Machine Learning for Predicting Postoperative Functional Disability and Mortality Among Older Patients With Cancer: Retrospective Cohort Study

Topic: Risk, prevention and care in population studies

This study included 33,355 patients in the training, 14,294 in the internal validation, and 6711 in the external validation sets. In the training set, 1406/33,355 (4.2%) patients experienced worse discharge. A total of 24 predictor variables were selected for the final models. CatBoost and XGBoost achieved the largest AUCs among the 6 models: 0.81 (95% CI 0.80-0.82) and 0.81 (95% CI 0.80-0.82), respectively. In the top 15 influential factors based on the mean absolute SHAP value, both models shared the same 14 factors such as dementia, age ≥85 years, and gastrointestinal cancer. The CatBoost model showed the largest AUCs in both internal (0.77, 95% CI 0.75-0.79) and external validation (0.72, 95% CI 0.68-0.75). The CatBoost model demonstrated good performance in predicting postoperative outcomes for older patients with cancer using routinely available preoperative factors. The robustness of these findings was supported by the identical top influential factors between the CatBoost and XGBoost models. This model could support surgical decision-making while considering postoperative quality of life and care burden, with potential for implementation through electronic health records.

Key findings

• This study included 33,355 patients in the training, 14,294 in the internal validation, and 6711 in the external validation sets.
• In the training set, 1406/33,355 (4.2%) patients experienced worse discharge.
• A total of 24 predictor variables were selected for the final models.
• CatBoost and XGBoost achieved the largest AUCs among the 6 models: 0.81 (95% CI 0.80-0.82) and 0.81 (95% CI 0.80-0.82), respectively.
• In the top 15 influential factors based on the mean absolute SHAP value, both models shared the same 14 factors such as dementia, age ≥85 years, and gastrointestinal cancer.

Why it matters

This retrospective cohort study developed machine learning models to predict postoperative functional disability and mortality in older patients with cancer, with dementia among the top influential factors. It is about surgical decision-making and care planning, not treating, curing or preventing Alzheimer's disease.

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Study details

• Study designcohort study
• Participants33,355
• Effect size15.9%; 4.2%
• Evidence strengthModerate

Read the original

• DOI: 10.2196/65898
• PubMed: 40369796
• Original source →
• Free full text: JMIR Aging →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{hashimoto2025-40369796,
  author  = {Hashimoto, Yuki and Inoue, Norihiko and Tani, Takuaki and Imai, Shinobu},
  title   = {{Machine Learning for Predicting Postoperative Functional Disability and Mortality Among Older Patients With Cancer: Retrospective Cohort Study}},
  journal = {JMIR Aging},
  year    = {2025},
  doi     = {10.2196/65898},
  pmid    = {40369796},
  url     = {https://doi.org/10.2196/65898}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Multidimensional frailty score for the prediction of postoperative mortality risk JAMA surgery · 2014-07-01 · usefulness score 52/100 · Getting a diagnosis

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