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Purpose <p>To 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.</p> Methods <p>In 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.</p> Results <p>At 5 years after surgery, 80.6% (<i>n</i> = 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.</p> Conclusion <p>Multiple 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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年5月6日

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

摘要

Purpose

To 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.

Methods

In 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.

Results

At 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.

Conclusion

Multiple 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.