Key result
Models using frailty, comorbidities, and ADL predict 30-day potentially avoidable readmissions better than all-cause readmissions.
Why the study?
Readmission-related healthcare reforms have shifted focus from all-cause to potentially avoidable readmissions, but the utility of administrative data tools in predicting potentially avoidable readmissions is unclear.
Are 30-day potentially avoidable readmissions more predictable than all-cause readmissions using frailty, comorbidities, and ADL scores in older hospitalized patients?
Cohort (n=16,313)
No
Are 30-day potentially avoidable readmissions more predictable than all-cause readmissions using frailty, comorbidities, and ADL scores in older hospitalized patients?
Answers are grounded in this paper and cite their sources.
Potentially avoidable readmissions are more predictable than all-cause readmissions using administrative data tools assessing frailty, comorbidities, and ADL in older adults.
Background and Objectives: Readmission-related health care reforms have shifted their focus from all-cause readmissions (ACR) to potentially avoidable readmissions (PAR). However, little is known about the utility of analytic tools from administrative data in predicting PAR. This study determined whether 30-day ACR or 30-day PAR is more predictable using tools that assess frailty, comorbidities, and activities of daily living (ADL) from administrative data. Research Design and Methods: This retrospective cohort study was conducted at a large general acute care hospital in Tokyo, Japan. We analyzed patients aged ≥70 years who had been admitted to and discharged from the subject hospital between July 2016 and February 2021. Using administrative data, we assessed each patient's Hospital Frailty Risk Score, Charlson Comorbidity Index, and Barthel Index on admission. To determine the influence of each tool on readmission predictions, we constructed logistic regression models with different combinations of independent variables for predicting unplanned ACR and PAR within 30 days of discharge. Results: Among 16 313 study patients, 4.1% experienced 30-day ACR and 1.8% experienced 30-day PAR. The full model (including sex, age, annual household income, frailty, comorbidities, and ADL as independent variables) for 30-day PAR showed better discrimination (C-statistic: 0.79, 95% confidence interval: 0.77-0.82) than the full model for 30-day ACR (0.73, 0.71-0.75). The other prediction models for 30-day PAR also had consistently better discrimination than their corresponding models for 30-day ACR. Discussion and Implications: PAR is more predictable than ACR when using tools that assess frailty, comorbidities, and ADL from administrative data. Our PAR prediction model may contribute to the accurate identification of at-risk patients in clinical settings who would benefit from transitional care interventions.
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Mitsutake et al. (2023) conducted a cohort in Hospitalized older adults (n=16,313). Prediction models for potentially avoidable readmissions (PAR) vs. Prediction models for all-cause readmissions (ACR) was evaluated on Discrimination (C-statistic) of the full model for 30-day PAR vs 30-day ACR (C-statistic, 95% CI 0.77-0.82). Prediction models using frailty, comorbidities, and ADL showed better discrimination for 30-day potentially avoidable readmissions (C-statistic 0.79) than for all-cause readmissions (C-statistic 0.73).
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