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Updated: Jan 7, 2026
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Development and validation of an interpretable machine learning model for predicting the risk of non-cardiac surgery
Qing Li1,2, Zizhou Liu1,2, Kunlun He3
1Department of Medicine, South China University of Technology, Guangzhou, China.
Background:
This study developed a machine learning model to predict postoperative heart failure (HF) risk in non-cardiac surgery patients.
Methods:
Using data from 489 patients (109 HF cases, 380 controls), the dataset was split 8:2 into training and testing sets, with under-sampling for class imbalance. Eight algorithms were evaluated, with random forest (RF) performing best.
Results:
The RF model achieved AUROCs of 0.919 (training) and 0.923 (testing), validated externally (AUC = 0.878). SHAP analysis identified key predictors: age, neutrophil-to-lymphocyte ratio, blood glucose, INR, pulse and serum creatinine (positively associated); serum albumin, MCHC, eGFR and diastolic blood pressure (negatively associated). A web-based tool was developed for clinical use.
Conclusion:
The model integrates 10 clinical variables reflecting age, inflammation, renal dysfunction, and hemodynamic instability, enabling preoperative risk stratification and guiding targeted interventions to improve perioperative outcomes.
