External validation of the Hospital Frailty Risk Score and comparison with the Hospital-patient One-year Mortality Risk Score to predict outcomes in elderly hospitalised patients: a retrospective cohort study

Abstract

ObjectiveFrailty is an important prognostic factor in hospitalised patients but typically requires face-to-face assessment by trained observers to detect. Thus, frail patients are not readily apparent from a systems perspective for those interested in implementing quality improvement measures to optimise their outcomes. This study was designed to externally validate and compare two recently described tools using administrative data as potential markers for frailty: the Hospital Frailty Risk Score (HFRS) and the Hospital-patient One-year Mortality Risk (HOMR) Score.DesignRetrospective cohort study.SettingOntario, Canada.ParticipantsAll patients over 75 with at least one urgent non-psychiatric hospitalisation between 2004 and 2010.Main outcome measuresProlonged hospital length of stay (>10 days), 30-day mortality after admission and 30-day postdischarge rates of urgent readmission or emergency department (ED) visits.ResultsIn 452 785 patients (25.9% with intermediate or high-risk HFRS), increased HFRS was associated with higher Charlson scores, older age and decreased likelihood of baseline independence. Patients with high or intermediate HFRS had significantly increased risks of prolonged hospitalisation (70.0% (OR 8.64, 95% CI 8.30 to 8.99) or 49.7% (OR 3.66, 95% CI 3.60 to 3.71) vs 21.3% in low-risk HFRS group) and 30-day mortality (15.5% (OR 1.27, 95% CI 1.20 to 1.33) or 16.8% (OR 1.39, 95% CI 1.36 to 1.41) vs 12.7% in low-risk), but decreased risks of 30-day readmission (10.0% (OR 0.74, 95% CI 0.69 to 0.79) and 11.2% (OR 0.84, 95% CI 0.82 to 0.86) vs 13.1%) or ED visit (7.3% (OR 0.41, 95% CI 0.38 to 0.45) and 11.1% (OR 0.66, 95% CI 0.38 to 0.45) vs 16.0%). Although only loosely associated (Pearson correlation coefficient 0.265, p<0.0001), both the HFRS and HOMR Score were independently associated with each outcome—HFRS was more strongly associated with prolonged length of stay (C-statistic 0.71) and HOMR Score was more strongly associated with 30-day mortality (C-statistic 0.71). Both poorly predicted 30-day readmissions (C-statistics 0.52 for HFRS and 0.54 for HOMR Score).ConclusionsThe HFRS best identified hospitalised older patients at higher risk of prolonged length of stay and the HOMR score better predicted 30-day mortality. However, neither score was suitable for predicting risk of readmission or ED visit in the 30 days after discharge. Thus, a single score is inadequate to prognosticate for all outcomes associated with frailty.

The hospital frailty risk score in patients with heart failure is strongly associated with outcomes but less so with pharmacotherapy

“…Secondly, the addition of the HFRS to standard risk prediction equations [2,3] for postdischarge outcomes provided little additional information in our cohort. This echoes an earlier report [6] that the HFRS only poorly predicted outcomes in the first 30 days after discharge in 452 785 hospitalized elderly patients in another Canadian province. Like that earlier study, we also found that the HFRS was a better predictor of prolonged hospital stay in this cohort than postdischarge outcomes.…”

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“…The hospital frailty risk score (range 0-50) is a comprehensive and validated measure of a patient’s function and comorbidity that reflects global illness severity and identifies a group of patients who are at greater risk of adverse outcomes, including hospital admission and 30 day mortality 42. We categorised hospital frailty scores into four groups based on the distribution of scores within our cohort: 0, 0.1-8.9, at least 9, and not admitted to hospital 4246. We did not account for clustering by physician or facility because most people receive end-of-life care from several physicians in multiple care settings.…”

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“…(ICD-10) coding system [13], was developed for frailty risk stratification among older hospitalized patients and has been validated for its ability to predict adverse outcomes in various settings [14,15].…”