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The kidney failure risk equation in people with CKD and multimorbidity: the effect of competing mortality risks
Heather Walker1, Juan-Jesus Carrero2, Michael K Sullivan1,3
1School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK.
Insights
Predicting kidney failure risk in chronic kidney disease (CKD) patients with multimorbidity requires accounting for competing mortality. An updated Kidney Failure Risk Equation (KFRE) model improves prediction accuracy for these complex cases.
Area of Science:
- Nephrology
- Epidemiology
- Biostatistics
Background:
- Guidelines recommend risk prediction models for chronic kidney disease (CKD) patients.
- Multimorbidity, common in CKD, significantly impacts kidney failure and mortality.
- The four-variable Kidney Failure Risk Equation (KFRE) is a key prediction tool.
Purpose of the Study:
- Validate the KFRE in CKD individuals with and without multimorbidity.
- Compare KFRE performance using creatinine vs. cystatin C eGFR.
- Update the KFRE to incorporate competing mortality risks.
Main Methods:
- Observational cohort study (UK Biobank, SCREAM).
- Defined multimorbidity as ≥2 long-term conditions plus CKD.
- Assessed KFRE discrimination, calibration, and fit at 2 and 5 years.
- Developed and validated an updated model for competing mortality.
Main Results:
- High prevalence of multimorbidity (61.2% UK Biobank, 70.3% SCREAM).
- KFRE showed good discrimination (AUC ≥0.86).
- KFRE underestimated risk in multimorbidity patients (UK Biobank O/E=1.75 at 5yr).
- Updated model improved calibration (UK Biobank O/E=0.98 at 5yr).
Conclusions:
- Competing mortality is crucial for kidney failure prediction in CKD, especially with multimorbidity.
- An updated model accounting for competing mortality enhances prediction accuracy.
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.
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