The Diagnostic Yield of the Multidisciplinary Discussion in Patients With COVID-19 Pneumonia

Prognostic value of acute cor pulmonale in COVID-19-related pneumonia: A prospective study

Background

It is known that acute cor pulmonale (ACP) worsens the prognosis of non-coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (NC-ARDS). The ACP risk score evaluates the risk of ACP occurrence in mechanically ventilated patients with NC-ARDS. There is less data on the risk factors and prognosis of ACP induced by COVID-19-related pneumonia.

Objective

The objective of this study was to evaluate the prognostic value of ACP, assessed by transthoracic echocardiography (TTE) and clinical factors associated with ACP in a cohort of patients with COVID-19-related pneumonia.

Materials and methods

Between February 2020 and June 2021, patients admitted to intensive care unit (ICU) at Amiens University Hospital for COVID-19-related pneumonia were assessed by TTE within 48 h of admission. ACP was defined as a right ventricle/left ventricle area ratio of >0.6 associated with septal dyskinesia. The primary outcome was mortality at 30 days.

Results

Among 146 patients included, 36% (n = 52/156) developed ACP of which 38% (n = 20/52) were non-intubated patients. The classical risk factors of ACP (found in NC-ARDS) such as PaCO₂ >48 mmHg, driving pressure >18 mmHg, and PaO₂/FiO₂ < 150 mmHg were not associated with ACP (all P-values > 0.1). The primary outcome occurred in 32 (22%) patients. More patients died in the ACP group (n = 20/52 (38%) vs. n = 12/94 (13%), P = 0.001). ACP [hazards ratio (HR) = 3.35, 95%CI [1.56–7.18], P = 0.002] and age >65 years (HR = 2.92, 95%CI [1.50–5.66], P = 0.002) were independent risk factors of 30-day mortality.

Conclusion

ACP was a frequent complication in ICU patients admitted for COVID-19-related pneumonia. The 30-day-mortality was 38% in these patients. In COVID-19-related pneumonia, the classical risk factors of ACP did not seem relevant. These results need confirmation in further studies.

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision

Background

Hypoxia is a potentially life-threatening condition that can be seen in pneumonia patients.

Objective

We aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT.

Materials and Methods

We enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO₂, HCO3-, K⁺, Na⁺, anion gap, C-reactive protein) served as ground truth.

Results

Radiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant.

Conclusion

The constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity.

Streamlining Patient Management of Suspected COVID-19 Patients in the Emergency Department: Incorporation of Pulmonary CT Angiography into the Triaging Algorithm

Purpose: To evaluate the use of pulmonary computed tomography (CT) angiography during initial admission at an emergency department (ED), to identify COVID-19 patients with accompanying pulmonary embolism (PE) and its impact on clinical management. Methods: We performed a retrospective analysis of COVID-19 patients that underwent pulmonary CT angiography at the ED. CT scans were evaluated for the presence and extent of PE and for imaging changes suspicious of COVID-19. Patients were subdivided into two groups: (1) Group A consisted of patients with proven COVID-19 based on real-time polymerase chain reaction (RT-PCR), and (2) Group B of patients suspected for COVID-19, comprising patients positive on RT-PCR and/or COVID-19-suspicious CT findings. To assess the differences between patients with and without pulmonary embolism, Fisher’s exact test was used. Results: A total of 308 patients were admitted to the ED for diagnostic work-up of dyspnea and suspected COVID-19, and 95 patients underwent pulmonary CT angiography. PE was detected in 13.6% (3/22) of patients in Group A and 20.7% (6/29) in Group B. No significant differences were observed between patients with and without PE concerning hospitalization (Group B: 100% (6/6) vs. 91.3% (21/23)), the necessity of oxygen therapy (Group B: 66% (4/6) vs. 43.5% (10/23)), and death (Group B: 33% (2/6) vs. 4.3% (1/23) p > 0.05, respectively). Conclusions: In 20.7% of COVID-19 patients, PE was detected upon admission to the ED. Although the incorporation of early pulmonary CT angiography in patients suspicious of COVID-19 may be beneficial to identify concomitant PE, further studies are necessary to corroborate these findings.

Lung Ultrasound in the Diagnosis of COVID-19 Pneumonia: Not Always and Not Only What Is COVID-19 "Glitters"

Background: In the current coronavirus disease-2019 (COVID-19) pandemic, lung ultrasound (LUS) has been extensively employed to evaluate lung involvement and proposed as a useful screening tool for early diagnosis in the emergency department (ED), prehospitalization triage, and treatment monitoring of COVID-19 pneumonia. However, the actual effectiveness of LUS in characterizing lung involvement in COVID-19 is still unclear. Our aim was to evaluate LUS diagnostic performance in assessing or ruling out COVID-19 pneumonia when compared with chest CT (gold standard) in a population of SARS-CoV-2-infected patients. Methods: A total of 260 consecutive RT-PCR confirmed SARS-CoV-2-infected patients were included in the study. All the patients underwent both chest CT scan and concurrent LUS at admission, within the first 6-12 h of hospital stay. Results: Chest CT scan was considered positive when showing a "typical" or "indeterminate" pattern for COVID-19, according to the RSNA classification system. Disease prevalence for COVID-19 pneumonia was 90.77%. LUS demonstrated a sensitivity of 56.78% in detecting lung alteration. The concordance rate for the assessment of abnormalities by both methods increased in the case of peripheral distribution and middle-lower lung location of lesions and in cases of more severe lung involvement. A total of nine patients had a "false-positive" LUS examination. Alternative diagnosis included chronic heart disease (six cases), bronchiectasis (two cases), and subpleural emphysema (one case). LUS specificity was 62.50%. Collateral findings indicative of overlapping conditions at chest CT were recorded also in patients with COVID-19 pneumonia and appeared distributed with increasing frequency passing from the group with mild disease (17 cases) to that with severe disease (40 cases). Conclusions: LUS does not seem to be an adequate tool for screening purposes in the ED, due to the risk of missing some lesions and/or to underestimate the actual extent of the disease. Furthermore, the not specificity of LUS implies the possibility to erroneously classify pre-existing or overlapping conditions as COVID-19 pneumonia. It seems more safe to integrate a positive LUS examination with clinical, epidemiological, laboratory, and radiologic findings to suggest a "virosis." Viral testing confirmation is always required.

Proven COVID-19-associated pulmonary aspergillosis in patients with severe respiratory failure

BACKGROUND

An increasing number of reports have described the COVID-19-associated pulmonary aspergillosis (CAPA) as being a further contributing factor to mortality. Based on a recent consensus statement supported by international medical mycology societies, it has been proposed to define CAPA as possible, probable, or proven on the basis of sample validity and thus diagnostic certainty. Considering current challenges associated with proven diagnoses, there is pressing need to study the epidemiology of proven CAPA.

METHODS

We report the incidence of histologically diagnosed CAPA in a series of 45 consecutive COVID-19 laboratory-confirmed autopsies, performed at Padova University Hospital during the first and second wave of the pandemic. Clinical data, laboratory data and radiological features were also collected for each case.

RESULTS

Proven CAPA was detected in 9 (20%) cases, mainly in the second wave of the pandemic (7/17 vs. 2/28 of the first wave). The population of CAPA patients consisted of seven males and two females, with a median age of 74 years. Seven patients were admitted to the intensive care unit. All patients had at least two comorbidities, and concomitant lung diseases were detected in three cases.

CONCLUSION

We found a high frequency of proven CAPA among patients with severe COVID-19 thus confirming at least in part the alarming epidemiological data of this important complication recently reported as probable CAPA.