Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages

Huda K. Saleh Kutrani; Jim Briggs; David Prytherch; Elisavet Andrikopoulou; Claire Spice

doi:10.3233/SHTI250400

Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages

Huda K. Saleh Kutrani, Jim Briggs, David Prytherch, Elisavet Andrikopoulou, Claire Spice

Portsmouth Hospitals University NHS Trust

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

The objective is to explore the relationship between the Hospital Frailty Risk Score (HFRS) and in-hospital mortality for all adults ages. Methods: This retrospective cohort study included patients aged 16 years and older who were admitted to Queen Alexandra Hospital in Portsmouth, UK from 01/01/2010 to 31/12/2019. 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 in-hospital mortality to assess model performance between (HFRS and in-hospital mortality) and (Charlson Comorbidity Index-CCI and in-hospital mortality), using AUROC. Results: The proportion of intermediate and high risk of frailty increased with prediction periods of in-hospital mortality. Crude HFRS models (AUROCs ranging from 0·782 to 0·829) were superior to crude CCI models (AUROCs ranging from 0·690 to 0·708) for all in-hospital mortality periods. However, HFRS combined with CCI was a better predictor of in-hospital mortality in all prediction periods. Conclusions: This study has demonstrated the utility of HFRS to predict in-hospital mortality in patients across all adult ages.

Original language	English
Title of host publication	Intelligent Health Systems – From Technology to Data and Knowledge
Subtitle of host publication	Proceedings of MIE 2025
Editors	Elisavet Andrikopoulou, Parisis Gallos, Theodoros N. Arvanitis, Rosalynn Austin, Arriel Benis, Ronald Cornet, Panagiotis Chatzistergos, Alexander Dejaco, Linda Dusseljee-Peute, Alaa Mohasseb, Pantelis Natsiavas, Haythem Nakkas, Philip Scott
Publisher	IOS Press
Pages	562-566
ISBN (Electronic)	9781643685960
DOIs	https://doi.org/10.3233/SHTI250400
Publication status	Published - 21 May 2025
Event	Medical Informatics Europe 2025: Intelligent health systems – From technology to data and knowledge - Glasgow, United Kingdom Duration: 19 May 2025 → 21 May 2025 https://mie2025.efmi.org/home-page

Publication series

Name	Studies in Health Technology and Informatics
Publisher	IOS Press
Volume	327
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	Medical Informatics Europe 2025
Abbreviated title	MIE 2025
Country/Territory	United Kingdom
City	Glasgow
Period	19/05/25 → 21/05/25
Internet address	https://mie2025.efmi.org/home-page

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Frailty
Hospital Frailty Risk Score
Age
In-hospital mortality

Access to Document

10.3233/SHTI250400

Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult agesFinal published version, 275 KBLicence: CC BY-NC

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 Access
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Using the Hospital Frailty Risk Score to predict length of stay across all adult ages
Kutrani, H. K. S., Briggs, J., Prytherch, D. & Spice, C., 23 Jan 2025, In: PLoS One. 20, 1, p. 1-14 e0317234.
Research output: Contribution to journal › Article › peer-review

Open Access
File
25 Downloads (Pure)

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 2025
Student thesis: Doctoral Thesis

File

Medical Informatics Europe 2025
Andrikopoulou, E. (Chair), Andrikopoulou, E. (Member of programme committee) & Andrikopoulou, E. (Organiser)
25 Feb 2024 → 22 May 2025
Activity: Participating in or organising an event types › Participation in conference

Cite this

Kutrani, H. K. S., Briggs, J., Prytherch, D., Andrikopoulou, E., & Spice, C. (2025). Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages. In E. Andrikopoulou, P. Gallos, T. N. Arvanitis, R. Austin, A. Benis, R. Cornet, P. Chatzistergos, A. Dejaco, L. Dusseljee-Peute, A. Mohasseb, P. Natsiavas, H. Nakkas, & P. Scott (Eds.), Intelligent Health Systems – From Technology to Data and Knowledge: Proceedings of MIE 2025 (pp. 562-566). (Studies in Health Technology and Informatics; Vol. 327). IOS Press. https://doi.org/10.3233/SHTI250400

Kutrani, Huda K. Saleh ; Briggs, Jim ; Prytherch, David et al. / Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages. Intelligent Health Systems – From Technology to Data and Knowledge: Proceedings of MIE 2025. editor / Elisavet Andrikopoulou ; Parisis Gallos ; Theodoros N. Arvanitis ; Rosalynn Austin ; Arriel Benis ; Ronald Cornet ; Panagiotis Chatzistergos ; Alexander Dejaco ; Linda Dusseljee-Peute ; Alaa Mohasseb ; Pantelis Natsiavas ; Haythem Nakkas ; Philip Scott. IOS Press, 2025. pp. 562-566 (Studies in Health Technology and Informatics).

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title = "Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages",

abstract = "The objective is to explore the relationship between the Hospital Frailty Risk Score (HFRS) and in-hospital mortality for all adults ages. Methods: This retrospective cohort study included patients aged 16 years and older who were admitted to Queen Alexandra Hospital in Portsmouth, UK from 01/01/2010 to 31/12/2019. 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 in-hospital mortality to assess model performance between (HFRS and in-hospital mortality) and (Charlson Comorbidity Index-CCI and in-hospital mortality), using AUROC. Results: The proportion of intermediate and high risk of frailty increased with prediction periods of in-hospital mortality. Crude HFRS models (AUROCs ranging from 0·782 to 0·829) were superior to crude CCI models (AUROCs ranging from 0·690 to 0·708) for all in-hospital mortality periods. However, HFRS combined with CCI was a better predictor of in-hospital mortality in all prediction periods. Conclusions: This study has demonstrated the utility of HFRS to predict in-hospital mortality in patients across all adult ages. ",

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author = "Kutrani, \{Huda K. Saleh\} and Jim Briggs and David Prytherch and Elisavet Andrikopoulou and Claire Spice",

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Kutrani, HKS, Briggs, J , Prytherch, D , Andrikopoulou, E & Spice, C 2025, Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages. in E Andrikopoulou, P Gallos, TN Arvanitis, R Austin, A Benis, R Cornet, P Chatzistergos, A Dejaco, L Dusseljee-Peute, A Mohasseb, P Natsiavas, H Nakkas & P Scott (eds), Intelligent Health Systems – From Technology to Data and Knowledge: Proceedings of MIE 2025. Studies in Health Technology and Informatics, vol. 327, IOS Press, pp. 562-566, Medical Informatics Europe 2025, Glasgow, United Kingdom, 19/05/25. https://doi.org/10.3233/SHTI250400

Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages. / Kutrani, Huda K. Saleh; Briggs, Jim ; Prytherch, David et al.
Intelligent Health Systems – From Technology to Data and Knowledge: Proceedings of MIE 2025. ed. / Elisavet Andrikopoulou; Parisis Gallos; Theodoros N. Arvanitis; Rosalynn Austin; Arriel Benis; Ronald Cornet; Panagiotis Chatzistergos; Alexander Dejaco; Linda Dusseljee-Peute; Alaa Mohasseb; Pantelis Natsiavas; Haythem Nakkas; Philip Scott. IOS Press, 2025. p. 562-566 (Studies in Health Technology and Informatics; Vol. 327).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages

AU - Kutrani, Huda K. Saleh

AU - Briggs, Jim

AU - Prytherch, David

AU - Andrikopoulou, Elisavet

AU - Spice, Claire

N1 - Will be Gold OA

PY - 2025/5/21

Y1 - 2025/5/21

N2 - The objective is to explore the relationship between the Hospital Frailty Risk Score (HFRS) and in-hospital mortality for all adults ages. Methods: This retrospective cohort study included patients aged 16 years and older who were admitted to Queen Alexandra Hospital in Portsmouth, UK from 01/01/2010 to 31/12/2019. 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 in-hospital mortality to assess model performance between (HFRS and in-hospital mortality) and (Charlson Comorbidity Index-CCI and in-hospital mortality), using AUROC. Results: The proportion of intermediate and high risk of frailty increased with prediction periods of in-hospital mortality. Crude HFRS models (AUROCs ranging from 0·782 to 0·829) were superior to crude CCI models (AUROCs ranging from 0·690 to 0·708) for all in-hospital mortality periods. However, HFRS combined with CCI was a better predictor of in-hospital mortality in all prediction periods. Conclusions: This study has demonstrated the utility of HFRS to predict in-hospital mortality in patients across all adult ages.

AB - The objective is to explore the relationship between the Hospital Frailty Risk Score (HFRS) and in-hospital mortality for all adults ages. Methods: This retrospective cohort study included patients aged 16 years and older who were admitted to Queen Alexandra Hospital in Portsmouth, UK from 01/01/2010 to 31/12/2019. 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 in-hospital mortality to assess model performance between (HFRS and in-hospital mortality) and (Charlson Comorbidity Index-CCI and in-hospital mortality), using AUROC. Results: The proportion of intermediate and high risk of frailty increased with prediction periods of in-hospital mortality. Crude HFRS models (AUROCs ranging from 0·782 to 0·829) were superior to crude CCI models (AUROCs ranging from 0·690 to 0·708) for all in-hospital mortality periods. However, HFRS combined with CCI was a better predictor of in-hospital mortality in all prediction periods. Conclusions: This study has demonstrated the utility of HFRS to predict in-hospital mortality in patients across all adult ages.

KW - Frailty

KW - Hospital Frailty Risk Score

KW - Age

KW - In-hospital mortality

UR - https://ebooks.iospress.nl/bookseries/studies-in-health-technology-and-informatics

UR - https://mie2025.efmi.org/

U2 - 10.3233/SHTI250400

DO - 10.3233/SHTI250400

M3 - Conference contribution

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SP - 562

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BT - Intelligent Health Systems – From Technology to Data and Knowledge

A2 - Andrikopoulou, Elisavet

A2 - Gallos, Parisis

A2 - Arvanitis, Theodoros N.

A2 - Austin, Rosalynn

A2 - Benis, Arriel

A2 - Cornet, Ronald

A2 - Chatzistergos, Panagiotis

A2 - Dejaco, Alexander

A2 - Dusseljee-Peute, Linda

A2 - Mohasseb, Alaa

A2 - Natsiavas, Pantelis

A2 - Nakkas, Haythem

A2 - Scott, Philip

PB - IOS Press

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Y2 - 19 May 2025 through 21 May 2025

ER -

Kutrani HKS, Briggs J , Prytherch D , Andrikopoulou E, Spice C. Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages. In Andrikopoulou E, Gallos P, Arvanitis TN, Austin R, Benis A, Cornet R, Chatzistergos P, Dejaco A, Dusseljee-Peute L, Mohasseb A, Natsiavas P, Nakkas H, Scott P, editors, Intelligent Health Systems – From Technology to Data and Knowledge: Proceedings of MIE 2025. IOS Press. 2025. p. 562-566. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI250400

Using the Hospital Frailty Risk Score to predict in-hospital mortality across all adult ages

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Fingerprint

Research output

Age does not improve the predictive ability of the Hospital Frailty Risk Score for length of stay

Using the Hospital Frailty Risk Score to predict length of stay across all adult ages

Student theses

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

Activities

Medical Informatics Europe 2025

Cite this