ABSTRACT
One of the challenges that emerged during the coronavirus disease 2019 (COVID-19) pandemic and is still relevant today is the need to identify patients with acute respiratory failure (ARF) who could benefit from conventional oxygen therapy (COT) - oxygen supplementation with nasal cannulas, Venturi masks, and non-rebreather masks - without recurring to advanced respiratory therapy, such as high-flow nasal cannula (HFNC), continuous positive airway pressure (CPAP), non-invasive ventilation (NIV), or invasive mechanical ventilation. The aim of the study was to develop a clinical tool able to predict the failure of COT in COVID-19 patients presenting to the emergency department (ED) with ARF. This was a retrospective monocentric cohort study carried out in the ED of the University Hospital of Bologna Sant'Orsola-Malpighi Polyclinic, Italy. The cohort comprised 101 COVID-19 patients with ARF from the first pandemic wave who received COT. This cohort was used to develop a scale that considers serum lactate concentration, partial arterial oxygen pressure/inspired oxygen fraction (PaO2/FiO2) ratio, and body temperature to predict COT failure, referred to as the Lactate, Oxygenation, and Temperature (LOT) score. The highest possible score was 17 points. The LOT score was associated with COT failure (area under the receiver operating curve or AUROC = 0.79, 95% CI 0.69 - 0.89, p < 0.001); the cut-off value of > 5 points had optimal predictive power and showed significantly higher 30-day mortality (log-rank χ2 = 28,828, p < 0.0001). The LOT score was able to effectively predict COT failure in COVID-19 patients with ARF. Patients with LOT score > 5 had a very high risk of therapy failure, and more advanced respiratory therapies must be considered in these patients.
ABSTRACT
INTRODUCTION AND AIM: As first receivers of suspected coronavirus disease 2019 (COVID-19) patients, clinicians of the Emergency Department (ED) have to rapidly perform the first clinical assessment evaluating the intensity of care needed. So far, clear management guidelines still lack. We identified variables associated with hospitalization in order to give a quick tool to assist clinicians in stratifying cases based on the severity at their arrival at the ED and in predicting the need for hospital care. Methods: This is a monocentric observational prospective study enrolling COVID-19 patients. A score for hospitalization prediction (CovHos Score) was created using variables associated with hospitalization at multivariate analysis and then validated on an internal subsequent cohort. RESULTS: A total of 667 patients were included; 465 (69.7%) were hospitalized and 108 (16.2%) died at 30-days follow-up. In a multivariate analysis, male sex, age>65, alveolar-to-arterial oxygen gradient percentage increase compared to that expected for age, neutrophils/lymphocytes ratio and C-reactive protein levels were significantly associated with a higher rate of hospital admission. A CovHos score cut-off of 12 points predicted hospitalization with 85% sensitivity and 82.4 % specificity (area under a receiver operating characteristic curve [AUROC] = 0.909, 95% CI 0.884 - 0.935). Similar results were obtained in the validation court. A cut-off of 22 has 79% sensitivity and 77% specificity in predicting mortality (AUROC = 0.824; 95% CI 0.782-0.866); sensitivity and specificity were respectively 71.4% and 71.3% in the validation group. CONCLUSIONS: Although medical judgment still remains crucial, the CovHos score is an effective tool to assist emergency clinicians in predicting the need for hospitalization or to optimize allocation in a shortage of hospital resources.