Establishment and validation of a prediction model to evaluate the prolonged hospital stay after anterior cervical discectomy and fusion
- VernacularTitle:颈椎前路椎间盘切除融合术后住院时间延长预测模型的建立与验证
- Author:
Hong-Wen GU
1
;
Hong-Wei WANG
;
Shi-Lei TANG
;
Kang-En HAN
;
Zhi-Hao ZHANG
;
Yin HU
;
Hai-Long YU
Author Information
- Keywords: cervical spondylosis myelopathy; anterior cervical discectomy and fusion; hospital stay; nomogram
- From: Journal of Regional Anatomy and Operative Surgery 2024;33(7):604-609
- CountryChina
- Language:Chinese
- Abstract: Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.
