The predictive role of identifying frailty in assessing the need for palliative care in the elderly - the application of machine learning algorithm

Aug 5, 2026

Palliative care is a key element of integrated care, improving quality and reducing hospitalization costs for people living with chronic obstructive pulmonary disease (COPD). This study uses machine learning to support early recognition of frailty as a long-term condition in COPD patients. Frailty was assessed with a checklist covering BMI reduction, fatigue, physical activity, walking speed, and disability via FEV1, then linked to palliative care needs through predictive models and validated criteria.

LanguageEnglish

CategoryHealthcare

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Pages13

Reading Time33 min

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Abstract

Background

Methods

Results

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Introduction

Nejatifar et al. Journal of Health, Population and Nutrition [https://doi.org/10.1186/s41043-025-00841-2](https://doi.org/10.1186/s41043-025-00841-2) Journal of Health, Population (2025) 44:133 and Nutrition RESEARCH Open Access The predictive role of identifying frailty in assessing the need for palliative care in the elderly: the application of machine learning algorithm Zahra Nejatifar 1, Ahad Alizadeh3, Mohammad Amerzadeh2, Shideh Omidian4 and Sima Rafiei5* Abstract Background Palliative care is a key component of integrated care to improve care quality and reduce hospitalization costs for patients with chronic obstructive pulmonary disease (COPD) . This study aims to use machine learning algorithms to create an effective approach to the early recognition and identification of frailty as a long-term condition in COPD patients. Methods The level of frailty in a sample of patients (total n = 140) was assessed using the checklist of frailty assessment, which encompasses five questions: measured decrease in body mass index (BMI), fatigue status, physical activity status, and walking speed. The last question assessed disability through forced expiratory volume in the first second (FEV1) measured using spirometry results. The next checklist was the Palliative Care Needs Assessment Tool, taken from the assessment checklist for palliative care needs in patients with COPD by Thoenesen et al. [28] . We used different machine learning algorithms, with performance assessed using an area under the receiver-operating characteristic curve, sensitivity, and specificity, to develop a validated set of criteria for frailty using machine learning. Results Study findings revealed that the palliative care needs assessment tool categorized 74% of all patients into two groups: those requiring palliative care and those not requiring it. Furthermore, the influential variables that contributed to predicting the need for palliative care included measured BMI reduction, fatigue status, physical activity level, slow walking, and FEV1 . The super-learning model demonstrated higher accuracy (92%) than other machine-learning algorithms. Conclusion The study highlights the need for more collaboration between clinicians and data scientists to use the potential of data collected from COPD patients in clinical settings with the purpose of early identification of frailty as a long-term condition. Predicting palliative care needs accurately is critical in these contexts, as it can lead to better resource allocation, improved healthcare delivery, and enhanced patient outcomes. Keywords Frailty, Palliative care, COPD, Patients *Correspondence: Sima Rafiei [sima.rafie@gmail.com](sima.rafie@gmail.com) Full list of author information is available at the end of the article © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/.)[.](http://creativecommons.org/licenses/by/4.0/.) Nejatifar et al. Journal of Health, Population and Nutrition (2025) 44:133 Introduction The World Health Organization (WHO) defines healthy aging as"the process of developing and maintaining the functional abi

FAQs

What is the study’s main goal for COPD patients?

To use machine learning algorithms to enable early recognition and identification of frailty as a long-term condition in COPD patients.

How was frailty assessed in the study sample?

Frailty was assessed using a checklist including BMI decrease, fatigue status, physical activity status, walking speed, and disability evaluated through FEV1 measured by spirometry.

Which factors were most influential in predicting palliative care needs?

Measured BMI reduction, fatigue status, physical activity level, slow walking, and FEV1 were reported as influential variables for predicting the need for palliative care.

Aug 5, 2026

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