Development and validation of a nomogram to predict the unfavorable outcomes in elderly patients undergoing lumbar fusion surgery for degenerative disease.

IF 1.8 3区 医学 Q2 SURGERY BMC Surgery Pub Date : 2024-12-21 DOI:10.1186/s12893-024-02720-9
Peng Cui, Shuaikang Wang, Haojie Zhang, Peng Wang, Xiaolong Chen, Chao Kong, Shibao Lu
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Abstract

Objective: Failure to understand long-term quality of life and functional outcomes hinders effective decision making and prognostication. Therefore, the study aims to predict and analyse the unfavorable outcomes (FOs) in elderly patients undergoing lumbar fusion surgery.

Methods: Consecutive 382 patients who underwent lumbar fusion surgery for lumbar degenerative disease from March 2019 to July 2022 were enrolled in this study. The risk factors were selected by the least absolute shrinkage and selection operator method (LASSO) regression. Then, a nomogram prediction model was established to predict unfavorable outcomes (UFOs) by using the risk factors selected from LASSO regression. The performance of the model was assessed by the calibration curve and receiver operating characteristic (ROC) curve. The decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical utility of the model.

Results: Finally, 147 of 382 patients showed UFOs. After splitting data in a 70 - 30 fashion, 267 patients were included in the training set. Ten potential risk factors were selected according to the LASSO regression, that identified the predictor to establish nomogram model. The area under the curve (AUC) value was 0.828, and the calibration curve gained from this prediction model suggested good predictive accuracy between the predicted probability and actual probability. In the validation set, the AUC for the model was 0.858. Likewise, the calibration curve gained from this prediction model suggested good predictive accuracy between the predicted probability and actual probability. And the results of DCA and CIC demonstrated that the model showed good clinical practicability in the validation set.

Conclusion: This nomogram model has good predictive performance and clinical practicability, which could provide a certain basis for predicting UFOs in elderly patients undergoing lumbar fusion surgery.

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发展和验证的nomogram预测因退行性疾病而行腰椎融合手术的老年患者的不良结果。
目的:不了解长期生活质量和功能结局会妨碍有效的决策和预后。因此,本研究旨在预测和分析老年腰椎融合手术患者的不良预后(FOs)。方法:2019年3月至2022年7月,连续382例腰椎退行性疾病患者行腰椎融合手术。采用最小绝对收缩法和选择算子法(LASSO)回归对危险因素进行筛选。然后,利用LASSO回归选择的危险因素,建立了对不良结果(ufo)的nomogram预测模型。通过标定曲线和受试者工作特征(ROC)曲线评价模型的性能。采用决策曲线分析(DCA)和临床影响曲线(CIC)评价模型的临床应用价值。结果:382例患者中有147例出现不明飞行物。在以70 - 30的方式分割数据后,267名患者被纳入训练集。通过LASSO回归筛选出10个潜在危险因素,确定预测因子,建立nomogram模型。曲线下面积(AUC)值为0.828,该预测模型得到的校准曲线在预测概率与实际概率之间具有较好的预测精度。在验证集中,模型的AUC为0.858。同样,该预测模型得到的校准曲线表明,预测概率与实际概率之间具有较好的预测精度。DCA和CIC结果表明,该模型在验证集中具有良好的临床实用性。结论:该模型具有良好的预测性能和临床实用性,可为老年腰椎融合术患者不明飞行物的预测提供一定依据。
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来源期刊
BMC Surgery
BMC Surgery SURGERY-
CiteScore
2.90
自引率
5.30%
发文量
391
审稿时长
58 days
期刊介绍: BMC Surgery is an open access, peer-reviewed journal that considers articles on surgical research, training, and practice.
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