Abstract: FR-PO1104
Integrating Clinical Decision Support Tools in CKD Management
Session Information
- CKD: Epidemiology, Risk Factors, and Prevention - 2
October 25, 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
- Thiessen Philbrook, Heather, Johns Hopkins Medicine, Baltimore, Maryland, United States
- Patel, Dipal M., Johns Hopkins Medicine, Baltimore, Maryland, United States
- Bitzel, Jack, Johns Hopkins Medicine, Baltimore, Maryland, United States
- Menez, Steven, Johns Hopkins Medicine, Baltimore, Maryland, United States
- Cervantes, C. Elena, Johns Hopkins Medicine, Baltimore, Maryland, United States
- Jaar, Bernard G., Johns Hopkins Medicine, Baltimore, Maryland, United States
- Grams, Morgan, NYU Langone Health, New York, New York, United States
- Parikh, Chirag R., Johns Hopkins Medicine, Baltimore, Maryland, United States
Background
Integrating clinical decision support (CDS) tools into nephrology outpatient clinics provides an opportunity to improve patient care by leveraging existing data in electronic medical records (EMR).
Methods
In January 2022, three CDS tools were deployed for use in Johns Hopkins nephrology clinics. A viewer within the EMR system displays Kidney Failure Risk Equation (KFRE) scores, risk of cardiovascular disease and longitudinal graphical visualizations including prescription status for the top 5 KDIGO-recommended proteinuria-reducing therapies, and laboratory measurements (Figure). Nephrologists can also view the 2-year KFRE on the EMR dashboard and utilize a dotphrase in clinic notes. We examined trends in clinical care practices before (2020-2021) and after (2022-2023) implementation of these CDS tools for patients with advanced kidney disease, and within a subgroup of diabetic patients.
Results
Practice patterns improved with a reduction in the percentage of patients without recent albuminuria measurements (Table). Prescription rates of evidence-based therapies to reduce proteinuria and protect kidney function increased over time.
Conclusion
CDS tools can highlight guideline-based recommendations for clinical care and provide an opportunity to evaluate their impact in real-world clinical settings. More analyses are needed to define mechanisms by which CDS tools impact clinical practices.
| All Patients | Patients with Diabetes | ||||
| Before (n=2037) | After (n=2223) | Before (n=1084) | After (n=1126) | ||
| Patient Care | # nephrology encounters, median (25th pct, 75th pct) | 3 (2,5) | 3 (2,5) | 3 (2,5) | 3 (2,5) |
| # eGFR measurements, median (25th pct, 75th pct) | 7 (4,13) | 8 (4,13) | 7 (4,13) | 8 (5,13) | |
| Missing urine albumin to creatinine, urine protein to creatinine, or urine protein dipstick measurement, % | 12% | 10% | 11% | 9% | |
| Prescription | Angiotensin-converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB), % | 66% | 70% | 73% | 76% |
| Glucagon-like peptide 1 receptor agonist (GLP1), % | 10% | 15% | 19% | 27% | |
| Sodium/glucose cotransporter-2 inhibitors (SGLT2), % | 15% | 30% | 21% | 40% | |
| Mineralocorticoid receptor antagonists (MRS), % | 11% | 14% | 13% | 16% | |
Funding
- Clinical Revenue Support