重症肺炎患者早期行机械通气的列线图预测模型构建及验证

目的　探索和建立重症肺炎患者是否需要机械通气的临床预测模型。方法　回顾性分析我院收治的重症肺炎患者185例，根据患者24 h内是否行机械通气治疗分为机械通气组（123例）和非机械通气组（62例）。统计2组患者性别、年龄，入院时动脉血氧分压［p（O2）］、动脉血二氧化碳分压［p（CO2）］、肺泡动脉血氧分压差［p（A-a）O2］、氧合指数（OI）等血气分析指标和实验室检查结果的差异。多因素Logistic回归筛选影响重症肺炎患者需要行机械通气的危险因素，根据筛选后的指标构建预测模型，并绘制列线图。通过受检者工作特征（ROC）曲线和校准曲线评价模型的预测价值。结果　与非机械通气组相比，机械通气组患者年龄、p（A-a）O2、急性生理学与慢性健康状况评分系统Ⅱ（APACHEⅡ）评分、动脉血p（CO2）升高；降钙素原（PCT）、中心静脉血氧饱和度（ScvO2）、OI、动脉血p（O2）降低（P＜0.05）。Logistic回归分析显示，年龄、OI、p（O2）、p（CO2）、p（A-a）O2是患者是否需要机械通气的独立影响因素，上述5个指标构建的列线图模型具有较好的区分度（AUC=0.827，95%CI：0.785~0.898）和精准度，优于传统的p（O2）+ p（CO2）+OI模型和OI模型。结论　基于患者年龄， p（O2）、p（CO2）、OI、p（A-a）O2等参数建立的列线图模型可准确预测重症肺炎患者早期是否需要机械通气。

Construction and verification of a prediction nomogram for early mechanical ventilation in patients with severe pneumonia

Objective　To explore and establish a clinical predictive model for patients with severe pneumonia. Methods　A total of 185 patients with severe pneumonia treated in our hospital were retrospectively analyzed. According to whether the patient received mechanical ventilation within 24 hours, they were divided into mechanical ventilation group (n=123) and non-mechanical ventilation group (n=62). Data of patient sex, age, blood gas analysis indicators such as arterial partial pressure of oxygen [p(O2)], arterial partial pressure of carbon dioxide [p(CO2)], alveolar-arterial partial pressure of oxygen [p(A-a)O2], oxygenation index (OI), and some laboratory findings at admission were analyzed. Multivariate Logistic regression was used to screen the risk factors affecting the need for mechanical ventilation in patients with severe pneumonia. A predictive model based on these selected indicators was constructed, and a nomogram was plotted. The predictive value of the model was evaluated through the receiver operating characteristic (ROC) curve and calibration curve. Results　Compared with the non-mechanical ventilation group, data of age, p(A-a)O2, APACHEⅡ score and p(CO2) were significantly higher in the mechanical ventilation group, while procalcitonin (PCT), central venous oxygen saturation (ScvO2), p(O2) and OI were significantly lower (P＜0.05). Logistic regression analysis showed that age, p(O2), p(CO2), p(A-a)O2 and OI were independent factors affecting whether the patients needed mechanical ventilation. The nomogram model constructed by the above five indicators showed a good discrimination (AUC=0.827, 95%CI: 0.785-0.898) and accuracy, which was better than the traditional p(O2)+ p(CO2)+OI model and OI model. Conclusion The nomogram model established based on age, p (O2), p (CO2), OI and p (A-a)O2 can accurately predict whether mechanical ventilation is required in the early stage of severe pneumonia patient.