Abstract
Background: Hospital Frailty Risk Score (HFRS) has recently been used to predict adverse health outcomes including length of stay (LOS) in hospital. LOS is an important indicator for patient quality of care, the measurement of hospital performance, efficiency and costs. Tools to predict LOS may enable earlier interventions in those identified at higher risk of a long stay. Previous work focused on patients over 75 years of age, but we explore the relationship between HFRS and LOS for all adults.
Methods: This is a retrospective cohort study using data from a large acute hospital during the period from 01/01/2010 to 30/06/2018. The study included patients aged 16 years and older. We calculated HFRS for patients who had been previously admitted to the hospital within the previous 2 years. The study developed Logistic Regression models (crude and adjusted) for nine prediction periods of LOS to assess association between (LOS and HFRS) and (LOS and Charlson Comorbidity Index-CCI), using odds ratios, and AUROC to assess model performance.
Results: An increase in HFRS is associated with prolonged LOS. HFRS alone or combined with CCI were more important predictor of long LOS in most of periods to predict LOS. However, crude HFRS was superior to the models where HFRS was combined with any other variable for LOS in excess of 21 days, which had AUROCs ranging from 0·867 to 0·890. Regarding eight age groups, crude HFRS remained the first or second most effective predictor of long LOS. HFRS alone or combined with CCI was superior to other models for patients older than 44 years for all periods of LOS; whereas for patients younger than 44 years it was superior for all LOS except 45, 60, and 90 days.
Conclusion: This study has demonstrated the utility of HFRS to predict hospital LOS in patients across all ages.
Methods: This is a retrospective cohort study using data from a large acute hospital during the period from 01/01/2010 to 30/06/2018. The study included patients aged 16 years and older. We calculated HFRS for patients who had been previously admitted to the hospital within the previous 2 years. The study developed Logistic Regression models (crude and adjusted) for nine prediction periods of LOS to assess association between (LOS and HFRS) and (LOS and Charlson Comorbidity Index-CCI), using odds ratios, and AUROC to assess model performance.
Results: An increase in HFRS is associated with prolonged LOS. HFRS alone or combined with CCI were more important predictor of long LOS in most of periods to predict LOS. However, crude HFRS was superior to the models where HFRS was combined with any other variable for LOS in excess of 21 days, which had AUROCs ranging from 0·867 to 0·890. Regarding eight age groups, crude HFRS remained the first or second most effective predictor of long LOS. HFRS alone or combined with CCI was superior to other models for patients older than 44 years for all periods of LOS; whereas for patients younger than 44 years it was superior for all LOS except 45, 60, and 90 days.
Conclusion: This study has demonstrated the utility of HFRS to predict hospital LOS in patients across all ages.
| Original language | English |
|---|---|
| Article number | e0317234 |
| Pages (from-to) | 1-14 |
| Journal | PLoS One |
| Volume | 20 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 23 Jan 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Frailty
- Hospital Frailty Risk Score
- Age
- Length of stay
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Age does not improve the predictive ability of the Hospital Frailty Risk Score for length of stay
Kutrani, H. K. S., Briggs, J., Prytherch, D. & Spice, C., 9 Sept 2025, In: PLoS One. 20, 9, 14 p., e0330930.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages
Kutrani, H. K. S., Briggs, J., Prytherch, D., Andrikopoulou, E. & Spice, C., 21 May 2025, Intelligent Health Systems – From Technology to Data and Knowledge: Proceedings of MIE 2025. Andrikopoulou, E., Gallos, P., Arvanitis, T. N., Austin, R., Benis, A., Cornet, R., Chatzistergos, P., Dejaco, A., Dusseljee-Peute, L., Mohasseb, A., Natsiavas, P., Nakkas, H. & Scott, P. (eds.). IOS Press, p. 562-566 (Studies in Health Technology and Informatics; vol. 327).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile
Student theses
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Using Hospital Frailty Risk Score to Identify Adverse Outcomes across all Adult Ages and Identify Factors Influencing its Power to Identify Adverse Outcomes and Determine its Association with Sepsis
Kutrani, H. K. S. (Author), Briggs, J. S. (Supervisor), Bader-El-Den, M. (Supervisor) & Andrikopoulou, E. (Supervisor), 11 Dec 2025Student thesis: Doctoral Thesis
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