Quantitative-analysis of computed tomography in COVID-19 and non COVID-19 ARDS patients: A case-control study.
Ontology highlight
ABSTRACT:
Materials and methods: The study is a single-center prospective observational study performed on adults with ARDS onset ?72 h and a PaO2/FiO2 ? 200 mmHg. CT scans were acquired at PEEP set using a PEEP-FiO2 table with VT adjusted to 6 ml/kg predicted body weight.
Results: 22 patients were included, of whom 13 presented with COVID-19 ARDS. Lung weight was significantly higher in COVID- patients, but all COVID+ patients presented supranormal lung weight values. Noninflated lung tissue was significantly higher in COVID- patients (36 ± 14% vs. 26 ± 15% of total lung weight at end-expiration, p < 0.01). Tidal recruitment was significantly higher in COVID- patients (20 ± 12 vs. 9 ± 11% of VT, p < 0.05). Lung density histograms of 5 COVID+ patients with high elastance (type H) were similar to those of COVID- patients, while those of the 8 COVID+ patients with normal elastance (type L) displayed higher aerated lung fraction.
SUBMITTER: Chauvelot L
PROVIDER: S-EPMC7423516 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
Quantitative-analysis of computed tomography in COVID-19 and non COVID-19 ARDS patients: A case-control study.
Chauvelot Louis L Bitker Laurent L Dhelft François F Mezidi Mehdi M Orkisz Maciej M Davila Serrano Eduardo E Penarrubia Ludmilla L Yonis Hodane H Chabert Paul P Folliet Laure L David Guillaume G Provoost Judith J Lecam Pierre P Boussel Loic L Richard Jean-Christophe JC
Journal of critical care 20200813
<h4>Purpose</h4>The aim of this study was to assess whether the computed tomography (CT) features of COVID-19 (COVID+) ARDS differ from those of non-COVID-19 (COVID-) ARDS patients.<h4>Materials and methods</h4>The study is a single-center prospective observational study performed on adults with ARDS onset ≤72 h and a PaO<sub>2</sub>/FiO<sub>2</sub> ≤ 200 mmHg. CT scans were acquired at PEEP set using a PEEP-FiO<sub>2</sub> table with VT adjusted to 6 ml/kg predicted body weight.<h4>Results</h4> ...[more]
Similar Datasets
Project description:BackgroundPEEP selection in severe COVID-19 patients under extracorporeal membrane oxygenation (ECMO) is challenging as no study has assessed the alveolar recruitability in this setting. The aim of the study was to compare lung recruitability and the impact of PEEP on lung aeration in moderate and severe ARDS patients with or without ECMO, using computed tomography (CT).MethodsWe conducted a two-center prospective observational case-control study in adult COVID-19-related patients who had an indication for CT within 72 h of ARDS onset in non-ECMO patients or within 72 h after ECMO onset. Ninety-nine patients were included, of whom 24 had severe ARDS under ECMO, 59 severe ARDS without ECMO and 16 moderate ARDS.ResultsNon-inflated lung at PEEP 5 cmH2O was significantly greater in ECMO than in non-ECMO patients. Recruitment induced by increasing PEEP from 5 to 15 cmH2O was not significantly different between ECMO and non-ECMO patients, while PEEP-induced hyperinflation was significantly lower in the ECMO group and virtually nonexistent. The median [IQR] fraction of recruitable lung mass between PEEP 5 and 15 cmH2O was 6 [4-10]%. Total superimposed pressure at PEEP 5 cmH2O was significantly higher in ECMO patients and amounted to 12 [11-13] cmH2O. The hyperinflation-to-recruitment ratio (i.e., a trade-off index of the adverse effects and benefits of PEEP) was significantly lower in ECMO patients and was lower than one in 23 (96%) ECMO patients, 41 (69%) severe non-ECMO patients and 8 (50%) moderate ARDS patients. Compliance of the aerated lung at PEEP 5 cmH2O corrected for PEEP-induced recruitment (CBABY LUNG) was significantly lower in ECMO patients than in non-ECMO patients and was linearly related to the logarithm of the hyperinflation-to-recruitment ratio.ConclusionsLung recruitability of COVID-19 pneumonia is not significantly different between ECMO and non-ECMO patients, with substantial interindividual variations. The balance between hyperinflation and recruitment induced by PEEP increase from 5 to 15 cmH2O appears favorable in virtually all ECMO patients, while this PEEP level is required to counteract compressive forces leading to lung collapse. CBABY LUNG is significantly lower in ECMO patients, independently of lung recruitability.
| S-EPMC9250720 | biostudies-literature
Project description:Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.
| S-EPMC9356575 | biostudies-literature
Project description:SARS-CoV-2 is a novel coronavirus that causes acute respiratory distress syndrome (ARDS), death and long-term sequelae. Innate immune cells are critical for host defense but are also the primary drivers of ARDS. The relationships between innate cellular responses in ARDS resulting from COVID-19 compared to other causes of ARDS, such as bacterial sepsis is unclear. Moreover, the beneficial effects of dexamethasone therapy during severe COVID-19 remain speculative, but understanding the mechanistic effects could improve evidence-based therapeutic interventions. To interrogate these relationships, we developed an scRNAseq atlas that is freely accessible (biernaskielab.ca/COVID_neutrophil). We discovered that compared to bacterial ARDS, COVID-19 was associated with distinct neutrophil polarization characterized by either interferon (IFN) or prostaglandin (PG) active states. Neutrophils from bacterial ARDS had higher expression of antibacterial molecules such as PLAC8 and CD83. Dexamethasone therapy in COVID patients rapidly altered the IFNactive state, downregulated interferon responsive genes, and activated IL1R2+ve neutrophils. Dexamethasone also induced the emergence of immature neutrophils expressing immunosuppressive molecules ARG1 and ANXA1, which were not present in healthy controls. Moreover, dexamethasone remodeled global cellular interactions by changing neutrophils from information receivers into information providers. Importantly, male patients had higher proportions of IFNactive neutrophils and a greater degree of steroid-induced immature neutrophil expansion. Indeed, the highest proportion of IFNactive neutrophils was associated with mortality. These results define neutrophil states unique to COVID-19 when contextualized to other life-threatening infections, thereby enhancing the relevance of our findings at the bedside. Furthermore, the molecular benefits of dexamethasone therapy are also defined. The identified molecular pathways can now be targeted to develop improved therapeutics.
2021-10-05 | PXD028429 | Pride
Project description:BackgroundCOVID-19 causes acute respiratory distress syndrome (ARDS) and depletes the lungs of surfactant, leading to prolonged mechanical ventilation and death. The feasibility and safety of surfactant delivery in COVID-19 ARDS patients have not been established.MethodsWe performed retrospective analyses of data from patients receiving off-label use of exogenous natural surfactant during the COVID-19 pandemic. Seven COVID-19 PCR positive ARDS patients received liquid Curosurf (720 mg) in 150 ml normal saline, divided into five 30 ml aliquots) and delivered via a bronchoscope into second-generation bronchi. Patients were matched with 14 comparable subjects receiving supportive care for ARDS during the same time period. Feasibility and safety were examined as well as the duration of mechanical ventilation and mortality.ResultsPatients showed no evidence of acute decompensation following surfactant installation into minor bronchi. Cox regression showed a reduction of 28-days mortality within the surfactant group, though not significant. The surfactant did not increase the duration of ventilation, and health care providers did not convert to COVID-19 positive.ConclusionsSurfactant delivery through bronchoscopy at a dose of 720 mg in 150 ml normal saline is feasible and safe for COVID-19 ARDS patients and health care providers during the pandemic. Surfactant administration did not cause acute decompensation, may reduce mortality and mechanical ventilation duration in COVID-19 ARDS patients. This study supports the future performance of randomized clinical trials evaluating the efficacy of meticulous sub-bronchial lavage with surfactant as treatment for patients with COVID-19 ARDS.
| S-EPMC7812332 | biostudies-literature
Project description:BackgroundCritically ill COVID-19 patients have pathophysiological lung features characterized by perfusion abnormalities. However, to date no study has evaluated whether the changes in the distribution of pulmonary gas and blood volume are associated with the severity of gas-exchange impairment and the type of respiratory support (non-invasive versus invasive) in patients with severe COVID-19 pneumonia.MethodsThis was a single-center, retrospective cohort study conducted in a tertiary care hospital in Northern Italy during the first pandemic wave. Pulmonary gas and blood distribution was assessed using a technique for quantitative analysis of dual-energy computed tomography. Lung aeration loss (reflected by percentage of normally aerated lung tissue) and the extent of gas:blood volume mismatch (percentage of non-aerated, perfused lung tissue-shunt; aerated, non-perfused dead space; and non-aerated/non-perfused regions) were evaluated in critically ill COVID-19 patients with different clinical severity as reflected by the need for non-invasive or invasive respiratory support.ResultsThirty-five patients admitted to the intensive care unit between February 29th and May 30th, 2020 were included. Patients requiring invasive versus non-invasive mechanical ventilation had both a lower percentage of normally aerated lung tissue (median [interquartile range] 33% [24-49%] vs. 63% [44-68%], p < 0.001); and a larger extent of gas:blood volume mismatch (43% [30-49%] vs. 25% [14-28%], p = 0.001), due to higher shunt (23% [15-32%] vs. 5% [2-16%], p = 0.001) and non-aerated/non perfused regions (5% [3-10%] vs. 1% [0-2%], p = 0.001). The PaO2/FiO2 ratio correlated positively with normally aerated tissue (ρ = 0.730, p < 0.001) and negatively with the extent of gas-blood volume mismatch (ρ = - 0.633, p < 0.001).ConclusionsIn critically ill patients with severe COVID-19 pneumonia, the need for invasive mechanical ventilation and oxygenation impairment were associated with loss of aeration and the extent of gas:blood volume mismatch.
| S-EPMC8215486 | biostudies-literature
Project description:BackgroundWe aimed to develop integrative machine-learning models using quantitative computed tomography (CT) parameters in addition to initial clinical features to predict the respiratory outcomes of coronavirus disease 2019 (COVID-19).MethodsThis was a retrospective study involving 387 patients with COVID-19. Demographic, initial laboratory, and quantitative CT findings were used to develop predictive models of respiratory outcomes. High-attenuation area (HAA) (%) and consolidation (%) were defined as quantified percentages of the area with Hounsfield units between -600 and -250 and between -100 and 0, respectively. Respiratory outcomes were defined as the development of pneumonia, hypoxia, or respiratory failure. Multivariable logistic regression and random forest models were developed for each respiratory outcome. The performance of the logistic regression model was evaluated using the area under the receiver operating characteristic curve (AUC). The accuracy of the developed models was validated by 10-fold cross-validation.ResultsA total of 195 (50.4%), 85 (22.0%), and 19 (4.9%) patients developed pneumonia, hypoxia, and respiratory failure, respectively. The mean patient age was 57.8 years, and 194 (50.1%) were female. In the multivariable analysis, vaccination status and levels of lactate dehydrogenase, C-reactive protein (CRP), and fibrinogen were independent predictors of pneumonia. The presence of hypertension, levels of lactate dehydrogenase and CRP, HAA (%), and consolidation (%) were selected as independent variables to predict hypoxia. For respiratory failure, the presence of diabetes, levels of aspartate aminotransferase, and CRP, and HAA (%) were selected. The AUCs of the prediction models for pneumonia, hypoxia, and respiratory failure were 0.904, 0.890, and 0.969, respectively. Using the feature selection in the random forest model, HAA (%) was ranked as one of the top 10 features predicting pneumonia and hypoxia and was first place for respiratory failure. The accuracies of the cross-validation of the random forest models using the top 10 features for pneumonia, hypoxia, and respiratory failure were 0.872, 0.878, and 0.945, respectively.ConclusionsOur prediction models that incorporated quantitative CT parameters into clinical and laboratory variables showed good performance with high accuracy.
| S-EPMC10089866 | biostudies-literature
Project description:BackgroundThe assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19.MethodsOne hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared.ResultsIn comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOICT) and the percentage of infection (POICT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOICT and clinical features, including age, cluster of differentiation 4 (CD4)+ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOICT, POICT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively).ConclusionsAlthough quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.
| S-EPMC7940921 | biostudies-literature
Project description:BackgroundIn acute respiratory distress syndrome (ARDS), extravascular lung water index (EVLWi) and pulmonary vascular permeability index (PVPI) measured by transpulmonary thermodilution reflect the degree of lung injury. Whether EVLWi and PVPI are different between non-COVID-19 ARDS and the ARDS due to COVID-19 has never been reported. We aimed at comparing EVLWi, PVPI, respiratory mechanics and hemodynamics in patients with COVID-19 ARDS vs. ARDS of other origin.MethodsBetween March and October 2020, in an observational study conducted in intensive care units from three university hospitals, 60 patients with COVID-19-related ARDS monitored by transpulmonary thermodilution were compared to the 60 consecutive non-COVID-19 ARDS admitted immediately before the COVID-19 outbreak between December 2018 and February 2020.ResultsDriving pressure was similar between patients with COVID-19 and non-COVID-19 ARDS, at baseline as well as during the study period. Compared to patients without COVID-19, those with COVID-19 exhibited higher EVLWi, both at the baseline (17 (14-21) vs. 15 (11-19) mL/kg, respectively, p = 0.03) and at the time of its maximal value (24 (18-27) vs. 21 (15-24) mL/kg, respectively, p = 0.01). Similar results were observed for PVPI. In COVID-19 patients, the worst ratio between arterial oxygen partial pressure over oxygen inspired fraction was lower (81 (70-109) vs. 100 (80-124) mmHg, respectively, p = 0.02) and prone positioning and extracorporeal membrane oxygenation (ECMO) were more frequently used than in patients without COVID-19. COVID-19 patients had lower maximal lactate level and maximal norepinephrine dose than patients without COVID-19. Day-60 mortality was similar between groups (57% vs. 65%, respectively, p = 0.45). The maximal value of EVLWi and PVPI remained independently associated with outcome in the whole cohort.ConclusionCompared to ARDS patients without COVID-19, patients with COVID-19 had similar lung mechanics, but higher EVLWi and PVPI values from the beginning of the disease. This was associated with worse oxygenation and with more requirement of prone positioning and ECMO. This is compatible with the specific lung inflammation and severe diffuse alveolar damage related to COVID-19. By contrast, patients with COVID-19 had fewer hemodynamic derangement. Eventually, mortality was similar between groups.Trial registration number and date of registrationClinicalTrials.gov (NCT04337983). Registered 30 March 2020-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04337983 .
| S-EPMC8169440 | biostudies-literature
Project description:BackgroundSevere immunopathology may drive the deleterious manifestations that are observed in the advanced stages of coronavirus disease 2019 (COVID-19) but are poorly understood.ObjectiveOur aim was to phenotype leukocyte subpopulations and the cytokine milieu in the lungs and blood of critically ill patients with COVID-19 acute respiratory distress syndrome (ARDS).MethodsWe consecutively included patients less than 72 hours after intubation following informed consent from their next of kin. Bronchoalveolar lavage fluid was evaluated by microscopy; bronchoalveolar lavage fluid and blood were assessed by 10-color flow cytometry and a multiplex cytokine panel.ResultsFour mechanically ventilated patients (aged 40-75 years) with moderate-to-severe COVID-19 ARDS were included. Immature neutrophils dominated in both blood and lungs, whereas CD4 and CD8 T-cell lymphopenia was observed in the 2 compartments. However, regulatory T cells and TH17 cells were found in higher fractions in the lung. Lung CD4 and CD8 T cells and macrophages expressed an even higher upregulation of activation markers than in blood. A wide range of cytokines were expressed at high levels both in the blood and in the lungs, most notably, IL-1RA, IL-6, IL-8, IP-10, and monocyte chemoattactant protein-1, consistent with hyperinflammation.ConclusionCOVID-19 ARDS exhibits a distinct immunologic profile in the lungs, with a depleted and exhausted CD4 and CD8 T-cell population that resides within a heavily hyperinflammatory milieu.
| S-EPMC7581505 | biostudies-literature
Project description:This study aimed to compare the chest computed tomography (CT) findings between survivors and non-survivors with Coronavirus Disease 2019 (COVID-19). Between 12 January 2020 and 20 February 2020, the records of 124 consecutive patients diagnosed with COVID-19 were retrospectively reviewed and divided into survivor (83/124) and non-survivor (41/124) groups. Chest CT findings were qualitatively compared on admission and serial chest CT scans were semi-quantitively evaluated between two groups using curve estimations. On admission, significantly more bilateral (97.6% vs. 73.5%, p?=?0.001) and diffuse lesions (39.0% vs. 8.4%, p?<?0.001) with higher total CT score (median 10 vs. 4, p?<?0.001) were observed in non-survivor group compared with survivor group. Besides, crazy-paving pattern was more predominant in non-survivor group than survivor group (39.0% vs. 12.0%, p?<?0.001). From the prediction of curve estimation, in survivor group total CT score increased in the first 20 days reaching a peak of 6 points and then gradually decreased for more than other 40 days (R2?=?0.545, p?<?0.001). In non-survivor group, total CT score rapidly increased over 10 points in the first 10 days and gradually increased afterwards until ARDS occurred with following death events (R2?=?0.711, p?<?0.001). In conclusion, persistent progression with predominant crazy-paving pattern was the major manifestation of COVID-19 in non-survivors. Understanding this CT feature could help the clinical physician to predict the prognosis of the patients.
| S-EPMC7347874 | biostudies-literature