■创伤患者的预后高度依赖于早期医学诊断。通过构建列线图模型,不良后果的风险可以直观和单独地显示,这对医学诊断具有重要的临床意义。
■开发和评估可用于中国不同数据可用性设置的创伤不良结局患者预测模型。
■这是一项回顾性预后研究,使用2018年中国8家公立三甲医院的数据。将数据随机分为开发集和验证集。简单,开发了预测不良结局的改进和扩展模型,不良结局定义为院内死亡或ICU转移,和患者的临床特征,生命体征,诊断,和实验室测试值作为预测因子。模型的结果以列线图的形式呈现,并使用接受者工作特征曲线下面积(ROC-AUC)评估性能,精度-召回(PR)曲线(PR-AUC),Hosmer-Lemeshow拟合优度测试,校正曲线,和决策曲线分析(DCA)。
■我们的最终数据集包括18,629名患者(40.2%为女性,平均年龄52.3),其中1,089人(5.85%)导致不良后果。在外部验证集中,三个模型的ROC-AUC分别为0.872、0.881和0.903,PR-AUC分别为0.339、0.337和0.403。就校准曲线和DCA而言,模型也表现良好。
■这项预后研究发现,包括患者临床特征在内的三种预测模型和列线图,生命体征,诊断,和实验室检测值可以支持临床医生基于数据可用性更准确地识别在不同环境中存在不良结局风险的患者.
UNASSIGNED: 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.
UNASSIGNED: To develop and evaluate models for predicting patients with adverse outcomes of trauma that can be used in different data availability settings in
China.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.