For older adults with heart failure (HF), leaving the hospital alive does not always mean returning to an independent life. Loss of independence can reduce quality of life, increase caregiver burden, and raise care needs. Yet most prognostic approaches focus on death or rehospitalization, making it difficult to identify patients likely to survive while losing independence.
Addressing this challenge, a research team led by Assistant Professor Kanji Yamada from the Department of Physical Therapy, Faculty of Health Science, Juntendo University, Japan, including Associate Professor Nobuyuki Kagiyama and Specially-Appointed Professor Tetsuya Takahashi, both from the same university, analyzed 6,382 patients aged 65 years or older who were hospitalized for HF, received physical therapy, and were independent in activities before admission. Using the prospective J-Proof HF Registry, they developed an eXtreme Gradient Boosting (XGBoost) model to predict functional maintenance, functional decline, or death 1 year after discharge. The study was published online in the Journal of Cardiac Failure on August 20, 2026.
The team used 77 candidate predictors. The model incorporated assessments performed by physical therapists at discharge, including maximum gait speed, the Short Physical Performance Battery (SPPB), handgrip strength, and the Barthel Index (BI), as well as preadmission frailty measured by the Kihon Checklist (KCL). A parsimonious Top-10 model was evaluated using nested leave-one-site-out validation and compared with KCL-based scores.
At 1 year, 2,076 patients experienced functional decline and 928 died, while 3,378 maintained functional independence. The Top-10 XGBoost model significantly outperformed the modified KCL score and KCL total score. Gait speed, preadmission KCL score, discharge BI, and SPPB were among the influential predictors, with greater predictive importance than cardiac measures such as ejection fraction.
“Being able to live independently is just as important to many patients as how long they live,” says Dr. Yamada. “ Our findings show that physical function at discharge can reveal a risk that mortality-focused models may overlook, identifying patients who are likely to survive but lose independence.”
The model identified four risk phenotypes by combining predicted risks of functional decline and mortality. Most notably, a low-mortality/high-decline group of 1,732 patients had a 57% rate of functional decline despite an 89% probability of survival at 1 year. Identifying this group could help medical professional prioritize post-discharge support, including more intensive outpatient or home-based rehabilitation.
“Instead of asking only whether a patient will survive, we need to ask whether they will be able to maintain the life they value after discharge ,” Dr. Yamada says. “ A practical risk estimate could help clinicians and patients discuss rehabilitation needs earlier and make better use of limited resources.”
To support clinical use, the researchers developed a prototype web application that estimates individual risk at discharge and simulates how changes in physical function, such as faster gait speed, may affect predicted risk. The application is a prototype, and implementation studies are needed to determine whether model-guided decisions improve patient outcomes.
Overall, the study demonstrates that machine learning can help make functional decline visible alongside mortality risk in older adults with HF. By combining clinical information with direct measures of physical performance, the approach could support targeted rehabilitation and patient-centered discharge planning, with the goal of preserving independence and quality of life.
About Assistant Professor Kanji Yamada from Juntendo University, Japan
Dr. Kanji Yamada is an Assistant Professor in the Department of Physical Therapy, Faculty of Health Science at Juntendo University, Japan. He holds qualifications in physical therapy and a PhD, and his research specializes in acute-phase and geriatric rehabilitation, focusing on physical function and clinical outcomes in patients with heart failure. He utilizes machine learning approaches to predict 1-year functional decline and mortality in older populations. He has authored multiple peer-reviewed publications on rehabilitation medicine and intensive care unit-acquired weaknesses. In addition to his academic research, he provides clinical care in the Department of Rehabilitation at Kobe City Medical Center General Hospital in Kobe, Japan.
Funding
This research was supported by research funding from the Japanese Society of Cardiovascular Physical Therapy (Tokyo, Japan) and JSPS KAKENHI Grant Number JP25K02969.
Reference
Authors: Kanji Yamada¹ , ², Nobuyuki Kagiyama³, Tomoyuki Morisawa⁴, Masakazu Saitoh¹ , ⁴, Kentaro Iwata² , ⁴, Michitaka Kato⁴, Koji Sakurada⁴, Yuji Kono⁴, Yuki Iida⁴, Masanobu Taya⁴, Yoshinari Funami⁴, Kentaro Kamiya⁴, Tetsuya Takahashi¹ , ⁴
Title of original paper: Machine Learning Predicts Functional Decline and Risk Phenotypes in Older Patients With Heart Failure
Journal: Journal of Cardiac Failure, pages 1–11, 2026
DOI: https://doi.org/10.1016/j.cardfail.2026.08.005
Affiliations: 1 Department of Physical Therapy, Faculty of Health Science, Juntendo University, Japan
2 Department of Rehabilitation, Kobe City Medical Center General Hospital, Japan
3 Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Japan
4 Committee of the J-Proof HF Registry, Japanese Society of Cardiovascular Physical Therapy, Japan
History of Juntendo University
Juntendo was originally founded in 1838 as a Dutch School of Medicine at a time when Western medical education was not yet embedded as a normal part of Japanese society. With the creation of Juntendo, the founders hoped to create a place where people could come together with the shared goal of helping society through the powers of medical education and practices. Their aspirations led to the establishment of Juntendo Hospital, the first private hospital in Japan. Through the years the institution's experience and perspective as an institution of higher education and a place of clinical practice has enabled Juntendo University to play an integral role in the shaping of Japanese medical education and practices. Along the way the focus of the institution has also expanded, now consisting of nine undergraduate programs and eight graduate programs, the university specializes in the fields of health science, health and sports science, nursing health care and sciences, and international liberal arts, as well as medicine. Today, Juntendo University continues to pursue innovative approaches to international level education and research with the goal of applying the results to society.
Mission Statement
The mission of Juntendo University is to strive for advances in society through education, research, and healthcare, guided by the motto “Jin – I exist as you exist” and the principle of “Fudan Zenshin - Continuously Moving Forward”. The spirit of “Jin”, which is the ideal of all those who gather at Juntendo University, entails being kind and considerate of others. The principle of “Fudan Zenshin” conveys the belief of the founders that education and research activities will only flourish in an environment of free competition. Our academic environment enables us to educate outstanding students to become healthcare professionals, patients can believe in, scientists capable of innovative discoveries and inventions, and global citizens ready to serve society.
Journal of Cardiac Failure
10.1016/j.cardfail.2026.08.005
Data/statistical analysis
People
Machine Learning Predicts Functional Decline and Risk Phenotypes in Older Patients With Heart Failure
20-Aug-2026
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article