Inaccurate Risk Assessment by the ACS NSQIP Risk Calculator in Aortic Surgery

Hers, Tessa M.; Schaik, Jan van; Keekstra, Niels; Putter, Hein; Hamming, Jaap F.; Vorst, Joost R. van der

doi:10.3390/jcm10225426

Cited by 12 publications

(1 citation statement)

References 28 publications

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“… 24 Because individuals with PAD are a high‐risk population with many vascular comorbidities, the ability for general risk prediction tools to perform well on this cohort could be limited. 62 By building ML models tailored to individuals receiving lower extremity endovascular revascularization, we attained better performance with AUROCs >0.90. Our algorithms can also predict clinically relevant limb‐related outcomes that existing tools may not be trained to predict such as major reintervention and major amputation, which are of importance to interventionalists and vascular surgeons.…”

Section: Discussionmentioning

confidence: 87%

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning

Li,

Aljabri,

Verma

et al. 2024

JAHA

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Background Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30‐day outcomes following lower extremity endovascular revascularization. Methods and Results The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30‐day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10‐fold cross‐validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30‐day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30‐day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92–0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70–0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb‐threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. Conclusions Our machine learning models accurately predict 30‐day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

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Section: Discussionmentioning

confidence: 87%