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预测腰椎融合手术患者术后住院时间延长的风险:一种新型预测列线图的开发与评估

Predicting prolonged postoperative length of stay risk in patients undergoing lumbar fusion surgery: Development and assessment of a novel predictive nomogram.

作者信息

Lu Chen-Xin, Huang Zhi-Bin, Chen Xiao-Mei, Wu Xiao-Dan

机构信息

Department of Anesthesiology, Fuzhou Second Hospital, Fuzhou, China.

Department of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, China.

出版信息

Front Surg. 2022 Aug 16;9:925354. doi: 10.3389/fsurg.2022.925354. eCollection 2022.

Abstract

OBJECTIVE

The purpose of this study was to develop and internally validate a prediction nomogram model in patients undergoing lumbar fusion surgery.

METHODS

A total of 310 patients undergoing lumbar fusion surgery were reviewed, and the median and quartile interval were used to describe postoperative length of stay (PLOS). Patients with PLOS > P were defined as prolonged PLOS. The least absolute shrinkage and selection operator (LASSO) regression was used to filter variables for building the prolonged PLOS risk model. Multivariable logistic regression analysis was applied to build a predictive model using the variables selected in the LASSO regression model. The area under the ROC curve (AUC) of the predicting model was calculated and significant test was performed. The Kappa consistency test between the predictive model and the actual diagnosis was performed. Discrimination, calibration, and the clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.

RESULTS

According to the interquartile range of PLOS in a total of 310 patients, the PLOS of 235 patients was ≤P (7 days) (normal PLOS), and the PLOS of 75 patients was > P (prolonged PLOS). The LASSO selected predictors that were used to build the prediction nomogram included BMI, diabetes, hypertension, duration of surgery, duration of anesthesia, anesthesia type, intraoperative blood loss, sufentanil for postoperative analgesia, and postoperative complication. The model displayed good discrimination with an AUC value of 0.807 (95% CI: 0.758-0.849, < 0.001), a Kappa value of 0.5186 (cutoff value, 0.2445,  < 0.001), and good calibration. A high C-index value of 0.776 could still be reached in the interval validation. Decision curve analysis showed that the prolonged PLOS nomogram was clinically useful when intervention was decided at the prolonged PLOS possibility threshold of 3%.

CONCLUSIONS

This study developed a novel nomogram with a relatively good accuracy to help clinicians access the risk of prolonged PLOS in lumbar fusion surgery patients. By an estimate of individual risk, surgeons and anesthesiologists may shorten PLOS and accelerate postoperative recovery of lumbar fusion surgery through more accurate individualized treatment.

摘要

目的

本研究旨在开发并内部验证一种用于接受腰椎融合手术患者的预测列线图模型。

方法

回顾性分析310例接受腰椎融合手术的患者,采用中位数和四分位数间距描述术后住院时间(PLOS)。PLOS>P的患者被定义为PLOS延长。采用最小绝对收缩和选择算子(LASSO)回归筛选变量,以构建PLOS延长风险模型。应用多变量逻辑回归分析,使用LASSO回归模型中选择的变量构建预测模型。计算预测模型的受试者工作特征曲线下面积(AUC)并进行显著性检验。对预测模型与实际诊断结果进行Kappa一致性检验。使用C指数、校准图和决策曲线分析评估预测模型的区分度、校准度及临床实用性。采用自举法验证进行内部验证。

结果

根据310例患者PLOS的四分位数间距,235例患者的PLOS≤P(7天)(正常PLOS),75例患者的PLOS>P(PLOS延长)。LASSO选择的用于构建预测列线图的预测因素包括体重指数、糖尿病、高血压、手术时间、麻醉时间、麻醉类型、术中失血量、术后镇痛用舒芬太尼及术后并发症。该模型显示出良好的区分度,AUC值为0.807(95%CI:0.758 - 0.849,P<0.001),Kappa值为0.5186(截断值,0.2445,P<0.001),且校准良好。在区间验证中仍可达到较高的C指数值0.776。决策曲线分析表明,当在3%的PLOS延长可能性阈值处决定干预时,PLOS延长列线图在临床上是有用的。

结论

本研究开发了一种准确性相对较高的新型列线图,以帮助临床医生评估腰椎融合手术患者PLOS延长的风险。通过估计个体风险,外科医生和麻醉医生可以通过更准确的个体化治疗缩短PLOS并加速腰椎融合手术患者的术后恢复。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a3c/9426777/4e86f2edb710/fsurg-09-925354-g001.jpg

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