Predictive value of a risk prediction model guided by the ratio of respiratory rate to diaphragmatic thickening fraction for the timing of noninvasive-invasive mechanical ventilation transition in patients with acute exacerbation of chronic obstructive pulmonary disease
10.3760/cma.j.cn121430-20231205-01051
- VernacularTitle:以呼吸频率与膈肌增厚分数比值为导向的风险预测模型对AECOPD患者无创-有创机械通气治疗切换时机的预测价值
- Author:
Xiaodong LI
1
;
Tian LI
;
Xingwei DI
;
Jingyu LIU
Author Information
1. 锦州医科大学附属第一医院重症医学科，辽宁锦州 121000
- Keywords:
Chronic obstructive pulmonary disease;
Diaphragm ultrasound;
Non-invasive ventilation;
Risk prediction
- From:
Chinese Critical Care Medicine
2024;36(5):503-507
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the predictive value of a risk prediction model guided by the ratio of respiratory rate to diaphragm thickening fraction (RR/DTF) for noninvasive-invasive mechanical ventilation transition timing in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), through ultrasound evaluation of diaphragm movement indicators.Methods:Sixty-four patients diagnosed with AECOPD and undergoing non-invasive ventilation (NIV), who were admitted to the department of critical care medicine of the First Affiliated Hospital of Jinzhou Medical University from January 2022 to July 2023 were enrolled. They were divided into NIV successful group and NIV failure group based on the outcome of NIV within 24 hours. Clinical indicators such as RR/DTF, diaphragmatic excursion (DE), tidal volume (VT), respiratory rate (RR), pH value, partial pressure of carbon dioxide (PaCO 2), and sputum excretion disorder were compared between the two groups after 2 hours of NIV. The factors influencing NIV failure were included in binary Logistic regression analysis, and an RR/DTF oriented risk prediction model was established. Receiver operator characteristic curve (ROC curve) analysis was used to assess the predictive value of this model for the timing of noninvasive-invasive mechanical ventilation transition in AECOPD patients. Results:Among 64 patients with AECOPD, with 43 in the NIV successful group and 21 in the NIV failure group. There were no statistically significant differences in baseline data such as age, gender, body mass index (BMI), oxygenation index (P/F), smoking history, and acute physiological and chronic health evaluation Ⅱ (APACHEⅡ) between the two groups of patients, indicating comparability. Compared to the NIV successful group, the NIV failure group showed a significantly increase in RR/DTF, RR, PaCO 2, and sputum retention, while VT and DE were significantly decreased [RR/DTF (%): 1.00±0.18 vs. 0.89±0.22, RR (bpm): 21.64±3.13 vs. 19.62±2.98, PaCO 2 (mmHg, 1 mmHg≈0.133 kPa): 70.82±8.82 vs. 65.29±9.47, sputum retention: 57.1% vs. 30.2%, VT (mL): 308.09±14.89 vs. 324.48±23.82, DE (mm): 19.91±2.94 vs. 22.05±3.30, all P < 0.05]. Binary Logistic regression analysis showed that RR/DTF [odds ratio ( OR) = 147.989, 95% confidence interval (95% CI) was 3.321-595.412, P = 0.010], RR ( OR = 1.296, 95% CI was 1.006-1.670, P = 0.045), VT ( OR = 0.966, 95% CI was 0.935-0.999, P = 0.044), PaCO 2 ( OR = 1.086, 95% CI was 1.006~1.173, P = 0.035), and sputum retention ( OR = 4.533, 95% CI was 1.025-20.049, P = 0.046) were independent risk factors for predicting NIV failure in AECOPD patients. ROC curve analysis showed that the area under the curve (AUC) of 0.713 with a 95% CI of 0.587-0.839 ( P = 0.005). The sensitivity was 72.73%, the specificity was 88.10%, the Youden index was 0.394, and the optimal cut-off value was 0.87. Conclusion:The RR/DTF risk prediction model has good predictive value for the timing of noninvasive-invasive mechanical ventilation transition in AECOPD patients.
