The First Affiliated Hospital of Jinan University, Guangzhou, China.
Eur J Med Res. 2024 Jan 29;29(1):80. doi: 10.1186/s40001-024-01655-4.
The incidence of cervical spine fractures is increasing every day, causing a huge burden on society. This study aimed to develop and verify a nomogram to predict the in-hospital mortality of patients with cervical spine fractures without spinal cord injury. This could help clinicians understand the clinical outcome of such patients at an early stage and make appropriate decisions to improve their prognosis.
This study included 394 patients with cervical spine fractures from the Medical Information Mart for Intensive Care III database, and 40 clinical indicators of each patient on the first day of admission to the intensive care unit were collected. The independent risk factors were screened using the Least Absolute Shrinkage and Selection Operator regression analysis method, a multi-factor logistic regression model was established, nomograms were developed, and internal validation was performed. A receiver operating characteristic (ROC) curve was drawn, and the area under the ROC curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. Moreover, the consistency between the actual probability and predicted probability was reflected using the calibration curve and Hosmer-Lemeshow (HL) test. A decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit.
The nomogram indicators included the systolic blood pressure, oxygen saturation, respiratory rate, bicarbonate, and simplified acute physiology score (SAPS) II. The results showed that our model had satisfactory predictive ability, with an AUC of 0.907 (95% confidence interval [CI] = 0.853-0.961) and 0.856 (95% CI = 0.746-0.967) in the training set and validation set, respectively. Compared with the SAPS-II system, the NRI values of the training and validation sets of our model were 0.543 (95% CI = 0.147-0.940) and 0.784 (95% CI = 0.282-1.286), respectively. The IDI values of the training and validation sets were 0.064 (95% CI = 0.004-0.123; P = 0.037) and 0.103 (95% CI = 0.002-0.203; P = 0.046), respectively. The calibration plot and HL test results confirmed that our model prediction results showed good agreement with the actual results, where the HL test values of the training and validation sets were P = 0.8 and P = 0.95, respectively. The DCA curve revealed that our model had better clinical net benefit than the SAPS-II system.
We explored the in-hospital mortality of patients with cervical spine fractures without spinal cord injury and constructed a nomogram to predict their prognosis. This could help doctors assess the patient's status and implement interventions to improve prognosis accordingly.
颈椎骨折的发病率日益增高，给社会带来了巨大负担。本研究旨在建立并验证一个列线图，以预测无脊髓损伤的颈椎骨折患者的院内病死率，帮助临床医生在早期了解此类患者的临床结局，并做出适当决策以改善其预后。
本研究纳入了来自医疗信息集中监测 III 数据库的 394 例颈椎骨折患者，收集了每位患者入重症监护病房第 1 天的 40 项临床指标。采用最小绝对收缩和选择算子回归分析方法筛选独立风险因素，建立多因素 logistic 回归模型，绘制列线图，并进行内部验证。绘制受试者工作特征（ROC）曲线，计算曲线下面积（AUC）、净重新分类改善（NRI）和综合判别改善（IDI）以评估模型的判别能力。此外，通过校准曲线和 Hosmer-Lemeshow（HL）检验评估模型预测概率与实际概率之间的一致性。进行决策曲线分析（DCA），比较列线图与临床实践中常用的评分系统，以评估临床净获益。
列线图指标包括收缩压、氧饱和度、呼吸频率、碳酸氢盐和简化急性生理学评分（SAPS）II。结果表明，我们的模型具有良好的预测能力，在训练集和验证集中的 AUC 分别为 0.907（95%置信区间[CI]：0.853-0.961）和 0.856（95% CI：0.746-0.967）。与 SAPS-II 系统相比，我们模型的训练集和验证集的 NRI 值分别为 0.543（95% CI：0.147-0.940）和 0.784（95% CI：0.282-1.286）。训练集和验证集的 IDI 值分别为 0.064（95% CI：0.004-0.123；P=0.037）和 0.103（95% CI：0.002-0.203；P=0.046）。校准曲线和 HL 检验结果证实，我们的模型预测结果与实际结果具有良好的一致性，训练集和验证集的 HL 检验值分别为 P=0.8 和 P=0.95。DCA 曲线表明，我们的模型比 SAPS-II 系统具有更好的临床净获益。
本研究探讨了无脊髓损伤的颈椎骨折患者的院内病死率，并构建了一个预测其预后的列线图，有助于医生评估患者的病情并采取相应的干预措施以改善预后。
