Orly Tonkikh1, Efrat Shadmi2,3, Natalie Flaks-Manov3, Moshe Hoshen3, Ran D Balicer3,4, Anna Zisberg2. 1. The Cheryl Spencer Department of Nursing, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel. orly.tonkikh@gmail.com. 2. The Cheryl Spencer Department of Nursing, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel. 3. Clalit Research Institute, Chief Physician's Office, Clalit Health Services, Tel-Aviv, Israel. 4. Epidemiology Department, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Abstract
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.
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.
Authors: Oanh Kieu Nguyen; Colin Washington; Christopher R Clark; Michael E Miller; Vivek A Patel; Ethan A Halm; Anil N Makam Journal: J Gen Intern Med Date: 2021-01-14 Impact factor: 6.473
Authors: Pedro L Valenzuela; Javier Ortiz-Alonso; Natalia Bustamante-Ara; María T Vidán; Gabriel Rodríguez-Romo; Jennifer Mayordomo-Cava; Marianna Javier-González; Mercedes Hidalgo-Gamarra; Myriel López-Tatis; Maria Isabel Valadés-Malagón; Alejandro Santos-Lozano; José Antonio Serra-Rexach; Alejandro Lucia Journal: J Clin Med Date: 2020-03-14 Impact factor: 4.241