Prediction of Noninvasive Ventilation Failure in COVID-19 Patients: When Shall We Stop?

Abstract

Introduction: In coronavirus disease 2019 (COVID-19), there are no tools available for the difficult task of recognizing which patients do not benefit from maintaining respiratory support, such as noninvasive ventilation (NIV). Identifying treatment failure is crucial to provide the best possible care and optimizing resources. Therefore, this study aimed to build a model that predicts NIV failure in patients who did not progress to invasive mechanical ventilation (IMV).

Methods: This retrospective observational study included critical COVID-19 patients treated with NIV who did not progress to IMV. Patients were admitted to a Portuguese tertiary hospital between October 1, 2020, and March 31, 2021. The outcome of interest was NIV failure, defined as COVID-19-related in-hospital death. A binary logistic regression was performed, where the outcome (mortality) was the dependent variable. Using the independent variables of the logistic regression a decision-tree classification model was implemented.

Results: The study sample, composed of 103 patients, had a mean age of 66.3 years (SD=14.9), of which 38.8% (40 patients) were female. Most patients (82.5%) were autonomous for basic activities of daily living. The prediction model was statistically significant with an area under the curve of 0.994 and a precision of 0.950. Higher age, a higher number of days with increases in the fraction of inspired oxygen (FiO₂), a higher number of days of maximum expiratory positive airway pressure, a lower number of days on NIV, and a lower number of days from disease onset to hospital admission were, with statistical significance, associated with increased odds of death. A decision-tree classification model was then obtained to achieve the best combination of variables to predict the outcome of interest.

Conclusions: This study presents a model to predict death in COVID-19 patients treated with NIV in patients who did not progress to IMV, based on easily applicable variables that mainly reflect patients' evolution during hospitalization. Along with the decision-tree classification model, these original findings may help clinicians define the best therapeutical approach to each patient, prioritizing life-comforting measures when adequate, and optimizing resources, which is crucial within limited or overloaded healthcare systems. Further research is needed on this subject of treatment failure, not only to understand if these results are reproducible but also, in a broader sense, helping to fill this gap in modern medicine guidelines.

Keywords: covid-19 retro; noninvasive mechanical ventilation; pandemics; prediction tools; resource management; respiratory insufficiency; treatment failure.

Figure 1. Flowchart of inclusion and exclusion of participants

Between October 1, 2020, and March 31, 2021, 139 COVID-19 patients, were treated with noninvasive ventilation. These patients were not treated with invasive mechanical ventilation at any point during their hospital stay. Applying exclusion criteria, 103 patients were admitted to analysis, of which 39 died during the hospital stay. COVID-19: coronavirus disease 2019; NIV: noninvasive ventilation; IMV: invasive mechanical ventilation

Figure 2. Receiver operating characteristic curve for the predictive model

The receiver operating characteristic curve for the predictive model has an area under the curve of 0.994 and a precision of 0.950.

Figure 3. Decision-tree classification model

The decision-tree classification model presented starts with a root node in which all the observations (n_total=103; n_alive=64; n_dead=39) are present. The first decision boundary was based on the average fraction of inspired oxygen (FiO₂) values: approximately 82% of the patients who die have average FiO₂ values greater than 72.6%. A new decision boundary is defined by age in the group of patients with average FiO₂ values equal to or lower than 72.6%. Of a total of seven patients in this branch, six patients with ages greater than 74 years die. Among the group of patients with average FiO₂ values greater than 72.6%, from a total of 32 patients, 30 patients with end positive airway pressure (EPAP) with average values greater than 9.9 die. Third-level decision boundaries are established according to FiO₂ average values, number of days with increases in FiO2, number of days between disease onset and hospitalization, and the total number of days on noninvasive ventilation (NIV). The word "gini" that is present in the figure refers to the Gini coefficient, which ranges from 0 to 1.