All-Cause Readmission or Potentially Avoidable Readmission: Which Is More Predictable Using Frailty, Comorbidities, and ADL?

Seigo Mitsutake; Tatsuro Ishizaki; Shohei Yano; Rumiko Tsuchiya-Ito; Kazuaki Uda; Kenji Toba; Hideki Ito

doi:10.1093/geroni/igad043

All-Cause Readmission or Potentially Avoidable Readmission: Which Is More Predictable Using Frailty, Comorbidities, and ADL?

Innov Aging. 2023 May 15;7(5):igad043. doi: 10.1093/geroni/igad043. eCollection 2023.

Authors

Seigo Mitsutake¹, Tatsuro Ishizaki¹, Shohei Yano^{1

2}, Rumiko Tsuchiya-Ito^{1

3}, Kazuaki Uda^{1

4}, Kenji Toba⁵, Hideki Ito⁵

Affiliations

¹ Human Care Research Team, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan.
² The Salvation Army Booth Memorial Hospital, Tokyo, Japan.
³ Research Department, Institute for Health Economics and Policy, Association for Health Economics Research and Social Insurance and Welfare, Tokyo, Japan.
⁴ Department of Health Services Research, Institute of Medicine, University of Tsukuba, Ibaraki, Japan.
⁵ Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan.

Abstract

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.

Keywords: Care coordination; Care transitions; Epidemiology; Health service; Quality of care.