Establishment of a prediction model for mechanical ventilation in ICU patients with nasal high-flow oxygen therapy
10.3760/cma.j.issn.1671-0282.2022.08.005
- VernacularTitle:ICU经鼻高流量氧疗患者机械通气风险预测模型的建立
- Author:
Meng CHONG
1
;
Yafang NIU
;
Xin MA
;
Li MA
Author Information
1. 兰州大学第二医院重症医学三科（急诊ICU）,兰州 730000
- Keywords:
High flow oxygen therapy;
Mechanical ventilation;
Nomogram;
Prediction model;
Prognosis
- From:
Chinese Journal of Emergency Medicine
2022;31(8):1042-1048
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish the prediction model of the ultimate risk of mechanical ventilation for patients undergoing nasal high-flow oxygen therapy in the intensive care unit (ICU), provide clinicians with a convenient and effective prediction method and accurate treatment timing, and improve the prognosis of ICU patients.Methods:Patients admitted to the ICU of our hospital from January 2019 to December 2021 were retrospectively enrolled. General clinical data of the patients were collected, including vital signs, biochemical indices of blood gas, inflammatory indices, acute comorbidities, APACHE Ⅱ score, length of stay in ICU and total length of stay, within 24 h after admission. Statistical analysis was performed on the above indicators and a chart was constructed.Results:Finally, 362 patients were enrolled in this study, and were divided into the transnasal high flow oxygen therapy group (HFNC group) and noninvasive positive pressure ventilation group (NIPPV group) according to whether mechanical ventilation was finally performed. The univariate and binary Logistic multivariate regression analysis showed that APACHE Ⅱ score ( OR=1.323, 95% CI: 1.818-1.483), ROX index ( OR=0.371, 95% CI: 0.226-0.609), total length of stay ( OR=1.097, 95% CI: 1.003-1.200) and complicating acute respiratory failure ( OR=2.456, 95% CI: 1.368-4.506) were independent influencing factors for determining whether patients underwent mechanical ventilation. Based on the above independent influencing factors, the lipopograms were constructed. The goodness of fit R2 and C-index of the model were 0.892 and 0.985, respectively through evaluation and verification model. The calibration curve of the model fitted well with the ideal curve, and the areas under the ROC curve of the rosettes and independent factors were 0.985, 0.959, 0.899, 0.656 and 0.576, respectively, indicating that the model was more effective than the independent index in predicting risk. Decision curve analysis also showed that the rosette had high clinical benefit. Conclusions:There are many related factors affecting whether patients undergo mechanical ventilation after nasal high-flow oxygen therapy. In this paper, after univariate and multivariate analysis, the most valuable indicators are combined to establish a line chart with better predictive performance to assess patients' risk, which can further provide clinicians with simple and effective prediction methods and improve the prognosis of patients.
