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用于预测颅骨骨折患者28天死亡率的列线图的建立及外部验证

Establishment and external validation of a nomogram for predicting 28-day mortality in patients with skull fracture.

作者信息

Tang Jia, Zhong Zhenguang, Nijiati Muyesai, Wu Changdong

机构信息

Graduate School of Xinjiang Medical University, Ürümqi, China.

Department of Bioengineering, Imperial College London, London, United Kingdom.

出版信息

Front Neurol. 2024 Jan 12;14:1338545. doi: 10.3389/fneur.2023.1338545. eCollection 2023.

Abstract

BACKGROUND

Skull fracture can lead to significant morbidity and mortality, yet the development of effective predictive tools has remained a challenge. This study aimed to establish and validate a nomogram to evaluate the 28-day mortality risk among patients with skull fracture.

MATERIALS AND METHODS

Data extracted from the Medical Information Mart for Intensive Care (MIMIC) database were utilized as the training set, while data from the eICU Collaborative Research Database were employed as the external validation set. This nomogram was developed using univariate Cox regression, best subset regression (BSR), and the least absolute shrinkage and selection operator (LASSO) methods. Subsequently, backward stepwise multivariable Cox regression was employed to refine predictor selection. Variance inflation factor (VIF), akaike information criterion (AIC), area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) were used to assess the model's performance.

RESULTS

A total of 1,527 adult patients with skull fracture were enrolled for this analysis. The predictive factors in the final nomogram included age, temperature, serum sodium, mechanical ventilation, vasoactive agent, mannitol, extradural hematoma, loss of consciousness and Glasgow Coma Scale score. The AUC of our nomogram was 0.857, and C-index value was 0.832. After external validation, the model maintained an AUC of 0.853 and a C-index of 0.829. Furthermore, it showed good calibration with a low Brier score of 0.091 in the training set and 0.093 in the external validation set. DCA in both sets revealed that our model was clinically useful.

CONCLUSION

A nomogram incorporating nine features was constructed, with a good ability in predicting 28-day mortality in patients with skull fracture.

摘要

背景

颅骨骨折可导致严重的发病率和死亡率,但开发有效的预测工具仍然是一项挑战。本研究旨在建立并验证一种列线图,以评估颅骨骨折患者28天的死亡风险。

材料与方法

从重症监护医学信息集市(MIMIC)数据库中提取的数据用作训练集,而来自电子重症监护病房协作研究数据库的数据用作外部验证集。该列线图采用单变量Cox回归、最佳子集回归(BSR)和最小绝对收缩和选择算子(LASSO)方法构建。随后,采用向后逐步多变量Cox回归来优化预测因子的选择。使用方差膨胀因子(VIF)、赤池信息准则(AIC)、受试者操作特征曲线下面积(AUC)、一致性指数(C-index)、校准曲线和决策曲线分析(DCA)来评估模型的性能。

结果

本分析共纳入1527例成年颅骨骨折患者。最终列线图中的预测因素包括年龄、体温、血清钠、机械通气、血管活性药物、甘露醇、硬膜外血肿、意识丧失和格拉斯哥昏迷量表评分。我们列线图的AUC为0.857,C-index值为0.832。外部验证后,该模型的AUC维持在0.853,C-index为0.829。此外,它在训练集中显示出良好的校准,Brier评分低至0.091,在外部验证集中为0.093。两组的DCA均显示我们的模型具有临床实用性。

结论

构建了一个包含九个特征的列线图,对颅骨骨折患者28天死亡率具有良好的预测能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9433/10811263/2d565ec44b9e/fneur-14-1338545-g0001.jpg

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