Chest CT scan and alveolar procollagen III to predict lung fibroproliferation in acute respiratory distress syndrome

AbstractIntroductionIn Acute Respiratory Distress Syndrome (ARDS), the heterogeneity of lung lesions results in a mis-match between ventilation and perfusion, leading to the development of hypoxia. The study aimed to examine the association between computed tomographic (CT scan) lung findings in patients with ARDS after abdominal surgery and improved hypoxia and mortality after prone ventilation.Material and MethodsA single site, retrospective observational study was performed at the Sapporo Medical University School of Medicine, Sapporo, Hokkaido, Japan, between 1st January 2004 and 31st October 2018. Patients were allocated to one of two groups after CT scanning according to the presence of ground-glass opacity (GGO) or alveolar shadow with predominantly dorsal lung atelectasis (DLA) on lung CT scan images. Also, Patients were divided into a prone ventilation group and a supine ventilation group when the treatment for ARDS was started.ResultsWe analyzed data for fifty-one patients with ARDS following abdominal surgery. CT scans confirmed GGO in five patients in the Group A and in nine patients in the Group B, and DLA in 17 patients in the Group A and nine patients in the Group B. Both GGO and DLA were present in two patients in the Group A and nine patients in the Group B. Prone ventilation significantly improved patients’ impaired ratio of arterial partial pressure of oxygen to fraction of inspired oxygen from 12 h after prone positioning compared with that in the supine position. Weaning from mechanical ventilation occurred significantly earlier in the Group A with DLA vs the Group B with DLA (P < 0.001). Twenty-eight-day mortality was significantly lower for the Group A with DLA vs the Group B with DLA (P = 0.035).ConclusionsThese results suggest that prone ventilation could be effective for treating patients with ARDS as showing the DLA.

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High frequency percussive ventilation increases alveolar recruitment in early acute respiratory distress syndrome: an experimental, physiological and CT scan study

Critical Care ◽

10.1186/s13054-017-1924-6 ◽

2018 ◽

Vol 22 (1) ◽

Cited By ~ 7

Author(s):

Thomas Godet ◽

Matthieu Jabaudon ◽

Raïko Blondonnet ◽

Aymeric Tremblay ◽

Jules Audard ◽

...

Keyword(s):

Acute Respiratory Distress Syndrome ◽

Respiratory Distress Syndrome ◽

Respiratory Distress ◽

Ct Scan ◽

High Frequency ◽

Distress Syndrome ◽

Alveolar Recruitment

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Adding Value of Chest CT Images to a Personalized Prognostic Model in Acute Respiratory Distress Syndrome: A Retrospective Study

10.21203/rs.3.rs-871622/v1 ◽

2021 ◽

Author(s):

Yuan-Cheng Wang ◽

Shan Huang ◽

Qian Yu ◽

Xiangpan Meng ◽

Jianfeng Xie ◽

...

Keyword(s):

Mechanical Ventilation ◽

Acute Respiratory Distress Syndrome ◽

Respiratory Distress Syndrome ◽

Respiratory Distress ◽

Predictive Model ◽

Distress Syndrome ◽

Clinical Information ◽

Ct Images ◽

Chest Ct ◽

High Morbidity

Abstract Background: Acute respiratory distress syndrome (ARDS) is a critical disease in the intensive care unit (ICU) with high morbidity and mortality. The accuracy for predicting outcomes of ARDS patients with mechanical ventilation is limited, and most predicting model are based on clinical information. This study aimed to explore the prognostic value of chest CT images in ARDS patients with mechanical ventilation and develop an outperformed predictive model.Methods: The patients diagnosed with ARDS between January 2014 and June 2019 were retrospectively recruited. Clinical information, ventilation parameters, primary causes, illness severity, and chest CT images were collected. Radiomics features were extracted from the levels of the upper, middle, and lower lungs, and were further screened according to the primary outcome (28-day mortality after ARDS onset). Radiomics Scores for each level were computed. The univariate and multivariate logistic regression analyses were applied to figure out risk factors. Various predictive models were constructed and compared.Results: Of 366 ARDS patients recruited in this study, 276 (median age, 64 years [interquartile range, 54–75 years]; 208 male) survive on the Day 28. Among all factors, the APACHE Ⅱ Score (OR, 2.607, 95% CI: 1.896-3.584, P < 0.001), the Radiomics_Score of the middle lung (OR, 2.230, 95% CI: 1.387-3.583, P = 0.01), the Radiomics_Score of the lower lung (OR, 1.633, 95% CI: 1.143-2.333, P = 0.01) was associated with the 28-day mortality. The clinical_radiomics predictive model (AUC, 0.813, 95% CI: 0.767-0.850) show the best performance compared with the clinical model (AUC, 0.758, 95% CI: 0.710-0.802), the radiomics model (AUC, 0.692, 95% CI: 0.641-0.739) and the various ventilator parameter-based models (highest AUC, 0.773, 95% CI: 0.726-0.815).Conclusions: The radiomics features of chest CT images have incremental values in predicting the 28-day mortality in ARDS patients with mechanical ventilation. These results help to build a personalized prognostic prediction model and to stratify high-risk patients.

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Compressive Forces and Computed Tomography–derived Positive End-expiratory Pressure in Acute Respiratory Distress Syndrome

Anesthesiology ◽

10.1097/aln.0000000000000373 ◽

2014 ◽

Vol 121 (3) ◽

pp. 572-581 ◽

Cited By ~ 29

Author(s):

Massimo Cressoni ◽

Davide Chiumello ◽

Eleonora Carlesso ◽

Chiara Chiurazzi ◽

Martina Amini ◽

...

Keyword(s):

Computed Tomography ◽

Acute Respiratory Distress Syndrome ◽

Respiratory Distress Syndrome ◽

Respiratory Distress ◽

Ct Scan ◽

Distress Syndrome ◽

Peep Level ◽

Transpulmonary Pressure ◽

Lung Parenchyma ◽

Positive End Expiratory Pressure

Abstract Background: It has been suggested that higher positive end-expiratory pressure (PEEP) should be used only in patients with higher lung recruitability. In this study, the authors investigated the relationship between the recruitability and the PEEP necessary to counteract the compressive forces leading to lung collapse. Methods: Fifty-one patients with acute respiratory distress syndrome (7 mild, 33 moderate, and 11 severe) were enrolled. Patients underwent whole-lung computed tomography (CT) scan at 5 and 45 cm H2O. Recruitability was measured as the amount of nonaerated tissue regaining inflation from 5 to 45 cm H2O. The compressive forces (superimposed pressure) were computed as the density times the sternum-vertebral height of the lung. CT-derived PEEP was computed as the sum of the transpulmonary pressure needed to overcome the maximal superimposed pressure and the pleural pressure needed to lift up the chest wall. Results: Maximal superimposed pressure ranged from 6 to 18 cm H2O, whereas CT-derived PEEP ranged from 7 to 28 cm H2O. Median recruitability was 15% of lung parenchyma (interquartile range, 7 to 21%). Maximal superimposed pressure was weakly related with lung recruitability (r 2 = 0.11, P = 0.02), whereas CT-derived PEEP was unrelated with lung recruitability (r 2 = 0.0003, P = 0.91). The maximal superimposed pressure was 12 ± 3, 12 ± 2, and 13 ± 1 cm H2O in mild, moderate, and severe acute respiratory distress syndrome, respectively, (P = 0.0533) with a corresponding CT-derived PEEP of 16 ± 5, 16 ± 5, and 18 ± 5 cm H2O (P = 0.48). Conclusions: Lung recruitability and CT scan–derived PEEP are unrelated. To overcome the compressive forces and to lift up the thoracic cage, a similar PEEP level is required in higher and lower recruiters (16.8 ± 4 vs. 16.6 ± 5.6, P = 1).

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