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Objective <p>This study aimed to estimate the prevalence of frailty among elderly patients undergoing spine surgery at 1 month postoperatively, and develop a perioperative risk model to support targeted medical prevention strategies and facilitate the early identification of high-risk patients.</p> Methods <p>This prospective study included 206 elderly patients scheduled for spine surgery between June and August 2023. Frailty was assessed using the FRAIL Scale at admission and one month postoperatively. Data collected included general information, clinical data, laboratory parameters, and pre- and postoperative Athens Insomnia Scale (AIS) scores. Based on the FRAIL Scale scores at one month postoperatively, patients were categorized into a non-frailty group (<i>n</i> = 91) and a frailty group (<i>n</i> = 115). Univariate analyses were conducted to identify variables associated with postoperative frailty. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify independent risk predictors A risk model was constructed. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis, calibration curves, decision curve analysis, discrimination metrics (C-index), and the Hosmer–Lemeshow goodness-of-fit test.</p> Results <p>The incidence of frailty and pre-frailty one month after spine surgery was 55.8%. Univariate analysis revealed significant differences between the two groups in age, preoperative frailty score, anesthesia duration, operation duration, total hospitalization costs, preoperative glycated hemoglobin (HbA1c), length of hospital stay, and incidence of postoperative complications (all <i>P</i> &lt; 0.05). Multivariate analysis identified age, length of hospital stay, preoperative frailty score, operation duration, anesthesia duration, pre- and postoperative AIS scores, and the number of perioperative medications as independent risk factors (all <i>P</i> &lt; 0.05). The LASSO-based risk model demonstrated excellent discrimination, with an area under the curve of 0.958 (95% CI: 0.931–0.985) and a C-index exceeding 0.90. The Hosmer–Lemeshow test indicated good calibration (<i>P</i> = 0.109).</p> Conclusion <p>The incidence of perioperative frailty is high among elderly patients undergoing spine surgery. A combination of demographic, surgical, and clinical factors contributes to postoperative frailty risk. The perioperative risk model developed in this study demonstrates excellent performance and can serve as a reference for the early identification of high-risk patients and the implementation of targeted preventive strategies.</p>

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Perioperative risk factors and risk modeling for frailty one month after spine surgery in elderly patients

  • 期刊论文

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  • 发表日期：

    2026年6月1日

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  • Yao Wang,
  • Pingxiu Zhang,
  • Yurong Zheng,
  • Xiaobo Wang,
  • Congrui Liao,
  • Meiying Li,
  • Wanyi Jiang,
  • Huiqing Li,
  • Xiaohuan Yi,
  • Zhongmin Zhang,
  • Lan Feng

摘要

Objective

This study aimed to estimate the prevalence of frailty among elderly patients undergoing spine surgery at 1 month postoperatively, and develop a perioperative risk model to support targeted medical prevention strategies and facilitate the early identification of high-risk patients.

Methods

This prospective study included 206 elderly patients scheduled for spine surgery between June and August 2023. Frailty was assessed using the FRAIL Scale at admission and one month postoperatively. Data collected included general information, clinical data, laboratory parameters, and pre- and postoperative Athens Insomnia Scale (AIS) scores. Based on the FRAIL Scale scores at one month postoperatively, patients were categorized into a non-frailty group (n = 91) and a frailty group (n = 115). Univariate analyses were conducted to identify variables associated with postoperative frailty. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify independent risk predictors A risk model was constructed. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis, calibration curves, decision curve analysis, discrimination metrics (C-index), and the Hosmer–Lemeshow goodness-of-fit test.

Results

The incidence of frailty and pre-frailty one month after spine surgery was 55.8%. Univariate analysis revealed significant differences between the two groups in age, preoperative frailty score, anesthesia duration, operation duration, total hospitalization costs, preoperative glycated hemoglobin (HbA1c), length of hospital stay, and incidence of postoperative complications (all P < 0.05). Multivariate analysis identified age, length of hospital stay, preoperative frailty score, operation duration, anesthesia duration, pre- and postoperative AIS scores, and the number of perioperative medications as independent risk factors (all P < 0.05). The LASSO-based risk model demonstrated excellent discrimination, with an area under the curve of 0.958 (95% CI: 0.931–0.985) and a C-index exceeding 0.90. The Hosmer–Lemeshow test indicated good calibration (P = 0.109).

Conclusion

The incidence of perioperative frailty is high among elderly patients undergoing spine surgery. A combination of demographic, surgical, and clinical factors contributes to postoperative frailty risk. The perioperative risk model developed in this study demonstrates excellent performance and can serve as a reference for the early identification of high-risk patients and the implementation of targeted preventive strategies.