Prediction of individuals at high-risk of chronic kidney disease during treatment with lithium for bipolar disorder

Abstract Background Lithium is the most effective treatment in bipolar disorder. Its use is limited by concerns about risk of chronic kidney disease (CKD). We aimed to develop a model to predict risk of CKD following lithium treatment initiation, by identifying individuals with a high-risk trajectory of renal function. Methods We used United Kingdom Clinical Practice Research Datalink (CPRD) electronic heath records (EHRs) from 2000-2018. CPRD Aurum for prediction model development and CPRD Gold for external validation. We used elastic net to generate a prediction model from potential features. We performed discrimination and calibration assessments in an external validation data set. We included all patients aged ≥16 with bipolar disorder prescribed lithium. To be included patients had to have ≥1 year of follow-up before lithium initiation, ≥3 estimated glomerular filtration rate (eGFR) measures after lithium initiation (to be able to determine a trajectory) and a normal (≥60 mL/min/1.73m2) eGFR at lithium initiation (baseline). In the Aurum development cohort 1609 fulfilled these criteria. The Gold external validation cohort included 934 patients. We included 44 potential baseline features in the prediction model, including sociodemographic, mental and physical heath and drug treatment characteristics. We compared a full model with the 3-variable five-year kidney failure risk equation (KFRE) and a 3-variable elastic net model. We used group-based trajectory modelling to identify latent trajectory groups for eGFR. We were interested in the group with deteriorating renal function (the high-risk group). Findings The high-risk group included 191 (11.87%) of the Aurum cohort and 137 (14.67%) of the Gold cohort, of these 168 (87.96%) and 117 (85.40%) respectively developed CKD 3a or more severe during follow-up. The model, developed in Aurum, had a ROC area of 0.879 (95%CI 0.853-0.904) in the Gold external validation data set. At the empirical optimal cut-point defined in the development dataset, the model had a sensitivity of 0. 91 (95%CI 0.84-0.97) and a specificity of 0.74 (95% CI 0.67-0.82). However, a 3-variable elastic net model (including only age, sex and baseline eGFR) performed similarly well (ROC area 0.888; 95%CI 0.864-0.912), as did the KFRE (ROC area 0.870; 95%CI 0.841-0.898). Conclusions Individuals at high-risk of a poor trajectory of renal function can be identified before initiation of lithium treatment by a simple equation including age, sex and baseline eGFR. We did not identify strong predicters of renal impairment specific to lithium treated patients.