key: cord-0741416-5wsvok08
authors: Revel, Marie-Pierre; Boussouar, Samia; de Margerie-Mellon, Constance; Saab, Inès; Lapotre, Thibaut; Mompoint, Dominique; Chassagnon, Guillaume; Milon, Audrey; Lederlin, Mathieu; Bennani, Souhail; Molière, Sébastien; Debray, Marie-Pierre; Bompard, Florian; Dangeard, Severine; Hani, Chahinez; Ohana, Mickaël; Bommart, Sébastien; Jalaber, Carole; El Hajjam, Mostafa; Petit, Isabelle; Fournier, Laure; Khalil, Antoine; Brillet, Pierre-Yves; Bellin, Marie-France; Redheuil, Alban; Rocher, Laurence; Bousson, Valérie; Rousset, Pascal; Grégory, Jules; Deux, Jean-François; Dion, Elisabeth; Valeyre, Dominique; Porcher, Raphael; Jilet, Léa; Abdoul, Hendy
title: Study of Thoracic CT in COVID-19: The STOIC Project
date: 2021-06-29
journal: Radiology
DOI: 10.1148/radiol.2021210384
sha: 2cce6f5695f57b36fb4b9b7e24c8c0dac49935b5
doc_id: 741416
cord_uid: 5wsvok08

BACKGROUND: There are conflicting data regarding the diagnostic performance of Chest computed tomography (CT) for COVID-19 pneumonia. Disease extent on CT has been reported to influence prognosis. PURPOSE: To create a large publicly available dataset and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. MATERIALS AND METHODS: This multicenter observational retrospective cohort study (ClinicalTrials.gov: NCT04355507) involved 20 French university hospitals. Eligible subjects presented at the emergency departments of the hospitals involved between March 1st and April 30th, 2020 and underwent both thoracic CT and RT-PCR 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 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 subjects positive for both RT-PCR and CT, using clinical and radiological features. RESULTS: Of 10,930 subjects screened for eligibility, 10,735 (median age 65 years, interquartile range, 51–77 years; 6,147 men) were included and 6,448 (60.0%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity and 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’s AC1 coefficient: 0.79) Of all the variables analysed, the extent of pneumonia on CT (OR 3.25, 95%CI: 2.71, 3.89) was the best predictor of severe outcome at one month. A score based solely on clinical variables predicted a severe outcome with an AUC 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 on CT. CONCLUSION: Using pre-defined criteria, CT reading is not influenced by reader’s experience and helps predict the outcome at one month. See also the editorial by Rubin.

The SARS-Cov-2 pandemic has caused more than 1.6 million deaths worldwide by the end of 2020 and has overwhelmed healthcare resources in most countries. SARS-Cov-2 infects the airway epithelial cells according to their expression of ACE2 receptors (1), with consequences ranging from no or few symptoms to acute respiratory distress, the main cause of death.

COVID-19 pneumonia resulting from SARS-Cov-2 infection is characterized by ground glass opacities not always detectable on chest radiography (2) . The advice from the European Society of Radiology was to use CT in subjects developing respiratory symptoms (3) . The use of CT in managing SARS-Cov-2 pandemic has been variable around the world. CT has been used as a screening test in China, following initial reports of sensitivity as high as 97% (4) .

Conversely, the American College of Radiologists estimated that imaging findings in COVID-19 were not specific, overlapping with other infections (5) . The risk that CT scanners could become vectors of infection and that the use of CT might have a disproportionate riskbenefit ratio was also raised (6, 7) . In northern Italy, CT helped patient triage by discarding from the COVID-19 protocol 29% of individuals who had normal or non-COVID-19 abnormalities on CT (8) . To determine the diagnostic value of CT more clearly, one objective of the STOIC project, collecting CT scans and 1-month outcomes in more than 10,000 individuals, was to evaluate the sensitivity and specificity of CT interpreted by readers of differing levels of experience, with RT-PCR as the reference standard. Another objective was to assess the influence of the extent of pneumonia and CT features related to comorbidities, on subjects' outcomes at 1-month follow-up.

The protocol of this multicenter observational retrospective cohort study can be accessed via ClinicalTrials.gov with identifier NCT04355507. Its design and execution were in accordance with the Standards for Reporting of Diagnostic Accuracy (STARD) initiative (9) .

The STOIC (Study of ThOracic computed tomography In Covid-19) project aimed to build a dataset of at least 10,000 CT scans from individuals with suspected COVID-19 pneumonia, evaluated during the first wave of the SARS-Cov-2 pandemic in France. The collected data and list of image annotations are described in Appendix E1-E3, together with the license agreement for data sharing. The project involved 20 university hospitals: 15 from Assistance Publique des Hôpitaux de Paris and 5 from other cities (Strasbourg, Lyon, Rennes, and Montpellier). Subjects were eligible if they had both thoracic CT scans and RT-PCR at initial presentation, between March 1 st and April 30 th , 2020. According to the recommendations of the French Health Authority (HAS), CT was not performed as a screening test in subjects with no thoracic symptoms but only in those presenting at the hospital with dyspnea and/or desaturation as measured by pulse oximetry (10). This study was approved by the Ethics committee of Cochin hospital in Paris, which waived the need for written informed consent.

All participating hospitals were equipped with multi-detector CT scanners from different manufacturers, allowing volumetric high-resolution CT acquisitions of the whole thorax. CT acquisitions were performed without contrast administration except when pulmonary I n p r e s s embolism was suspected as a confounding diagnosis to COVID-19 pneumonia at presentation (11).

After anonymized export, CT images were independently read using a dedicated 3D image viewer web application by 7 junior (IS, TL, AM, FB, SD, CH, CJ) and 13 senior (MPR, SBou, CDM, DM, GC, ML, SBen, SM, MPD, MO, SBom, MEH, IP) chest radiologists who were blinded to initial reports, RT-PCR results, demographic characteristics, clinical symptoms, and outcomes. Senior radiologists had at least 4-years of experience in chest imaging (Table E1 ). Readers were asked to classify CT scans as either positive or negative for COVID-19 based on the criteria of the COVID-19 structured report of the French Society of Radiology (12) . Positive diagnosis required the presence of predominantly subpleural ground glass opacities and the absence of mucoid impactions, bronchiolar nodules, or focal consolidation suggesting bacterial infection. Only the initial CT, performed at presentation, was analyzed. CT scans were only read by one radiologist, except those which were unintentionally exported twice and received different anonymization numbers. Again, some CT scans not marked as read were re-annotated by the same reader. This provided the opportunity to evaluate inter and intra reader agreement in a post-hoc analysis. For CT scans they considered as COVID-19 positive, readers had to visually quantify lung disease extent using a 5-point scale. They also evaluated lung emphysema on a 5-point scale (0%, <25%, 25-50%, 50-75%, >75%), severity of coronary artery calcifications using a semi-quantitative visual method (13) , and measured the amount of chest wall fat anterior to the sternum.

Patient demographic characteristics were retrieved from the electronic medical records, as was the time elapsed since the onset of symptoms, the need for oxygen supplementation at presentation and any pre-existing comorbidities such as diabetes, hypertension, coronary artery disease, and any other pre-existing cardiovascular or respiratory disease. Initial (or repeated) RT-PCR results were collected. For subjects with positive RT-PCR, the outcome at 1 month was analyzed, with unfavorable outcome being defined as death or intubation.

RT-PCR was used as reference standard. If the first RT-PCR result was negative but turned out to be positive in the following 7 days after CT, the patient was considered as positive for SARS-Cov-2 infection.

For the power analysis, we considered that with an enrollment target of 10,000 subjects, a disease prevalence of at least 50% and expected CT sensitivity and specificity between 80 and 85%, the width of the confidence intervals for both sensitivity and specificity would be less than 1.0%.

CT diagnostic performance was evaluated by its sensitivity, specificity, positive and negative predictive values, accuracy and area under the curve (AUC) with RT-PCR results as the reference standard. The McNemar test was used for paired comparisons. Cohen's kappa coefficient was used to measure intra-reader agreement for readers who re-annotated the same CT scan. Gwet's AC1 coefficient for multiple readers was used to evaluate inter-reader agreement on CT scans annotated by two different readers.

The analysis was conducted on all included subjects. Continuous data are presented as mean ± standard deviation, while categorical data are summarized as counts and percentages.

For modeling purposes, missing clinical data were handled with multiple imputations by chained equations. The risk model for severe outcome was developed on subjects positive for both RT-PCR and CT, who had unenhanced CT allowing quantifying coronary artery calcifications on CT.

Multivariable logistic regression was used to estimate the risk model. We selected 11 clinical and CT variables to serve as candidate predictors. These included: age, sex, need for oxygen supplementation at presentation, comorbidities (diabetes, hypertension, coronary artery disease, respiratory disease), chest wall fat thickness anterior to the sternum, disease extent, coronary artery ordinal calcium score ( Figure 4 ) and emphysema score. Multiple imputations were performed to handle missing data using the SAS procedure PROC MI.

Severity of the disease (subjects who were intubated at one point or deceased were considered as severe cases) was the dependent variable. Stepwise variable selection was applied to select predictors in the final model, with a 0.20 significance level for entry and a 0.05 significance level for retention. Discrimination was assessed by calculating the AUC and goodness of fit by the Hosmer-Lemeshow test. A simplified score was obtained by multiplying the regression coefficient by 5 and rounding to the nearest integer (14) .

All statistical analyses were performed with SAS software Version 9.4 (SAS Institute Inc.). A P value of ≤.05 was considered significant for all statistical tests conducted.

Of the 10,930 subjects who were assessed for eligibility, 195 were excluded either because they did not fulfill the inclusion criteria or were erroneously included twice ( Figure 1 ). A total of 10,735 subjects (median age 65 years, interquartile range, 51-77 years; 6,147 men) were I n p r e s s finally included. The study demographics are presented in Table 1 . RT-PCR was positive for 6,448 subjects, corresponding to a disease prevalence of 60.0% during the study period. These results and the corresponding AUC, accuracy and predictive values are presented in Table 2 .

Annotations from different radiologists ( Figure 2 ) were available for 235 CT scans exported twice and had received different anonymization numbers (Table E1) .

Gwet's AC1 coefficient was 0.79, indicating strong interreader agreement for the classification of CT scans as COVID-19 positive or negative. Regarding disease extent, Gwet's AC1 coefficient was 0.38 for classification into one of the five categories (< 10%/10-24%/25-49%/50-74%/≥ 75%), but 0.85 for classification as < 50% vs ≥ 50%. (Figure 3 ). Different annotations from the same observer were also available for 324 CT scans. This happened when CT scans of a reading list were not labeled as "already read". Intra observer agreement was perfect for 16 of the 20 readers (=1.00), and almost perfect for the remaining four ( ranging from 0.82 to 0.92).

The proportion of double readings related to the whole number of readings was homogeneous amongst the readers (median 4.3%, IQR: 3.9, 5.2).

Emphysema was rarely present, with 86.4% (3663/4238) of CT and RT-PCR positive subjects having no emphysema or, when present, affecting more than 25% of the lung in only 1.8% (79/4238). imaging findings for subjects with severe and non-severe outcome is presented in Table 3 .

There was no evidence of an association between adipose thickness anterior to the sternum with disease severity (p=.10) contrary to emphysema percentage, coronary artery calcifications, and disease extent on CT (p< .001 for all 3 parameters). Table   4 displays the adjusted odds ratios of all significant predictors for severe outcome. The final risk model achieved an AUC of 0.69 (95%CI: 0.67, 0.71), without evidence for miscalibration (Hosmer-Lemeshow test: p ranging from 0.10 to 0.96 across imputed datasets) and was better than the clinical model (Likelihood ratio test p<.001) ( Figure 6 ).

Taking into account the only two significant interactions (coronary artery disease and hypertension, oxygen supplementation and disease extent) did not improve the performance of the model (Table E2 , Figure E1 ).

After multiplying the regression coefficient by 5 and rounding up to the nearest integer, a nomogram for severity prediction was derived ( Figure E2 ), ranging from 0 (lowest risk) to 18 (highest risk), and achieved the same AUC.

Despite abundant literature, CT diagnostic performance for COVID-19 pneumonia remains a subject of debate with diagnostic specificities ranging from less than 20% to 90%, depending on the diagnostic criteria and tests used for confirming infection (15) (16) (17) .In this retrospective cohort study of 10,735 subjects suspected of COVID-19 pneumonia, the overall CT diagnostic accuracy was 80.0%, considering RT-PCR as the reference standard, and increased to 86.3% after 5 days of symptoms. Male predominance, increased BMI, diabetes and other characteristics of our study sample were in line with those already reported as risk factors for developing symptomatic COVID-19 pneumonia requiring hospitalization (18) (19) (20) .

Conversely the presence of emphysema was a rare finding in our study. An age-and sexadjusted meta-analysis suggested that cigarette smoking might be protective against contracting COVID-19 (21) . It can be argued that smoking is inconsistently reported, whereas emphysema was systematically assessed by the readers in our study.

We report high intra and inter-reader agreement with no influence of reader's experience on CT diagnostic performance, supporting the use of predefined criteria. CT sensitivity increased after 5 days of symptoms, in line with other reports of higher sensitivity of CT after 2 to 5 days of symptoms (22,23).

In our study, CT specificity, although insufficient for a confident diagnosis, was much higher than that of the first reports (4). A first Cochrane review based on studies mainly from Asian countries reported a pooled CT specificity of only 18.1% (24) . Even though an update of this first review reported a pooled specificity of 61.1% (25) , the authors estimated that the heterogeneity and insufficient quality of the included studies limited their possibility to draw conclusions and highlighted the need to conduct future diagnostic accuracy studies that should predefine positive imaging findings.

The structured reporting of COVID-19 pneumonia proposed by the French Society of Radiology, used in this study, requires that bronchiolar nodules, mucoid impactions, and focal consolidation should be absent for diagnosis (12) . Other proposals for structured reporting also include CT features favoring diagnoses other than COVID-19 pneumonia (26-28). CO-RADS (COVID-19 Reporting and Data System) 2 category corresponds to a low level of COVID-19 suspicion when centrilobular nodules, lobar or segmental consolidation, or lung cavitation are observed. COVID-RADS includes pulmonary nodules and airway secretions as inconsistent with COVID-19 (27) . The Radiological Society of North America consensus document suggested one atypical appearance category: bronchiolar nodules or cavitation (28) .

As recommended by the American College of Radiology (5) RT-PCR is the only specific method of diagnosis even though it has imperfect sensitivity (29) . The first RT-PCR showed a sensitivity of 95.8% in our study but not all negative tests were repeated. When RT-PCR was negative and CT was interpreted as positive, 39.3% of the initially negative RT-PCR results were positive on repeat testing, highlighting the need for further testing in cases of discrepancies.

In addition to the evaluation of diagnostic performance, another important objective of the STOIC project was to evaluate the risk factors for a poor outcome. As previously reported (18, 30) , we found that advanced age, male sex, and hypertension were risk factors for severe outcome, contrary to the amount of chest wall fat, a substitute for missing BMI. Emphysema was not found to be an independent predictor of severity in our study, in line with the report 

Artificial intelligence could help better predict disease severity in subjects with Covid-19 (31, 32) , but requires large datasets, one of the reasons the STOIC project was launched.

Our study had limitations. First, we did not repeat all first negative PCR tests, probably leading to an underestimation of CT specificity. Second, double readings were not systematically planned for evaluating intra and inter reader agreements. This evaluation resulted from post hoc analysis and was based on CT exams that received double annotations by chance, thus with a low risk of bias. Third, BMI data was missing in too many of our subjects to be able to be included in the clinical model. However, chest wall fat thickness could be used as a surrogate and was not retained in the final model. Fourth, we also did not include biological parameters, because the biological tests to be performed at presentation were not standardized during the first wave of the pandemic in our country. Lastly, we defined poor outcome as the risk for intubation at one point or death at 1-month follow-up.

We know now that dexamethasone results in lower 28-day mortality (33,34), however subjects from our series who were intubated and/or died had developed a severe form of the disease. Thus, the definition of poor outcome in our study remains valid even though the therapeutic management of severe forms of COVID-19 has improved.

In conclusion, when using predefined criteria, CT interpretation performance was not influenced by reader experience and enabled a correct diagnosis of COVID-19 pneumonia with an overall 80.0% accuracy, increasing to 86.3% after 5 days of symptoms. For subjects with negative RT-PCR but signs of COVID-19 pneumonia on CT, RT-PCR testing should be repeated since it might become positive in 39.3% of such subjects. Lastly, the extent of lung disease on CT at initial presentation is a strong predictor of poor outcome within 1-month I n p r e s s I n p r e s s *Calcification in each of the four main coronary arteries was categorized as none, mild, moderate, or severe ( Figure 4) . Calcification was classified as mild when less than one-third of the length of the entire artery showed calcification, moderate when one-third to two-thirds of the artery showed calcification and severe when more than two-thirds of the artery showed calcification. **Visual quantification of lung disease extent and emphysema using a 5-point scale (0%, <25%, 25-50%, 50-75%, >75%) I n p r e s s Any collaborative research project led by an academic partner who requires access to the STOIC data shall be analyzed, validated, and authorized by the Steering Committee of STOIC. To this end, the academic partner shall send a document describing the research project to the Stoic Steering Committee at the following email address: Marie-pierre.revel@aphp.fr, with the following subject: STOIC DATA ACCESS PERMISSION

After acceptance by the Steering Committee, the academic partner shall sign a specific agreement (Data Transfer Agreement -DTA) with AP-HP, who are legally responsible for the STOIC data as Sponsor of the STOIC research. The DTA signed by the parties will use the AP-HP template and will be governed by applicable French laws. Each collaborative research project shall be subject to a specific DTA.

In addition to the previous indication, any and all access to the STOIC data is provided according to the following conditions:

-The STOIC data shall remain at all times under the governance of AP-HP.

-The academic partner will only use the STOIC data for the collaborative research project presented to the Steering Committee ; -The STOIC data shall be used by the academic partner in accordance with all applicable regulations and laws -During the collaborative research project, STOIC data shall be under the sole responsibility of the academic partner and should not be shared with any other parties -The academic partner shall inform the STOIC Steering Committee on a regular basis the progress of their collaborative research project -At the end of the collaborative research project, the academic partner shall destroy the STOIC data in its possession, and shall not keep any copies ; -The intellectual property related to the results of the collaborative research on the STOIC data shall be shared with APHP, as defined on the specific DTA -The academic partner shall respect and follow the « STOIC PUBLICATION POLICY / PROCEDURES » (Appendix 1) for any publication and communication on STOIC data ; -French Law and the GDPR shall be applicable, only non-identifying, retrospective data will be shared Description on Appendices : -Appendix E2 : STOIC PUBLICATION POLICY / PROCEDURES -Appendix E3 : Description of the available STOIC data

SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor

Diagnostic Performance of Chest X-Ray for COVID-19 Pneumonia During the SARS-CoV-2 Pandemic in Lombardy, Italy

COVID-19 patients and the radiology department -advice from the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI)

Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases

ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection. American College of Radiology

A role for CT in COVID-19? What data really tell us so far

CT screening for early diagnosis of SARS-CoV-2 infection

Integrated Radiologic Algorithm for COVID-19 Pandemic

Réponses rapides dans le cadre de la COVID-19 -Place du scanner thoracique. Haute Autorité de Santé

Compte-rendu structuré de scanner thoracique pour les patients suspects de COVID

Ordinal scoring of coronary artery calcifications on low-dose CT scans of the chest is predictive of death from cardiovascular disease

Should scoring rules be based on odds ratios or regression coefficients?

Diagnostic Performance of Chest CT for SARS-CoV-2 Infection in Individuals with or without COVID-19 Symptoms

The sensitivity and specificity of chest CT in the diagnosis of COVID-19

Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region

Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease

Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area

COVID-19 and the nicotinic cholinergic system

Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection

COVID-19 pneumonia: Diagnostic and prognostic role of CT based on a retrospective analysis of 214 consecutive patients from

Thoracic imaging tests for the diagnosis of COVID-19

Thoracic imaging tests for the diagnosis of COVID-19

CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation

Coronavirus disease 2019 (COVID-19) imaging reporting and data system (COVID-RADS) and common lexicon: a proposal based on the imaging data of 37 studies

Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA. Radiology: Cardiothoracic Imaging

Clinical Performance of SARS-CoV-2 Molecular Tests

Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study

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

CT scan AI-aided triage for patients with COVID-19 in China

The higher accuracy after 5 days of symptoms, despite a loss of specificity greater than the gain in sensitivity (hence the decrease in AUC), was due to an increased disease prevalence (2892/3141, 92.1%) in this subgroup

PARCC UMRS 970, INSERM

We thank all the clinical research technicians who collected the clinical data and exported the CT scans in an anonymized format, especially W Hadi, J Pierson, S Chenaf and A Bellino for coordination. We also thank C Villeret from the Clinical Research and Innovation Department in of Assistance Publique des Hôpitaux de Paris for her supervision.We thank General Electric Healthcare for providing the 3D image viewer web application and Orange Healthcare for providing data storage resources. I n p r e s s

The STOIC Publication Policy/Procedures must be accepted by the principal investigator in charge of a project endorsed or accepted by the STOIC Steering Committee before any access to the STOIC research material. Such procedures include the following items: -The STOIC-Research Collaborative group will be included in the team in charge of the writing of the manuscript, for critical review and approval of the final version. For this objective, the draft of the manuscript must be submitted to the chair of the STOIC steering committee at least 2 weeks before the submission to the targeted journal -The STOIC-Research Collaborative group will be included in the list of co-authors, as having fulfilled the 4 following ICMJE criteria  Substantial contribution to the acquisition of data for the work; AND  Critical revision for important intellectual content; AND  Final approval of the version to be published; AND  Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved -Refer to the acronym STOIC in the title or the sub-title of any publication. In case the policy of the journal does not accept this, refer to the acronym STOIC in the abstract of the publication.-Inform the president of the STOIC Steering Committee of the status of the manuscript every 3 months (i.e., before each STOIC Steering Committee meeting) until the publication of the manuscript.-Provide to the president of the STOIC Steering Committee an electronic copy of the manuscript once published.-Mention in the "Acknowledgements section" the sponsor (AP-HP). The current proposal is to use the following sentences: « The STOIC study was sponsored by Assistance Publique Hôpitaux de Paris and was funded by Fondation APHP pour la Recherche, Guerbet, Innothera, Fondation CentraleSupélec. General Electric Healthcare provided a 3D image visualization web application and Orange Healthcare a data repository" -Reference the publication which describes the main baseline characteristics of the patients (Revel et al, Radiology 2021).-The ClinicalTrials.gov NCT04355507 should be mentioned in the manuscript.

Description of the available STOIC Data Format of data: -NIfti for CT scan images -The clinical data will be shared as age category, delay from symptoms onset, or binary information: for instance: severe/non severe outcome, severe/non severe coronary artery calcium score so that any re-identification is made impossible, according to GDPRThe baseline data of the 10,735 patients of STOIC include the following information: