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Purpose <p>Frailty is increasingly recognized as a predictor of poor surgical outcomes, yet its preoperative assessment in patients with non-small-cell lung cancer (NSCLC) remains challenging. This study aimed to develop and validate a high-performance predictive model for assessing frailty risk using routinely available clinical parameters and machine learning (ML) techniques.</p> Methods <p>This single-center, cross-sectional study enrolled 489 preoperative patients with NSCLC hospitalized from April to October 2024. Participants were randomly divided into training (<i>n</i>&#xa0;=&#xa0;342) and validation (<i>n</i>&#xa0;=&#xa0;147) sets. Frailty was assessed using the FRAIL scale. We developed a logistic regression-based nomogram and compared it with six ML models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis.</p> Results <p>Frailty/pre-frailty prevalence was 36.1%. Logistic regression identified age, body mass index, comorbidity grade, fatigue, walking difficulty, single-breath diffusing capacity of the lung for carbon monoxide and the triglyceride-glucose (TyG) index as independent predictors. Although the nomogram achieved an AUC of 0.77 (95% confidence interval 0.69–0.85) in the validation set, the Light Gradient Boosting Machine (LGBM) model demonstrated superior discrimination, with an AUC of 0.965 in the training set and 0.807 in the validation set. Feature importance analysis highlighted the TyG index, comorbidity grade, and maximal voluntary ventilation as top predictors.</p> Conclusions <p>This study developed a robust frailty risk prediction framework. The integration of ML algorithms with objective physiological markers (TyG index and respiratory reserve) significantly enhanced predictive accuracy over traditional methods, providing a reliable tool for preoperative risk stratification in patients with NSCLC.</p>

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Development and Validation of a Frailty Risk Prediction Model for Preoperative Non-Small-Cell Lung Cancer Patients: A Cross-Sectional Study

  • 期刊论文

  • 发表日期：

    2026年2月19日

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  • Hang Yi,
  • Miao Liu,
  • Yihao Chen,
  • Lu Liu,
  • Man Liu,
  • Zhen Wang,
  • Jingchen Kan,
  • Miao Sun,
  • Yifan Xu,
  • Jiali Yan,
  • Yinyan Gao,
  • Yousheng Mao,
  • Fengyan Ma

摘要

Purpose

Frailty is increasingly recognized as a predictor of poor surgical outcomes, yet its preoperative assessment in patients with non-small-cell lung cancer (NSCLC) remains challenging. This study aimed to develop and validate a high-performance predictive model for assessing frailty risk using routinely available clinical parameters and machine learning (ML) techniques.

Methods

This single-center, cross-sectional study enrolled 489 preoperative patients with NSCLC hospitalized from April to October 2024. Participants were randomly divided into training (n = 342) and validation (n = 147) sets. Frailty was assessed using the FRAIL scale. We developed a logistic regression-based nomogram and compared it with six ML models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis.

Results

Frailty/pre-frailty prevalence was 36.1%. Logistic regression identified age, body mass index, comorbidity grade, fatigue, walking difficulty, single-breath diffusing capacity of the lung for carbon monoxide and the triglyceride-glucose (TyG) index as independent predictors. Although the nomogram achieved an AUC of 0.77 (95% confidence interval 0.69–0.85) in the validation set, the Light Gradient Boosting Machine (LGBM) model demonstrated superior discrimination, with an AUC of 0.965 in the training set and 0.807 in the validation set. Feature importance analysis highlighted the TyG index, comorbidity grade, and maximal voluntary ventilation as top predictors.

Conclusions

This study developed a robust frailty risk prediction framework. The integration of ML algorithms with objective physiological markers (TyG index and respiratory reserve) significantly enhanced predictive accuracy over traditional methods, providing a reliable tool for preoperative risk stratification in patients with NSCLC.