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Home > Vascular Annual Meeting > schanzer_risk_stratification

Risk Stratification in Critical Limb Ischemia (CLI): Derivation and Validation of a Simplified Model to Predict Amputation-Free Survival (AFS) Using Multi-Center Surgical Outcomes Data

Andres Schanzer¹, Jessica Mega², Judith Meadows³, Russell H. Samson⁴, Dennis F. Bandyk⁵, Michael S. Conte.^3
1University of Massachusetts Medical School, Worcester, Mass.;²Massachusetts General Hospital, Boston, Mass.;³Brigham and Women's Hospital, Boston, Mass.;⁴Florida State University, Sarasota, Fla.;⁵University of South Florida, Tampa, Fla.

OBJECTIVES: Patients with CLI are a heterogeneous population with respect to risk for mortality and limb loss, complicating clinical decision-making. Endovascular options, as compared to bypass, offer a tradeoff between reduced procedural risk and inferior durability. Risk stratified data predictive of AFS may improve clinical decision making and allow for better assessment of new technology in the CLI population.

METHODS: Two datasets of CLI patients who underwent surgical bypass were used: the PREVENT III (PIII) randomized trial (n=1404) and a multicenter registry (n=716) from 3 distinct vascular centers (2 academic, 1 community-based). The PIII cohort was randomly assigned to a derivation set (n=953) and to a validation set (n=451). Predictors of AFS identified on univariate screen (inclusion threshold, p<0.20) were included in a stepwise selection Cox model. The resulting 5 significant predictors were assigned an integer score to stratify patients into 3 risk groups. The prediction rule was internally validated in the PIII validation set and externally validated in the multicenter cohort.

RESULTS: The estimated 1 year AFS in the derivation, internal validation, and external validation sets were 76.3%, 72.5%, and 77.0%, respectively. In the derivation set, dialysis (HR 2.81, p<.0001), tissue loss (HR 2.22, p=.0004), age ≥75 (HR 1.64, p=.001), hematocrit <30 (HR 1.61, p=.012), and advanced CAD (HR 1.41, p=.021) were significant predictors for AFS in the multivariable model. An integer score, derived from the ß coefficients, was used to generate 3 risk categories (low <4, medium 4-8, high >8). Stratification of the patients, in each dataset, according to risk category yielded 3 significantly different Kaplan-Meier estimates for AFS [Figure 1]. For a given risk category, the AFS estimate was consistent between the derivation and validation sets [Figure 2]. High risk classification (9% of all patients) was associated with a >50% risk of either death or major amputation within 1 year of bypass.

CONCLUSIONS: Among patients selected to undergo surgical bypass, this parsimonious risk stratification model [Figures 3a, 3b] reliably identified CLI patients with a >50% chance of death or major amputation at 1 year. Calculation of a “CLI risk score” may be useful for surgical decision making and for clinical trial designs in the CLI population.

AUTHOR DISCLOSURES: A. Schanzer, None; J. Mega, None; J. Meadows, None; R.H. Samson, None; D.F. Bandyk, None; M.S. Conte, None.