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A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure Using a Large Administrative Claims
Dingwei Dai1, Paula J Alvarez2, Steven D Woods2
1Clinical Trial Services, Part of the CVS Health Family of Companies, Woonsocket, RI, USA.
Insights
A new predictive model accurately identifies patients with chronic kidney disease (CKD) stages 3 or 4 at high risk for kidney failure. Early detection can improve outcomes and reduce costs for this patient group.
Area of Science:
- Nephrology
- Health Informatics
- Predictive Analytics
Background:
- Chronic kidney disease (CKD) management requires identifying high-risk patients.
- CKD stages 3 and 4 represent a critical window for intervention.
- Administrative claims data can be leveraged for population health management.
Purpose of the Study:
- To develop and validate a predictive model for identifying patients with CKD stages 3-4 at high risk of progression to kidney failure.
- To utilize a large administrative claims database for model development and validation.
- To inform the creation of targeted CKD management programs.
Main Methods:
- A predictive model was developed using multivariate logistic regression on a large Aetna claims database.
- The study included 74,114 patients with CKD stages 3-4 over a 36-month period (12-month baseline, 24-month prediction).
- Model performance was assessed using AUROC, calibration, and gain/lift charts.
Main Results:
- 3.3% of identified patients progressed to kidney failure.
- Key predictors included CKD stage, hypertension, diabetes mellitus, and hyperkalemia.
- The model demonstrated good predictive accuracy (AUROC=0.844) and calibration, identifying 70.8% of progressive cases in the top two deciles.
Conclusions:
- A novel predictive model accurately identifies high-risk CKD patients from a national database.
- Early identification facilitates timely interventions, potentially improving patient outcomes.
- This model may lead to reduced healthcare expenditures for the at-risk CKD population.
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.
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