Short-term outcomes after major lower limb amputation:a systematic review and meta-analysis

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Introduction: Mortality after major lower limb amputation is a key factor in the decision to perform surgery. This review synthesises the evidence on in-hospital and 30-day post-operative mortality after major amputation. Methods: Studies published globally and reporting in-hospital or 30-day mortality after major lower limb amputation (defined as at- or above-ankle amputation, or transtibial amputation [TTA] and/or transfemoral amputation [TFA]) and published between 2008 and 2025 were included. Only population level studies were included. Meta-analysis was performed and prediction intervals used to primarily report findings, as they account for between-study heterogeneity and represent the plausible range of mortality outcomes in a new study drawn from the same underlying data distribution. Results: Fifty-two studies were included (n =1 067 616). Only three studies were from low- and middle-income countries (two from Brazil, one from Togo). Most studies were either from North America (42.3%) or Europe and Central Asia (36.5%). Twenty-six studies reported in-hospital mortality, 26 reported 30-day mortality. Prediction intervals were 3.6 – 24.5% (point estimate: 10.0%, 95% confidence interval [CI] 8.2 – 12.1%, n = 664 608), and 4.0 – 20.4% (point estimate: 9.4%, 95% CI 8.0 – 11.0%, n =390 030) for in-hospital and 30-day mortality, respectively. In-hospital mortality prediction intervals were 2.9 – 12.7% (point estimate: 6.2%, 95% CI 5.1 to 7.7%, n =77 204) for TTAs and 5.6 – 23.3% (point estimate: 11.8%, 95% CI: 9.6 – 14.4%, n =78 632) for TFAs. Prediction intervals for 30-day mortality were 2.5 – 11.6% (point estimate 5.5%, 95% CI: 4.5 – 6.7%, n =87 971) for TTAs and 4 – 26.6% (point estimate: 10.9%, 95% CI 8.3 – 14.2%, n =42 145) for TFAs. Heterogeneity was high (>75%) across all analyses. Conclusion: Most studies reporting short-term outcomes after major lower limb amputation are from high-income countries. Reported mortality is highly variable, but prediction intervals can be used to estimate mortality in future studies and inform shared decision making.

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