Predicting the risk of acute kidney injury: Derivation and validation of STRATIFY-AKI

Background: Antihypertensives reduce the risk of cardiovascular disease but are also associated with harms including acute kidney injury (AKI). Few data exist to guide clinical decision making regarding these risks. Aim: To develop a prediction model estimating the risk of AKI in people potentially indicated for antihypertensive treatment. Design and setting: This observational cohort study used routine Primary Care data from the Clinical Practice Research Datalink (CPRD) in England. Methods: People aged 40+ years, with at least one blood pressure measurement between 130-179 mmHg were included. Outcomes were hospitalisation or death with AKI within 1, 5 and 10 years. The model was derived with data from CPRD GOLD (n=1,772,618), using a Fine-Gray competing risks approach, with subsequent recalibration using pseudo-values. External validation used data from CPRD Aurum (n=3,805,322). Results: The mean age of participants was 59 years and 52% were female. The final model consisted of 27 predictors and showed good discrimination at 1, 5 and 10 years (C-statistic 0.82, 95%CI 0.82-0.82). These was some over-prediction at the highest predicted probabilities (O/E 0.63, 95%CI 0.62-0.65), affecting patients with the highest risk. Most patients (>95%) had a low 1-5 year risk of AKI. Conclusions: This clinical prediction model enables GPs to accurately identify patients at high risk of AKI which will aid treatment decisions. Since the vast majority of patients were at low risk, such a model may provide useful reassurance that most antihypertensive treatment is safe and appropriate whilst flagging the few for whom this is not the case.