Objectives: Estimated glomerular filtration rate (eGFR) and albuminuria, the current standard-of-care tests that predict risk of kidney function decline in early-stage diabetic kidney disease (DKD), are only modestly useful. We evaluated the decision-making impact of an artificial intelligence-enabled prognostic test, KidneyIntelX, in the management of DKD by primary care physicians (PCPs).

Study Design: This was a prospective web-based survey administered among PCPs in the United States.

Methods: We used conjoint analysis with multivariable logit models to estimate PCP preferences. The survey included hypothetical patient profiles with 6 attributes: albuminuria, eGFR, age, blood pressure (BP), hemoglobin A1c (HbA1c), and KidneyIntelX result. Each PCP viewed 8 patient profiles randomly selected from 42 unique profiles having 1 level from each attribute. For each patient, PCPs were asked to indicate whether they would prescribe a sodium-glucose cotransporter-2 (SGLT2) inhibitor, increase angiotensin receptor blocker (ARB) dose, and/or refer to a nephrologist.

Results: A total of 401 PCPs completed the survey (response rate, 8.8%). The relative importance of the top 2 attributes for each decision were HbA1c (52%) and KidneyIntelX result (23%) for prescribing SGLT2 inhibitors, BP (62%) and KidneyIntelX result (13%) for increasing ARB dose, and eGFR (42%) and KidneyIntelX result (27%) for nephrologist referral. A high-risk KidneyIntelX result was associated with significantly higher odds of PCPs prescribing SGLT2 inhibitors (odds ratio [OR], 1.64; 95% CI, 1.29-2.08), increasing ARB dose (OR, 1.49; 95% CI, 1.17-1.89), and referring to a nephrologist (OR, 2.47; 95% CI, 1.99-3.08) compared with no test.

Conclusions: The KidneyIntelX test had greater relative importance than albuminuria and eGFR to PCPs in making treatment decisions and was second only to eGFR for nephrologist referrals. Because of its significant impact on decision-making, KidneyIntelX has high clinical utility in DKD management.

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Source
http://dx.doi.org/10.37765/ajmc.2022.89207DOI Listing

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