ABSTRACT
Background Since January 2020, coronavirus disease 19 (COVID-19) has rapidly spread all over the world. An early assessment of illness severity is crucial for the stratification of patients in order to address them to the right intensity path of care. We performed an analysis on a large cohort of COVID-19 patients (n=581) hospitalized between March 2020 and May 2021 in our intensive care unit (ICU) at Policlinico Riuniti di Foggia hospital. Through an integration of the scores, demographic data, clinical history, laboratory findings, respiratory parameters, a correlation analysis, and the use of machine learning our study aimed to develop a model to predict the main outcome. Methods We deemed eligible for analysis all adult patients (age >18 years old) admitted to our department. We excluded all the patients with an ICU length of stay inferior to 24 h and the ones that declined to participate in our data collection. We collected demographic data, medical history, D-dimers, NEWS2, and MEWS scores on ICU admission and on ED admission, PaO2/FiO2 ratio on ICU admission, and the respiratory support modalities before the orotracheal intubation and the intubation timing (early vs late with a 48-h hospital length of stay cutoff). We further collected the ICU and hospital lengths of stay expressed in days of hospitalization, hospital location (high dependency unit, HDU, ED), and length of stay before and after ICU admission;the in-hospital mortality;and the in-ICU mortality. We performed univariate, bivariate, and multivariate statistical analyses. Results SARS-CoV-2 mortality was positively correlated to age, length of stay in HDU, MEWS, and NEWS2 on ICU admission, D-dimer value on ICU admission, early orotracheal intubation, and late orotracheal intubation. We found a negative correlation between the PaO2/FiO2 ratio on ICU admission and NIV. No significant correlations with sex, obesity, arterial hypertension, chronic obstructive pulmonary disease, chronic kidney disease, cardiovascular disease, diabetes mellitus, dyslipidemia, and neither MEWS nor NEWS on ED admission were observed. Considering all the pre-ICU variables, none of the machine learning algorithms performed well in developing a prediction model accurate enough to predict the outcome although a secondary multivariate analysis focused on the ventilation modalities and the main outcome confirmed how the choice of the right ventilatory support with the right timing is crucial. Conclusion In our cohort of COVID patients, the choice of the right ventilatory support at the right time has been crucial, severity scores, and clinical judgment gave support in identifying patients at risk of developing a severe disease, comorbidities showed a lower weight than expected considering the main outcome, and machine learning method integration could be a fundamental statistical tool in the comprehensive evaluation of such complex diseases.
ABSTRACT
BACKGROUND: Pneumonia induced by 2019 Coronavirus (COVID-19) is characterized by hypoxemic respiratory failure that may present with a broad spectrum of clinical phenotypes. At the beginning, patients may have normal lung compliance and be responsive to noninvasive ventilatory support, such as CPAP. However, the transition to more severe respiratory failure - Severe Acute Respiratory Syndrome (SARS-CoV-2), necessitating invasive ventilation is often abrupt and characterized by a severe V/Q mismatch that require cycles of prone positioning. The aim of this case is to report the effect on gas exchange, respiratory mechanics and hemodynamics of tripod (or orthopneic sitting position) used as an alternative to prone position in a patient with mild SARS-CoV-2 pneumonia ventilated with helmet CPAP. CASE PRESENTATION: A 77-year-old awake and collaborating male patient with mild SARS-CoV-2 pneumonia and ventilated with Helmet CPAP, showed sudden worsening of gas exchange without dyspnea. After an unsuccessful attempt of prone positioning, we alternated three-hours cycles of semi-recumbent and tripod position, still keeping him in CPAP. Arterial blood gases (PaO2/FiO2, PaO2, SaO2, PaCO2 and A/a gradient), respiratory (VE, VT, RR) and hemodynamic parameters (HR, MAP) were collected in the supine and tripod position. Cycles of tripod position were continued for 3 days. The patient had a clinically important improvement in arterial blood gases and respiratory parameters, with stable hemodynamic and was successfully weaned and discharged to ward 10 days after pneumonia onset. CONCLUSIONS: Tripod position during Helmet CPAP can be applied safely in patients with mild SARS-CoV-2 pneumonia, with improvement of oxygenation and V/Q matching, thus reducing the need for intubation.