Why the study?
The study was conducted to develop and validate a machine learning tool to predict survival in peripheral artery disease patients after surgical treatment.
Does a gradient boosted decision tree machine learning model accurately predict all-cause mortality in patients undergoing first surgery for peripheral artery disease?
Does a gradient boosted decision tree machine learning model accurately predict all-cause mortality in patients undergoing first surgery for peripheral artery disease?
A machine learning model using simple clinical and demographic parameters can accurately predict one-, three-, and five-year mortality in patients undergoing their first surgery for peripheral artery disease.
May support preoperative risk stratification in PAD surgery; hypothesis-generating pending prospective validation.
The aim of this study was to develop and validate a machine learning tool for predicting survival in PAD patients who received surgical treatment. We used the data from 1,615 patients who underwent PAD surgery from 2005 to 2020. Gradient boosted decision trees (GBDTs) were used to predict mortality at one, three and five years after the first surgery, while predictor importance was assessed using the SHAP values method. The area under the curve (AUC) of the receiver operating characteristic curve of the one-, three and five-year prediction models were 0.86, 0.84 and 0.80, respectively. Disease stage was the most important predictor, along with age, chronic kidney disease status, hospital length-of-stay and total number of comorbidities. Presence of dyslipidemia was slightly predictive of one- and three-year mortality. Simple clinical and demographic parameters can be used to train a GBDT model capable of predicting PAD follow-up mortality.
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Doneda et al. (2025) studied this question.
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