Predicting readmission and death after hospital discharge: a comparison of conventional frailty measurement with an electronic health record-based score.
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ABSTRACT: Background
frailty measurement may identify patients at risk of decline after hospital discharge, but many measures require specialist review and/or additional testing.Objective
to compare validated frailty tools with routine electronic health record (EHR) data at hospital discharge, for associations with readmission or death.Design
observational cohort study.Setting
hospital ward.Subjects
consented cardiology inpatients ≥70 years old within 24 hours of discharge.Methods
patients underwent Fried, Short Physical Performance Battery (SPPB), PRISMA-7 and Clinical Frailty Scale (CFS) assessments. An EHR risk score was derived from the proportion of 31 possible frailty markers present. Electronic follow-up was completed for a primary outcome of 90-day readmission or death. Secondary outcomes were mortality and days alive at home ('home time') at 12 months.Results
in total, 186 patients were included (79 ± 6 years old, 64% males). The primary outcome occurred in 55 (30%) patients. Fried (hazard ratio [HR] 1.47 per standard deviation [SD] increase, 95% confidence interval [CI] 1.18-1.81, P < 0.001), CFS (HR 1.24 per SD increase, 95% CI 1.01-1.51, P = 0.04) and EHR risk scores (HR 1.35 per SD increase, 95% CI 1.02-1.78, P = 0.04) were independently associated with the primary outcome after adjustment for age, sex and co-morbidity, but the SPPB and PRISMA-7 were not. The EHR risk score was independently associated with mortality and home time at 12 months.Conclusions
frailty measurement at hospital discharge identifies patients at risk of poorer outcomes. An EHR-based risk score appeared equivalent to validated frailty tools and may be automated to screen patients at scale, but this requires further validation.
SUBMITTER: Tew YY
PROVIDER: S-EPMC8437069 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric health-care costs. The symptoms or characteristics of illness course necessary to develop reliable predictors are not available in coded billing data, but may be present in narrative electronic health record (EHR) discharge summaries. We identified a cohort of individuals admitted to a psychiatric inpatient unit between 1994 and 2012 with a principal diagnosis of major depressive disorder, and extracted inpatient psychiatric discharge narrative notes. Using these data, we trained a 75-topic Latent Dirichlet Allocation (LDA) model, a form of natural language processing, which identifies groups of words associated with topics discussed in a document collection. The cohort was randomly split to derive a training (70%) and testing (30%) data set, and we trained separate support vector machine models for baseline clinical features alone, baseline features plus common individual words and the above plus topics identified from the 75-topic LDA model. Of 4687 patients with inpatient discharge summaries, 470 were readmitted within 30 days. The 75-topic LDA model included topics linked to psychiatric symptoms (suicide, severe depression, anxiety, trauma, eating/weight and panic) and major depressive disorder comorbidities (infection, postpartum, brain tumor, diarrhea and pulmonary disease). By including LDA topics, prediction of readmission, as measured by area under receiver-operating characteristic curves in the testing data set, was improved from baseline (area under the curve 0.618) to baseline+1000 words (0.682) to baseline+75 topics (0.784). Inclusion of topics derived from narrative notes allows more accurate discrimination of individuals at high risk for psychiatric readmission in this cohort. Topic modeling and related approaches offer the potential to improve prediction using EHRs, if generalizability can be established in other clinical cohorts.
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Project description:Frailty, a clinical syndrome of decreased physiologic reserve and dysregulation in multiple physiologic systems, is associated with increased risk for adverse outcomes.The aim of this retrospective, cross-sectional, correlational study was to characterize frailty in older adults admitted to a tertiary-care hospital using a biopsychosocial frailty assessment and to determine associations between frailty and time to in-hospital mortality and 30-day rehospitalization.The sample included 278 patients ?55 years old admitted to medicine units. Frailty was determined using clinical data from the electronic health record (EHR) for symptoms, syndromes, and conditions and laboratory data for four serum biomarkers. A frailty risk score (FRS) was created from 16 risk factors, and relationships between the FRS and outcomes were examined.The mean age of the sample was 70.2 years and mean FRS was 9.4 ( SD, 2.2). Increased FRS was significantly associated with increased risk of death (hazard ratio = 1.77-2.27 for 3 days ? length of stay (LOS) ?7 days), but depended upon LOS ( p < .001). Frailty was marginally associated with rehospitalization for those who did not die in hospital (adjusted odds ratio = 1.18, p = .086, area under the curve [AUC] = 0.66, 95% confidence interval for AUC = [0.57, 0.76]).Clinical data in the EHR can be used for frailty assessment. Informatics may facilitate data aggregation and decision support. Because frailty is potentially preventable and treatable, early detection is crucial to delivery of tailored interventions and optimal patient outcomes.
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Project description:BackgroundWe aimed to assess the utility of the combination of the mini-mental state examination (MMSE) + clock drawing test (CDT) and the Fried phenotype for predicting non-elective hospital readmission or death within 6 months in elderly inpatients with cardiovascular disease (CVD).MethodsA single-center prospective cohort was conducted from September 2018 to February 2019. Inpatients ≥65 years old were recruited. Predictive validity was tested using a Cox proportional hazards regression model analysis, and the discriminative ability was evaluated by the receiver operating characteristic (ROC) curve.ResultsA total of 542 patients were included. Overall, 12% (64/542) screened positive for cognitive impairment, 16% (86/542) were physically frail and 8% (44/542) had cognitive impairment combined with physical frailty, showing an older age (P < 0.001) and a lower education level (P < 0.001) than physically frail patients. A total of 113 patients (20.9%) died or were readmitted at 6 months. Frail participants with a normal (hazard ratio [HR]: 1.73, 95% confidence interval [CI]: 1.06-2.82, P = 0.028) or impaired cognition (HR: 2.50, 95% CI: 1.27-4.91, P = 0.008) had a higher risk of non-elective hospital readmission or death than robust patients after adjusting for the age, sex, education level, marital status, the presence of diabetes mellitus, heart failure, and history of stroke. The area under the ROC curve (AUC) showed that the discriminative ability in relation to 6 months readmission and death for the MMSE + CDT + Fried phenotype was 0.65 (95% CI: 0.60-0.71), and the AUC for men was 0.71 (95% CI: 0.63-0.78), while that for women was 0.60 (95% CI: 0.51-0.69).ConclusionsAccounting for cognitive impairment in the frailty phenotype may allow for the better prediction of non-elective hospital readmission or death in elderly inpatients with CVD in the short term.Trial registrationChiCTR1800017204; date of registration: 07/18/2018.
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