RESUMO
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.
RESUMO
Background: Several studies have demonstrated an increased risk of severe coronavirus disease 2019 (COVID-19) in chronic kidney disease (CKD) patients. However, few have investigated the impact of CKD stage and dialysis modality. The primary aim of this study was to investigate the association between CKD stage, dialysis modality and risk of severe COVID-19. Secondly, we aimed to study the impact of comorbidities and drugs on the risk of severe COVID-19 in the CKD population. Methods: This nationwide observational study was based on data from the Swedish Renal Registry and three other national registries. Patients with non-dialysis CKD stage 3b-5 or dialysis on 1 January 2020 were included and followed until 31 December 2021. The primary outcome was COVID-19 hospitalization; the secondary outcome was COVID-19 mortality. Associations were investigated using logistic regression models, adjusting for confounders. Results: The study population comprised 7856 non-dialysis CKD patients and 4018 dialysis patients. The adjusted odds ratios (aOR) for COVID-19 hospitalization and mortality were highest in the dialysis group [aOR 2.24, 95% confidence interval (CI) 1.79-2.81; aOR 3.10, Cl 95% 2.03-4.74], followed by CKD 4 (aOR 1.33, 95% CI 1.05-1.68; aOR 1.66, Cl 95% 1.07-2.57), as compared with CKD 3b. No difference in COVID-19 outcomes was observed between patients on hemodialysis and peritoneal dialysis. Overall comorbidity burden was one of the strongest risk factors for severe COVID-19 and the risk was also increased in patients prescribed insulin, proton pump inhibitors, diuretics, antiplatelets or immunosuppressants. Conclusions: Worsening CKD stage and comorbidity are independent risk factors for severe COVID-19 in the Swedish CKD population.