Prediction of Conventional Oxygen Therapy Failure in COVID-19 Patients With Acute Respiratory Failure by Assessing Serum Lactate Concentration, PaO2/FiO2 Ratio, and Body Temperature.

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.

Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study.

BACKGROUND: Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness. OBJECTIVE: This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission. METHODS: This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical. RESULTS: Out of a total of 5601 patients, 4455 (79.5%), 330 (5.9%), 512 (9.1%), and 301 (5.4%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models. CONCLUSIONS: Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.

Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study).

OBJECTIVES: We aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19). METHODS: We performed a multicentre cohort study among hospitalized (>24 hours) patients diagnosed with COVID-19 from 22 February to 3 April 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospital discharge and was defined as: Spo2 <93% with 100% Fio2, respiratory rate >30 breaths/min or respiratory distress. Multivariable logistic regression models were built to identify predictors of SRF, ß-coefficients were used to develop a risk score. Trial Registration NCT04316949. RESULTS: We analysed 1113 patients (644 derivation, 469 validation cohort). Mean (±SD) age was 65.7 (±15) years, 704 (63.3%) were male. SRF occurred in 189/644 (29%) and 187/469 (40%) patients in the derivation and validation cohorts, respectively. At multivariate analysis, risk factors for SRF in the derivation cohort assessed at hospitalization were age ≥70 years (OR 2.74; 95% CI 1.66-4.50), obesity (OR 4.62; 95% CI 2.78-7.70), body temperature ≥38°C (OR 1.73; 95% CI 1.30-2.29), respiratory rate ≥22 breaths/min (OR 3.75; 95% CI 2.01-7.01), lymphocytes ≤900 cells/mm3 (OR 2.69; 95% CI 1.60-4.51), creatinine ≥1 mg/dL (OR 2.38; 95% CI 1.59-3.56), C-reactive protein ≥10 mg/dL (OR 5.91; 95% CI 4.88-7.17) and lactate dehydrogenase ≥350 IU/L (OR 2.39; 95% CI 1.11-5.11). Assigning points to each variable, an individual risk score (PREDI-CO score) was obtained. Area under the receiver-operator curve was 0.89 (0.86-0.92). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 71.6% (65%-79%), 89.1% (86%-92%), 74% (67%-80%) and 89% (85%-91%), respectively. PREDI-CO score showed similar prognostic ability in the validation cohort: area under the receiver-operator curve 0.85 (0.81-0.88). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 80% (73%-85%), 76% (70%-81%), 69% (60%-74%) and 85% (80%-89%), respectively. CONCLUSION: PREDI-CO score can be useful to allocate resources and prioritize treatments during the COVID-19 pandemic.