Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China.
Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China.
Front Cell Infect Microbiol. 2022 Mar 10;12:850741. doi: 10.3389/fcimb.2022.850741. eCollection 2022.
pneumonia (PCP) is a life-threatening disease associated with a high mortality rate among immunocompromised patient populations. Invasive mechanical ventilation (IMV) is a crucial component of treatment for PCP patients with progressive hypoxemia. This study explored the risk factors for IMV and established a model for early predicting the risk of IMV among patients with PCP.
A multicenter, observational cohort study was conducted in 10 hospitals in China. Patients diagnosed with PCP were included, and their baseline clinical characteristics were collected. A Boruta analysis was performed to identify potentially important clinical features associated with the use of IMV during hospitalization. Selected variables were further analyzed using univariate and multivariable logistic regression. A logistic regression model was established based on independent risk factors for IMV and visualized using a nomogram.
In total, 103 patients comprised the training cohort for model development, and 45 comprised the validation cohort to confirm the model's performance. No significant differences were observed in baseline clinical characteristics between the training and validation cohorts. Boruta analysis identified eight clinical features associated with IMV, three of which were further confirmed to be independent risk factors for IMV, including age (odds ratio [OR] 2.615 [95% confidence interval (CI) 1.110-6.159]; = 0.028), oxygenation index (OR 0.217 [95% CI 0.078-0.604]; = 0.003), and serum lactate dehydrogenase level (OR 1.864 [95% CI 1.040-3.341]; = 0.037). Incorporating these three variables, the nomogram achieved good concordance indices of 0.829 (95% CI 0.752-0.906) and 0.818 (95% CI 0.686-0.950) in predicting IMV in the training and validation cohorts, respectively, and had well-fitted calibration curves.
The nomogram demonstrated accurate prediction of IMV in patients with PCP. Clinical application of this model enables early identification of patients with PCP who require IMV, which, in turn, may lead to rational therapeutic choices and improved clinical outcomes.
肺炎（PCP）是一种危及生命的疾病，在免疫功能低下的患者群体中死亡率较高。有创机械通气（IMV）是治疗 PC 患者进行性低氧血症的重要组成部分。本研究探讨了 IMV 的危险因素，并建立了一个预测 PC 患者 IMV 风险的早期模型。
在中国 10 家医院进行了一项多中心、观察性队列研究。纳入了诊断为 PCP 的患者，并收集了他们的基线临床特征。进行了 Boruta 分析，以确定与住院期间使用 IMV 相关的潜在重要临床特征。使用单变量和多变量逻辑回归分析选定的变量。根据 IMV 的独立危险因素建立逻辑回归模型，并使用列线图可视化。
共有 103 例患者纳入模型开发的训练队列，45 例患者纳入验证队列以确认模型的性能。在训练和验证队列中，基线临床特征无显著差异。Boruta 分析确定了 8 个与 IMV 相关的临床特征，其中 3 个被进一步确认为 IMV 的独立危险因素，包括年龄（比值比 [OR] 2.615 [95% 置信区间 [CI] 1.110-6.159]； = 0.028）、氧合指数（OR 0.217 [95% CI 0.078-0.604]； = 0.003）和血清乳酸脱氢酶水平（OR 1.864 [95% CI 1.040-3.341]； = 0.037）。纳入这三个变量后，列线图在训练和验证队列中分别达到了 0.829（95% CI 0.752-0.906）和 0.818（95% CI 0.686-0.950）的良好一致性指数，且校准曲线拟合良好。
该列线图准确预测了 PC 患者的 IMV。该模型的临床应用可早期识别需要 IMV 的 PC 患者，从而可能做出合理的治疗选择并改善临床结局。
