Li Peihan, Wang Xuejuan, Li Li
Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China.
Drug Healthc Patient Saf. 2025 Mar 5;17:63-74. doi: 10.2147/DHPS.S497413. eCollection 2025.
Respiratory failure (RF) after trauma is one of the major causes of patients being admitted to the ICU and leads to a high mortality rate. However, we cannot predict mortality rates based on patients' various indicators. The aim of this study is to develop and validate a nomogram for predicting mortality in patients in the intensive care unit (ICU).
A total of 377 patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database were included in the study. All participants were systematically divided into a development cohort for modelling and a validation cohort for internal validation at a ratio of 7:3. Following patient admission, a comprehensive collection of 30 clinical indicators was performed. The least absolute shrinkage and selection operator (LASSO) regression technique was employed to discern pivotal risk factors. A multivariate Cox regression model was established, and a receiver operating curve (ROC) was plotted, and the area under the curve (AUC) was calculated. Furthermore, the decision curve analysis (DCA) was performed, and the nomogram was compared with the acute physiology score III (APSIII) and Oxford acute severity of illness score (OASIS) scoring systems to assess the net clinical benefit.
The indicators included in our model were age, OASIS score, SAPS III score, respiratory rate (RR), blood urea nitrogen (BUN) and hematocrit. The results demonstrated that our model yielded satisfied performance on the development cohort and on internal validation. The calibration curve underscored a robust concordance between predicted and actual outcomes. The DCA showed a superior clinical utility of our model in contrast to previously reported scoring systems.
In summary, we devised a nomogram for predicting mortality during the ICU stay of RF patients following trauma and established a prediction model that facilitates clinical decision making. However, external validation is needed in the future.
创伤后呼吸衰竭（RF）是患者入住重症监护病房（ICU）的主要原因之一，且死亡率很高。然而，我们无法根据患者的各项指标预测死亡率。本研究的目的是开发并验证一种用于预测重症监护病房（ICU）患者死亡率的列线图。
本研究纳入了重症监护医学信息数据库（MIMIC）-IV中的377例患者。所有参与者按7:3的比例系统地分为用于建模的开发队列和用于内部验证的验证队列。患者入院后，全面收集30项临床指标。采用最小绝对收缩和选择算子（LASSO）回归技术来识别关键危险因素。建立多变量Cox回归模型，绘制受试者工作特征曲线（ROC），并计算曲线下面积（AUC）。此外，进行决策曲线分析（DCA），并将列线图与急性生理学评分III（APSIII）和牛津急性疾病严重程度评分（OASIS）评分系统进行比较，以评估净临床获益。
我们模型中纳入的指标包括年龄、OASIS评分、SAPS III评分、呼吸频率（RR）、血尿素氮（BUN）和血细胞比容。结果表明，我们的模型在开发队列和内部验证中均表现良好。校准曲线强调了预测结果与实际结果之间的高度一致性。与先前报道的评分系统相比，DCA显示我们的模型具有更高的临床实用性。
总之，我们设计了一种用于预测创伤后RF患者在ICU住院期间死亡率的列线图，并建立了一个有助于临床决策的预测模型。然而，未来还需要进行外部验证。
