Vascular health and risk management · 2026
A Dynamic Prognosis Model of Patients with Chronic Heart Failure: A Prospective Cohort Study Using Follow-Up Data and Recurrent Neural Networks.
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Zhang Yujia, Dou Mengyi, Ding Fengqin, Yan Jingjing, Li Yixin, Tian Jing, Wang Ruihua
Аннотация
BACKGROUND: Prediction models for mortality risk in patients with chronic heart failure (CHF) have traditionally relied on static admission data, which restricts capturing disease dynamics. Longitudinal follow-up data were used to develop a dynamic model to improve accuracy and provide evidence for tailored interventions.
METHODS: We enrolled 1,333 CHF patients from 3 Shanxi centres. Data included CHF patient-reported outcome (PRO) measures (CHF-PROM), lifestyle, medications, and prognosis. Endpoint: all-cause mortality. Using sequential data, we developed Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Multi-layer Perceptron (MLP), and Logistic Regression (LR) for 3-year risk. Performance assessed by area under the receiver operating characteristic curve (AUC), accuracy, true negative rate (TNR), true positive rate (TPR), Brier score, and F1-score. Temporal Shapley Additive exPlanations (TimeSHAP) provided interpretability, and a web tool built.
RESULTS: Among models tested, the GRU model demonstrated strongest predictive accuracy, with performance steadily increasing as follow-up progressed. By 24 months, the GRU-based model attained its peak predictive performance, yielding an AUC of 0.765 (95% confidence interval [CI]: 0.761-0.768), an F1-score of 0.537 (95% CI: 0.531-0.542), and a Brier score of 0.208 (95% CI: 0.199-0.216). TimeSHAP indicated that physical condition, appetite, sleep, physical independence, and anxiety within the CHF-PROM, together with age and New York Heart Association Functional Classification functional class, were key predictors of 3-year all-cause mortality in patients with CHF.
CONCLUSION: PRO data from multiple follow-ups, combined with a model constructed using GRU, provides promising tool for predicting mortality risk in patients with chronic heart failure (CHF). The self-developed web-based decision support system allows users to calculate risk scores simply by entering patient information.
TRIAL REGISTRATION: Study registered with the China Clinical Trial Registry [identifier: ChiCTR2100043337]. Experimental registration date is February 11, 2021.
METHODS: We enrolled 1,333 CHF patients from 3 Shanxi centres. Data included CHF patient-reported outcome (PRO) measures (CHF-PROM), lifestyle, medications, and prognosis. Endpoint: all-cause mortality. Using sequential data, we developed Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Multi-layer Perceptron (MLP), and Logistic Regression (LR) for 3-year risk. Performance assessed by area under the receiver operating characteristic curve (AUC), accuracy, true negative rate (TNR), true positive rate (TPR), Brier score, and F1-score. Temporal Shapley Additive exPlanations (TimeSHAP) provided interpretability, and a web tool built.
RESULTS: Among models tested, the GRU model demonstrated strongest predictive accuracy, with performance steadily increasing as follow-up progressed. By 24 months, the GRU-based model attained its peak predictive performance, yielding an AUC of 0.765 (95% confidence interval [CI]: 0.761-0.768), an F1-score of 0.537 (95% CI: 0.531-0.542), and a Brier score of 0.208 (95% CI: 0.199-0.216). TimeSHAP indicated that physical condition, appetite, sleep, physical independence, and anxiety within the CHF-PROM, together with age and New York Heart Association Functional Classification functional class, were key predictors of 3-year all-cause mortality in patients with CHF.
CONCLUSION: PRO data from multiple follow-ups, combined with a model constructed using GRU, provides promising tool for predicting mortality risk in patients with chronic heart failure (CHF). The self-developed web-based decision support system allows users to calculate risk scores simply by entering patient information.
TRIAL REGISTRATION: Study registered with the China Clinical Trial Registry [identifier: ChiCTR2100043337]. Experimental registration date is February 11, 2021.
Предметные рубрики MeSH
AgedFemaleHumansMaleMiddle AgedChinaChronic DiseaseDecision Support TechniquesHeart FailureLong Short Term MemoryMultilayer PerceptronsPatient Reported Outcome MeasuresPrediction AlgorithmsPredictive Learning ModelsPredictive Value of TestsPrognosisProspective StudiesRecurrent Neural NetworksReproducibility of ResultsRisk AssessmentRisk FactorsTime Factors