A simple index predicting 30-day readmissions in acutely hospitalized patients.
inpatient care
quality of care
readmissions
Journal
Journal of evaluation in clinical practice
ISSN: 1365-2753
Titre abrégé: J Eval Clin Pract
Pays: England
ID NLM: 9609066
Informations de publication
Date de publication:
Aug 2021
Aug 2021
Historique:
revised:
18
10
2020
received:
12
08
2020
accepted:
23
10
2020
pubmed:
4
12
2020
medline:
12
8
2021
entrez:
3
12
2020
Statut:
ppublish
Résumé
There are various models attempting to predict 30-day readmissions of acutely admitted internal medicine patients. However, it is uncertain how to create a parsimonious index that has equivalent predictive ability and can be extrapolated to other settings. We developed a regression equation to predict 30-day readmissions from all acute hospitalizations in internal medicine departments in a regional hospital in 2015-2016 and validated the model in 2019. The independent (predictor) variables were age, past hospitalizations, admission laboratory test results, length of stay in hospital and discharge diagnoses. We compared the predictive value of a logistic regression model and index that included discharge diagnoses and admission laboratory test results with one that included only age, past hospitalizations, and hospital length of stay. Readmission rates were associated with age, time since last hospitalization, number of previous hospitalizations, and length of stay, as well as with a diagnosis of chronic obstructive lung disease and congestive heart failure and several laboratory data. Logistic regressions of the independent variables for 30-day readmission rates were similar in 2015-2016 and 2019. An index was derived from number of previous admissions to hospitals, time since last admission, age, and length of stay. In 2019, for every unit of the index, the odds of readmission increased by 1.33 (95% CI- 1.30-1.37), and ranged from 2.1% to 37.1%. Addition of discharge diagnoses and laboratory variables did not significantly improve the risk differentiation of the index. The c-statistic for the final parsimonious model was 0.704. An index derived from the number of previous hospital admissions, days since last admission, age, and length of stay in days differentiated between the risks of readmission within 30 days without the need for discharge diagnosis and laboratory variables.
Sections du résumé
BACKGROUND
BACKGROUND
There are various models attempting to predict 30-day readmissions of acutely admitted internal medicine patients. However, it is uncertain how to create a parsimonious index that has equivalent predictive ability and can be extrapolated to other settings.
METHODS
METHODS
We developed a regression equation to predict 30-day readmissions from all acute hospitalizations in internal medicine departments in a regional hospital in 2015-2016 and validated the model in 2019. The independent (predictor) variables were age, past hospitalizations, admission laboratory test results, length of stay in hospital and discharge diagnoses. We compared the predictive value of a logistic regression model and index that included discharge diagnoses and admission laboratory test results with one that included only age, past hospitalizations, and hospital length of stay.
RESULTS
RESULTS
Readmission rates were associated with age, time since last hospitalization, number of previous hospitalizations, and length of stay, as well as with a diagnosis of chronic obstructive lung disease and congestive heart failure and several laboratory data. Logistic regressions of the independent variables for 30-day readmission rates were similar in 2015-2016 and 2019. An index was derived from number of previous admissions to hospitals, time since last admission, age, and length of stay. In 2019, for every unit of the index, the odds of readmission increased by 1.33 (95% CI- 1.30-1.37), and ranged from 2.1% to 37.1%. Addition of discharge diagnoses and laboratory variables did not significantly improve the risk differentiation of the index. The c-statistic for the final parsimonious model was 0.704.
CONCLUSIONS
CONCLUSIONS
An index derived from the number of previous hospital admissions, days since last admission, age, and length of stay in days differentiated between the risks of readmission within 30 days without the need for discharge diagnosis and laboratory variables.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
942-948Informations de copyright
© 2020 John Wiley & Sons Ltd.
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