KFRE aangepast voor multimorbiditeit verbetert schatting nierfalenrisico

BACKGROUND AND HYPOTHESIS: Guidelines recommend using risk prediction models for predicting kidney failure in chronic kidney disease (CKD). Many people with CKD have multiple long-term conditions (multimorbidity), which influences outcomes including kidney failure and mortality. This study validated the four-variable kidney failure risk equation (KFRE) in individuals with CKD, with and without multimorbidity, comparing performance of KFRE using creatinine and cystatin C to calculate estimated glomerular filtration rate (eGFR) and updated the model to account for competing mortality risks. METHODS: Observational cohort study using research-based (UK Biobank) and population-based cohorts (Stockholm Creatinine Measurements project: SCREAM). Multimorbidity was defined as two or more long-term conditions in addition to CKD. Kidney failure was defined as long-term dialysis or kidney transplantation. KFRE performance assessment included discrimination, calibration, and overall fit at 2 and 5 years. An updated model (using the same variables as KFRE) accounting for competing mortality risks was developed and validated. RESULTS: 14 998 of 24 489 individuals in UK Biobank (61.2%) and 30 147 of 42 902 individuals in SCREAM (70.3%) had multimorbidity. Discrimination of KFRE was good (area under curve $\ge $0.86 across eGFR equations in all cohorts, multimorbidity groups and time horizons). Kidney failure risk was under-estimated in people with multimorbidity in UK Biobank (observed/expected (O/E) ratio 1.75 at 5 years; eGFR creatinine). Conversely, calibration-in-the-large (O/E ratio) at 5 years in SCREAM was 1.05 in the multimorbidity group (eGFR creatinine). Using cystatin C compared to creatinine did not improve model performance.Cumulative incidence of death was higher with multimorbidity compared to no multimorbidity. An updated model considering competing mortality improved calibration performance over KFRE, O/E ratio 0.98 in multimorbidity group of the validation cohort (UK Biobank) at 5 years. CONCLUSION: Competing mortality risk is important when predicting kidney failure, particularly for people with multimorbidity. An updated model accounting for competing mortality risk, permits improved model performance.