A clinical decision support system for predicting coronary artery stenosis in patients with suspected coronary heart disease.

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.

The Causal Effects of Anxiety-Mediated Social Support on Death in Patients with Chronic Heart Failure: A Multicenter Cohort Study.

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.

Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities.

AIMS: Chronic heart failure (CHF) has an increasing burden of comorbidities, which affect clinical outcomes. Few studies have focused on the clustering and hierarchical management of patients with CHF based on comorbidity. This study aimed to explore the cluster model of CHF patients based on comorbidities and to verify their relationship with clinical outcomes. METHODS AND RESULTS: Electronic health records of patients hospitalized with CHF from January 2014 to April 2019 were collected, and 12 common comorbidities were included in the latent class analysis. The Fruchterman-Reingold layout was used to draw the comorbidity network, and analysis of variance was used to compare the weighted degrees among them. The incidence of clinical outcomes among different clusters was presented on Kaplan-Meier curves and compared using the log-rank test, and the hazard ratio was calculated using the Cox proportional risk model. Sensitivity analysis was performed according to the left ventricular ejection fraction. Four different clinical clusters from 4063 total patients were identified: metabolic, ischaemic, high comorbidity burden, and elderly-atrial fibrillation. Compared with the metabolic cluster, patients in the high comorbidity burden cluster had the highest adjusted risk of combined outcome and all-cause mortality {1.67 [95% confidence interval (CI), 1.40-1.99] and 2.87 [95% CI, 2.17-3.81], respectively}, followed by the elderly-atrial fibrillation and ischaemic clusters. The adjusted readmission risk of patients with ischaemic, high comorbidity burden, and elderly-atrial fibrillation clusters were 1.35 (95% CI, 1.08-1.68), 1.39 (95% CI, 1.13-1.72), and 1.42 (95% CI, 1.14-1.77), respectively. The comorbidity network analysis found that patients in the high comorbidity burden cluster had more and higher comorbidity correlations than those in other clusters. Sensitivity analysis revealed that patients in the high comorbidity burden cluster had the highest risk of combined outcome and all-cause mortality (P < 0.05). CONCLUSIONS: The difference in adverse outcomes among clusters confirmed the heterogeneity of CHF and the importance of hierarchical management. This study can provide a basis for personalized treatment and management of patients with CHF, and provide a new perspective for clinical decision making.

Development and validation of a patient-reported outcome measure for patients with chronic respiratory failure: The CRF-PROM scale.

BACKGROUND: Various health-related quality-of-life (HRQOL) tools are used to evaluate patients with chronic respiratory failure (CRF), but there is a relative lack of tools available for the evaluation of social support and treatment in these patients. The present study focused on the development of a systematic patient-reported outcome measure (PROM) tool for use in patients with CRF. METHODS: The CRF-PROM scale conceptual framework and item bank were generated after reviewing the corresponding literature and HRQOL scales, interviewing CRF patients and focus groups. After creation of the initial scale, the items in the scale were selected through two item selection theories, and the final scale was created. The reliability, validity and feasibility of the final scale were assessed. RESULTS: The CRF-PROM scale includes four domains (i.e., physiological domain, psychological domain, social domain and therapeutic domain) and 10 dimensions. After the item selection process, the final scale included 50 items. Cronbach's α coefficients, which were all above 0.7, indicated the reliability of the scale. The results of structural validity met the relevant standards of confirmatory factor analysis. The response rates of the preinvestigation and the formal investigation were 93.3% and 97.6%, respectively. CONCLUSIONS: The CRF-PROM scale developed in the present study is effective and reliable. It could be used widely in the posthospital management of patients, in CRF studies and in clinical trials of new medical products and interventions. PATIENT OR PUBLIC CONTRIBUTION: Participants from eight different hospitals and communities participated in the development or validation phase of the CRF-PROM scale.