Social and therapeutic decline earlier than physical and psychological domains after discharge in heart failure patients: A patient-reported outcome measurements of latent transition analysis.

Background: Among patients with chronic heart failure (CHF), response shifts are common in assessing treatment effects. However, few studies focused on potential response shifts in these patients. Materials and methods: Data of CHF patient-reported outcome measures (PROMs) were obtained from three hospitals in Shanxi, China, from 2017 to 2019. A total of 497 patients were enrolled and followed up at 1 month and 6 months after discharge. Latent transition analysis (LTA) was employed to determine the longitudinal transition trajectories of latent subtypes in CHF patients in the physiological, psychological, social, and therapeutic domains. Results: The patients were divided into high- and low-level groups in the four domains according to the LTA. One month after discharge, the physiological and psychological domains improved, while the social and therapeutic domains remained unchanged. Six months after discharge, the former remained stable, but the latter deteriorated. The factors affecting the state transition in four domains were as follows. The influencing factor of the physiological domains are gender, age, tea consumption, smoking, alcohol consumption, physical activity, and light diet; those of the psychological domain are gender, occupation, smoking, alcohol consumption, and physical activity; those of the social domains are age; those of the therapeutic domains are education and income. Conclusion: The disease status of CHF patients has shifted over time. Risk factors accelerate the deterioration of patients' condition. Furthermore, the risk factors of social and therapeutic domains deteriorate patients' condition faster than those of physiological and psychological domains. Therefore, individualized intervention programs should be given for CHF patients who may be transferred to the low-level groups to maintain the treatment effect and improve the prognosis.

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.

Time-to-event prediction analysis of patients with chronic heart failure comorbid with atrial fibrillation: a LightGBM model.

BACKGROUND: Chronic heart failure (CHF) comorbid with atrial fibrillation (AF) is a serious threat to human health and has become a major clinical burden. This prospective cohort study was performed to design a risk stratification system based on the light gradient boosting machine (LightGBM) model to accurately predict the 1- to 3-year all-cause mortality of patients with CHF comorbid with AF. METHODS: Electronic medical records of hospitalized patients with CHF comorbid with AF from January 2014 to April 2019 were collected. The data set was randomly divided into a training set and test set at a 3:1 ratio. In the training set, the synthetic minority over-sampling technique (SMOTE) algorithm and fivefold cross validation were used for LightGBM model training, and the model performance was performed on the test set and compared using the logistic regression method. The survival rate was presented on a Kaplan-Meier curve and compared by a log-rank test, and the hazard ratio was calculated by a Cox proportional hazard model. RESULTS: Of the included 1796 patients, the 1-, 2-, and 3-year cumulative mortality rates were 7.74%, 10.63%, and 12.43%, respectively. Compared with the logistic regression model, the LightGBM model showed better predictive performance, the area under the receiver operating characteristic curve for 1-, 2-, and 3-year all-cause mortality was 0.718 (95%CI, 0.710-0.727), 0.744(95%CI, 0.737-0.751), and 0.757 (95%CI, 0.751-0.763), respectively. The net reclassification index was 0.062 (95%CI, 0.044-0.079), 0.154 (95%CI, 0.138-0.172), and 0.148 (95%CI, 0.133-0.164), respectively. The differences between the two models were statistically significant (P < 0.05). Patients in the high-risk group had a significantly higher hazard of death than those in the low-risk group (hazard ratios: 12.68, 13.13, 14.82, P < 0.05). CONCLUSION: Risk stratification based on the LightGBM model showed better discriminative ability than traditional model in predicting 1- to 3-year all-cause mortality of patients with CHF comorbid with AF. Individual patients' prognosis could also be obtained, and the subgroup of patients with a higher risk of mortality could be identified. It can help clinicians identify and manage high- and low-risk patients and carry out more targeted intervention measures to realize precision medicine and the optimal allocation of health care resources.

Assessment of chronic disease self-management in patients with chronic heart failure based on the MCID of patient-reported outcomes by the multilevel model.

PURPOSE: The minimal clinically important difference (MCID) of a patient-reported outcome (PRO) represents the threshold value of the change in the score for that PRO. It is deemed to have an important implication in clinical management. This study was performed to evaluate the clinical significance of chronic disease self-management (CDSM) for patients with chronic heart failure based on the MCID of the chronic heart failure-PRO measure (CHF-PROM). METHODS: A multicenter, prospective cohort study of 555 patients with heart failure were enrolled from July 2018. Advice of CDSM was provided in written form at discharge to all patients. Information regarding CHF-PROM and CDSM were collected during follow-up. Multilevel models were applied to dynamically evaluate the effects of CDSM for CHF-PROM scores, as well as its physical and psychological domains. MCID changes of the PRO were introduced and compared with ß values of CDSM obtained from the multi-level models to further evaluate the clinical significance. The STROBE checklist is shown in Additional file 1. RESULTS: Scores for CHF-PROM improved significantly after discharge. The multilevel models showed that a regular schedule, avoidance of over-eating, a low-sodium diet and exercise increased scores on CHF-PROM. Compared with the MCID, avoidance of over-eating (12.39 vs. 9.75) and maintenance of a regular schedule often (10.98 vs. 9.75), and exercise almost every day (11.36 vs. 9.75) reached clinical significance for the overall summary. Avoidance of over-eating (5.88 vs. 4.79) and a regular schedule almost every day (4.96 vs. 4.79) reached clinical significance for the physical scores. Avoidance of over-eating half of the time (5.26 vs. 4.87) and a regular schedule almost every day (5.84 vs. 4.87) demonstrated clinical significance for the psychological scores. CONCLUSIONS: This study observed an association of avoidance of over-eating and maintenance of a regular schedule with the improvement of CHF-PROM. It provides further evidence for management of heart failure. TRIAL REGISTRATION: Current Prospective Trials NCT02878811; registered August 25, 2016; https://clinicaltrials.gov/ct2/show/NCT02878811?term=NCT02878811&draw=2&rank=1 .

A nomogram based on a patient-reported outcomes measure: predicting the risk of readmission for patients with chronic heart failure.

BACKGROUND: Health-related quality of life, as evaluated by a patient-reported outcomes measure (PROM), is an important prognostic marker in patients with chronic heart failure. This study aimed to use PROM to establish an effective readmission nomogram for chronic heart failure. METHODS: Using a PROM as a measurement tool, we conducted a readmission nomogram for chronic heart failure on a prospective observational study comprising of 454 patients with chronic heart failure hospitalized between May 2017 to January 2020. A Concordance index and calibration curve were used to evaluate the discriminative ability and predictive accuracy of the nomogram. A bootstrap resampling method was used for internal validation of results. RESULTS: The median follow-up period in the study was 372 days. After a final COX regression analysis, the gender, income, health care, appetite-sleep, anxiety, depression, paranoia, support, and independence were identified and included in the nomogram. The nomogram showed moderate discrimination, with a concordance index of 0.737 (95% CI 0.673-0.800). The calibration curves for the probability of readmission for patients with chronic heart failure showed high consistency between the probability, as predicted, and the actual probability. CONCLUSIONS: This model offers a platform to assess the risk of readmission for different populations with CHF and can assist clinicians with personalized treatment recommendations.