Key result
Five-variable biomarker model predicts 90-day HF readmission with an AUC of ~0.75.
Why the study?
Ninety days is a crucial time point for readmission risk assessment in chronic heart failure, but risk factor exploration and predictive modeling for 90-day readmission were lacking.
Cohort (n=542)
No
Effect estimate: AUC 0.746 (95% CI 0.685-0.808)
A novel prediction model incorporating cardiac troponin, fasting blood glucose, serum sodium, eGFR, and neutrophils demonstrated good discriminative ability for assessing 90-day readmission risk in patients with chronic heart failure.
May support readmission risk stratification in chronic HF; leaves open need for external validation before clinical use.
Background: Chronic heart failure (CHF) is associated with high morbidity and mortality rates, which is not curable currently, resulting in an increasing risk of readmission and imposing a considerable burden on healthcare systems. Predictive modeling is a critical tool for guiding the clinical management of CHF. 90-day is a crucial time point for readmission risk assessment in patients with CHF. However, there is a lack of risk factor exploration, as well as predictive modeling for 90-day readmission risk in these patients. The aim of this study is to identify prognostic risk biomarkers and develop a novel prediction model for 90-day readmission for patients with CHF. Methods: = 162) cohort at a 7:3 ratio. Demographic, comorbidities, laboratory tests, and echocardiography results were analyzed through Least Absolute Shrinkage and Selection Operator (LASSO) regression to select predictive variables. Furthermore, receiver operating characteristic (ROC) curve, the area under the curve (AUC), decision curve analysis (DCA), and calibration curves were used to access the discriminative power, clinical validities, and calibration of the model. Results: Of the included 542 patients, the readmission rates were 18.7% and 19.1% in 90-day follow-up in the training and validation cohort respectively. Five variables, including cardiac troponin (cTn), fasting blood glucose (FBG), serum sodium, estimated glomerular filtration rate (eGFR), neutrophil (NEU) showed the strongest correlation with 90-day readmission according to LASSO regression. These selected variables were then combined into a novel prediction model, with an AUC of 0.746 [95% (confidence interval) CI: 0.685-0.808] in the training cohort and 0.705 (95% CI: 0.605-0.804) in the validation cohort. Conclusions: Our findings suggest that a predictive model incorporating the variables of cTn, FBG, serum sodium, eGFR and NEU demonstrating a good predictive ability for 90-day readmission risk in patients with CHF, which can aid clinicians in clinical decisions and personalized management.
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He et al. (2025) conducted a cohort in Chronic heart failure (n=542). Prediction model (cTn, FBG, serum sodium, eGFR, NEU) was evaluated on Unplanned heart failure readmission within 90 days (AUC 0.746, 95% CI 0.685-0.808). A predictive model incorporating cardiac troponin, fasting blood glucose, serum sodium, eGFR, and neutrophils demonstrated good discriminative ability for 90-day readmission risk in patients with chronic heart failure, with an AUC of 0.746.
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