OBJECTIVE: To determine if the results of a questionnaire mailed to older patients can help identify those patients at greatest risk of hospital admission. DESIGN: A longitudinal cohort study. SETTING: A prepaid managed care plan in the Denver metropolitan area. PARTICIPANTS: Of the 4414 eligible patients at least 81 years old, 3745 (84.8%) responded. MEASUREMENTS: We studied the predictive power of self-reported demographic, health status, medical history, health habits, functional status (including Katz' activities of daily living and OARS instrumental activities of daily living), and socioeconomic status data to identify those older adults at greatest risk of hospitalization within 4.5 months of completing the survey. We derived our predictive model on one-half the subjects and tested its validity it on the other half. RESULTS: Univariate analysis revealed 25 variables significantly associated with hospital admission. In a logistic regression model, four significant variables successfully stratified the patients by risk of admission. These four variables are: the presence of heart disease, the presence of diabetes, need for help preparing meals, and limited physical independence (requiring the help of a person or mechanical aid to get around). In addition, there was an antagonistic interaction between the presence of heart disease and limited physical independence. The model stratified patients from low risk (4.5% chance of admission) to high risk (39% chance of admission). As measured by the Hosmer-Lemeshow statistic and the area under a receiver-operator characteristic (ROC) curve, this model fit both the derivation and validation subjects well. CONCLUSIONS: A mailed questionnaire achieved a high response rate, and the information collected produced an effective model predictive of hospitalization in the short term. Four easily ascertained pieces of information identify those patients older than age 81 at increased risk.
OBJECTIVE: To determine if the results of a questionnaire mailed to older patients can help identify those patients at greatest risk of hospital admission. DESIGN: A longitudinal cohort study. SETTING: A prepaid managed care plan in the Denver metropolitan area. PARTICIPANTS: Of the 4414 eligible patients at least 81 years old, 3745 (84.8%) responded. MEASUREMENTS: We studied the predictive power of self-reported demographic, health status, medical history, health habits, functional status (including Katz' activities of daily living and OARS instrumental activities of daily living), and socioeconomic status data to identify those older adults at greatest risk of hospitalization within 4.5 months of completing the survey. We derived our predictive model on one-half the subjects and tested its validity it on the other half. RESULTS: Univariate analysis revealed 25 variables significantly associated with hospital admission. In a logistic regression model, four significant variables successfully stratified the patients by risk of admission. These four variables are: the presence of heart disease, the presence of diabetes, need for help preparing meals, and limited physical independence (requiring the help of a person or mechanical aid to get around). In addition, there was an antagonistic interaction between the presence of heart disease and limited physical independence. The model stratified patients from low risk (4.5% chance of admission) to high risk (39% chance of admission). As measured by the Hosmer-Lemeshow statistic and the area under a receiver-operator characteristic (ROC) curve, this model fit both the derivation and validation subjects well. CONCLUSIONS: A mailed questionnaire achieved a high response rate, and the information collected produced an effective model predictive of hospitalization in the short term. Four easily ascertained pieces of information identify those patients older than age 81 at increased risk.
Authors: Anthony V Perruccio; Elena Losina; Elizabeth A Wright; Jeffrey N Katz Journal: J Gerontol A Biol Sci Med Sci Date: 2012-08-09 Impact factor: 6.053
Authors: Michael A LaMantia; Timothy F Platts-Mills; Kevin Biese; Christine Khandelwal; Cory Forbach; Charles B Cairns; Jan Busby-Whitehead; John S Kizer Journal: Acad Emerg Med Date: 2010-03 Impact factor: 3.451
Authors: Jet H Klunder; Sofie L Panneman; Emma Wallace; Ralph de Vries; Karlijn J Joling; Otto R Maarsingh; Hein P J van Hout Journal: PLoS One Date: 2022-09-23 Impact factor: 3.752