Abstract：
BACKGROUND: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in the intensive care unit (ICU), with high in-hospital mortality. Current scoring systems are limited in predicting nosocomial death of VAP. This study aimed to develop and validate a more accurate and effective prediction model for in-hospital mortality in ICU patients with VAP.
METHODS: This was a retrospective cohort study. The demographic and clinical data of 8,182 adult patients with VAP were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. All patients were randomly classified as a training set (n=4,629) and a test set (n=1,984) with a ratio of 7:3. The outcome was in-hospital mortality and the follow-up was terminated at discharge. Univariate and multivariate logistic regression analyses were used to identify the independent predictors and develop the prediction model in the training set, and internal validation was carried out in the test set. The receiver operating characteristic (ROC) curve and calibration curve were plotted to evaluate the performance of the model.
RESULTS: Ethnicity, lung cancer history, septicemia history, hospital length of stay (LOS), fraction of inspired oxygen (FIO2) level, oxygen saturation (SPO2) level, Simplified Acute Physiology Score (SAPS II) score, Sequential Organ Failure Assessment (SOFA) score, and duration of invasive ventilation were all independently associated with the mortality of VAP. The algorithm of the prediction model was as follows: lnP/(1-P) = -0.700 + 0.493 Other Ethnicity + 0.789 Lung Cancer (Yes) + 0.693 Septicemia (Yes) - 0.074 Hospital LOS - 0.008 FIO2 - 0.032 SPO2 + 0.104 SOFA Score + 0.047 SAPS II + 0.004 Invasive Ventilation. The AUC was 0.837 in the training set and 0.817 in the test set, which indicated that the model performed well. The calibration curve also confirmed good calibration.
CONCLUSIONS: A model with good performance was developed to predict the individual death risk of VAP patients in the ICU, which might have the potential to provide ancillary data to support decision-making by physicians. External validation requires further evaluation of the model performance.
METHODS: This was a retrospective cohort study. The demographic and clinical data of 8,182 adult patients with VAP were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. All patients were randomly classified as a training set (n=4,629) and a test set (n=1,984) with a ratio of 7:3. The outcome was in-hospital mortality and the follow-up was terminated at discharge. Univariate and multivariate logistic regression analyses were used to identify the independent predictors and develop the prediction model in the training set, and internal validation was carried out in the test set. The receiver operating characteristic (ROC) curve and calibration curve were plotted to evaluate the performance of the model.
RESULTS: Ethnicity, lung cancer history, septicemia history, hospital length of stay (LOS), fraction of inspired oxygen (FIO2) level, oxygen saturation (SPO2) level, Simplified Acute Physiology Score (SAPS II) score, Sequential Organ Failure Assessment (SOFA) score, and duration of invasive ventilation were all independently associated with the mortality of VAP. The algorithm of the prediction model was as follows: lnP/(1-P) = -0.700 + 0.493 Other Ethnicity + 0.789 Lung Cancer (Yes) + 0.693 Septicemia (Yes) - 0.074 Hospital LOS - 0.008 FIO2 - 0.032 SPO2 + 0.104 SOFA Score + 0.047 SAPS II + 0.004 Invasive Ventilation. The AUC was 0.837 in the training set and 0.817 in the test set, which indicated that the model performed well. The calibration curve also confirmed good calibration.
CONCLUSIONS: A model with good performance was developed to predict the individual death risk of VAP patients in the ICU, which might have the potential to provide ancillary data to support decision-making by physicians. External validation requires further evaluation of the model performance.
摘要：
背景：呼吸机相关性肺炎（VAP）是重症监护病房（ICU）中常见的医院感染，医院死亡率高。当前的评分系统在预测VAP的医院死亡方面受到限制。本研究旨在开发和验证更准确有效的ICUVAP患者院内死亡率预测模型。
方法：这是一项回顾性队列研究。从重症监护医学信息集市（MIMIC-III）数据库中提取了8,182名成人VAP患者的人口统计学和临床数据。将所有患者随机分类为比率为7:3的训练集(n=4,629)和测试集(n=1,984)。结果为住院死亡率，出院时终止随访。使用单变量和多变量逻辑回归分析来识别独立预测因子并在训练集中开发预测模型。并在测试集中进行内部验证。绘制了接收器工作特性（ROC）曲线和校准曲线以评估模型的性能。
结果：种族，肺癌病史,败血症史,住院时间（LOS），吸入氧气（FIO2）水平的分数，氧饱和度(SPO2)水平,简化急性生理学评分（SAPSII）评分，序贯器官衰竭评估（SOFA）评分，有创通气时间均与VAP的死亡率独立相关。预测模型的算法如下:lnP/(1-P)=-0.700+0.493其他种族+0.789肺癌(是)+0.693败血症(是)-0.074医院LOS-0.008FIO2-0.032SPO2+0.104SOFA评分+0.047SAPSII+0.004有创通气。训练集中的AUC为0.837，测试集中的AUC为0.817，这表明该模型表现良好。校准曲线也证实了良好的校准。
结论：建立了一个性能良好的模型来预测ICU中VAP患者的个体死亡风险，这可能有可能提供辅助数据来支持医生的决策。外部验证需要进一步评估模型性能。
方法：这是一项回顾性队列研究。从重症监护医学信息集市（MIMIC-III）数据库中提取了8,182名成人VAP患者的人口统计学和临床数据。将所有患者随机分类为比率为7:3的训练集(n=4,629)和测试集(n=1,984)。结果为住院死亡率，出院时终止随访。使用单变量和多变量逻辑回归分析来识别独立预测因子并在训练集中开发预测模型。并在测试集中进行内部验证。绘制了接收器工作特性（ROC）曲线和校准曲线以评估模型的性能。
结果：种族，肺癌病史,败血症史,住院时间（LOS），吸入氧气（FIO2）水平的分数，氧饱和度(SPO2)水平,简化急性生理学评分（SAPSII）评分，序贯器官衰竭评估（SOFA）评分，有创通气时间均与VAP的死亡率独立相关。预测模型的算法如下:lnP/(1-P)=-0.700+0.493其他种族+0.789肺癌(是)+0.693败血症(是)-0.074医院LOS-0.008FIO2-0.032SPO2+0.104SOFA评分+0.047SAPSII+0.004有创通气。训练集中的AUC为0.837，测试集中的AUC为0.817，这表明该模型表现良好。校准曲线也证实了良好的校准。
结论：建立了一个性能良好的模型来预测ICU中VAP患者的个体死亡风险，这可能有可能提供辅助数据来支持医生的决策。外部验证需要进一步评估模型性能。
