Kidney Week

Abstract: TH-PO1031

Prediction of Medication Therapy Problems in Patients with Moderate- to High-Risk CKD

Session Information

• CKD: Epidemiology, Risk Factors, and Prevention - 1
  October 24, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: CKD (Non-Dialysis)

• 2301 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

• Weltman, Melanie R., University of Pittsburgh School of Pharmacy, Pittsburgh, Pennsylvania, United States

Melanie R. Weltman, PharmD

• Alghwiri, Alaa A., University of Pittsburgh Division of Renal-Electrolyte, Pittsburgh, Pennsylvania, United States

Alaa A. Alghwiri, PhD

• Han, Zhuoheng, University of Pittsburgh Division of Renal-Electrolyte, Pittsburgh, Pennsylvania, United States

Zhuoheng Han

• Lavenburg, Linda-Marie Ustaris, University of Pittsburgh Division of Renal-Electrolyte, Pittsburgh, Pennsylvania, United States

Linda-Marie Ustaris Lavenburg, DO, MS

• Nolin, Thomas D., University of Pittsburgh School of Pharmacy, Pittsburgh, Pennsylvania, United States

Thomas D. Nolin, PharmD, PhD, FASN

• Yabes, Jonathan Guerrero, University of Pittsburgh Department of Medicine, Pittsburgh, Pennsylvania, United States

Jonathan Guerrero Yabes, PhD

• Chen, Yi-Fan, University of Pittsburgh Department of Medicine, Pittsburgh, Pennsylvania, United States

Yi-Fan Chen, MS, PhD

• Jhamb, Manisha, University of Pittsburgh Division of Renal-Electrolyte, Pittsburgh, Pennsylvania, United States

Manisha Jhamb, MD, MPH

Background

Patients with chronic kidney disease (CKD) are at risk of medication therapy problems (MTP) due to high comorbidity and medication burden. Using data from the Kidney Coordinated HeAlth Management Partnership (K-CHAMP) trial, we used machine learning to build a predictive model to identify MTP high-risk patients with CKD in the primary care setting.

Methods

We used baseline data from patients enrolled in the intervention arm of the K-CHAMP trial, completed May 2019 to July 2022, which tested a population health management strategy, including medication management, for improving CKD care. The dataset was divided into 80% training and 20% testing subsets. The area under the ROC curve (AUROC) was used to assess classification accuracy in distinguishing between patients with and without MTP. Eight candidate models were considered, and the top three performing models (Random Forest, Support Vector Machines, and Gradient Boosting), based on cross-validated AUROC on training data, underwent further refinement. The model with the highest AUROC in the testing set, while considering the bias/variance trade-off, was selected as the best-performing model.

Results

Among 730 patients who received medication review at baseline, 566 (77.5%) had at least 1 MTP. Key demographics were mean age 74 years, 55% females, 92% White, 64% with diabetes, and the mean number of medications was 5.8 at baseline. The Random Forest model had the best performance on the testing set with AUROC 0.72, sensitivity 0.80, and specificity 0.64. The five most influential variables, ranked in descending order of importance for predicting individuals with MTP, were diabetes status (yes/no), HbA1C level, UACR level, age, and number of comorbidities.

Conclusion

The Random Forest model provided the highest performance in predicting MTP for patients with moderate-to high-risk CKD. Future work will focus on developing a user-friendly online tool aimed at identifying patients who may benefit most from pharmacist-led medication management.

Funding

• NIDDK Support