Key result
An electronic health record frailty index independently predicted all-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (all p < .001).
Why the study?
Electronic health record-based frailty index approaches have been incorporated into British guidelines, but applications in the United States have been limited.
Does an EHR-based frailty index predict mortality and healthcare utilization in Medicare Accountable Care Organization patients?
Cohort (n=12,798)
Does an EHR-based frailty index predict mortality and healthcare utilization in Medicare Accountable Care Organization patients?
p-value: p=<.001
An EHR-based frailty index is feasible to construct in a managed care population and independently predicts mortality, hospitalizations, and falls.
eFI may support frailty identification in Medicare ACOs; leaves open whether screening improves outcomes in trials.
BACKGROUND: The accumulation of deficits model for frailty has been used to develop an electronic health record (EHR) frailty index (eFI) that has been incorporated into British guidelines for frailty management. However, there have been limited applications of EHR-based approaches in the United States. METHODS: We constructed an adapted eFI for patients in our Medicare Accountable Care Organization (ACO, N = 12,798) using encounter, diagnosis code, laboratory, medication, and Medicare Annual Wellness Visit (AWV) data from the EHR. We examined the association of the eFI with mortality, health care utilization, and injurious falls. RESULTS: The overall cohort was 55.7% female, 85.7% white, with a mean age of 74.9 (SD = 7.3) years. In the prior 2 years, 32.1% had AWV data. The eFI could be calculated for 9,013 (70.4%) ACO patients. Of these, 46.5% were classified as prefrail (0.10 < eFI ≤ 0.21) and 40.1% frail (eFI > 0.21). Accounting for age, comorbidity, and prior health care utilization, the eFI independently predicted all-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (all p < .001). Having at least one functional deficit captured from the AWV was independently associated with an increased risk of hospitalizations and injurious falls, controlling for other components of the eFI. CONCLUSIONS: Construction of an eFI from the EHR, within the context of a managed care population, is feasible and can help to identify vulnerable older adults. Future work is needed to integrate the eFI with claims-based approaches and test whether it can be used to effectively target interventions tailored to the health needs of frail patients.
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Pajewski et al. (2019) conducted a cohort in Frailty (n=12,798). Electronic health record frailty index (eFI) was evaluated on All-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (p=<.001). An electronic health record frailty index independently predicted all-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (all p < .001).
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