Israel Medical Association Journal

Background: The coronavirus disease 2019 (COVID-19) pandemic resulted in repeated surges of patients, sometimes challenging triage protocols and appropriate control of patient flow. Available models, such as the National Early Warning Score (NEWS), have shown significant limitations. Still, they are used by some centers to triage COVID-19 patients due to the lack of better tools.

Objectives: To establish a practical and automated triage tool based on readily available clinical data to rapidly determine a distinction between patients who are prone to respiratory failure.

Methods: The electronic medical records of COVID-19 patients admitted to the Sheba Medical Center March–April 2020 were analyzed. Population data extraction and exploration were conducted using a MDClone (Israel) big data platform. Patients were divided into three groups: non-intubated, intubated within 24 hours, and intubated after 24 hours. The NEWS and our model where applied to all three groups and a best fit prediction model for the prediction of respiratory failure was established.

Results: The cohort included 385 patients, 42 of whom were eventually intubated, 15 within 24 hours or less. The NEWS score was significantly lower for the non-intubated patients compared to the two other groups. Our improved model, which included NEWS elements combined with other clinical data elements, showed significantly better performance. The model's receiver operating characteristic curve had area under curve (AUC) of 0.92 with of sensitivity 0.81, specificity 0.89, and negative predictive value (NPV) 98.4% compared to AUC of 0.63 with NEWS. As patients deteriorate and require further support with supplemental O₂, the need for re-triage emerges. Our model was able to identify those patients on supplementary O₂ prone to respiratory failure with an AUC of 0.86 sensitivity 0.95, and specificity 0.7 NPV 98.9%, whereas NEWS had an AUC of 0.76. For both groups positive predictive value was approximately 35%.

Conclusions: Our model, based on readily available and simple clinical parameters, showed an excellent ability to predict negative outcome among patients with COVID-19 and therefore might be used as an initial screening tool for patient triage in emergency departments and other COVID-19 specific areas of the hospital.

Background: The single-breath diffusing capacity of the lungs (DLCO_SB) test measures the extent to which carbon monoxide (CO) passes from the lung air sacs into the blood. The accessible alveolar volume (VA_SB) is measured by inert gas during a 10-second period. The single-breath transfer coefficient of the lung for carbon monoxide (KCO_SB) is the DLCO_SB divided by VA_SB. Cystic fibrosis (CF) disease comprises progressive airway obstruction with bronchiectasis and parenchyma fibrosis. Yet, the KCO_SB appears insignificant in the assessment of pulmonary function in CF.

Objectives: To challenge the precision of normal KCO_SB in CF.

Methods: The authors collected pulmonary function tests (PFT) data from 74 confirmed CF patients (mean age 26 ± 10 years) with various levels of pulmonary disease severity. Tests included spirometry, DLCO_BP, and body plethysmography (_BP). Anatomical dead space was calculated by deducting anatomical dead space from total lung capacity TLC(_BP) to establish alveolar volume (VA_BP) and to determine KCO_BP. We also included individual data of arterial pCO₂ blood-gas level.  

Results: KCO_SB values were normal or higher in most patients, regardless of patient FEV1 value (R² = 0.2204; P < 0.02). In contrast, the measurements of KCO_BP were low corresponding with low FEV1 values, and negatively correlated with the elevation of trapped air and pCO₂ levels (R² = 0.1383; P = 0.0133, P > 0.05, respectively).

Conclusions: The 10- second perfusion time of the inert gas during DLCO_SB represent the communicative alveolar volume in CF patients with advanced pulmonary disease. The findings justify the use of DLCO_SB with the deterioration of FEV1 and elevation of pCO₂ levels.