The deadliest lung lobe in COVID-19: a retrospective cohort study of elderly patients hospitalized for COVID-19

Respiratory epithelium in the conducting airways of the human body is one of the primary targets of SARS-CoV-2 infection, however, there is a paucity of studies describing the association between COVID-19 and physical characteristics of the conducting airways. To better understand the pathophysiology of COVID-19 on the size of larger conducting airways, we determined the luminal area of the central airways in patients with a history of COVID-19 compared to a height-matched cohort of controls using a case–control study design. Using three-dimensional reconstruction from low-dose high-resolution computed tomography, we retrospectively assessed airway luminal cross-sectional area in 114 patients with COVID-19 (66 females, 48 males) and 114 healthy, sex- and height-matched controls (66 females, 48 males). People with a history of smoking, cardiopulmonary disease, or a body mass index greater than 40 kg·m−2 were excluded. Luminal areas of seven conducting airways were analyzed, including trachea, left and right main bronchus, intermediate bronchus, left and right upper lobe, and left lower lobe. For the central conducting airways, luminal area was ~ 15% greater patients with COVID-19 compared to matched controls (p < 0.05). Among patients with COVID-19, there were generally no differences in the luminal areas of the conducting airways between hospitalized patients compared to patients who did not require COVID-19-related hospitalization. Our findings suggest that males and females with COVID-19 have pathologically larger conducting airway luminal areas than healthy, sex- and height-matched controls.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Greater central airway luminal area in people with COVID-19: a case–control series

Jeltema

Gorman

Ovrom

et al. 2022

“…www.nature.com/scientificreports/ Numerous prognostic models have been developed for COVID-19, using both traditional statistics and ML. Blood parameters, like ferritin 22 , troponin, and myoglobin [23][24][25][26] , demographic parameters, including Charlson comorbidity index score 27 , age and gender 28,29 , chest CT images 30 , routine chest X-rays 31 , age-related dementia 32 , cardiac auscultation 33 and other factors are suggested to predict severity or death in patients with COVID-19. However, all these studies were aimed at a single-time assessment of patients' prognosis, close to admission.…”

Section: Discussionmentioning

confidence: 99%

Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19

Kogan

Robinson

Itelman

et al. 2022

Our study was aimed at developing and validating a new approach, embodied in a machine learning-based model, for sequentially monitoring hospitalized COVID-19 patients and directing professional attention to patients whose deterioration is imminent. Model development employed real-world patient data (598 prediction events for 210 patients), internal validation (315 prediction events for 97 patients), and external validation (1373 prediction events for 307 patients). Results show significant divergence in longitudinal values of eight routinely collected blood parameters appearing several days before deterioration. Our model uses these signals to predict the personal likelihood of transition from non-severe to severe status within well-specified short time windows. Internal validation of the model's prediction accuracy showed ROC AUC of 0.8 and 0.79 for prediction scopes of 48 or 96 h, respectively; external validation showed ROC AUC of 0.7 and 0.73 for the same prediction scopes. Results indicate the feasibility of predicting the forthcoming deterioration of non-severe COVID-19 patients by eight routinely collected blood parameters, including neutrophil, lymphocyte, monocyte, and platelets counts, neutrophil-to-lymphocyte ratio, CRP, LDH, and D-dimer. A prospective clinical study and an impact assessment will allow implementation of this model in the clinic to improve care, streamline resources and ease hospital burden by timely focusing the medical attention on potentially deteriorating patients.

“…This is of importance, since COVID-19 pneumonia can present with several radiological findings of varying degree, all correlated with clinical outcome 5 , 28 , 29 . A traditional division of lung lesions, according to its anatomical location in one of the five lung lobes, was found to be useful for predicting patient outcomes at the time of presenting to the emergency department 30 . However, the MBrixia score allows for quantification of the overall lesion burden, and it is advantageous in COVID-19 pneumonia, since studies using higher-resolution imaging, such as CT imaging, have reported COVID-19 lesions in lobar segments—as opposed to entire lung lobes 31 , 32 .…”

Section: Discussionmentioning

confidence: 99%

Chest x-ray imaging score is associated with severity of COVID-19 pneumonia: the MBrixia score

Jensen

Costa

Nørgaard

et al. 2022

Spatial resolution in existing chest x-ray (CXR)-based scoring systems for coronavirus disease 2019 (COVID-19) pneumonia is low, and should be increased for better representation of anatomy, and severity of lung involvement. An existing CXR-based system, the Brixia score, was modified to increase the spatial resolution, creating the MBrixia score. The MBrixia score is the sum, of a rule-based quantification of CXR severity on a scale of 0 to 3 in 12 anatomical zones in the lungs. The MBrixia score was applied to CXR images from COVID-19 patients at a single tertiary hospital in the period May 4th–June 5th, 2020. The relationship between MBrixia score, and level of respiratory support at the time of performed CXR imaging was investigated. 37 hospitalized COVID-19 patients with 290 CXRs were identified, 22 (59.5%) were admitted to the intensive care unit and 10 (27%) died during follow-up. In a Poisson regression using all 290 MBrixia scored CXRs, a higher MBrixia score was associated with a higher level of respiratory support at the time of performed CXR. The MBrixia score could potentially be valuable as a quantitative surrogate measurement of COVID-19 pneumonia severity, and future studies should investigate the score’s validity and capabilities of predicting clinical outcomes.