Analysis of a Machine Learning-Based Risk Stratification Scheme for Chronic Limb-Threatening Ischemia

• Published in: JAMA network open Vol. 5; no. 3; p. e223424
• Main Authors: Chung, Jayer, Freeman, Nikki L B, Kosorok, Michael R, Marston, William A, Conte, Michael S, McGinigle, Katharine L
• Format: Journal Article
• Language: English
• Published: United States American Medical Association 01.03.2022
• Subjects: Aged; Amputation; Chronic Limb-Threatening Ischemia; Clinical trials; Cluster analysis; Cohort Studies; Humans; Ischemia; Ischemia - surgery; Limb Salvage - adverse effects; Machine Learning; Male; Online Only; Original Investigation; Pain - etiology; Patients; Peripheral Arterial Disease; Retrospective Studies; Risk Assessment; Risk Factors; Surgery; Time Factors; Treatment Outcome
• ISSN: 2574-3805; 2574-3805
• DOI: 10.1001/jamanetworkopen.2022.3424

Valid risk stratification schemes are key to performing comparative effectiveness research; however, for chronic limb-threatening ischemia (CLTI), risk stratification schemes have limited efficacy. Improved, accurate, comprehensive, and reproducible risk stratification models for CLTI are needed. To evaluate the use of topic model cluster analysis to generate an accurate risk prediction model for CLTI. This multicenter, nested cohort study of existing Project of Ex Vivo Vein Graft Engineering via Transfection (PREVENT) III clinical trial data assessed data from patients undergoing infrainguinal vein bypass for the treatment of ischemic rest pain or ischemic tissue loss. Original data were collected from January 1, 2001, to December 31, 2003, and were analyzed in September 2021. All patients had 1 year of follow-up. Supervised topic model cluster analysis was applied to nested cohort data from the PREVENT III randomized clinical trial. Given a fixed number of clusters, the data were used to examine the probability that a patient belonged to each of the clusters and the distribution of the features within each cluster. The primary outcome was 1-year CLTI-free survival, a composite of survival with remission of ischemic rest pain, wound healing, and freedom from major lower-extremity amputation without recurrent CLTI. Of the original 1404 patients, 166 were excluded because of a lack of sufficient feature and/or outcome data, leaving 1238 patients for analysis (mean [SD] age, 68.4 [11.2] years; 800 [64.6%] male; 894 [72.2%] White). The Society for Vascular Surgery Wound, Ischemia, and Foot Infection grade 2 wounds were present in 543 patients (43.8%), with rest pain present in 645 (52.1%). Three distinct clusters were identified within the cohort (130 patients in stage 1, 578 in stage 2, and 530 in stage 3), with 1-year CLTI-free survival rates of 82.3% (107 of 130 patients) for stage 1, 61.1% (353 of 578 patients) for stage 2, and 53.4% (283 of 530 patients) for stage 3. Stratified by stage, 1-year mortality was 10.0% (13 of 130 observed deaths in stage 1) for stage 1, 13.5% (78 of 578 patients) for stage 2, and 20.2% (105 of 521 patients) for stage 3. Similarly, stratifying by stage revealed major limb amputation rates of 4.2% (5 of 119 observed major limb amputations in stage 1) for stage 1, 10.8% (55 of 509 patients) for stage 2, and 18.4% (81 of 440 patients) for stage 3. Among survivors without a major amputation, the rates of CLTI recurrence were 9.2% (11 of 119 observed recurrences in stage 1) for stage 1, 24.9% (130 of 523 patients) for stage 2, and 29.6% (132 of 446 patients) for stage 3. The topic model cluster analysis in this cohort study identified 3 distinct stages within CLTI. Findings suggest that CLTI-free survival is an end point that can be accurately and reproducibly quantified and may be used as a patient-centric outcome.