目的：建立预测重症颅脑损伤（TBI）患者在重症监护病房期间继发肺部感染发生的列线图。进一步优化患者的个性化治疗，并支持有效的发展，循证预防和干预策略。
方法：本研究使用来自公开的MIMIC-IV（重症监护医学信息集市IV）数据库的患者数据。
方法：基于人群的回顾性队列研究。
方法：在这项回顾性队列研究中，纳入1780例TBI患者，随机分为训练集（n=1246）和发展集（n=534）。使用Kaplan-Meier曲线分析肺部感染对生存的影响。在训练集中建立单变量逻辑回归模型，以确定肺部感染的潜在因素。并在多变量逻辑回归模型中确定独立危险因素以建立列线图模型。用接受者工作特征(ROC)曲线评估列线图性能，校正曲线和Hosmer-Lemeshow试验，并通过决策曲线分析（DCA）评估预测值。
结果：本研究共纳入1780例TBI患者，其中186例患者（约10%）发生继发性肺部感染，21名患者在住院期间死亡。在1594例未发生肺部感染的患者中，只有85例患者死亡(占5.3%)。存活曲线表明，重症监护病房入院后第7天和第14天，患有肺部感染的TBI患者存在明显的生存劣势（p<0.001）。单变量和多变量逻辑回归分析均显示，除白人或黑人以外的种族等因素，呼吸频率,温度,机械通气,抗生素和充血性心力衰竭是TBI患者肺部感染的独立危险因素(OR>1,p<0.05)。基于这些因素,以及格拉斯哥昏迷量表和国际标准化比率变量，构建训练集模型来预测TBI患者肺部感染的风险，训练集中的ROC曲线下面积为0.800，验证集中为0.768。校准曲线证明了模型的良好校准和与实际观测的一致性。DCA表明了预测模型在临床实践中的实用性。
结论：本研究建立了TBI患者肺部感染的预测模型,这可能有助于临床医生早期识别高危患者并预防肺部感染的发生。
OBJECTIVE: To develop a nomogram for predicting occurrence of secondary pulmonary infection in patients with critically traumatic brain injury (TBI) during their stay in the intensive care unit, to further optimise personalised treatment for patients and support the development of effective, evidence-based prevention and intervention strategies.
METHODS: This study used patient data from the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database.
METHODS: A population-based retrospective cohort study.
METHODS: In this retrospective cohort study, 1780 patients with TBI were included and randomly divided into a training set (n=1246) and a development set (n=534). The impact of pulmonary infection on survival was analysed using Kaplan-Meier curves. A univariate logistic regression model was built in training set to identify potential factors for pulmonary infection, and independent risk factors were determined in a multivariate logistic regression model to build nomogram model. Nomogram performance was assessed with receiver operating characteristic (ROC) curves, calibration curves and Hosmer-Lemeshow test, and predictive value was assessed by decision curve analysis (DCA).
RESULTS: This study included a total of 1780 patients with TBI, of which 186 patients (approximately 10%) developed secondary lung infections, and 21 patients died during hospitalisation. Among the 1594 patients who did not develop lung infections, only 85 patients died (accounting for 5.3%). The survival curves indicated a significant survival disadvantage for patients with TBI with pulmonary infection at 7 and 14 days after intensive care unit admission (p<0.001). Both univariate and multivariate logistic regression analyses showed that factors such as race other than white or black, respiratory rate, temperature, mechanical ventilation, antibiotics and congestive heart failure were independent risk factors for pulmonary infection in patients with TBI (OR>1, p<0.05). Based on these factors, along with Glasgow Coma Scale and international normalised ratio variables, a training set model was constructed to predict the risk of pulmonary infection in patients with TBI, with an area under the ROC curve of 0.800 in the training set and 0.768 in the validation set. The calibration curve demonstrated the model\'s good calibration and consistency with actual observations, while DCA indicated the practical utility of the predictive model in clinical practice.
CONCLUSIONS: This study established a predictive model for pulmonary infections in patients with TBI, which may help clinical doctors identify high-risk patients early and prevent occurrence of pulmonary infections.
