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Objective: Despite guidelines recommending less stringent glycemic goals for older adults with type 2 diabetes, overtreatment is prevalent. Pragmatic approaches for prioritizing patients for optimal prescribing are lacking. We describe glycemic control and medication patterns for older adults with type 2 diabetes in a contemporary cohort, exploring variability by frailty status. Research Design and Methods: This was a cross-sectional observational study based on electronic health record (EHR) data, within an accountable care organization (ACO) affiliated with an academic medical center/health system. Participants were ACO-enrolled adults with type 2 diabetes who were ≥65 years of age as of 1 November 2020. Frailty status was determined by an automated EHR-based frailty index (eFI). Diabetes management was described by the most recent A1C in the past 2 years and use of higher-risk medications (insulin and/or sulfonylurea). Results: Among 16,973 older adults with type 2 diabetes (mean age 75.2 years, 9,154 women [53.9%], 77.8% White), 9,134 (53.8%) and 6,218 (36.6%) were classified as pre-frail (0.10 < eFI ≤0.21) or frail (eFI >0.21), respectively. The median A1C level was 6.7% (50 mmol/mol) with an interquartile range of 6.2-7.5%, and 74.1 and 38.3% of patients had an A1C <7.5% (58 mmol/mol) and <6.5% (48 mmol/mol), respectively. Frailty status was not associated with level of glycemic control (P = 0.08). A majority of frail patients had an A1C <7.5% (58 mmol/mol) (n = 4,544, 73.1%), and among these patients, 1,755 (38.6%) were taking insulin and/or a sulfonylurea. Conclusion: Treatment with insulin and/or a sulfonylurea to an A1C levels <7.5% is common in frail older adults. Tools such as the eFI may offer a scalable approach to targeting optimal prescribing interventions.
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BACKGROUND: Frailty is associated with numerous post-operative adverse outcomes in older adults. Current pre-operative frailty screening tools require additional data collection or objective assessments, adding expense and limiting large-scale implementation. OBJECTIVE: To evaluate the association of an automated measure of frailty integrated within the Electronic Health Record (EHR) with post-operative outcomes for nonemergency surgeries. DESIGN: Retrospective cohort study. SETTING: Academic Medical Center. PARTICIPANTS: Patients 65 years or older that underwent nonemergency surgery with an inpatient stay 24 hours or more between October 8th, 2017 and June 1st, 2019. EXPOSURES: Frailty as measured by a 54-item electronic frailty index (eFI). OUTCOMES AND MEASUREMENTS: Inpatient length of stay, requirements for post-acute care, 30-day readmission, and 6-month all-cause mortality. RESULTS: Of 4,831 unique patients (2,281 females (47.3%); mean (SD) age, 73.2 (5.9) years), 4,143 (85.7%) had sufficient EHR data to calculate the eFI, with 15.1% categorized as frail (eFI > 0.21) and 50.9% pre-frail (0.10 < eFI ≤ 0.21). For all outcomes, there was a generally a gradation of risk with higher eFI scores. For example, adjusting for age, sex, race/ethnicity, and American Society of Anesthesiologists class, and accounting for variability by service line, patients identified as frail based on the eFI, compared to fit patients, had greater needs for post-acute care (odds ratio (OR) = 1.68; 95% confidence interval (CI) = 1.36-2.08), higher rates of 30-day readmission (hazard ratio (HR) = 2.46; 95%CI = 1.72-3.52) and higher all-cause mortality (HR = 2.86; 95%CI = 1.84-4.44) over 6 months' follow-up. CONCLUSIONS: The eFI, an automated digital marker for frailty integrated within the EHR, can facilitate pre-operative frailty screening at scale.
