Comparison of optimization parametrizations for regional lung compliance estimation using personalized pulmonary poromechanical modeling.

Interstitial lung diseases, such as idiopathic pulmonary fibrosis (IPF) or post-COVID-19 pulmonary fibrosis, are progressive and severe diseases characterized by an irreversible scarring of interstitial tissues that affects lung function. Despite many efforts, these diseases remain poorly understood and poorly treated. In this paper, we propose an automated method for the estimation of personalized regional lung compliances based on a poromechanical model of the lung. The model is personalized by integrating routine clinical imaging data - namely computed tomography images taken at two breathing levels in order to reproduce the breathing kinematic-notably through an inverse problem with fully personalized boundary conditions that is solved to estimate patient-specific regional lung compliances. A new parametrization of the inverse problem is introduced in this paper, based on the combined estimation of a personalized breathing pressure in addition to material parameters, improving the robustness and consistency of estimation results. The method is applied to three IPF patients and one post-COVID-19 patient. This personalized model could help better understand the role of mechanics in pulmonary remodeling due to fibrosis; moreover, patient-specific regional lung compliances could be used as an objective and quantitative biomarker for improved diagnosis and treatment follow up for various interstitial lung diseases.

Strategies to safely rule out pulmonary embolism in COVID-19 outpatients: a multicenter retrospective study.

OBJECTIVES: The objective was to define a safe strategy to exclude pulmonary embolism (PE) in COVID-19 outpatients, without performing CT pulmonary angiogram (CTPA). METHODS: COVID-19 outpatients from 15 university hospitals who underwent a CTPA were retrospectively evaluated. D-Dimers, variables of the revised Geneva and Wells scores, as well as laboratory findings and clinical characteristics related to COVID-19 pneumonia, were collected. CTPA reports were reviewed for the presence of PE and the extent of COVID-19 disease. PE rule-out strategies were based solely on D-Dimer tests using different thresholds, the revised Geneva and Wells scores, and a COVID-19 PE prediction model built on our dataset were compared. The area under the receiver operating characteristics curve (AUC), failure rate, and efficiency were calculated. RESULTS: In total, 1369 patients were included of whom 124 were PE positive (9.1%). Failure rate and efficiency of D-Dimer > 500 µg/l were 0.9% (95%CI, 0.2-4.8%) and 10.1% (8.5-11.9%), respectively, increasing to 1.0% (0.2-5.3%) and 16.4% (14.4-18.7%), respectively, for an age-adjusted D-Dimer level. D-dimer > 1000 µg/l led to an unacceptable failure rate to 8.1% (4.4-14.5%). The best performances of the revised Geneva and Wells scores were obtained using the age-adjusted D-Dimer level. They had the same failure rate of 1.0% (0.2-5.3%) for efficiency of 16.8% (14.7-19.1%), and 16.9% (14.8-19.2%) respectively. The developed COVID-19 PE prediction model had an AUC of 0.609 (0.594-0.623) with an efficiency of 20.5% (18.4-22.8%) when its failure was set to 0.8%. CONCLUSIONS: The strategy to safely exclude PE in COVID-19 outpatients should not differ from that used in non-COVID-19 patients. The added value of the COVID-19 PE prediction model is minor. KEY POINTS: • D-dimer level remains the most important predictor of pulmonary embolism in COVID-19 patients. • The AUCs of the revised Geneva and Wells scores using an age-adjusted D-dimer threshold were 0.587 (95%CI, 0.572 to 0.603) and 0.588 (95%CI, 0.572 to 0.603). • The AUC of COVID-19-specific strategy to rule out pulmonary embolism ranged from 0.513 (95%CI: 0.503 to 0.522) to 0.609 (95%CI: 0.594 to 0.623).

Respiratory recovery trajectories after severe-to-critical COVID-19: a 1-year prospective multicentre study.

BACKGROUND: Survivors of severe-to-critical coronavirus disease 2019 (COVID-19) may have functional impairment, radiological sequelae and persistent symptoms requiring prolonged follow-up. This pragmatic study aimed to describe their clinical follow-up and determine their respiratory recovery trajectories, and the factors that could influence them and their health-related quality of life. METHODS: Adults hospitalised for severe-to-critical COVID-19 were evaluated at 3 months and up to 12 months post-hospital discharge in this prospective, multicentre, cohort study. RESULTS: Among 485 enrolled participants, 293 (60%) were reassessed at 6 months and 163 (35%) at 12 months; 89 (51%) and 47 (27%) of the 173 participants initially managed with standard oxygen were reassessed at 6 and 12 months, respectively. At 3 months, 34%, 70% and 56% of the participants had a restrictive lung defect, impaired diffusing capacity of the lung for carbon monoxide (D LCO) and significant radiological sequelae, respectively. During extended follow-up, both D LCO and forced vital capacity percentage predicted increased by means of +4 points at 6 months and +6 points at 12 months. Sex, body mass index, chronic respiratory disease, immunosuppression, pneumonia extent or corticosteroid use during acute COVID-19 and prolonged invasive mechanical ventilation (IMV) were associated with D LCO at 3 months, but not its trajectory thereafter. Among 475 (98%) patients with at least one chest computed tomography scan during follow-up, 196 (41%) had significant sequelae on their last images. CONCLUSIONS: Although pulmonary function and radiological abnormalities improved up to 1 year post-acute COVID-19, high percentages of severe-to-critical disease survivors, including a notable proportion of those managed with standard oxygen, had significant lung sequelae and residual symptoms justifying prolonged follow-up.

Epicardial adipose tissue and severe Coronavirus Disease 19.

BACKGROUND: Both visceral adipose tissue and epicardial adipose tissue (EAT) have pro-inflammatory properties. The former is associated with Coronavirus Disease 19 (COVID-19) severity. We aimed to investigate whether an association also exists for EAT. MATERIAL AND METHODS: We retrospectively measured EAT volume using computed tomography (CT) scans (semi-automatic software) of inpatients with COVID-19 and analyzed the correlation between EAT volume and anthropometric characteristics and comorbidities. We then analyzed the clinicobiological and radiological parameters associated with severe COVID-19 (O2 [Formula: see text] 6 l/min), intensive care unit (ICU) admission or death, and 25% or more CT lung involvement, which are three key indicators of COVID-19 severity. RESULTS: We included 100 consecutive patients; 63% were men, mean age was 61.8 ± 16.2 years, 47% were obese, 54% had hypertension, 42% diabetes, and 17.2% a cardiovascular event history. Severe COVID-19 (n = 35, 35%) was associated with EAT volume (132 ± 62 vs 104 ± 40 cm3, p = 0.02), age, ferritinemia, and 25% or more CT lung involvement. ICU admission or death (n = 14, 14%) was associated with EAT volume (153 ± 67 vs 108 ± 45 cm3, p = 0.015), hypertension and 25% or more CT lung involvement. The association between EAT volume and severe COVID-19 remained after adjustment for sex, BMI, ferritinemia and lung involvement, but not after adjustment for age. Instead, the association between EAT volume and ICU admission or death remained after adjustment for all five of these parameters. CONCLUSIONS: Our results suggest that measuring EAT volume on chest CT scans at hospital admission in patients diagnosed with COVID-19 might help to assess the risk of disease aggravation.

Study of Thoracic CT in COVID-19: The STOIC Project.

Background There are conflicting data regarding the diagnostic performance of chest CT for COVID-19 pneumonia. Disease extent at CT has been reported to influence prognosis. Purpose To create a large publicly available data set and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. Materials and Methods This multicenter, observational, retrospective cohort study involved 20 French university hospitals. Eligible patients presented at the emergency departments of the hospitals involved between March 1 and April 30th, 2020, and underwent both thoracic CT and reverse transcription-polymerase chain reaction (RT-PCR) testing for suspected COVID-19 pneumonia. CT images were read blinded to initial reports, RT-PCR, demographic characteristics, clinical symptoms, and outcome. Readers classified CT scans as either positive or negative for COVID-19 based on criteria published by the French Society of Radiology. Multivariable logistic regression was used to develop a model predicting severe outcome (intubation or death) at 1-month follow-up in patients positive for both RT-PCR and CT, using clinical and radiologic features. Results Among 10 930 patients screened for eligibility, 10 735 (median age, 65 years; interquartile range, 51-77 years; 6147 men) were included and 6448 (60%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity of CT were 80.2% (95% CI: 79.3, 81.2) and 79.7% (95% CI: 78.5, 80.9), respectively, with strong agreement between junior and senior radiologists (Gwet AC1 coefficient, 0.79). Of all the variables analyzed, the extent of pneumonia at CT (odds ratio, 3.25; 95% CI: 2.71, 3.89) was the best predictor of severe outcome at 1 month. A score based solely on clinical variables predicted a severe outcome with an area under the curve of 0.64 (95% CI: 0.62, 0.66), improving to 0.69 (95% CI: 0.6, 0.71) when it also included the extent of pneumonia and coronary calcium score at CT. Conclusion Using predefined criteria, CT reading is not influenced by reader's experience and helps predict the outcome at 1 month. ClinicalTrials.gov identifier: NCT04355507 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Rubin in this issue.

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.