Renal dysfunction (RD) often characterizes the worse course of patients with advanced heart failure (AHF). Despite many prognostic parameters, predicting outcomes remains difficult. Researcher biases and limited knowledge may lead to the underperformance and underuse of modeling algorithms. Many prognosis assessments hindered by excessive reliance on redundant predictors, lack practical clinical applicability. This study analyzed electronic health records of AHF&RD participants encompassing Henan Province Clinical Research Center for Cardiovascular Diseases compassing 11 hospital subcenters and the Beth Israel Deaconess Medical Center.
We employed an AI hybrid modeling framework to develop and validate AIHFLevel, a web-based system designed for evaluating the long-term survival profiles of patients with AHF and RD. The system integrated outcome prediction, clinical interpretability and prognostic stratification, and outperformed other clinical traits and composite risk models. Through 13 straightforward queries, the system empowered users to understand the influence of each predictor on every single individual survival outcome, thereby enabling the optimization of management strategies and targeted interventions in clinical practice.
'AIHFLevel' emerged as a user-friendly tool, empowering healthcare professionals with enhanced capabilities for continuous risk monitoring and precise patient risk profiling.
If you use this research or code, please cite the associated peer-reviewed article:
AI hybrid survival assessment for advanced heart failure patients with renal dysfunction. Nature Communications (2024). https://doi.org/10.1038/s41467-024-50415-9
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