The e-Frail CH project: using clinical data to measure older inpatients’ frailty at admission and discharge (Preprint)

Abstract

Frailty is a widespread geriatric syndrome among older adults, including hospitalised older inpatients. Some countries use electronic frailty measurement tools to identify frailty at the primary care level, but this method has rarely been investigated during hospitalisation in acute care hospitals. An electronic frailty measurement instrument based on population-based hospital electronic health records (EHRs) could effectively detect frailty and anticipate frailty-related problems and complications. The early identification of older adults in great need of healthcare support could be a valuable public health strategy.

To explore a data-driven frailty measurement instrument for older adult inpatients using data routinely collected at hospital admission and discharge.

A retrospective electronic patient register study included inpatients aged ≥ 65 years old admitted to and discharged from a public hospital between 2015 and 2017. A dataset of 53,690 hospitalisations was used to customise this data-driven frailty measurement instrument inspired by the Edmonton Frailty Scale developed by Rolfson et al. A two-step hierarchical cluster procedure was applied to compute e-Frail-CH scores at hospital admission and discharge. Prevalence, central tendency, comparative and validation statistics were computed.

Mean patient age at admission was 78.4 years old (SD 7.9), with more women admitted (n = 28,018; 52.2%) than men (n = 25,672; 47.8%). Our two-step hierarchical clustering approach computed 46,743 lines for hospital admissions and 47,361 for discharges. Clustering solutions scored from 0.5 to 0.8 on a scale from 0 to 1. Patients considered frail comprised 42.0% (n = 19,643) of admissions and 48.2% (n = 22,845) of discharges. Within e-Frail-CH’s 0–12 range, a score ≥ 6 indicated frailty. We found a statistically significant mean e-Frail-CH score change between hospital admission (5.3; SD 2.6) and discharge (5.75; SD 2.7; P < .001). Sensitivity and specificity cut-point values were 0.82 and 0.88, respectively. The area under the ROC curve was 0.85. Comparing the e-Frail-CH instrument to the existing Functional Independence Measure (FIM) instrument, FIM scores indicating severe dependence equated to e-Frail-CH scores of ≥ 9, with a sensitivity and specificity of 0.97 and 0.88, respectively. The area under the ROC curve was 0.92. There was a strong negative association between e-Frail-CH scores at hospital discharge and FIM scores (rs = -0.844; P < .001).

An electronic frailty measurement instrument was constructed and validated using patient data routinely collected at hospital admission and discharge. The mean e-Frail-CH score was higher at discharge than at admission. The routine calculation of e-Frail-CH scores could help clinicians select interventions to prevent or mitigate frailty among discharged adults.

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