Purpose: This study sought to develop models with good identification for adverse outcomes in patients with heart failure (HF) and find strong factors that affect prognosis. Patients and Methods: A total of 5004 qualifying cases were selected, among which 498 cases had adverse outcomes and 4506 cases were discharged after improvement. The study subjects were hospitalized patients diagnosed with HF from a regional cardiovascular hospital and the cardiology department of a medical university hospital in Shanxi Province of China between January 2014 and June 2019. Synthesizing minority oversampling technology combined with edited nearest neighbors (SMOTE+ENN) was used to pre-process unbalanced data. Traditional logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) were used to build risk identification models, and each model was repeated 100 times. Model discrimination and calibration were estimated using F1-score, the area under the receiver-operating characteristic curve (AUROC), and Brier score. The best performing of the five models was used to identify the risk of adverse outcomes and evaluate the influencing factors. Results: The SME-XGBoost was the best performing model with means of F1-score (0.3673, 95% confidence interval [CI]: 0.3633– 0.3712), AUC (0.8010, CI: 0.7974– 0.8046), and Brier score (0.1769, CI: 0.1748– 0.1789). Age, N-terminal pronatriuretic peptide, pulmonary disease, etc. were the most significant factors of adverse outcomes in patients with HF. Conclusion: The combination of SMOTE+ENN and advanced machine learning methods effectively improved the discrimination efficacy of adverse outcomes in HF patients, accurately stratified patients at risk of adverse outcomes, and found the top factors of adverse outcomes. These models and factors emphasize the importance of health status data in determining adverse outcomes in patients with HF.

Invasive coronary angiography imposes risks and high medical costs. Therefore, accurate, reliable, non-invasive, and cost-effective methods for diagnosing coronary stenosis are required. We designed a machine learning-based risk-prediction system as an accurate, noninvasive, and cost-effective alternative method for evaluating suspected coronary heart disease (CHD) patients. Electronic medical record data were collected from suspected CHD patients undergoing coronary angiography between May 1, 2017, and December 31, 2019. Multi-Class XGBoost, LightGBM, Random Forest, NGBoost, logistic models and MLP were constructed to identify patients with normal coronary arteries (class 0: no coronary artery stenosis), minimum coronary artery stenosis (class 1: 0 < stenosis <50%), and CHD (class 2: stenosis ≥50%). Model stability was verified externally. A risk-assessment and management system was established for patient-specific intervention guidance. Of 1577 suspected CHD patients, 81 (5.14%) had normal coronary arteries. The XGBoost model demonstrated the best overall classification performance (micro-average receiver operating characteristic [ROC] curve: 0.92, macro-average ROC curve: 0.89, class 0 ROC curve: 0.88, class 1 ROC curve: 0.90, class 2 ROC curve: 0.89), with good external verification. In class-specific classification, the XGBoost model yielded F1 values of 0.636, 0.850, and 0.858, for Classes 0, 1, and 2, respectively. The visualization system allowed disease diagnosis and probability estimation, and identified the intervention focus for individual patients. Thus, the system distinguished coronary artery stenosis well in suspected CHD patients. Personalized probability curves provide individualized intervention guidance. This may reduce the number of invasive inspections in negative patients, while facilitating decision-making regarding appropriate medical intervention, improving patient prognosis.

Due to a lack of an appropriate disease-specific patient-reported outcome (PRO) instrument for chronic heart failure including its social support and treatment aspects in China, this study was performed to develop a patient-reported outcome measure (PROM) for patients with chronic heart failure and evaluate its reliability, validity, and feasibility. According to the standard PROM guidelines established by the Food and Drug Administration, an item pool was formed by reviewing a large amount of relevant literature and interviewing patients with chronic heart failure about their main symptoms. Thus, the primary scale was created after adjusting the items and language with the help of patients and experts in the field. Next, 155 patients from 8 hospitals in different districts were recruited for a pilot survey using questionnaires containing these items. The patients’ responses were analyzed using the classical test theory and item response theory to select high-quality items and determine the subdomains of the scale. This was followed by a formal investigation in the same eight hospitals. In total, 360 patients and 100 healthy subjects were included to evaluate the reliability, validity, and feasibility of the items. Through this process, the final scale was established. The final scale comprised 12 subdomains with 57 items related to physical, psychological, social, and therapeutic areas. The data analysis results of the formal investigation showed that the PROM for chronic heart failure had good reliability, validity, and feasibility. Reliability was verified by Cronbach’s alpha coefficient, which was 0.913 for the total scale, 0.903 for the physical domain, 0.941 for the psychological domain, 0.827 for the social domain, and 0.839 for the therapeutic domain. The construct validity results met the relative criteria of confirmatory factor analysis. Discriminant validity was represented by score comparisons of nine subdomains. The response rate and the effective rate of return of the CHF-PROM were 98.94% and 98.92%, respectively. The final scale coincides with the theoretical framework and better reflects the overall quality of life of patients with chronic heart failure. This scale can be used as a valid instrument to evaluate clinical treatment and clinical trials of chronic heart failure.

Background: Chronic heart failure (CHF) affects more than 3.8 million people worldwide. There is a paucity of studies focusing on psychosocial issues in CHF patients. This study aimed to investigate the association of social support, mental adjustment and death to exploring whether mental adjustment could mediate the relationship. Methods: From May 2017 to June 2021, we conducted a multicenter clinical study to collect 1552 patients data. The Patient Report Outcome (PRO) scale were disseminated to collect information in the physical, psychological, social and therapeutic domains of patients. Marginal structural model was used to investigate the association of social support and CHF death, and the role of mental adjustment in their mediation. Results: The direct effect of social support accounted for 44.76% of the total effect. High social support (≥ 14 points) reduced mortality by 46.3% (RR=0.537, P=0.027), medium social support (11– 14 points) reduced mortality by 45.3% (RR=0.547, P=0.042). Anxiety (effect percentage: 24.63%) and appetite-sleep quality (effect percentage: 30.61%) played a mediating role between social support and death in CHF patients. In women, aged > 75 years, divorced or widowed patients were most prone to anxiety due to inadequate support (RR=0.519, P=0.019; RR=0.403, P=0.002; RR=0.413, P=0.041). Family care and socioeconomic assistance significantly reduced the risk of death (RR=0.689, P=0.040; RR=0.584, P=0.012). Conclusion: Social support can reduce patient mortality, especially family care and social economic assistance. The elderly, female, divorced or widowed patients are more likely to cause mental illness due to inadequate social support.

Background: The re-hospitalization rate of patients with heart failure remains at a high level, and studies of the subject have focused mainly on event-time outcomes. In addition to using re-hospitalization data with the outcomes of the event-time-count, this study introduces the conditional frailty model, which could help obtain more reasonable results. Materials and methods: This prospective observational cohort study enrolled 1484 patients with heart failure caused by coronary heart disease. The outcomes of heart failure readmissions and the case report form data were collected. Based on the traditional Cox model with event-time outcomes, the mixed effects of a conditional frailty model were added to analyze the event-time-count longitudinal data. Results: The Cox regression model showed that non-manual work, diastolic dysfunction, and better medical compensation increased the risk of heart failure readmission, whereas treatment with beta-blockers decreased the risk. The conditional frailty model further revealed that age, female sex, non-manual work, better medical compensation, longer QRS duration, and treatment with percutaneous coronary intervention increased the risk of heart failure readmission. Conclusion: This study obtained more reliable, reasonable results based on longitudinal data and a mixed model. The results could provide more clinical epidemiological evidence for the management of heart failure.

Purpose: This study aimed to develop an integrative dynamic nomogram, including N-terminal pro-B type natural peptide (NT-proBNP) and estimated glomerular filtration rate (eGFR), for predicting the risk of all-cause mortality in HFmrEF patients. Patients and Methods: 790 HFmrEF patients were prospectively enrolled in the development cohort for the model. The least absolute shrinkage and selection operator (LASSO) regression and Random Survival Forest (RSF) were employed to select predictors for all-cause mortality. Develop a nomogram based on the Cox proportional hazard model for predicting long-term mortality (1-, 3-, and 5-year) in HFmrEF. Internal validation was conducted using Bootstrap, and the final model was validated in an external cohort of 338 consecutive adult patients. Discrimination and predictive performance were evaluated by calculating the time-dependent concordance index (C-index), area under the ROC curve (AUC), and calibration curve, with clinical value assessed via decision curve analysis (DCA). Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to assess the contributions of NT-proBNP and eGFR to the nomogram. Finally, develop a dynamic nomogram using the “Dynnom” package. Results: The optimal independent predictors for all-cause mortality (APSELNH: A: angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitor (ACEI/ARB/ARNI), P: percutaneous coronary intervention/coronary artery bypass graft (PCI/CABG), S: stroke, E: eGFR, L: lg of NT-proBNP, N: NYHA, H: healthcare) were incorporated into the dynamic nomogram. The C-index in the development cohort and validation cohort were 0.858 and 0.826, respectively, with AUCs exceeding 0.8, indicating good discrimination and predictive ability. DCA curves and calibration curves demonstrated clinical applicability and good consistency of the nomogram. NT-proBNP and eGFR provided significant net benefits to the nomogram. Conclusion: In this study, the dynamic APSELNH nomogram developed serves as an accessible, functional, and effective clinical decision support calculator, offering accurate prognostic assessment for patients with HFmrEF.