Study Results
Results pending
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Basic Information
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Recruitment Status
COMPLETED
Total Enrollment
44 participants
Study Classification
OBSERVATIONAL
Study Start Date
2020-05-07
Study Completion Date
2022-03-31
Brief Summary
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This is a multicenter observational retrospective cohort study that aims to study the morphological characteristics of the lung parenchyma of SARS-CoV2 positive patients identifiable in patterns through artificial intelligence techniques and their impact on patient outcome.
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Detailed Description
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BACKGROUND:
In February, the first case of SARS-CoV2 positive patient was recorded in Lombardy (Italy), a virus capable of causing a severe form of acute respiratory failure called Coronavirus Disease 2019 (COVID-19).
Qualitative assessments of lung morphology have been identified to describe macroscopic characteristics of this infection upon admission and during the hospitalization of patients.
At the moment, there are no studies that have exhaustively described the parenchymal lung damage induced by SARS-CoV2 by quantitative analysis.
The hypothesis of this study is that specific morphological and quantitative alterations of the lung parenchyma assessed by means of CT scan in patients suffering from severe respiratory insufficiency induced by SARS-CoV2 may have an impact on the severity of the degree of alteration of the respiratory exchanges (oxygenation and clearance of the CO2) and have an impact on patient outcome.
The presence of characteristic lung morphological patterns assessed by CT scan could allow the recognition of specific patient clusters who can benefit from intensive treatment differently, making a significant contribution to stratifying the severity of patients and their risk of mortality.
This is an exploratory clinical descriptive study of lung CT images in a completely new patient population who are nucleic acid amplification test confirmed SARS-CoV2 positive.
SAMPLE SIZE (n. patients):
The study will collect all patients with the inclusion criteria; a total of 500 patients are expected to be collected.
About 80 patients will be enrolled for each local experimental center.
The following patient data will be analyzed:
* blood gas analytical data assigned to the CT scan, checks performed upon entering the hospital, at the time of performing the CT scan, admission to intensive care and 7 days after entry
* patient characteristics such as age, gender and body mass index (BMI)
* comorbidity
* presence of organ dysfunction with the Sequential Organ Failure Assessment (SOFA)
* laboratory data relating to hospital admission and symptoms prior to hospitalization.
* ventilator and hemodynamic parameters upon entering the hospital, at the time of carrying out the CT scan, upon admission to intensive care and 7 days after entry.
The machine learning approach of lung CT scan analysis will aim at evaluating:
1. Quantitative and qualitative lung alterations;
2. The stratification of such morphological characteristics in specific morphological lung clusters identified by the means of artificial intelligence using deep learning algorithms.
ETHICAL ASPECTS:
The lung CT scan images will be collected and anonymized. Images will be subsequently sent by University of Milano-Bicocca Institutional google drive account to the University of Pennsylvania, Department of Anesthesiology and Critical Care and the Department of Radiology in a deidentified format for advanced quantitative analysis taking advantage of artificial intelligence using deep learning algorithms.
The data will be collected in a pseudo-anonymous way through paper Case Report Form (CRF) and analyzed by the scientific coordinator of the project.
Given the retrospective nature of the study and in the presence of technical difficult in obtaining an informed consent of patients in this period of pandemic emergency, informed consent will be waived.
STATISTICAL ANALYSIS:
Continuous data will be expressed as mean ± standard deviation or median and interquartile range, according to data distribution that will be evaluated by the Shapiro-Wilk test. Categorical variables will be expressed as proportions (frequency).
The deep learning segmentation algorithm will segment the lung parenchyma from the entire CT lung. Lung volume, lung weight and opacity intensity distribution analysis will be applied. Second, clustering analysis to stratify the patients will be performed. Both an intensity and a spatial clustering algorithm will be tested. Third, a model will be trained to predict the injury progression using the images and all other patient data. Statistical significance will be considered in the presence of a p\<0.05 (two-tailed).
In February, the first case of SARS-CoV2 positive patient was recorded in Lombardy (Italy), a virus capable of causing a severe form of acute respiratory failure called Coronavirus Disease 2019 (COVID-19).
Qualitative assessments of lung morphology have been identified to describe macroscopic characteristics of this infection upon admission and during the hospitalization of patients.
At the moment, there are no studies that have exhaustively described the parenchymal lung damage induced by SARS-CoV2 by quantitative analysis.
The hypothesis of this study is that specific morphological and quantitative alterations of the lung parenchyma assessed by means of CT scan in patients suffering from severe respiratory insufficiency induced by SARS-CoV2 may have an impact on the severity of the degree of alteration of the respiratory exchanges (oxygenation and clearance of the CO2) and have an impact on patient outcome.
The presence of characteristic lung morphological patterns assessed by CT scan could allow the recognition of specific patient clusters who can benefit from intensive treatment differently, making a significant contribution to stratifying the severity of patients and their risk of mortality.
This is an exploratory clinical descriptive study of lung CT images in a completely new patient population who are nucleic acid amplification test confirmed SARS-CoV2 positive.
SAMPLE SIZE (n. patients):
The study will collect all patients with the inclusion criteria; a total of 500 patients are expected to be collected.
About 80 patients will be enrolled for each local experimental center.
The following patient data will be analyzed:
* blood gas analytical data assigned to the CT scan, checks performed upon entering the hospital, at the time of performing the CT scan, admission to intensive care and 7 days after entry
* patient characteristics such as age, gender and body mass index (BMI)
* comorbidity
* presence of organ dysfunction with the Sequential Organ Failure Assessment (SOFA)
* laboratory data relating to hospital admission and symptoms prior to hospitalization.
* ventilator and hemodynamic parameters upon entering the hospital, at the time of carrying out the CT scan, upon admission to intensive care and 7 days after entry.
The machine learning approach of lung CT scan analysis will aim at evaluating:
1. Quantitative and qualitative lung alterations;
2. The stratification of such morphological characteristics in specific morphological lung clusters identified by the means of artificial intelligence using deep learning algorithms.
ETHICAL ASPECTS:
The lung CT scan images will be collected and anonymized. Images will be subsequently sent by University of Milano-Bicocca Institutional google drive account to the University of Pennsylvania, Department of Anesthesiology and Critical Care and the Department of Radiology in a deidentified format for advanced quantitative analysis taking advantage of artificial intelligence using deep learning algorithms.
The data will be collected in a pseudo-anonymous way through paper Case Report Form (CRF) and analyzed by the scientific coordinator of the project.
Given the retrospective nature of the study and in the presence of technical difficult in obtaining an informed consent of patients in this period of pandemic emergency, informed consent will be waived.
STATISTICAL ANALYSIS:
Continuous data will be expressed as mean ± standard deviation or median and interquartile range, according to data distribution that will be evaluated by the Shapiro-Wilk test. Categorical variables will be expressed as proportions (frequency).
The deep learning segmentation algorithm will segment the lung parenchyma from the entire CT lung. Lung volume, lung weight and opacity intensity distribution analysis will be applied. Second, clustering analysis to stratify the patients will be performed. Both an intensity and a spatial clustering algorithm will be tested. Third, a model will be trained to predict the injury progression using the images and all other patient data. Statistical significance will be considered in the presence of a p\<0.05 (two-tailed).
Conditions
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covid19
Study Design
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Observational Model Type
COHORT
Study Time Perspective
RETROSPECTIVE
Study Groups
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covid-19 pneumonia related patients
The study aims to collect the highest number possible of lung CT scan images performed in patients with COVID-19, in order to obtain a large sample size that will allow us to characterize the extent of lung injury, the presence of specific patterns of lung alteration, and their potential association with the outcome of patients - in view of assisting the medical staff in better understanding the grade of the severity impairment in these patients which might be potentially candidates to more intensive therapeutic strategies.
Lung CT scan analysis in COVID-19 patients
Intervention Type
OTHER
This research project will evaluate the morphological characteristics of the lung by CT scan analysis in COVID-19 patients which will be identified as specific patterns using artificial intelligence technology and their impact on outcome.
Interventions
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Lung CT scan analysis in COVID-19 patients
This research project will evaluate the morphological characteristics of the lung by CT scan analysis in COVID-19 patients which will be identified as specific patterns using artificial intelligence technology and their impact on outcome.
Intervention Type
OTHER
Eligibility Criteria
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Inclusion Criteria
* Patients 18 years old or above;
* Positive confirmation with nucleic acid amplification test or serology of SARS-CoV2 by naso-pharyngeal swab, bronchoaspirate sample or bronchoalveolar lavage;
* Lung CT scan performed within 7 days of hospital admission;
* Patients above 18 years old or above;
* Patients admitted to the hospital with a diagnosis of ARDS according to the Berlin criteria;
* Lung CT scan performed within 7 days of ARDS diagnosis;
* Positive confirmation with nucleic acid amplification test or serology of SARS-CoV2 by naso-pharyngeal swab, bronchoaspirate sample or bronchoalveolar lavage;
* Lung CT scan performed within 7 days of hospital admission;
* Patients above 18 years old or above;
* Patients admitted to the hospital with a diagnosis of ARDS according to the Berlin criteria;
* Lung CT scan performed within 7 days of ARDS diagnosis;
Exclusion Criteria
● Positive confirmation with nucleic acid amplification test or serology of SARS-CoV2 by naso-pharyngeal swab, bronchoaspirate sample or bronchoalveolar lavage
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
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University of Milano Bicocca
OTHER
Sponsor Role
lead
Responsible Party
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Responsibility Role
SPONSOR
Locations
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Ospedale Papa Giovanni XXIII
Bergamo, , Italy
Site Status
Policlinico San Marco-San Donato group
Bergamo, , Italy
Site Status
Azienda Ospedaliero-Universitaria di Ferrara
Ferrara, , Italy
Site Status
ASST di Lecco Ospedale Alessandro Manzoni
Lecco, , Italy
Site Status
ASST Melegnano-Martesana, Ospedale Santa Maria delle Stelle
Melzo, , Italy
Site Status
ASST Monza
Monza, , Italy
Site Status
AUSL Romagna-Ospedale Infermi di Rimini
Rimini, , Italy
Site Status
Istituto per la Sicurezza Sociale-Ospedale della Repubblica di San Marino
San Marino, , San Marino
Site Status
Countries
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Italy
San Marino
References
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Grasselli G, Pesenti A, Cecconi M. Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response. JAMA. 2020 Apr 28;323(16):1545-1546. doi: 10.1001/jama.2020.4031. No abstract available.
Reference Type
BACKGROUND
Remuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet. 2020 Apr 11;395(10231):1225-1228. doi: 10.1016/S0140-6736(20)30627-9. Epub 2020 Mar 13.
Reference Type
BACKGROUND
Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020 May;20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1. Epub 2020 Feb 19. No abstract available.
Reference Type
BACKGROUND
Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020 Mar 28;395(10229):1054-1062. doi: 10.1016/S0140-6736(20)30566-3. Epub 2020 Mar 11.
Reference Type
BACKGROUND
Zhou S, Wang Y, Zhu T, Xia L. CT Features of Coronavirus Disease 2019 (COVID-19) Pneumonia in 62 Patients in Wuhan, China. AJR Am J Roentgenol. 2020 Jun;214(6):1287-1294. doi: 10.2214/AJR.20.22975. Epub 2020 Mar 5.
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BACKGROUND
Xiong Y, Sun D, Liu Y, Fan Y, Zhao L, Li X, Zhu W. Clinical and High-Resolution CT Features of the COVID-19 Infection: Comparison of the Initial and Follow-up Changes. Invest Radiol. 2020 Jun;55(6):332-339. doi: 10.1097/RLI.0000000000000674.
Reference Type
BACKGROUND
Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. AJR Am J Roentgenol. 2020 Jul;215(1):87-93. doi: 10.2214/AJR.20.23034. Epub 2020 Mar 14.
Reference Type
BACKGROUND
Dai WC, Zhang HW, Yu J, Xu HJ, Chen H, Luo SP, Zhang H, Liang LH, Wu XL, Lei Y, Lin F. CT Imaging and Differential Diagnosis of COVID-19. Can Assoc Radiol J. 2020 May;71(2):195-200. doi: 10.1177/0846537120913033. Epub 2020 Mar 4.
Reference Type
BACKGROUND
Li Y, Xia L. Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management. AJR Am J Roentgenol. 2020 Jun;214(6):1280-1286. doi: 10.2214/AJR.20.22954. Epub 2020 Mar 4.
Reference Type
BACKGROUND
Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J. Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing. Radiology. 2020 Aug;296(2):E41-E45. doi: 10.1148/radiol.2020200343. Epub 2020 Feb 12.
Reference Type
BACKGROUND
Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020 Aug;296(2):E32-E40. doi: 10.1148/radiol.2020200642. Epub 2020 Feb 26.
Reference Type
BACKGROUND
Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, Ji W. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology. 2020 Aug;296(2):E115-E117. doi: 10.1148/radiol.2020200432. Epub 2020 Feb 19. No abstract available.
Reference Type
BACKGROUND
Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, Pan I, Shi LB, Wang DC, Mei J, Jiang XL, Zeng QH, Egglin TK, Hu PF, Agarwal S, Xie FF, Li S, Healey T, Atalay MK, Liao WH. Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT. Radiology. 2020 Aug;296(2):E46-E54. doi: 10.1148/radiol.2020200823. Epub 2020 Mar 10.
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BACKGROUND
Pan F, Ye T, Sun P, Gui S, Liang B, Li L, Zheng D, Wang J, Hesketh RL, Yang L, Zheng C. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology. 2020 Jun;295(3):715-721. doi: 10.1148/radiol.2020200370. Epub 2020 Feb 13.
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BACKGROUND
Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, Cui J, Xu W, Yang Y, Fayad ZA, Jacobi A, Li K, Li S, Shan H. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology. 2020 Apr;295(1):202-207. doi: 10.1148/radiol.2020200230. Epub 2020 Feb 4.
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Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, Diao K, Lin B, Zhu X, Li K, Li S, Shan H, Jacobi A, Chung M. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology. 2020 Jun;295(3):200463. doi: 10.1148/radiol.2020200463. Epub 2020 Feb 20.
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Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020 Feb 15;395(10223):497-506. doi: 10.1016/S0140-6736(20)30183-5. Epub 2020 Jan 24.
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Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, Fan Y, Zheng C. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020 Apr;20(4):425-434. doi: 10.1016/S1473-3099(20)30086-4. Epub 2020 Feb 24.
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BACKGROUND
Guo L, Wei D, Zhang X, Wu Y, Li Q, Zhou M, Qu J. Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score. Front Microbiol. 2019 Dec 3;10:2752. doi: 10.3389/fmicb.2019.02752. eCollection 2019.
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BACKGROUND
Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, Zhao Y, Li Y, Wang X, Peng Z. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020 Mar 17;323(11):1061-1069. doi: 10.1001/jama.2020.1585.
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BACKGROUND
Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020 Feb 15;395(10223):507-513. doi: 10.1016/S0140-6736(20)30211-7. Epub 2020 Jan 30.
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Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, Wu Y, Zhang L, Yu Z, Fang M, Yu T, Wang Y, Pan S, Zou X, Yuan S, Shang Y. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020 May;8(5):475-481. doi: 10.1016/S2213-2600(20)30079-5. Epub 2020 Feb 24.
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Rezoagli E, Xin Y, Signori D, Sun W, Gerard S, Delucchi KL, Magliocca A, Vitale G, Giacomini M, Mussoni L, Montomoli J, Subert M, Ponti A, Spadaro S, Poli G, Casola F, Herrmann J, Foti G, Calfee CS, Laffey J, Bellani G, Cereda M; CT-COVID19 Multicenter Study Group. Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan. Crit Care. 2024 Aug 5;28(1):263. doi: 10.1186/s13054-024-05046-3.
Reference Type
DERIVED
Related Links
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Other Identifiers
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TAC-COVID19
Identifier Type: -
Identifier Source: org_study_id
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