Derivation and Validation of Risk Prediction Models for Cardiovascular and Kidney Outcomes of Non‐ST‐Segment–Elevation Acute Coronary Syndrome in Patients With Chronic Kidney Disease

Background: Patients with chronic kidney disease are at high risk of adverse outcomes after acute coronary syndrome (ACS) and need optimized treatment decisions. We derived and validated a series of risk models for predicting 1‐year mortality, readmission for myocardial infarction, and progression to kidney failure following ACS. Methods: The development cohort included adults with chronic kidney disease who had an admission for non‐ST‐segment–elevation ACS, in Alberta, Canada between April 1, 2004 and March 31, 2017. Cox proportional hazard and Fine and Gray competing risk models were externally validated and updated in a temporally distinct cohort of patients in Alberta and a geographically distinct cohort of patients with chronic kidney disease and ACS in British Columbia, Canada. Results: The derivation cohort included 11 980 patients, the temporal validation cohort 4204, and the geographic validation cohort 1787. All models showed comparable discrimination and calibration in the temporal validation cohort; comparable model performance was achieved in the geographic validation cohort after updating. In the temporal and geographic validation cohorts, respectively, discrimination was very good for kidney failure (C‐indexes, 0.93 95% CI, 0.89–0.97 and 0.80 95% CI, 0.78–0.82), and modest for mortality (0.77 95% CI, 0.75–0.78 and 0.71 95% CI, 0.69–0.73), and readmission for myocardial infarction (0.65 95% CI, 0.62–0.67 and 0.57 95% CI, 0.53–0.62). All models were well calibrated after updating. Conclusions: We have developed, validated, and updated risk models for cardiac and renal outcomes in patients with chronic kidney disease and ACS, which can help facilitate shared decision‐making surrounding diagnostic testing, treatment, and monitoring of these patients.