Department of Innovative Medical Research, Chinese People's Liberation Army General Hospital, Beijing, China.
Department of Health Statistics, Naval Medical University, Shanghai, China.
Ann Med. 2024 Dec;56(1):2391018. doi: 10.1080/07853890.2024.2391018. Epub 2024 Aug 19.
The prognosis of trauma patients is highly dependent on early medical diagnosis. By constructing a nomogram model, the risk of adverse outcomes can be displayed intuitively and individually, which has important clinical implications for medical diagnosis.
To develop and evaluate models for predicting patients with adverse outcomes of trauma that can be used in different data availability settings in China.
This was a retrospective prognostic study using data from 8 public tertiary hospitals in China from 2018. The data were randomly divided into a development set and a validation set. Simple, improved and extended models predicting adverse outcomes were developed, with adverse outcomes defined as in-hospital death or ICU transfer, and patient clinical characteristics, vital signs, diagnoses, and laboratory test values as predictors. The results of the models were presented in the form of nomograms, and performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), precision-recall (PR) curves (PR-AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis (DCA).
Our final dataset consisted of 18,629 patients (40.2% female, mean age of 52.3), 1,089 (5.85%) of whom resulted in adverse outcomes. In the external validation set, three models achieved ROC-AUC of 0.872, 0.881, and 0.903, and a PR-AUC of 0.339, 0.337, and 0.403, respectively. In terms of the calibration curves and DCA, the models also performed well.
This prognostic study found that three prediction models and nomograms including the patient clinical characteristics, vital signs, diagnoses, and laboratory test values can support clinicians in more accurately identifying patients who are at risk of adverse outcomes in different settings based on data availability.
创伤患者的预后高度依赖于早期的医学诊断。通过构建列线图模型，可以直观地个体化显示不良结局的风险，这对医学诊断具有重要的临床意义。
构建并验证可用于中国不同数据可用性情境下预测创伤不良结局的患者的模型。
这是一项回顾性预后研究，使用了来自中国 8 家公立三级医院的 2018 年数据。数据随机分为开发集和验证集。开发集和验证集中分别构建了简单、改进和扩展的预测创伤不良结局的模型，将住院死亡或转入 ICU 定义为不良结局，将患者的临床特征、生命体征、诊断和实验室检查值作为预测因素。模型的结果以列线图的形式呈现，通过接受者操作特征曲线（ROC）下面积（AUC）、精确性-召回率（PR）曲线（PR-AUC）、Hosmer-Lemeshow 拟合优度检验、校准曲线和决策曲线分析（DCA）来评估模型的性能。
最终数据集包括 18629 例患者（40.2%为女性，平均年龄为 52.3 岁），其中 1089 例（5.85%）患者发生了不良结局。在外部验证集中，3 个模型的 ROC-AUC 分别为 0.872、0.881 和 0.903，PR-AUC 分别为 0.339、0.337 和 0.403。在校准曲线和 DCA 方面，模型也表现良好。
本研究发现，纳入患者临床特征、生命体征、诊断和实验室检查值的 3 个预测模型和列线图，可根据数据可用性，帮助不同情境下的临床医生更准确地识别发生不良结局风险较高的患者。
