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57954780,"identity":"6e26e0ac-ec5e-408d-9078-cb0d2e1f07df","added_by":"auto","created_at":"2024-06-07 23:17:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82394,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study.\u003c/p\u003e","description":"","filename":"Figure1Flowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-4459657/v1/e28e029ab2368de7d5c24a5e.png"},{"id":57954782,"identity":"5a89aed6-b34f-4837-8f1d-6d8d45726b57","added_by":"auto","created_at":"2024-06-07 23:17:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218993,"visible":true,"origin":"","legend":"\u003cp\u003eResults of a one-time screening by LASSO regression. A, The shrinkage plot of coefficients. The x axis shows log λ while the y axis shows coefficients. B, The x axis shows log λ while the y axis shows the misclassification error. The left bold dotted line represents the lasso.min, which means no standard error of misclassification is allowed; the right bold dotted line represents lasso.1se, which stands as 1 standard error of misclassification.\u003c/p\u003e","description":"","filename":"Figure2LassoLambda.png","url":"https://assets-eu.researchsquare.com/files/rs-4459657/v1/892929593d0d8528d0dacf6d.png"},{"id":57955324,"identity":"c154859f-402c-4fb1-ac7f-54c38ef57d7a","added_by":"auto","created_at":"2024-06-07 23:25:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50114,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram of the model with four factors (Patients’ age, sex, serum creatinine, and the use of beta-blockers). It lists calculation of probability for 1y, 2y and 3y.\u003c/p\u003e","description":"","filename":"Figure3Nomogram.png","url":"https://assets-eu.researchsquare.com/files/rs-4459657/v1/78556fb4c4c6cfbdac4213aa.png"},{"id":57954784,"identity":"187782c4-d10d-4f76-81b7-c46eec019395","added_by":"auto","created_at":"2024-06-07 23:17:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1870537,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operator Characteristics (ROC) curves, calibration curves and decision curve analysis (DCA) for the training and validation group. A, ROC curve of the training group. B, ROC curve of the validation group. C, calibration curve for the training group. D, calibration curve for the validation group. E, DCA of the training group. F, DCA of the validation group.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4459657/v1/30596445cb9ee76c6ad29355.png"},{"id":59155860,"identity":"7b720470-4654-4401-887b-088d1030014e","added_by":"auto","created_at":"2024-06-27 03:49:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3056814,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4459657/v1/9d740f1f-9833-4e57-bf45-868a52fedf6b.pdf"},{"id":57954783,"identity":"a280e481-729e-4750-9f23-4701b846855a","added_by":"auto","created_at":"2024-06-07 23:17:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23504,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1S2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4459657/v1/77c52cd13b5cb42ab7e48928.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Prognostic Model for Patients with Heart Failure with reduced Ejection Fraction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) is characterized by cardinal symptoms and signs (e.g. breathlessness, ankle swelling, fatigue, elevated jugular venous pressure, pulmonary crackles, and peripheral oedema). It\u0026rsquo;s caused by structural and/or functional abnormality of the heart that results in elevated intracardiac pressures and/or inadequate cardiac output at rest and/or during exercise\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince the industrial revolution and rapid development of modern society, the population of HF patients is skyrocketing\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The prevalence in 2014 in the UK was reported as 1.4%, slowly increasing by 23% from 2002 to 2014\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In a long-term cohort\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, HF patients suffered a high mortality (67.4%), and patients with heart failure with reduced ejection fraction (HFrEF) were worse\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Thus, there is a need to verify specific predictive factors for HFrEF for the purpose of reducing long-term mortality. Although risk scores constructed for prognostic prediction are fruitful, studies specifically focus on HFrEF are scarce. Previous studies\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e dug into the development of a risk model for the prognosis of patients with HFrEF produced different results and had a moderate accuracy. The pressing challenge is to identify particular predictive factors of mortality for patients with HFrEF and develop an accurate model for clinical application.\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study is to investigate the independent factors that determined the prognosis of patients with HFrEF, and to develop a prognosis model based on a retrospective cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eAll cases in this retrospective cohort study were selected from hospitalized patients diagnosed with chronic heart failure at the First Affiliated Hospital of Guangzhou University of Traditional Medicine, Guangzhou, GuangDong from January 2015 to June 2022. Patients were included in the study based on the inclusion criteria. Data were retrieved from the hospital's electronic medical record, including patients' general conditions, NYHA classification, length of hospitalization, comorbidities, medicinal prescription, laboratory test and transthoracic echocardiography.\u003c/p\u003e \u003cp\u003ePatients with HFrEF who met the inclusion criteria and were discharged uneventfully, were included in this cohort study.\u003c/p\u003e \u003cp\u003eOutcomes were ascertained by reviewing medical records during hospitalization or death certificates. Those died in our hospital were excluded and only those who had been discharged uneventfully were included. The survival time was marked on the date of death announcement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInclusion Criterion\u003c/h2\u003e \u003cp\u003eThe inclusion criterion are as follows: (1) Meet the diagnostic criteria for heart failure in the Framingham study\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e; (2) Inpatients diagnosed with chronic heart failure between January 2015 and June 2022 at the First Affiliated Hospital of Guangzhou University of Traditional Medicine; (iii) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (4) Left ventricular ejection fraction (LVEF)\u0026thinsp;\u0026le;\u0026thinsp;40% after or before admission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExclusion Criterion\u003c/h2\u003e \u003cp\u003eThe exclusion criterion are as follows: (1) Acute heart failure; (2) Patients who were to undergo coronary artery bypass grafting, valve replacement, or cardiac resynchronization therapy; (3) Complicated with acute myocardial infarction, intrinsic fatal arrhythmia, obstructive cardiomyopathy, constrictive pericarditis, or pulmonary embolism; (4) Complicated with congenital heart disease; (5) Complicated with severe liver disease or kidney disease requiring maintenance dialysis; (6) Complicated with severe psychosomatic diseases, malignant blood diseases, malignant tumors, or other uncontrolled systemic diseases; (7) Other conditions that were unsuitable for participation in this study.\u003c/p\u003e \u003cp\u003eThose who died (of any cause) during the follow-up period from January 1, 2015 to June 30, 2022, were recorded as an all-cause mortality outcome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDiagnosis Standard\u003c/h2\u003e \u003cp\u003eThe diagnostic criteria for heart failure in this study were based on the Framingham study criteria\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCoronary atherosclerotic heart disease was defined as \u0026gt;\u0026thinsp;50% stenosis diagnosed by coronary CTA or coronary angiography\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Hypertension was defined as resting SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg with or without DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Diabetes mellitus (DM) was defined as a group of metabolic disorders characterized by chronic elevation of blood glucose due to impaired insulin secretion or utilization\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Based on the 1995 World Health Organization criteria, cardiomyopathy was defined as myocardial diseases that cause cardiac dysfunction, including hypertrophic cardiomyopathy, dilated cardiomyopathy, restrictive cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Valvular heart disease is defined as a cardiac disorder characterized by structural or functional abnormalities of the heart valves from various causes, resulting in stenosis with (or without) incomplete closure of the orifice\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Atrial fibrillation was defined as a supraventricular arrhythmia characterized by uncoordinated atrial excitation accompanied by decreased cardiac function\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Rheumatic heart disease was defined as myocarditis or heart valve damage secondary to rheumatic fever\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Pulmonary heart disease was defined as heart disease caused by lesions in the lung tissue or pulmonary artery and its branches resulting in increased resistance in the pulmonary circulation and pulmonary hypertension, followed by hypertrophy and enlargement of the right ventricle and even right heart failure\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Anemia was defined as hemoglobin\u0026thinsp;\u0026le;\u0026thinsp;120 g/L in men or \u0026le;\u0026thinsp;110 g/L in non-pregnant women\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003e \u003cb\u003eand Statement\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e The study followed the ethical guidelines of the Declaration of Helsinki on human medical research and the principle of Good Clinical Practice (GCP). It was approved by the Medical Ethics Committee of Guangzhou University of Chinese Medicine (JY2022-148).\u003c/p\u003e \u003cp\u003e Due to the retrospective nature of the study, the Medical Ethics Committee of Guangzhou University of Chinese Medicine waived the need of obtaining informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistic Analysis\u003c/h2\u003e \u003cp\u003eCox regression was used to analyze the correlation between different variables and the occurrence of adverse outcomes (primary outcome indicators). The extent of correlation was expressed as odds ratio (OR), hazard ratio (HR) and 95% Confidence Interval (CI). Least absolute shrinkage and selection operator (LASSO) method was used to identify the optimal variables with non-zero coefficients as independent risk factors. LASSO cross validation (LASSO-CV) was used to find the best combination for the model. Thereafter, a stepwise backward Wald Cox regression was employed to screen for variables for the development of the predictive model, based on the result of LASSO regression. The model with the least Akaike information criterion (AIC) was chosen.\u003c/p\u003e \u003cp\u003eReceiver operating characteristic (ROC) curves were plotted and then, C's Index, area under curve (AUC), sensitivity and specificity were calculated. The highest adding-up number of sensitivity and specificity was selected as the best cut-off Point. AUC value was used to evaluate the discrimination of the model, and the model was considered to have good discrimination when AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7. Calibration of the model was evaluated by the calibration plot. Nomogram was used to demonstrate the parameters in the final prediction model, and decision curve analysis (DCA) was used to test the clinical validity of the model.\u003c/p\u003e \u003cp\u003eOur data was processed by SPSS 26.0 (IBM SPP, Chiacago, IL, USA), Stata 15.0 (StataCorp, College Station, Texas, USA) as well as R software 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided) was considered statistically significant in all of the above tests.\u003c/p\u003e \u003cp\u003ePrior to statistical analysis, all variables were retrieved to detect missing values. Cases with missing values would be firstly traced back to the electronic medical record in order to fill in the missing values. If it was not traceable, then the missing values would be filled in by Multiple Imputation by Chain Equations (MICE) using SPSS 26.0, with a default of k\u0026thinsp;=\u0026thinsp;5.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eBaseline Characteristics of the Cohort\u003c/h2\u003e\n \u003cp\u003eDuring January 2015 to June 2022, a total of 806 CHF patients were enrolled in this study in the First Affiliated Hospital of Guangzhou University of Chinese Medicine. There were 211 patients with HFrEF included eventually (see details in Fig. \u003cspan\u003e1\u003c/span\u003e). A total of 89(42%, 89/211) patients with HFrEF in this cohort experienced all-cause death after discharge during January 2015 to June 30, 2022, the median follow-up was 16 months, the one-year mortality rate was 26.1%(55/211) in the study population. The cohort was divided into group D (those who survived) and group S (those who died) by the state of survival at the end of follow-up (see details in \u003cstrong\u003eSupplementary Table \u003cspan\u003eS1\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eEstablishment of the Predictive Model\u003c/h2\u003e\n \u003cp\u003eA predictive model for the occurrence of all-cause mortality outcomes after hospital discharge in patients with HFrEF was established on the basis of this cohort. All cases were randomly divided and assigned to the training set and validation set in a ratio of 6:4. The training set was used to construct the predictive model, and the validation set was used to verify the model. Comparison of baseline data between two data sets is depicted in \u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eWe used Lasso Regression and LASSO-CV to help the selection of variables for the development of a model (see Fig. \u003cspan\u003e2\u003c/span\u003e). Based on the results of lambda.min and lambda.1se (see \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e), a total of 10 variables, including sex, age, heart rate, duration of hospitalization, admission times, CAD, hemoglobin (Hb), platelet (PLT), sCr, beta-blockers (BB), were selected for further analysis. Subsequently, backward Wald Cox regression revealed that the independent predictors of all-cause mortality were age, sex, sCr and \u0026beta;-blockers, as shown in \u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e. All four variables met the proportional hazards assumption.\u003c/p\u003e\n \u003cp\u003eThe parameters of the model are shown in Table \u003cspan\u003e3\u003c/span\u003e. As shown below, the predictive model\u0026rsquo;s equation was obtained as follows:\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComparison of Basic Characteristics between two Data Sets\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraining Set\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;120\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValidation Set\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;91\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (73.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (62, 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (61, 81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.5 (19.7, 25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospitalization days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (7, 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (7, 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospitalization Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1, 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1, 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (113, 142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (119, 142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (71, 94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (75, 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvival Time (month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (9, 37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (5, 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (26, 37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (25, 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVEDd (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (55, 67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNP (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1131.3 (415.9, 3079.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1532.7 (659.6, 3242.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAbbreviations: BMI, body mass index; BNP, B-type natriuretic peptide; HR, heart rate; IQR, interquartile range; LVEDd, left ventricular end-diastolic diameter; LVEF, left ventricular ejection fraction; SBP, systolic blood pressure.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCox regression of variables selection for establishment of predictive model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR for One-way Cox Regression\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR for Multivariate Cox Regression\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.037 (1.013\u0026ndash;1.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.055 (1.027\u0026ndash;1.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.576 (0.302\u0026ndash;1.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.370 (0.185\u0026ndash;0.740)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.984 (0.968\u0026ndash;1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospitalization Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.034 (0.995\u0026ndash;1.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission Times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.123 (1.052\u0026ndash;1.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.930 (1.091\u0026ndash;3.417)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.990 (0.979-1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.997 (0.993\u0026ndash;1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.002 (1.000-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004 (1.001\u0026ndash;1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.303 (0.166\u0026ndash;0.552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.245 (0.132\u0026ndash;0.455)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: BB, beta-blockers; CAD, coronary atherosclerotic disease; Hb, hemoglobin; HR, heart rate; PLT, platelet; sCr, serum creatinine.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eParameters for the development of the predictive model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI for Exp(B)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.027\u0026ndash;1.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.185\u0026ndash;0.740)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.001\u0026ndash;1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.132\u0026ndash;0.455)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eAbbreviations: BB, beta-blockers; CAD, coronary atherosclerotic disease; sCr, serum creatinine.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eParameters of the Predictive Model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYouden\u0026rsquo;s index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive Predictive Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNegative Predictive Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCut-Off Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.695\u0026ndash;0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn[P/(1-P)]\u0026thinsp;=\u0026thinsp;0.053 \u0026times; (Age) \u0026minus;\u0026thinsp;0.993\u0026times;(Sex)\u0026thinsp;+\u0026thinsp;0.004 \u0026times; (sCr) \u0026minus;\u0026thinsp;1.406 \u0026times; (BB)\u003c/p\u003e\n \u003cp\u003eBy employing the above variables, we built a predictive model for measurement of survival rate of patients with HFrEF (see Fig. \u003cspan\u003e3\u003c/span\u003e). The C indexes were 0.7567 (95%CI 0.6743\u0026ndash;0.8392) and 0.7520 (95%CI 0.6647\u0026ndash;0.8393) for the training set and the validation set, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eParameters of the Predictive Model\u003c/h2\u003e\n \u003cp\u003eBy calculating the predicted outcomes and the ROC curve of the model, parameters of the predictive model was evaluated. In the cut-point value of 0.210, the sensitivity, specificity, Youden\u0026rsquo;s index and accuracy of this model were 67.4%, 75.4%, 43% and 76.8%, respectively (see \u003cstrong\u003eTable\u0026nbsp;4\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eEvaluation of the Predictive Model\u003c/h2\u003e\n \u003cp\u003eROC curves of 1 year mortality of the training set and the validation set were draw. The AUC for the training set and the validation set were 0.7869 (95%CI 0.68018\u0026ndash;0.89361) and 0.7023 (95%CI 0.5885\u0026ndash;0.8160), respectively (see Fig. \u003cspan\u003e4\u003c/span\u003e, A, B).\u003c/p\u003e\n \u003cp\u003eThe calibration plots are shown in Fig. \u003cspan\u003e4\u003c/span\u003e, C, D. Good calibration was observed for the probability of mortality in the timing of 24 and 36 months, indicating well accordance with the actual prediction for the long-term mortality.\u003c/p\u003e\n \u003cp\u003eDCA curves were employed to evaluate the clinical utility of the model. As indicated in Fig. \u003cspan\u003e4\u003c/span\u003e, E, F, the blue lines represent treating all patients as dead and the red lines (threshold) represent treating all patients as alive, while the green lines represent the probability of survival calculated by the model. The net benefit is calculated by subtracting the proportion of all patients who are false negative from the proportion who are true positive, weighting by the relative harm of forgoing treatment compared with the negative consequences of an unnecessary treatment\u003csup\u003e[\u003cspan\u003e18\u003c/span\u003e]\u003c/sup\u003e. The DCA plots show that if the threshold probability is between 3\u0026thinsp;~\u0026thinsp;77% (development group) and 9\u0026thinsp;~\u0026thinsp;75% (validation group), using this model adds greater benefit than either treating patients as all survived or all dead. This suggests that the model is clinically efficacious.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOn the basis of a retrospective setting, have developed a predictive model for prognosis of patients with HFrEF by four easily-attained factors which are patients\u0026rsquo; age, sex, serum creatinine and the application of beta-blockers. It has a good discrimination and calibration, with a good clinical utility. A total of 43 potential variables were screen by Lasso regression and were further examined in the use of cox regression. Although the results of lambda.1se were two variables (Age and BB), cox regression analysis proved its AIC was not the best combination.\u003c/p\u003e \u003cp\u003eThe application of beta-blockers can make a life or death in terms of prognosis for patients with HF. In our cohort, nearly a quarter of all the patients (45/211, 22%) fail to receive the medicine. There are many reasons that beta-blockers are not prescribed for heart failure patients, like consistent hypotension and hypoperfusion or severe bradycardia, in the case of HFrEF. The paucity of beta-blockers could lead to progression of heart failure and frequent hospital admission, which also increases the likelihood of getting infection\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn one side, the unused beta-blocker may represent the perilous condition that the patients situated in due to end-stage heart failure\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Patients with SBP\u0026thinsp;\u0026lt;\u0026thinsp;85mmHg were excluded in the COPERNICUS trial\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In this cohort, we believed the main reason of paucity of beta-blockers in the prescription was the incapacity of sustaining a normal circulation, which was reflected by the patients\u0026rsquo; blood pressure and other signs. To keep a stable state, many physicians tended to prescribe hemodynamic drugs for the patients. Beta-blockers are commonly, contradicting to these medicine. Therefore, beta-blockers were often off the table. On the other side, it also indicates the protective effect of beta-blockers in terms of reducing mortality rate of HF patients while it has been proven long time ago\u003csup\u003e[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies with similar models provide valuable information for this study. We were not surprised to observed that namely all our developed schemes for the final model contained the factors of patients\u0026rsquo; age and the application of beta-blockers, since the majority of past developed models contained one or two of them\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Kurtulus et al.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e constructed a discharge risk model for HFrEF patients, 5 variables were screened in (age\u0026thinsp;\u0026ge;\u0026thinsp;75, sodium\u0026thinsp;\u0026lt;\u0026thinsp;130mEq/L, hepatomegaly at admission, not using beta-blocker at discharge and LEF\u0026thinsp;\u0026le;\u0026thinsp;20%). The model was performing well (C-statistic 0.74). Similar results were seen in research from Imen et al.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, age and BB were enlisted in the risk model, while the most influential factor was uric acid. Fang et al.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e had developed a model based on 4150 ICU patients with HF. Its C-index was 0.70 (95CI 0.67\u0026ndash;0.73), including 13 variables like patients\u0026rsquo; HF subtype (HFpEF, HFmrEF, HFrEF), age, gender, weight, ICU stay time, ICU type (CSRU, CCU, SICU, TSICU, MICU), atrial fibrillation (AF), COPD, Spironolactone, CABG, Hypertension, GFR, Hb. Among other studies\u003csup\u003e[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e that focused on all subtype HF, age was nonetheless a significant factor. This conveys a message that mortality rate increases along side with age increases for all patients with HF.\u003c/p\u003e \u003cp\u003eGender has long been an associated factor for HF patients. The female are predisposed to HFpEF as the male are tend to have HFrEF because of difference in etiology and pathophysiology\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In addition, women are more likely to have a higher blood concentration of angiotensin converting enzyme inhibitor (ACEI)/ angiotensin receptor blockers (ARB)/BB, and the reason behind this is they have smaller body composition, which leads to reduction of glomerular and hepatic filtration rate\u003csup\u003e[\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. It in turn, decelerates chemicals clearance and prolongs duration. This explains why female patients with HF lived longer than men\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, along with fewer hospitalization times when taking ARNI/ARB\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Our study discovered that sex was a crucial predictive factor for the prognosis of patients with HFrEF. In the long run, women have a lower mortality compared to men. A previous study\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e investigated obstructive coronary disease event in HFrEF patients, the researchers discovered that male gender had a significantly higher odds ratio (OR) (5.34, 95%CI 1.87\u0026ndash;15.2) comparing to other variables. On the contrary, Imen et al.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e reached a different conclusion with our result. In the study, female gender served to be a hazardous factor rather than a protective one. Our cohort showed that the median mortality for men and women was 14 and 21 month (Mann-Whitney U test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), respectively, indicating that female did have a better survival rate. To reach a conclusion, further research with larger population for analysis is needed.\u003c/p\u003e \u003cp\u003eRenal function has also been a pivotal factor of mortality for HF patients. Since 2006 people found that the HR of chronic kidney disease was ranging from 1.56\u0026ndash;2.34, depending on the study\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. A meta-analysis\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e suggested that renal impairment played a key role in adverse outcomes of HF patients. Additionally, a risk model\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e developed from machine learning showed the combination of ejection fraction and serum creatinine was sufficient to predict the prognosis of patients with HF. Unfortunately, the dataset didn\u0026rsquo;t include beta-blockers or other mortality-improving medicine. In the case of severe kidney failure or even dialysis, patients may suffer from serious complications, like frequent acute decompensated heart failure (ADHF) or acute pulmonary oedema, concomitant hyperkaliemia and fatal arrhythmia. In a word, HF patients suffered form chronic kidney disease not only have higher mortality, but are also life-saving therapies greatly limited in these cases, forming a vicious cycle\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Thus, examining serum creatinine and electrolytes is an important step for evaluation of medical treatment and long-term mortality for HFrEF.\u003c/p\u003e \u003cp\u003eIt\u0026rsquo;s surprising that factors like left ventricular ejection fraction (LVEF) and B-type natriuretic peptide (BNP) were excluded as independent predictors of mortality rate in our study. After all, the association between LVEF and mortality of HF patients has been long established\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. LVEF was one of the predictive factors of long-term mortality for HF patients, especially for HFrEF\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Compared to heart failure with middle reduced ejection fraction (HFmrEF) and heart failure with preserved ejection fraction (HFpEF), patients with HFrEF evidently has a much higher mortality\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. However, LVEF and BNP was not one of the hazardous factors of long-term mortality in our study. One cause of this may be the paucity of large samples in our cohort. Insufficient cases lead to the imbalance of data distribution, as most of our variables ranged in a non-normal distribution, which included LVEF. It was reasonable to speculate that a portion of HFrEF patients with lower level of LVEF was not included in our cohort. Likewise, both BNP and N-terminal pro-B-type natriuretic peptide (NT-proBNP) are useful tools for diagnosis of HF\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Previous studies also confirmed they are prognostic markers for HF patients\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Exclusion of BNP in our model may be due to the same reason that LVEF had.\u003c/p\u003e \u003cp\u003eAccording to the literature, the best model for evaluating prognosis of HF is Barcelona Bio-Heart Failure (BCN-Bio-HF) risk calculator so far\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e, with a studied population of 864 HFrEF patients. This study eventually developed a prognosis calculator with an average C-statistic of 0.79. It has 14 predictive factors, including patients\u0026rsquo; age, sex, LVEF, serum sodium, estimated glomerular filtration rate, hemoglobin, use of loop diuretics, use of BB, use of ACEI/ARB, and use of statins\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. The BCN-Bio-HF model was externally validated by the PROTECT cohort and the PARADIGM-HF cohort, proving its clinical utility. This model has all the four variables our model owned, it apparently takes more factors into account. Yet, more factors does not guarantee greater accuracy. Future comparison between these models and external validation is needed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe investigated the risk factors and mortality of patients with HFrEF. A risk model was developed and internally validated, it has a good accuracy and clinical utility. Having only four variables enables convenient usage of this nomogram for the prediction of prognosis of patients with HFrEF. To note, our study reflects a problem that HFrEF patients without taking beta-blockers cast a shadow on their prognosis. Attention should be\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study has several limitations. First and foremost, our study was a single-center, small-sampled, retrospective cohort inspecting the prognosis of patients with HFrEF. Its small population and study design may inevitably contribute to systemic bias and selection bias. Using the large cohort from accessable online database may be the key. What\u0026rsquo;s more, we didn\u0026rsquo;t perform comparison with similar models due to a lack of comprehensive dataset which contains all the variables that previous models had dived into. Finally, external validation from other centers and other countries in the future is needed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting of Interest\u003c/strong\u003e: The author(s) declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of Interest\u003c/strong\u003e: none.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Source\u003c/strong\u003e: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: Data is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMcDonagh TA, et al.Skibelund AK. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. \u003cem\u003eEUR HEART J\u003c/em\u003e, 42(36), 3599-726. https://doi.org/10.1093/eurheartj/ehab368 (2021)\u003c/li\u003e\n\u003cli\u003eDunlay SM, Roger VL. 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Development of a novel heart failure risk tool: the barcelona bio-heart failure risk calculator (BCN bio-HF calculator). \u003cem\u003ePLOS ONE\u003c/em\u003e, 9(1), e85466. https://doi.org/10.1371/journal.pone.0085466 (2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prognostic Factor, Mortality, Heart failure with reduced ejection fraction, Nomogram, Validation","lastPublishedDoi":"10.21203/rs.3.rs-4459657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4459657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrognostic predictive model for patients with heart failure with reduced ejection fraction (HFrEF) is scarce. This study aimed to develop a prognostic model for HFrEF patients. This retrospective cohort enrolled 211 eligible patients with HFrEF, the median follow-up of this cohort was 16 months, the one-year mortality rate was 26.1%(55/211). Cox regression showed that age, history of coronary artery disease (CAD), glucose (Glu), and the use of beta-blockers were independent predictors of the occurrence of all-cause mortality in patients with HFrEF after discharge. The cohort was divided into the development set (N\u0026thinsp;=\u0026thinsp;120) and the validation set (N\u0026thinsp;=\u0026thinsp;91) in a ratio of 6:4. Least absolute shrinkage and selection operator (LASSO) regression and cox regression screened out 4 variables for the development of the model, including age, sex, serum creatinine (sCr) and use of beta-blockers). A nomogram was constructed, it has a C indexes 0.76 (95%CI 0.67\u0026ndash;0.84), AUC of ROC curve of 1 year mortality of the model was 0.79 (95%CI 0.68\u0026ndash;0.89), indicating a good differentiation of the model. Calibration plot of the model was drawn and revealed a good calibration. The DCA plots showed that when the threshold probability was between 3\u0026thinsp;~\u0026thinsp;77% (development group), the model adds benefit to the patients. This model is sure to cast some light on clinical medicine for HFrEF.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Prognostic Model for Patients with Heart Failure with reduced Ejection Fraction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 23:17:35","doi":"10.21203/rs.3.rs-4459657/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3f1ed51-a957-43af-84cb-4b053b545573","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-27T03:41:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 23:17:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4459657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4459657","identity":"rs-4459657","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}