Predictors for mortality after lower-extremity amputations in geriatric patients

Abstract

Background The identification of independent predictors for operative and long-term mortality after lower-extremity amputations in the geriatric population would allow targeted management for high-risk patients and appropriate allocation of resources. Methods Univariate and multivariate logistic regression analyses were used to identify independent predictors for operative mortality. Life tables and Kaplan-Meier survival curves were generated. Independent predictors for long-term mortality were tested by log-rank test followed by Cox regression analysis. Results Female gender, congestive heart failure, and high-level amputation were identified as independent predictors for operative mortality (odds ratios 4.14, 4.59, and 4.77, respectively). The logistic regression model showed good calibration and discriminative power. Female gender, high-level amputation, cerebrovascular accident, congestive heart failure, noncommunity ambulation, and institutionalization before amputation were associated with an increased risk for long-term mortality. However, only high-level amputation, congestive heart failure, and noncommunity ambulation remained as independent risk factors after Cox regression analysis (relative risks 1.68, 2.08, and 2.10, respectively). Conclusions Extra care should be given to patients identified with independent predictors for operative and long-term mortality.