Abstract：
OBJECTIVE: Quantifying breathing effort in non-intubated patients is important but difficult. We aimed to develop two models to estimate it in patients treated with high-flow oxygen therapy.
METHODS: We analyzed the data of 260 patients from previous studies who received high-flow oxygen therapy. Their breathing effort was measured as the maximal deflection of esophageal pressure (ΔPes). We developed a multivariable linear regression model to estimate ΔPes (in cmH2O) and a multivariable logistic regression model to predict the risk of ΔPes being >10 cmH2O. Candidate predictors included age, sex, diagnosis of the coronavirus disease 2019 (COVID-19), respiratory rate, heart rate, mean arterial pressure, the results of arterial blood gas analysis, including base excess concentration (BEa) and the ratio of arterial tension to the inspiratory fraction of oxygen (PaO2:FiO2), and the product term between COVID-19 and PaO2:FiO2.
RESULTS: We found that ΔPes can be estimated from the presence or absence of COVID-19, BEa, respiratory rate, PaO2:FiO2, and the product term between COVID-19 and PaO2:FiO2. The adjusted R2 was 0.39. The risk of ΔPes being >10 cmH2O can be predicted from BEa, respiratory rate, and PaO2:FiO2. The area under the receiver operating characteristic curve was 0.79 (0.73-0.85). We called these two models BREF, where BREF stands for BReathing EFfort and the three common predictors: BEa (B), respiratory rate (RE), and PaO2:FiO2 (F).
CONCLUSIONS: We developed two models to estimate the breathing effort of patients on high-flow oxygen therapy. Our initial findings are promising and suggest that these models merit further evaluation.
METHODS: We analyzed the data of 260 patients from previous studies who received high-flow oxygen therapy. Their breathing effort was measured as the maximal deflection of esophageal pressure (ΔPes). We developed a multivariable linear regression model to estimate ΔPes (in cmH2O) and a multivariable logistic regression model to predict the risk of ΔPes being >10 cmH2O. Candidate predictors included age, sex, diagnosis of the coronavirus disease 2019 (COVID-19), respiratory rate, heart rate, mean arterial pressure, the results of arterial blood gas analysis, including base excess concentration (BEa) and the ratio of arterial tension to the inspiratory fraction of oxygen (PaO2:FiO2), and the product term between COVID-19 and PaO2:FiO2.
RESULTS: We found that ΔPes can be estimated from the presence or absence of COVID-19, BEa, respiratory rate, PaO2:FiO2, and the product term between COVID-19 and PaO2:FiO2. The adjusted R2 was 0.39. The risk of ΔPes being >10 cmH2O can be predicted from BEa, respiratory rate, and PaO2:FiO2. The area under the receiver operating characteristic curve was 0.79 (0.73-0.85). We called these two models BREF, where BREF stands for BReathing EFfort and the three common predictors: BEa (B), respiratory rate (RE), and PaO2:FiO2 (F).
CONCLUSIONS: We developed two models to estimate the breathing effort of patients on high-flow oxygen therapy. Our initial findings are promising and suggest that these models merit further evaluation.
摘要：
目的：定量非插管患者的呼吸努力很重要，但很困难。我们旨在开发两种模型来评估高流量氧疗患者的疗效。
方法：我们分析了260例接受高流量氧气治疗的患者的数据。他们的呼吸努力被测量为食道压力的最大偏转(ΔPes)。我们开发了多变量线性回归模型来估计ΔPes（以cmH2O为单位），并开发了多变量逻辑回归模型来预测ΔPes>10cmH2O的风险。候选预测因素包括年龄，性别,2019年冠状病毒病的诊断（COVID-19），呼吸频率,心率,平均动脉压,动脉血气分析结果,包括碱过量浓度（BEa）和动脉张力与氧气吸入分数的比率（PaO2：FiO2），COVID-19和PaO2之间的产品术语：FiO2。
结果：我们发现可以从COVID-19，BEa，呼吸频率,PaO2：FiO2，COVID-19和PaO2之间的产品术语：FiO2。调整后的R2为0.39。从BEa可以预测ΔPes>10cmH2O的风险，呼吸频率,和PaO2：FiO2。受试者工作特征曲线下面积为0.79(0.73-0.85)。我们称这两个模型为BREF，其中BREF代表BReathingEFfort和三个常见的预测因子：BEa(B)，呼吸频率（RE），和PaO2:FiO2(F)。
结论：我们开发了两种模型来评估高流量氧疗患者的呼吸努力。我们的初步发现是有希望的，并表明这些模型值得进一步评估。
方法：我们分析了260例接受高流量氧气治疗的患者的数据。他们的呼吸努力被测量为食道压力的最大偏转(ΔPes)。我们开发了多变量线性回归模型来估计ΔPes（以cmH2O为单位），并开发了多变量逻辑回归模型来预测ΔPes>10cmH2O的风险。候选预测因素包括年龄，性别,2019年冠状病毒病的诊断（COVID-19），呼吸频率,心率,平均动脉压,动脉血气分析结果,包括碱过量浓度（BEa）和动脉张力与氧气吸入分数的比率（PaO2：FiO2），COVID-19和PaO2之间的产品术语：FiO2。
结果：我们发现可以从COVID-19，BEa，呼吸频率,PaO2：FiO2，COVID-19和PaO2之间的产品术语：FiO2。调整后的R2为0.39。从BEa可以预测ΔPes>10cmH2O的风险，呼吸频率,和PaO2：FiO2。受试者工作特征曲线下面积为0.79(0.73-0.85)。我们称这两个模型为BREF，其中BREF代表BReathingEFfort和三个常见的预测因子：BEa(B)，呼吸频率（RE），和PaO2:FiO2(F)。
结论：我们开发了两种模型来评估高流量氧疗患者的呼吸努力。我们的初步发现是有希望的，并表明这些模型值得进一步评估。
