A Machine Learning Tool to Predict Survival After First Surgery in Peripheral Artery Disease Patients.

Martina Doneda, Ettore Lanzarone, Fabio Riccardo Pisa, Bianca Pane, Giovanni Pratesi, Giovanni Spinella

Abstract readValidation Study

In one paragraph

Article in Journal of cardiovascular translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Martina DonedaDepartment of Electronics Information and Bioengineering (DEIB), Politecnico Di Milano, Milan, Italy.

Ettore LanzaroneDepartment of Management, Information and Production Engineering (DIGIP), University of Bergamo, Dalmine (Bg), Italy.

Fabio Riccardo PisaDepartment of Surgical and Integrated Diagnostic Sciences (DISC), University of Genoa, Viale Benedetto XV 6, Genoa, Italy.

Bianca PaneDepartment of Surgical and Integrated Diagnostic Sciences (DISC), University of Genoa, Viale Benedetto XV 6, Genoa, Italy.

Giovanni PratesiDepartment of Surgical and Integrated Diagnostic Sciences (DISC), University of Genoa, Viale Benedetto XV 6, Genoa, Italy.

Giovanni SpinellaDepartment of Surgical and Integrated Diagnostic Sciences (DISC), University of Genoa, Viale Benedetto XV 6, Genoa, Italy. giovanni.spinella@unige.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Decision Support TechniquesMachine LearningPeripheral Arterial DiseaseVascular Surgical ProceduresAgedAged, 80 and overClinical Decision-MakingDecision TreesFemaleHumansMaleMiddle AgedPredictive Value of TestsReproducibility of ResultsRetrospective StudiesRisk AssessmentMachine learningMortalityPeripheral artery diseasePrediction

Identifiers

PMID41060529

DOI10.1007/s12265-025-10692-1

PMCPMC12630312

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.