A tool to predict the risk of lower extremity amputation in patients starting dialysis.

ABSTRACT

BACKGROUND AND HYPOTHESIS: Non-traumatic lower extremity amputation (LEA) is a severe complication during dialysis. To inform decision-making for physicians, we developed a multivariable prediction model for LEA after starting dialysis. METHODS: Data from the Swedish Renal Registry (SNR) between 2010 and 2020 were geographically split into a development and validation cohort. Data from NECOSAD between 1997 and 2009 were used for validation targeted at Dutch patients. Inclusion criteria were no previous LEA and kidney transplant and age ≥ 40 years at baseline. A Fine-Gray model was developed with LEA within 3 years after starting dialysis as outcome of interest. Death and kidney transplant were treated as competing events. One coefficient, ordered by expected relevance, per 20 events was estimated. Performance was assessed with calibration and discrimination. RESULTS: SNR was split into an urban development cohort with 4 771 individuals experiencing 201 (4.8%) events and a rural validation cohort with 4.876 individuals experiencing 155 (3.2%) events. NECOSAD contained 1 658 individuals experiencing 61 (3.7%) events. Ten predictors were included female sex, age, diabetes mellitus, peripheral artery disease, cardiovascular disease, congestive heart failure, obesity, albumin, haemoglobin and diabetic retinopathy. In SNR, calibration intercept and slope were -0.003 and 0.912 respectively. The C-index was estimated as 0.813 (0.783-0.843). In NECOSAD, calibration intercept and slope were 0.001 and 1.142 respectively. The C-index was estimated as 0.760 (0.697-0.824). Calibration plots showed good calibration. CONCLUSION: A newly developed model to predict LEA after starting dialysis showed good discriminatory performance and calibration. By identifying high-risk individuals this model could help select patients for preventive measures.