| 彭小玲,陈卓敏.急诊多发伤患者早期死亡的危险因素及列线图 预测模型建立[J].安徽医药,待发表. |
| 急诊多发伤患者早期死亡的危险因素及列线图 预测模型建立 |
|
| 投稿时间：2024-03-18 录用日期：2024-04-15 |
| DOI： |
| 中文关键词: 损伤严重程度评分 格拉斯哥昏迷量表 碱剩余 乳酸 多发伤 列线图 |
| 英文关键词: |
| 基金项目:江西省卫生健康委科技计划项目（20204808） |
|
| 摘要点击次数: 130 |
| 全文下载次数: 0 |
| 中文摘要: |
| 目的 分析急诊多发伤患者早期死亡的危险因素，建立并验证列线图预测模型。方法 对2019年1月1日—2023年8月31日期间联勤保障部队第九〇八医院收治的289例急诊多发伤患者的临床资料进行回顾性研究分析。应用单因素及多因素logistic回归分析探讨影响急诊多发伤患者早期死亡的危险因素，并绘制列线图预测模型。使用受试者工作特征曲线（ROC）和校准曲线评估模型的区分度和预测性能，使用决策曲线（DCA）评估模型的临床适用性。结果 多因素logistic回归分析显示，年龄≥43岁、乳酸（Lac）≥2.3 mmol.L-1和损伤严重程度评分（ISS）≥25分是早期死亡的独立危险因素（P均<0.001）；碱剩余（BE）>－2.8 mmol.L-1和格拉斯哥昏迷评分（GCS）>7分对早期死亡具有保护作用（P均<0.05）；列线图的ROC曲线下面积为0.840（95% CI：0.756~0.923）；Hosmer-Lemeshow（HL）拟合优度检验=3.734，P=0.880；DCA结果显示具有正向净获益，表明列线图对急诊多发伤患者早期死亡具有较好的预测性能。结论 本研究构建的列线图预测模型对急诊多发伤患者早期死亡具有较好的预测效果，有助于临床医生识别高危患者并制定相应的临床决策。 |
| 英文摘要: |
| Objective To analyze the risk factors of early death in emergency patients with multiple injuries, and establish and verify the nomogram prediction model. Methods The clinical data of 289 patients with emergency multiple injuries admitted to our hospital from January 1, 2019 to August 31, 2023 were retrospectively analyzed.Univariate and multivariate logistic regression analysis was used to investigate the factors affecting the early death of patients, and the prediction model was drawn with a column graph.The model's differentiability and predictive performance were evaluated using receiver operating characteristic curves (ROC) and calibration curves, and the model's clinical applicability was evaluated by decision curve analysis (DCA). Results Multivariate logistic regression analysis showed that age≥43 years old, lactic acid (Lac)≥2.3mmol/L and injury severity score (ISS)≥25 points were independent risk factors for early death (P <0.001).Alkali residual (BE) >-2.8mmol/L and Glasgow Coma Score (GCS) >7 were protective against early death (P <0.05).The area under ROC curve of the histogram was 0.840 (95%CI: 0.756~0.923), the Hosmer-Lemeshow (HL) goodness of fit test was 3.734, P=0.880, and the DCA results showed a positive net benefit, indicating that the histogram had a good predictive performance for early death in emergency patients with multiple injuries. Conclusion The nomogram prediction model established in this study has a good predictive effect on early death of emergency patients with multiple injuries, which is helpful for clinicians to identify high-risk patients and make corresponding clinical decisions. |
|
查看/发表评论 下载PDF阅读器 |
| 关闭 |
