Prospective evaluation of social risks, physical function, and cognitive function in prediction of non-elective rehospitalization and post-discharge mortality.

Autor: Clancy HA; Systems Research Initiative, Kaiser Permanente Division of Research, 2000 Broadway Avenue, Oakland, CA, 94612, USA., Zhu Z; Systems Research Initiative, Kaiser Permanente Division of Research, 2000 Broadway Avenue, Oakland, CA, 94612, USA., Gordon NP; Systems Research Initiative, Kaiser Permanente Division of Research, 2000 Broadway Avenue, Oakland, CA, 94612, USA., Kipnis P; Systems Research Initiative, Kaiser Permanente Division of Research, 2000 Broadway Avenue, Oakland, CA, 94612, USA. Patricia.Kipnis@kp.org., Liu VX; Systems Research Initiative, Kaiser Permanente Division of Research, 2000 Broadway Avenue, Oakland, CA, 94612, USA.; Intensive Care Unit, Kaiser Permanente Medical Center, 700 Lawrence Expressway, Santa Clara, CA, 95051, USA., Escobar GJ; Systems Research Initiative, Kaiser Permanente Division of Research, 2000 Broadway Avenue, Oakland, CA, 94612, USA.
Jazyk: angličtina
Zdroj: BMC health services research [BMC Health Serv Res] 2022 Apr 29; Vol. 22 (1), pp. 574. Date of Electronic Publication: 2022 Apr 29.
DOI: 10.1186/s12913-022-07910-w
Abstrakt: Background: Increasing evidence suggests that social factors and problems with physical and cognitive function may contribute to patients' rehospitalization risk. Understanding a patient's readmission risk may help healthcare providers develop tailored treatment and post-discharge care plans to reduce readmission and mortality. This study aimed to evaluate whether including patient-reported data on social factors; cognitive status; and physical function improves on a predictive model based on electronic health record (EHR) data alone.
Methods: We conducted a prospective study of 1,547 hospitalized adult patients in 3 Kaiser Permanente Northern California hospitals. The main outcomes were non-elective rehospitalization or death within 30 days post-discharge. Exposures included patient-reported social factors and cognitive and physical function (obtained in a pre-discharge interview) and EHR-derived data for comorbidity burden, acute physiology, care directives, prior utilization, and hospital length of stay. We performed bivariate comparisons using Chi-square, t-tests, and Wilcoxon rank-sum tests and assessed correlations between continuous variables using Spearman's rho statistic. For all models, the results reported were obtained after fivefold cross validation.
Results: The 1,547 adult patients interviewed were younger (age, p = 0.03) and sicker (COPS2, p < 0.0001) than the rest of the hospitalized population. Of the 6 patient-reported social factors measured, 3 (not living with a spouse/partner, transportation difficulties, health or disability-related limitations in daily activities) were significantly associated (p < 0.05) with the main outcomes, while 3 (living situation concerns, problems with food availability, financial problems) were not. Patient-reported cognitive (p = 0.027) and physical function (p = 0.01) were significantly lower in patients with the main outcomes. None of the patient-reported variables, singly or in combination, improved predictive performance of a model that included acute physiology and longitudinal comorbidity burden (area under the receiver operator characteristic curve was 0.716 for both the EHR model and maximal performance of a random forest model including all predictors).
Conclusions: In this insured population, incorporating patient-reported social factors and measures of cognitive and physical function did not improve performance of an EHR-based model predicting 30-day non-elective rehospitalization or mortality. While incorporating patient-reported social and functional status data did not improve ability to predict these outcomes, such data may still be important for improving patient outcomes.
(© 2022. The Author(s).)
Databáze: MEDLINE
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