covid19TAC-COVID19NCT04395482
Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury
An observational study of covid19, run by University of Milano Bicocca at 8 sites in 2 countries.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- covid19
- What is being tested?
- Lung CT scan analysis in COVID-19 patients
- How many people
- 44 participants
- Where
- 8 sitesin 2 countries
- Study dates
- 7 May 2020to 31 Mar 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The goal is to collect as many lung CT scan images as possible in patients with COVID-19; according to the preliminary evaluation estimate, a total of 500 patients are expected to be collected.
Who may be able to join
- 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;
Who cannot join
- ● Positive confirmation with nucleic acid amplification test or serology of SARS-CoV2 by naso-pharyngeal swab, bronchoaspirate sample or bronchoalveolar lavage
Trial locations and contacts
8 sites in 2 countries.
8 sites in 2 countries
Italy7 sites
Ospedale Papa Giovanni XXIII
Bergamo
Policlinico San Marco-San Donato group
Bergamo
Azienda Ospedaliero-Universitaria di Ferrara
Ferrara
ASST di Lecco Ospedale Alessandro Manzoni
Lecco
ASST Melegnano-Martesana, Ospedale Santa Maria delle Stelle
Melzo
ASST Monza
Monza
AUSL Romagna-Ospedale Infermi di Rimini
Rimini
San Marino1 site
Istituto per la Sicurezza Sociale-Ospedale della Repubblica di San Marino
San Marino
About this study
Official title: Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury by Machine Learning: a Multicenter Retrospective Cohort Study.
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.
Read the detailed description
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:
- Quantitative and qualitative lung alterations;
- 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
- covid19
- Being tested
- Lung CT scan analysis in COVID-19 patients
- Keywords
- Lung injury, sars-covid-2, coronavirus infection
- Medical subject headings
- COVID-19, Lung Injury, Coronavirus Infections
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Lung CT scan analysis in COVID-19 patientsOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Lung CT scan analysis in COVID-19 patients |
What is being measured
The primary outcome is the main question the study is designed to answer.
A qualitative analysis of parenchymal lung damage induced by COVID-19
Describe the parenchymal lung damage induced by COVID-19 through a qualitative analysis with chest CT through artificial intelligence techniques.
Measured: Until patient discharge from the hospital (approximately 6 months)
A quantitative analysis of parenchymal lung damage induced by COVID-19
Describe the parenchymal lung damage induced by COVID-19 through a quantitative analysis with chest CT through artificial intelligence techniques.
Measured: Until patient discharge from the hospital (approximately 6 months)
Secondary outcomes (6)
The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.
The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure assessed as intensive care mortality.
Measured: Until patient discharge from the hospital (approximately 6 months)
The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.
The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure assessed as hospital mortality.
Measured: Until patient discharge from the hospital (approximately 6 months)
The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.
The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure assessed as days free from mechanical ventilation.
Measured: Until patient discharge from the hospital (approximately 6 months)
Automated segmentation of lung scans of patients with COVID-19 and ARDS.
The hypothesis is that the uso of deep neural network models for lung segmentation in Acute Respiratory Distress Syndrome (ARDS) in animal models and Chronic Obstructive Pulmonary Disease (COPD) in patients that could be applied to self-segment the lungs of COVID-19 patients through a learning transfer mechanism with artificial intelligence.
Measured: Until patient discharge from the hospital (approximately 6 months)
Knowledge of chest CT features in COVID-19 patients and their detail through the use of machine learning and other quantitative techniques.
Expand the knowledge of chest CT features in COVID-19 patients and their detail through the use of machine learning and other quantitative techniques comparing CT patterns of COVID-19 patients to those of patients with ARDS.
Measured: Until patient discharge from the hospital (approximately 6 months)
The ability within which the analysis of artificial intelligence that uses deep learning models can be used to predict clinical outcomes
Determine the capacity within which the artificial intelligence analysis that uses deep learning models can be used to predict clinical outcomes from the analysis of the characteristics of the chest CT obtained within 7 days of hospital admission; combining quantitative CT data with clinical data.
Measured: Until patient discharge from the hospital (approximately 6 months)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 May 2020
- Study start7 May 2020
- Primary completion15 Jun 2021
- Study completion31 Mar 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (24)Papers the sponsor has linked to this study.
- Other trials for covid19Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 44 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Milano Bicocca
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04395482
- Sponsor's protocol ID
- TAC-COVID19
- Acronym
- TAC-COVID19
- Status last verified
- Jul 2022
- First submitted
- 18 May 2020
- Last updated
- 21 Jul 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 32167538Remuzzi 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.
PubMed 32178769Dong 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.
PubMed 32087114Zhou 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.
PubMed 32171076Zhou 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.
PubMed 32134681Xiong 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.
PubMed 32134800Salehi 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.
PubMed 32174129Dai 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.
PubMed 32129670Li 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.
PubMed 32130038Xie 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.
PubMed 32049601Ai 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.
PubMed 32101510Fang 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.
PubMed 32073353Bai 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.
PubMed 32155105Pan 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.
PubMed 32053470Chung 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.
PubMed 32017661
Study at a glance
From the registry record, last updated by the sponsor on 21 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury by Machine Learning: a Multicenter Retrospective Cohort Study.
- ClinicalTrials.gov ID
- NCT04395482
- Acronym / sponsor ID
- TAC-COVID19 / TAC-COVID19
- Conditions
- covid19
- Keywords
- Lung injury, sars-covid-2, coronavirus infection
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 44 participants (actual)
- Lead sponsor
- University of Milano BicoccaUniversity, hospital or other
- Study start
- 7 May 2020 (actual)
- Primary completion
- 15 Jun 2021 (actual)
- Study completion
- 31 Mar 2022 (actual)
- First posted
- 20 May 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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:
- Quantitative and qualitative lung alterations;
- 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).
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| covid-19 pneumonia related patients | – | Other: Lung CT scan analysis in COVID-19 patientsThe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The goal is to collect as many lung CT scan images as possible in patients with COVID-19; according to the preliminary evaluation estimate, a total of 500 patients are expected to be collected.
Key inclusion criteria (6 listed)
- 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;
Key exclusion criteria (1 listed)
- ● Positive confirmation with nucleic acid amplification test or serology of SARS-CoV2 by naso-pharyngeal swab, bronchoaspirate sample or bronchoalveolar lavage
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Lung CT scan analysis in COVID-19 patientsThis 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. | Other | covid-19 pneumonia related patients |
Primary outcome
A qualitative analysis of parenchymal lung damage induced by COVID-19
Time frame: Until patient discharge from the hospital (approximately 6 months)
Describe the parenchymal lung damage induced by COVID-19 through a qualitative analysis with chest CT through artificial intelligence techniques.
A quantitative analysis of parenchymal lung damage induced by COVID-19
Time frame: Until patient discharge from the hospital (approximately 6 months)
Describe the parenchymal lung damage induced by COVID-19 through a quantitative analysis with chest CT through artificial intelligence techniques.
6 secondary outcomes
The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.
Time frame: Until patient discharge from the hospital (approximately 6 months)
The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.
Time frame: Until patient discharge from the hospital (approximately 6 months)
The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.
Time frame: Until patient discharge from the hospital (approximately 6 months)
Automated segmentation of lung scans of patients with COVID-19 and ARDS.
Time frame: Until patient discharge from the hospital (approximately 6 months)
Knowledge of chest CT features in COVID-19 patients and their detail through the use of machine learning and other quantitative techniques.
Time frame: Until patient discharge from the hospital (approximately 6 months)
The ability within which the analysis of artificial intelligence that uses deep learning models can be used to predict clinical outcomes
Time frame: Until patient discharge from the hospital (approximately 6 months)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Jul 2022.
- Background: PubMed 32167538Grasselli 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.
- Background: PubMed 32178769Remuzzi 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.
- Background: PubMed 32087114Dong 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.
- Background: PubMed 32171076Zhou 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.
- Background: PubMed 32134681Zhou 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.
- Background: PubMed 32134800Xiong 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.
- Background: PubMed 32174129Salehi 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.
- Background: PubMed 32129670Dai 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.
- Background: PubMed 32130038Li 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.
- Background: PubMed 32049601Xie 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.
- Background: PubMed 32101510Ai 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.
- Background: PubMed 32073353Fang 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.
- Related InfoLink provided by the sponsor.
- Related InfoLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
8 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of covid19, run by University of Milano Bicocca at 8 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 21 Jul 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 8 sites in Italy, San Marino. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
A qualitative analysis of parenchymal lung damage induced by COVID-19 (time frame: Until patient discharge from the hospital (approximately 6 months)). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04395482, which also lists 24 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Jul 2022.