A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure Using a Large Administrative Claims Database

Abstract

Background To create an appropriate chronic kidney disease (CKD) management program, we developed a predictive model to identify patients in a large administrative claims database with CKD stages 3 or 4 who were at high risk for progression to kidney failure. Methods The predictive model was developed and validated utilizing a subset of patients with CKD stages 3 or 4 derived from a large Aetna claims database. The study spanned 36 months, comprised of a 12-month (2015) baseline period and a 24-month (2016–2017) prediction period. All patients were ≥18 years of age and continuously enrolled for 36 months. Multivariate logistic regression was used to develop models. Prediction model performance measures included area under the receiver operating characteristic curve (AUROC), calibration, and gain and lift charts. Results Of the 74,114 patients identified as having CKD stages 3 or 4 during the baseline period, 2476 (3.3%) had incident kidney failure during the prediction period. The predictive model included the effect of numerous variables, including age, gender, CKD stage, hypertension (HTN), diabetes mellitus (DM), congestive heart failure, peripheral vascular disease, anemia, hyperkalemia (HK), prospective episode risk group score, and poor adherence to renin-angiotensin-aldosterone system inhibitors. The strongest predictors of progression to kidney failure were CKD stage (4 vs 3), HTN, DM, and HK. The ROC and calibration analyses in the validation sample demonstrated good predictive accuracy (AUROC=0.844) and calibration. The top two prediction deciles identified 70.8% of patients who progressed to kidney failure during the prediction period. Conclusion This novel predictive model had good accuracy for identifying, from a large national database, patients with CKD who were at high risk of progressing to kidney failure within 2 years. Early identification using this model could potentially lead to improved health outcomes and reduced healthcare expenditures in this at-risk population.

Prediction models used in the progression of chronic kidney disease: A scoping review

“…The eGFR formula was also not consistent across studies, 13 studies used the CKD Epidemiology Collaboration (CKD-EPI) equation [ 9 , 41 , 42 , 44 , 46 , 51 , 54 – 56 , 58 , 63 , 65 , 66 ] and 9 studies used the Modification of Diet in Renal Disease (MDRD) equation [ 43 , 45 , 47 , 49 , 53 , 60 , 62 , 64 , 71 ]. Two studies used unique equations customised for their specific cohort [ 48 , 69 ].…”

“…The smallest study [ 50 ] had 43 participants and the largest study included over 300,000 patient records [ 54 ]. Included study populations were from the United States [ 45 , 52 – 56 , 60 , 68 , 69 , 72 ], Canada [ 41 , 46 , 58 , 59 ], Taiwan [ 9 , 42 , 70 ], Germany [ 64 , 66 ], Japan [ 48 , 50 , 61 , 67 ], France [ 43 ], Croatia [ 44 ], Korea [ 49 , 51 ], United Kingdom [ 62 ], Iran [ 71 ], Romania [ 65 ], Spain [ 63 ], Netherlands [ 47 ] and China [ 57 ].…”

“…30 papers reported on the performance of their respective predictive models (regardless of the type of prediction model used) with 25 studies assessing the performance of their model by measuring the Area Under the Curve (AUC) [ 9 , 41 , 43 , 45 – 54 , 56 – 62 , 64 , 66 – 68 , 70 ]. Both supervised and unsupervised techniques were shown to have used AUC to validate their prediction model, each having a relatively high value that indicated that their model was reliable in predicting their defined outcome.…”

“…Studies tried to overcome this issue by filling in the missing data using imputation techniques and internal validation techniques to help justify the dataset [ 42 , 60 ]. There were also studies that reported having unbalanced data and outlined the methods applied to re-balance the data before initiating model development [ 9 , 42 , 45 , 62 , 70 ]. Knowing these limitations, studies recognised that their prediction models would only be applicable to their own given study population and would require external validation to allow generalisation of their model to other populations [ 41 , 44 – 46 , 49 , 50 , 53 , 56 , 59 , 61 , 64 – 66 , 69 , 73 , 74 ].…”

Development and validation of risk models to predict chronic kidney disease among people living with HIV: protocol for a systematic review

“…Predictive models have been developed to assist with CKD risk evaluation in both the HIV and general population. These predictive models have been employed to identify persons at greater risk of CKD (diagnosis or prognosis) and found to be helpful in clinical decision-making as well as public health interventions to mitigate against CKD 12 13. These models use biological markers (urinary protein and albumin, serum creatinine and cystine C, uric acid) along with other traditional risk factors for CKD (age, sex, blood pressure readings and diabetes mellitus) in their formulation.…”