BACKGROUND: Changes in N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels may not fully translate into patient-reported health status in patients with heart failure (HF). We aimed to evaluate the correlation between NT-proBNP levels and patient-reported health status changes at one month after discharge of patients, and their associations with risk of death and rehospitalization in patients with acute HF. METHODS: We used data from the China Patient-centered Evaluative Assessment of Cardiac Events Prospective Heart Failure Study (PEACE 5p-HF Study). Patient-reported health status was measured by the 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12). Patients who were hospitalized for HF and completed the KCCQ-12 and NT-proBNP tests before and one month after discharge were eligible in our study. We stratified patients into different groups based on NT-proBNP levels (i.e., improved, stable, and deteriorated) and KCCQ-12 scores (i.e., not deteriorated and deteriorated). We also examined the associations of the joint NT-proBNP and KCCQ-12 change with the risk of one-year and four-year clinical outcomes. RESULTS: A total of 2461 patients were included in the analysis. The mean age was 64.06 ± 13.51 years, and 36.37% (895/2461) of the study population were female. Among patients with improved NT-proBNP levels, 115 (10.95%) patients had deteriorated KCCQ-12 scores. The correlation between the change in the KCCQ-12 score and NT-proBNP level was weak ( r2 = 0.002, P = 0.013). Stratification by changes in the KCCQ-12 score revealed subgroups with distinctive risks, such that patients with deteriorated KCCQ-12 scores in any of the NT-proBNP change groups exhibited an increased risk of one-year all-cause death than participants with not deteriorated KCCQ-12 scores in any of the NT-proBNP change groups. Patients with improved NT-proBNP levels and deteriorated KCCQ-12 scores presented greater risks of one-year all-cause death (hazard ratio [HR]: 2.45, 95% confidence interval [CI]: 1.34-4.48) than patients with stable NT-proBNP levels and not deteriorated KCCQ-12 scores (HR [95% CI], 1.77 [1.25-2.53]). CONCLUSIONS: A discrepancy between changes in NT-proBNP levels and KCCQ-12 scores was common. The change in NT-proBNP levels was not sufficient to characterize critical aspects related to HF during one month after discharge of patients. Changes in the KCCQ-12 score exhibit complementary information to NT-proBNP levels for the prediction of clinical outcomes in patients with acute HF. REGISTRATION www.clinicaltrials.gov (No. NCT02878811).
Aged ; Female ; Humans ; Male ; Middle Aged ; Health Status ; Heart Failure/metabolism* ; Natriuretic Peptide, Brain/metabolism* ; Peptide Fragments/metabolism* ; Prospective Studies

Heart failure is a serious and end-stage status of various heart diseases, characterized by comparatively high rate of readmission and mortality, and has become an important public health issue. The risk of readmission and mortality following discharge of an index hospitalization are key indicators to evaluate the quality of medical care among patients with acute heart failure. Therefore, it is important to carry out risk prediction research for patients with acute heart failure, quantify the disease risk, perform risk stratification, optimize clinical decision-making, elevate patients' quality of life and prognosis, and comprehensively improve the medical quality of acute heart failure. During the past 20 years, foreign researchers have developed dozens of models to predict the risk of acute heart failure readmission and mortality, and Chinese researchers have also developed up to 10 models applicable to the Chinese population. However, there is no recommended risk prediction model for acute heart failure in current clinical guidelines across China. In this report, we aim to introduce the major models for predicting the risk of acute heart failure readmission and mortality from home and abroad, focus on putting forward limitations of established models, and initiating potential directions for future studies from the following aspects: integrate multi-source data, mine emerging biomarkers, establish polygenic risk scores, optimize machine learning methods, promote flexible adjustment, and broaden approaches that applicable for various scenarios. Accordingly, this study will help facilitate domestic research in predicting the risk of readmission and mortality among patients hospitalized for acute heart failure.