Designing and validating a clinical decision support algorithm for diabetic nephroprotection in older patients.

Area of Science:

• Geriatric Medicine
• Nephrology
• Pharmacology

Background:

• Older adults with diabetic kidney disease (DKD) frequently receive suboptimal pharmacological treatment.
• Current clinical practice guidelines (CPGs) lack personalized care integration for this demographic.
• Clinical decision support (CDS) tools can enhance patient outcomes by combining evidence with personalized care.

Purpose of the Study:

• To design and validate a CDS algorithm for optimizing renin-angiotensin-aldosterone system inhibitor (RAASi) prescribing in older diabetic patients.
• To address the gap in personalized care within existing CPGs for DKD management.

Main Methods:

• Systematic reviews and meta-analyses identified number needed to treat (NNT) and time-to-benefit (TTB) values.
• Clinical scenarios for RAASi prescribing were developed.
• NNT and TTB values were matched with clinical cases, incorporating patient life expectancy and functional capacity.
• The CDS algorithm underwent validation using the Delphi technique with expert review.

Main Results:

• A comprehensive CDS algorithm was created, addressing 15 distinct prescribing scenarios.
• The algorithm generated 36 personalized and 9 general recommendations.
• Expert validation through a three-round Delphi study confirmed the algorithm's utility.

Conclusions:

• An evidence-informed CDS algorithm was successfully designed, integrating personalized care considerations often omitted in CPGs.
• The developed algorithm represents a significant step towards improving RAASi prescribing for older adults with DKD.
• Future clinical trials are planned to test the efficacy of this CDS algorithm in real-world settings.