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<p>For the treatment of osteoporotic vertebral compression fractures (OVCFs), robot-assisted percutaneous vertebroplasty (PVP) under local anesthesia has shown effectiveness; however, the precision of the puncture process varies significantly across spinal segments, and no tool currently exists to assess an individual’s risk of inaccurate puncture. In this secondary analysis of a previously published cohort of 312 patients who underwent robot-assisted PVP for single-level OVCFs under local anesthesia (2023–2024), we developed and internally validated a predictive model for clinically unacceptable puncture (Gertzbein Grade C–E). Patients were randomly split into training (n = 218) and validation (n = 94) cohorts. Candidate predictors included demographic, imaging, and early intraoperative variables. Feature selection was conducted via LASSO regression followed by multivariate logistic regression. Model performance was assessed by AUC, calibration plots, and decision curve analysis, with bootstrapping for internal validation. Unacceptable puncture occurred in 11.9% of cases (37/312). Four independent predictors were identified: T1–8 vertebral segment (OR = 4.89, 95% CI 2.67–8.95), pedicle width (OR = 0.71 per mm, 0.60–0.84), a semi-quantitative Respiratory Motion Impact Score (RMIS; OR = 1.95 per point, 1.38–2.76), and registration and planning time (OR = 1.32 per min, 1.12–1.56). The nomogram demonstrated good discrimination (training AUC = 0.862, validation AUC = 0.841, optimism-corrected AUC = 0.851) and calibration, with decision curve analysis showing positive net benefit across threshold probabilities of 5%–65%. This first internally validated model for puncture accuracy in robot-assisted PVP enables early intraoperative risk stratification using both preoperative and immediately available procedural variables. However, given the semi-quantitative and subjective nature of the RMIS component and the single-center design, rigorous external validation in multi-center cohorts is required before widespread clinical adoption.</p>

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Development and validation of a predictive model for puncture accuracy in robot-assisted percutaneous vertebroplasty under local anesthesia

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Chunyan Zhang,
  • Tairui Zhang,
  • Gen Li,
  • Xuebin Tang,
  • Hua Li,
  • Yunqing Wang,
  • Junwei Zhang

摘要

For the treatment of osteoporotic vertebral compression fractures (OVCFs), robot-assisted percutaneous vertebroplasty (PVP) under local anesthesia has shown effectiveness; however, the precision of the puncture process varies significantly across spinal segments, and no tool currently exists to assess an individual’s risk of inaccurate puncture. In this secondary analysis of a previously published cohort of 312 patients who underwent robot-assisted PVP for single-level OVCFs under local anesthesia (2023–2024), we developed and internally validated a predictive model for clinically unacceptable puncture (Gertzbein Grade C–E). Patients were randomly split into training (n = 218) and validation (n = 94) cohorts. Candidate predictors included demographic, imaging, and early intraoperative variables. Feature selection was conducted via LASSO regression followed by multivariate logistic regression. Model performance was assessed by AUC, calibration plots, and decision curve analysis, with bootstrapping for internal validation. Unacceptable puncture occurred in 11.9% of cases (37/312). Four independent predictors were identified: T1–8 vertebral segment (OR = 4.89, 95% CI 2.67–8.95), pedicle width (OR = 0.71 per mm, 0.60–0.84), a semi-quantitative Respiratory Motion Impact Score (RMIS; OR = 1.95 per point, 1.38–2.76), and registration and planning time (OR = 1.32 per min, 1.12–1.56). The nomogram demonstrated good discrimination (training AUC = 0.862, validation AUC = 0.841, optimism-corrected AUC = 0.851) and calibration, with decision curve analysis showing positive net benefit across threshold probabilities of 5%–65%. This first internally validated model for puncture accuracy in robot-assisted PVP enables early intraoperative risk stratification using both preoperative and immediately available procedural variables. However, given the semi-quantitative and subjective nature of the RMIS component and the single-center design, rigorous external validation in multi-center cohorts is required before widespread clinical adoption.