z-logo
Premium
Functional status before and during acute hospitalization and readmission risk identification
Author(s) -
Tonkikh Orly,
Shadmi Efrat,
FlaksManov Natalie,
Hoshen Moshe,
Balicer Ran D.,
Zisberg Anna
Publication year - 2016
Publication title -
journal of hospital medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.128
H-Index - 65
eISSN - 1553-5606
pISSN - 1553-5592
DOI - 10.1002/jhm.2595
Subject(s) - medicine , logistic regression , activities of daily living , confidence interval , odds ratio , hospital medicine , emergency medicine , risk assessment , prospective cohort study , medical record , framingham risk score , percentile , physical therapy , disease , statistics , computer security , mathematics , computer science
BACKGROUND Recent efforts to prevent readmissions are increasingly focusing on early identification of high‐risk patients. OBJECTIVE To test whether information on functioning during hospitalization contributes to the ability to accurately identify older adults at high risk of readmission beyond their baseline risk. DESIGN Prospective cohort study. SETTING Internal medicine wards at 2 medical centers. PATIENTS Five hundred fifty‐nine community‐dwelling older adults (aged ≥70 years) discharged to their homes. MEASUREMENTS Data on unplanned 30‐day readmissions were retrieved from electronic health records. Data on at‐admission activities of daily living (ADL) and in‐hospital ADL decline were collected using validated questionnaires. Multivariate logistic regression was used to model the association between functioning and readmission controlling for known risk factors. RESULTS Higher in‐hospital ADL decline was significantly associated with readmission (odds ratio for each 10‐point decrease in ADL = 1.32, 95% confidence interval = 1.02‐1.72) but did not contribute to the overall discrimination of the model, as compared with the at‐admission data (C statistic = 0.81 for each model). Identifying high‐risk (10th highest percentile) patients by the at‐admission model did not detect 7/55 (12.7%) of patients who would have been categorized as high risk if risk identification was postponed to the discharge date and included data on in‐hospital ADL decline. CONCLUSIONS The study highlights the ability to identify patients at high risk for readmission already early in the index hospitalization using data on functioning, nutrition, chronic morbidity, and prior hospitalizations. Nonetheless, at‐discharge functional assessment can detect additional patients whose readmission risk changes during the index hospitalization. Journal of Hospital Medicine 2016;11:636–641. © 2016 Society of Hospital Medicine

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here