Alessandri Francesco, Tosi Antonella, De Lazzaro Francesco, Andreoli Chiara, Cicchinelli Andrea, Carrieri Cosima, Lai Quirino, Pugliese Francesco
Department of General Surgery and Organ Transplantation Unit, Sapienza University of Rome, Policlinico Umberto I, 00161 Rome, Italy.
Department of Anesthesiology, Critical Care and Pain Medicine, Sapienza University of Rome, Policlinico Umberto I, 00161 Rome, Italy.
J Clin Med. 2022 May 5;11(9):2593. doi: 10.3390/jcm11092593.
(1) Background: the aim of this study was to create a score to predict the incidence of CPAP failure in COVID-19 patients early. (2) Methods: in this retrospective observational study, we included all consecutive adult patients admitted between February and April 2021. The main outcome was the failure of CPAP support (intubation or death). (3) Results: two-hundred and sixty-three COVID-19 patients were managed with CPAP. The population was divided in short-CPAP (CPAP days ≤ 10; 72.6%) and long-CPAP (>10; 27.4%) groups. After balancing the entire population using a stabilized IPTW method, we applied a multivariable logistic regression analysis to identify the risk factors for CPAP failure. We used the identified covariates to create a mathematical model, the CPAP Failure Score (CPAP-FS). The multivariable logistic regression analysis identified four variables: SpO2 (OR = 0.86; p-value = 0.001), P/F ratio (OR = 0.99; p-value = 0.008), the Call Score (OR = 1.44; p-value = 0.02), and a pre-existing chronic lung disease (OR = 3.08; p-value = 0.057). The beta-coefficients obtained were used to develop the CPAP-FS, whose diagnostic ability outperformed other relevant COVID-19-related parameters (AUC = 0.87; p-value < 0.0001). We validated the CPAP-FS using a 10-fold internal cross-validation method which confirmed the observed results (AUCs 0.76−0.80; p-values < 0.0001). (4) Conclusions: the CPAP-FS can early identify COVID-19 patients who are at risk of CPAP failure.
（1）背景：本研究旨在创建一个评分系统，以早期预测新冠肺炎患者持续气道正压通气（CPAP）失败的发生率。（2）方法：在这项回顾性观察研究中，我们纳入了2021年2月至4月期间收治的所有连续成年患者。主要结局是CPAP支持失败（插管或死亡）。（3）结果：263例新冠肺炎患者接受了CPAP治疗。将患者分为短CPAP组（CPAP使用天数≤10天；72.6%）和长CPAP组（>10天；27.4%）。使用稳定的逆概率加权法（IPTW）对整个人群进行平衡后，我们进行了多变量逻辑回归分析，以确定CPAP失败的风险因素。我们使用确定的协变量创建了一个数学模型，即CPAP失败评分（CPAP-FS）。多变量逻辑回归分析确定了四个变量：血氧饱和度（SpO2）（比值比[OR]=0.86；p值=0.001）、氧合指数（P/F）（OR=0.99；p值=0.008）、Call评分（OR=1.44；p值=0.02）和既往慢性肺病（OR=3.08；p值=0.057）。获得的β系数用于开发CPAP-FS，其诊断能力优于其他相关的新冠肺炎相关参数（曲线下面积[AUC]=0.87；p值<0.0001）。我们使用10倍内部交叉验证方法验证了CPAP-FS，该方法证实了观察结果（AUC为0.76 - 0.80；p值<0.0001）。（4）结论：CPAP-FS可以早期识别有CPAP失败风险的新冠肺炎患者。
