Evidence map›Paper›PMID 42089911›Full record

ArticleJournal of anesthesia2026

Prevalence of disability-free survival at 5 years after surgery: a prospective observational study.

Yusuke Naito, Mitsuru Ida, Soshiro Ogata, Yoko Yabuno, Satoki Inoue, Masahiko Kawaguchi

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Article in Journal of anesthesia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yusuke NaitoDepartment of Anesthesiology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara, 634-0813, Japan. naito623@naramed-u.ac.jp.ORCID http://orcid.org/0000-0002-1423-812X
Mitsuru IdaDepartment of Anesthesiology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara, 634-0813, Japan.
Soshiro OgataDepartment of Preventive Medicine and Epidemiology, National Cerebral and Cardiovascular Center, Research Institute, Osaka, Japan.
Yoko YabunoDepartment of Anesthesiology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara, 634-0813, Japan.
Satoki InoueDepartment of Anesthesiology, Fukushima Medical University, Fukushima, Japan.
Masahiko KawaguchiDepartment of Anesthesiology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara, 634-0813, Japan.

Funding

Nara Prefecture Nara Medical University Grant-in-Aid for a large-scale prospective cohort study on healthy life expectancy
6 · The paper itself

Abstract

purposeTo determine the prevalence of disability-free survival (DFS) five years after elective non-cardiac surgery in older adults, and to identify preoperative factors associated with DFS using conventional statistical analyses and machine learning methods.

methodsIn this prospective cohort study conducted at a single tertiary hospital in Japan, 2878 patients aged ≥55 years who underwent elective non-cardiac surgery under general anesthesia between 2016 and 2018 were enrolled and followed for 5 years. DFS was defined as survival without significant functional disability, assessed using the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0). Multivariable logistic regression and machine learning models were used to identify preoperative predictive factors.

resultsAt 5 years after surgery, 80.6% (n = 2321) of the patients achieved DFS. Factors significantly associated with non-DFS included older age (odds ratio [OR]: 1.76), symptomatic cerebrovascular disease (OR: 1.94), low serum albumin (OR: 0.70), poor nutritional status (OR: 0.80), and malignancy (OR: 1.53). The machine learning ensemble model, incorporating support vector machine, neural network, and XGBoost algorithms, achieved a balanced accuracy of 0.69 and an area under the receiver operating characteristic curve (AUC) of 0.76.

conclusionMultiple preoperative factors were independently associated with long-term DFS after surgery. Machine learning methods demonstrated moderate predictive accuracy, indicating potential clinical utility pending further model refinement.

Indexed as

Disability-free survivalFunctional outcomeMachine learning

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.