Optimizing guideline-directed medical therapy in chronic kidney disease and type 2 diabetes mellitus patients in primary care.

BACKGROUND

Despite KDIGO (Kidney Disease: Improving Global Outcomes) recommendations for renin-angiotensin-aldosterone system inhibitors (RAASi's) and sodium-glucose cotransporter-2 inhibitors (SGLT2i's) as first-line medications in chronic kidney disease and Type 2 diabetes mellitus patients, implementation gaps persist. We aimed to identify the reasons behind these gaps and develop strategies to address them.

METHODS

We conducted a retrospective study examining RAASi and SGLT2i prescription patterns among chronic kidney disease and Type 2 diabetes mellitus patients at an inner-city community hospital. Primary care providers were surveyed about barriers to prescribing these medications, after which we implemented an educational intervention and reassessed prescription rates.

RESULTS

We identified hyperkalemia and estimated glomerular filtration rate (eGFR) below 30 ml/min/1.73m² as major barriers to initiating RAASi's while hyperkalemia and acute kidney injury were the primary limiting factors for medication continuation. Insurance coverage and medication intolerance were the main obstacles to initiating and continuing SGLT2i's respectively. Prior hyperkalemia significantly reduced the odds ratio of RAASi prescription (P = 0.01) whereas urine albumin-creatinine ratio (UACR) screening significantly increased them (P = 0.03). UACR screening within the past year was associated with higher odds of SGLT2i prescription rates (P = 0.019) while insurance status showed no statistically significant impact (P = 0.317). Following educational intervention, annual UACR screening increased by 44%, while prescription rates for SGLT2i's and RAASi's increased by 95% and 4%, respectively. These increased rates were distributed proportionally across racial and ethnic groups.

CONCLUSIONS

We demonstrated that targeted educational interventions can improve evidence-based care delivery in a racially and ethnically equitable manner. Future research should explore artificial intelligence-based implementation strategies and conduct outcome-based studies.