Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old
Einstein (Säo Paulo)
; 20: eAO8012, 2022. tab, graf
Article
in English
|
LILACS-Express
| LILACS
| ID: biblio-1384783
Responsible library:
BR1.1
ABSTRACT
ABSTRACT 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.
Full text:
Available
Collection:
International databases
Database:
LILACS
Type of study:
Observational study
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Prognostic study
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Risk factors
Language:
English
Journal:
Einstein (Säo Paulo)
Journal subject:
Medicine
Year:
2022
Document type:
Article
Affiliation country:
Brazil
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United States
Institution/Affiliation country:
Faculdade Israelita de Ciências da Saúde Albert Einstein/BR
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Hospital Israelita Albert Einstein/BR
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Universidade Federal de São Paulo/BR
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University of Pittsburgh/US