English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Numerous frailty tools and definitions have been described. Amongst hospitalised patients, the validity of face-to-face instruments may be confounded by acute illness. However, patient assessment after recovery at the point of hospital discharge, or recognition of electronic health record (EHR) frailty markers, may overcome this issuep. Methods In a consented, prospective observational cohort study, we recruited patients ≥70 years old within 24 hours of expected discharge from the cardiology ward of the Royal Infirmary of Edinburgh. Three established frailty instruments were tested: the Fried phenotype, Short Physical Performance Battery and nurse-administered Clinical Frailty Scale (CFS). An unweighted 32-item EHR score was generated using frailty markers (e.g. falls risk, continence, cognition) recorded within mandated admission documentation. Comorbidity was assessed by count of chronic health conditions. Outcomes were a 90-day composite of unplanned readmission or death and 12-month mortality. Adjusted Cox modelling determined the hazard ratio (HR) per standard deviation increase in each frailty score. Results 186 patients (mean age 79 ± 6 years, 64% male) were included, of whom 55 (30%) had a 90-day composite outcome, and 21 (11%) died within 12 months. All four frailty tools were moderately correlated with age and comorbidity (Pearson’s r 0.21 to 0.43, all p &amp;lt; 0.05). The Fried phenotype (HR 1.47, 95% CI 1.18–1.81), CFS (HR 1.24, 95% CI 1.01–1.51) and EHR score (HR 1.26, 95% CI 1.03–1.55) independently predicted 90-day readmission or death, after adjustment for age, sex and comorbidity. All frailty instruments were independent predictors of 12-month mortality, with age, sex and comorbidity losing predictive power (p &amp;gt; 0.05) once frailty was included in modelling. Conclusions At hospital discharge, the Fried phenotype and CFS added to age and comorbidity in risk prediction for future unplanned readmission or death. EHR frailty markers appeared comparable to face-to-face assessment. An automated trigger for high-risk patients using routine EHR data merits prospective evaluation.

29 Predicting Unplanned Readmission and Death After Hospital Discharge: How Do Frailty Tools Compare to Electronic Health Record Frailty Markers?