ELMO, a new helmet interface for CPAP to treat COVID-19-related acute hypoxemic respiratory failure outside the ICU: a feasibility study

COVID-19 outbreaks surveillance through text mining applied to electronic health records

BACKGROUND

The COVID-19 pandemic has caused significant disruptions to everyday life and has had social, political, and financial consequences that will persist for years. Several initiatives with intensive use of technology were quickly developed in this scenario. However, technologies that enhance epidemiological surveillance in contexts with low testing capacity and healthcare resources are scarce. Therefore, this study aims to address this gap by developing a data science model that uses routinely generated healthcare encounter records to detect possible new outbreaks early in real-time.

METHODS

We defined an epidemiological indicator that is a proxy for suspected cases of COVID-19 using the health records of Emergency Care Unit (ECU) patients and text mining techniques. The open-field dataset comprises 2,760,862 medical records from nine ECUs, where each record has information about the patient's age, reported symptoms, and the time and date of admission. We also used a dataset where 1,026,804 cases of COVID-19 were officially confirmed. The records range from January 2020 to May 2022. Sample cross-correlation between two finite stochastic time series was used to evaluate the models.

RESULTS

For patients with age 18 years, we find time-lag () = 72 days and cross-correlation () ~ 0.82, = 25 days and ~ 0.93, and = 17 days and ~ 0.88 for the first, second, and third waves, respectively.

CONCLUSIONS

In conclusion, the developed model can aid in the early detection of signs of possible new COVID-19 outbreaks, weeks before traditional surveillance systems, thereby anticipating in initiating preventive and control actions in public health with a higher likelihood of success.

ELMO CPAP: an innovative type of ventilatory support for COVID-19-related acute respiratory distress syndrome

OBJECTIVE

To assess whether the use of ELMO, a helmet for noninvasive ventilation created in Brazil, had a positive impact on the prognosis of patients with hypoxemic respiratory failure caused by severe COVID-19.

METHODS

This is a retrospective study of 50 critically ill COVID-19 patients. Epidemiological, clinical, and laboratory data were collected on ICU admission, as well as before, during, and after ELMO use. Patients were divided into two groups (success and failure) according to the outcome.

RESULTS

ELMO use improved oxygenation parameters such as Pao2, Fio2, and the Pao2/Fio2 ratio, and this contributed to a gradual reduction in Fio2, without an increase in CO2, as determined by arterial blood gas analysis. Patients in the success group had significantly longer survival (p < 0.001), as determined by the Kaplan-Meier analysis, less need for intubation (p < 0.001), fewer days of hospitalization, and a lower incidence of acute kidney injury in comparison with those in the failure group.

CONCLUSIONS

The significant improvement in oxygenation parameters, the longer survival, as reflected by the reduced need for intubation and by the mortality rate, and the absence of acute kidney injury suggest that the ELMO CPAP system is a promising tool for treating ARDS and similar clinical conditions.

Non-invasive Ventilation Delivered by Helmet vs Face Mask in Patients with COVID-19 Infection: Additional Measures to Reap Further Benefits

How to cite this article: Mandal M, Bhattacharya D, Esquinas AM. Non-invasive Ventilation Delivered by Helmet vs Face Mask in Patients with COVID-19 Infection: Additional Measures to Reap Further Benefits. Indian J Crit Care Med 2022;26(10):1159-1160.