Wajekar AS, Gangakhedkar GR, Rane AD. Letter to the Editor: Machine learning models for hospital-acquired functional decline in geriatric surgical patients - from prediction to prevention. World J Clin Cases 2026; 14(12): 119786 [DOI: 10.12998/wjcc.v14.i12.119786]
Corresponding Author of This Article
Anjana S Wajekar, Professor, Department of Anesthesia, Critical Care and Pain, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute, Sector 22, Utsav Chowk-CISF Road, Owe Camp, Kharghar, Navi Mumbai 410210, Maharashtra, India. anjanawajekar@gmail.com
Research Domain of This Article
Anesthesiology
Article-Type of This Article
Correspondence
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Apr 26, 2026 (publication date) through Apr 15, 2026
Times Cited of This Article
Times Cited (0)
Journal Information of This Article
Publication Name
World Journal of Clinical Cases
ISSN
2307-8960
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Wajekar AS, Gangakhedkar GR, Rane AD. Letter to the Editor: Machine learning models for hospital-acquired functional decline in geriatric surgical patients - from prediction to prevention. World J Clin Cases 2026; 14(12): 119786 [DOI: 10.12998/wjcc.v14.i12.119786]
World J Clin Cases. Apr 26, 2026; 14(12): 119786 Published online Apr 26, 2026. doi: 10.12998/wjcc.v14.i12.119786
Letter to the Editor: Machine learning models for hospital-acquired functional decline in geriatric surgical patients - from prediction to prevention
Anjana S Wajekar, Gauri R Gangakhedkar, Ashwini D Rane
Anjana S Wajekar, Gauri R Gangakhedkar, Ashwini D Rane, Department of Anesthesia, Critical Care and Pain, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute, Navi Mumbai 410210, Maharashtra, India
Author contributions: Wajekar AS made outline of manuscript; Wajekar AS, Gangakhedkar GR, and Rane AD contributed to writing of manuscript, have read and approved the final manuscript.
Conflict-of-interest statement: All authors declare that they have no conflict of interest to disclose.
Corresponding author: Anjana S Wajekar, Professor, Department of Anesthesia, Critical Care and Pain, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute, Sector 22, Utsav Chowk-CISF Road, Owe Camp, Kharghar, Navi Mumbai 410210, Maharashtra, India. anjanawajekar@gmail.com
Received: February 6, 2026 Revised: February 13, 2026 Accepted: March 16, 2026 Published online: April 26, 2026 Processing time: 68 Days and 12.3 Hours
Abstract
Hospital-acquired functional decline (HAFD), even after a successful surgery, is found in up to one-third of the geriatric population. Early and accurate prediction of HAFD can provide timepoints for early intervention to improve postoperative outcomes. While machine learning (ML) offers a promising approach, several pitfalls must be addressed before clinical adoption. When combined with clinically interpretable outputs and continuous model recalibration, ML can support patient-centred decision-making, helping reduce preventable disability, shorten recovery time, and improve overall quality of care for vulnerable hospitalized elders.
Core Tip: Machine learning (ML) for hospital-acquired functional decline will be truly successful not when it merely predicts decline, but when it enables timely interventions that prevent it. When combined with clinically interpretable outputs and continuous model recalibration, ML can support patient-centred decision-making, helping reduce preventable disability, shorten recovery time, and improve overall quality of care for vulnerable hospitalized elders.
