Key result
A functional, clinical, and cognitive model identifies high-risk older hospitalized patients with ~0.77 AUC.
Why the study?
The study aimed to develop and validate a high-risk predictive model to identify common adverse events in older hospitalized patients, including early readmission, long hospital stays, or in-hospital death.
Cohort (n=508)
No
Effect estimate: AUC 0.77 (95% CI 0.71-0.83)
A predictive model incorporating functional status, comorbidities, and medication burden showed acceptable discrimination for predicting adverse hospital outcomes in older adults.
May inform bedside risk assessment in older inpatients; leaves open external validation and outcome trials.
OBJECTIVE: To develop and validate a high-risk predictive model that identifies, at least, one common adverse event in older population: early readmission (up to 30 days after discharge), long hospital stays (10 days or more) or in-hospital deaths. METHODS: This was a retrospective cohort study including patients aged 60 years or older (n=340) admitted at a 630-beds tertiary hospital, located in the city of São Paulo, Brazil. A predictive model of high-risk indication was developed by analyzing logistical regression models. This model prognostic capacity was assessed by measuring accuracy, sensitivity, specificity, and positive and negative predictive values. Areas under the receiver operating characteristic curve with 95% confidence intervals were also obtained to assess the discriminatory power of the model. Internal validation of the prognostic model was performed in a separate sample (n=168). RESULTS: Statistically significant predictors were identified, such as current Barthel Index, number of medications in use, presence of diabetes mellitus, difficulty chewing or swallowing, extensive surgery, and dementia. The study observed discrimination model acceptance in the construction sample 0.77 (95% confidence interval: 0.71-0.83) and good calibration. The characteristics of the validation samples were similar, and the receiver operating characteristic curve area was 0.687 (95% confidence interval: 0.598-0.776). We could assess an older patient's adverse health events during hospitalization after admission. CONCLUSION: A predictive model with acceptable discrimination was obtained, with satisfactory results for early readmission (30 days), long hospital stays (10 days), or in-hospital death.
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Costa et al. (2022) conducted a cohort in Hospitalized older adults (n=508). Predictive risk model was evaluated on Discrimination of the predictive model for early readmission, long hospital stay, or in-hospital death (construction sample) (AUC 0.77, 95% CI 0.71-0.83). A predictive model incorporating functional, clinical, and cognitive factors demonstrated acceptable discrimination (AUC 0.77) for identifying older hospitalized patients at high risk of adverse outcomes.
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