Daily ROX index can predict transitioning to mechanical ventilation within the next 24 h in COVID-19 patients on HFNC

Impact of Surgical Mask Placement Over High-Flow Nasal Cannula on Oxygenation Parameters in COVID-19 Patients Experiencing Hypoxemic Respiratory Failure

BACKGROUND

During the COVID-19 pandemic, managing respiratory failure in critically ill patients has presented significant challenges. A high-flow nasal cannula (HFNC) has been established as an effective respiratory support modality, offering heated, humidified oxygen at high flow rates. However, concerns persist regarding the potential for aerosol dispersion and the risk of viral transmission, particularly in COVID-19. This study investigates the impact of surgical mask (3-ply surgical mask) placement over HFNC on oxygenation parameters in COVID-19 patients experiencing hypoxemic respiratory failure.

METHODS

A retrospective analysis of clinical data from a tertiary medical facility was conducted. The study included 35 patients with confirmed COVID-19 and moderate to severe hypoxemia. Oxygenation indices such as the SpO2/FiO2 (SF) ratio, flow rate, and the ratio of oxygen saturation index (ROX index) were monitored before and after the application of surgical masks over HFNC. Statistical analyses were performed to compare these parameters before and after surgical mask placement.

RESULTS

The adjunctive use of surgical masks over HFNC significantly improved oxygenation parameters compared to HFNC alone. Despite these improvements, there was no significant change in heart and respiratory rates, quick sequential organ failure assessment (qSOFA) scores, or Glasgow Coma Scale (GCS) levels. Subgroup analysis showed an increase in SF ratio ranging between 5.49% and 6.04% in patients with ROX indices, but these trends were not statistically significant.

CONCLUSION

This study provides preliminary evidence that surgical masks over HFNC may enhance oxygenation in critically ill COVID-19 patients with hypoxemic respiratory failure. These results underscore the potential importance of infection control measures in respiratory therapy during pandemics and suggest that further investigation in more extensive prospective studies is warranted.

Predicting High-Flow Nasal Cannula Oxygen Therapy Failure in Patients With Acute Hypoxaemic Respiratory Failure Using Machine Learning: Model Development and External Validation

AIMS AND OBJECTIVES

To develop and validate a prediction model for high-flow nasal cannula (HFNC) failure in patients with acute hypoxaemic respiratory failure (AHRF).

BACKGROUND

AHRF accounts for a major proportion of intensive care unit (ICU) admissions and is associated with high mortality. HFNC is a non-invasive respiratory support technique that can improve patient oxygenation. However, HFNC failure, defined as the need for escalation to invasive mechanical ventilation, can lead to delayed intubation, prolonged mechanical ventilation and increased risk of mortality. Timely and accurate prediction of HFNC failure has important clinical implications. Machine learning (ML) can improve clinical prediction.

DESIGN

Multicentre observational study.

METHODS

This study analysed 581 patients from an academic medical centre in Boston and 180 patients from Guangzhou, China treated with HFNC for AHRF. The Boston dataset was randomly divided into a training set (90%, n = 522) and an internal validation set (10%, n = 59), and the model was externally validated using the Guangzhou dataset (n = 180). A random forest (RF)-based feature selection method was used to identify predictive factors. Nine machine learning algorithms were selected to build the predictive model. The area under the receiver operating characteristic curve (AUC) and performance evaluation parameters were used to evaluate the models.

RESULTS

The final model included 38 features selected using the RF method, with additional input from clinical specialists. Models based on ensemble learning outperformed other models (internal validation AUC: 0.83; external validation AUC: 0.75). Important predictors of HFNC failure include Glasgow Coma Scale scores and Sequential Organ Failure Assessment scores, albumin levels measured during HFNC treatment, ROX index at ICU admission and sepsis.

CONCLUSIONS

This study developed an interpretable ML model that accurately predicts the risk of HFNC failure in patients with AHRF.

RELEVANCE TO CLINICAL PRACTICE

Clinicians and nurses can use ML models for early risk assessment and decision support in AHRF patients receiving HFNC.

REPORTING METHOD

TRIPOD checklist for prediction model studies was followed in this study.

PATIENT OR PUBLIC CONTRIBUTION

Patients were involved in the sample of the study.