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To describe diagnostic categories and comorbidities associated with increased risk of readmission within 28 days among older adults.
Retrospective observational study of all hospital admissions following ED attendance by patients aged ≥ 60 years between July 2020 and June 2023. Index and subsequent 28-day readmission were identified using ED data and hospital discharge records. ED diagnosis, Australian Refined Diagnosis-Related Group (AR-DRG) discharge codes, and ICD-10-AM comorbidities were extracted. Multivariate logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations with 28-day readmission. The study and findings have been reported against the STROBE-RECORD guideline.
Of the 28,730 initial patient visits, 7.9% re-presented within 28 days. The most common ED diagnoses at initial and readmission were chest pain (5.4% vs. 4.6%), falls (5.2% vs. 4.1%), dyspnoea (3.5% vs. 3.1%), abdominal pain (3.1% vs. 3.3%) and cerebrovascular accident (1.7% vs. 1.7%). The most frequent AR-DRGs were respiratory infections/inflammations, kidney and urinary signs/symptoms, and other digestive system disorders. Key ICD-10-AM codes associated with a higher likelihood of readmission within 28 days were obstructive/reflux uropathy (OR 2.66, 95% CI 1.78–3.96), urinary retention (OR 1.84, 95% CI 1.38–2.46), chronic ischaemic heart disease (OR 1.57, 95% CI 1.10–2.25), delirium (OR 1.35, 95% CI 1.07–1.71) and disorders of fluid, electrolyte, and acid–base balance (OR 1.29, 95% CI 1.09–1.54).
Nearly 8% of older adults are readmitted within 28 days. Our described approach offers a potential framework to identify at-risk groups and intervene to reduce avoidable representations and/or admissions.
The results reported here create the opportunity for clinicians to identify areas for improvement in clinical practice, care coordination, and service delivery. Our approach and methodology can be replicated in other health services.
No patient or public contribution.
To develop and validate a model to predict cognitive decline within 12 months for home care clients without a diagnosis of dementia.
We included all adults aged ≥ 18 years who had at least two interRAI Home Care assessments within 12 months, no diagnosis of dementia and a baseline Cognitive Performance Scale score ≤ 1. The sample was randomly split into a derivation cohort (75%) and a validation cohort (25%). Significant cognitive decline was defined as an increase (deterioration) in Cognitive Performance Scale scores from ‘0’ or ‘1’ at baseline to a score of ≥ 2 at the follow-up assessment.
Using the derivation cohort, a multivariable logistic regression model was used to predict cognitive decline within 12 months. Covariates included demographics, disease diagnoses, sensory and communication impairments, health conditions, physical and social functioning, service utilisation, informal caregiver status and eight interRAI-derived health index scales. The predicted probability of cognitive decline was calculated for each person in the validation cohort. The c-statistic was used to assess the model's discriminative ability. This study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guidelines.
A total of 6796 individuals (median age: 82; female: 60.4%) were split into a derivation cohort (n = 5098) and a validation cohort (n = 1698). Logistic regression models using the derivation cohort resulted in a c-statistic of 0.70 (95% CI 0.70, 0.73). The final regression model (including 21 main effects and 8 significant interaction terms) was applied to the validation cohort, resulting in a c-statistic of 0.69 (95% CI 0.66, 0.72).
interRAI data can be used to develop a model for identifying individuals at risk of cognitive decline. Identifying this group enables proactive clinical interventions and care planning, potentially improving their outcomes. While these results are promising, the model's moderate discriminative ability highlights opportunities for improvement.
To identify the reasons and/or risk factors for hospital admission and/or emergency department attendance for older (≥60 years) residents of long-term care facilities.
Older adults' use of acute services is associated with significant financial and social costs. A global understanding of the reasons for the use of acute services may allow for early identification and intervention, avoid clinical deterioration, reduce the demand for health services and improve quality of life.
Systematic review registered in PROSPERO (CRD42022326964) and reported following PRISMA guidelines.
The search strategy was developed in consultation with an academic librarian. The strategy used MeSH terms and relevant keywords. Articles published since 2017 in English were eligible for inclusion. CINAHL, MEDLINE, Scopus and Web of Science Core Collection were searched (11/08/22). Title, abstract, and full texts were screened against the inclusion/exclusion criteria; data extraction was performed two blinded reviewers. Quality of evidence was assessed using the NewCastle Ottawa Scale (NOS).
Thirty-nine articles were eligible and included in this review; included research was assessed as high-quality with a low risk of bias. Hospital admission was reported as most likely to occur during the first year of residence in long-term care. Respiratory and cardiovascular diagnoses were frequently associated with acute services use. Frailty, hypotensive medications, falls and inadequate nutrition were associated with unplanned service use.
Modifiable risks have been identified that may act as a trigger for assessment and be amenable to early intervention. Coordinated intervention may have significant individual, social and economic benefits.
This review has identified several modifiable reasons for acute service use by older adults. Early and coordinated intervention may reduce the risk of hospital admission and/or emergency department.
This systematic review was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology.
No patient or public contribution.
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