DeepSurv Heart Failure (HF) Prognostic Model
- DeepSurv HF Prognostic Model — Explanation and Clinical Context
DeepSurv is a Cox-proportional-hazards deep neural network that learns a nonlinear risk function from patient covariates and outputs individualized survival/hazard estimates. Applied to heart failure (HF), DeepSurv-based models can integrate many continuous and interacting predictors (clinical variables, biomarkers, and high-resolution signals) and may improve discrimination and personalization versus traditional Cox models. However, DeepSurv requires a trained model (weights) and an inference runtime; it cannot be computed from a formula in PHP alone. The MAGGIC risk score is a validated, simple clinical score for 1- and 3-year mortality in HF and is provided here as an approximate fallback when a deployed DeepSurv service is not available.
Reference:
Katzman J, Shaham U, Bates J, et al. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18:24.
Ross HJ, et al. Predicting heart failure outcomes by integrating breath-by-breath measurements from cardiopulmonary exercise testing and clinical data through a deep learning survival neural network (DeepSurv application to HF). Eur Heart J Digit Health. 2024.
Pocock SJ, Ariti CA, McMurray JJ, et al. Predicting survival in heart failure: the MAGGIC risk score (Meta-Analysis Global Group in Chronic Heart Failure). Eur Heart J. 2013;34(19):1404–1413.
For practical fallback calculation and point mapping examples see MDCalc's MAGGIC calculator (web tool) and multiple external validations.
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