1
|
Hariri LP, North CM, Shih AR, Israel RA, Maley JH, Villalba JA, Vinarsky V, Rubin J, Okin DA, Sclafani A, Alladina JW, Griffith JW, Gillette MA, Raz Y, Richards CJ, Wong AK, Ly A, Hung YP, Chivukula RR, Petri CR, Calhoun TF, Brenner LN, Hibbert KA, Medoff BD, Hardin CC, Stone JR, Mino-Kenudson M. Lung Histopathology in Coronavirus Disease 2019 as Compared With Severe Acute Respiratory Sydrome and H1N1 Influenza: A Systematic Review. Chest 2020; 159:73-84. [PMID: 33038391 PMCID: PMC7538870 DOI: 10.1016/j.chest.2020.09.259] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 08/20/2020] [Accepted: 09/14/2020] [Indexed: 12/13/2022] Open
Abstract
Background Patients with severe coronavirus disease 2019 (COVID-19) have respiratory failure with hypoxemia and acute bilateral pulmonary infiltrates, consistent with ARDS. Respiratory failure in COVID-19 might represent a novel pathologic entity. Research Question How does the lung histopathology described in COVID-19 compare with the lung histopathology described in SARS and H1N1 influenza? Study Design and Methods We conducted a systematic review to characterize the lung histopathologic features of COVID-19 and compare them against findings of other recent viral pandemics, H1N1 influenza and SARS. We systematically searched MEDLINE and PubMed for studies published up to June 24, 2020, using search terms for COVID-19, H1N1 influenza, and SARS with keywords for pathology, biopsy, and autopsy. Using PRISMA-Individual Participant Data guidelines, our systematic review analysis included 26 articles representing 171 COVID-19 patients; 20 articles representing 287 H1N1 patients; and eight articles representing 64 SARS patients. Results In COVID-19, acute-phase diffuse alveolar damage (DAD) was reported in 88% of patients, which was similar to the proportion of cases with DAD in both H1N1 (90%) and SARS (98%). Pulmonary microthrombi were reported in 57% of COVID-19 and 58% of SARS patients, as compared with 24% of H1N1 influenza patients. Interpretation DAD, the histologic correlate of ARDS, is the predominant histopathologic pattern identified in lung pathology from patients with COVID-19, H1N1 influenza, and SARS. Microthrombi were reported more frequently in both patients with COVID-19 and SARS as compared with H1N1 influenza. Future work is needed to validate this histopathologic finding and, if confirmed, elucidate the mechanistic underpinnings and characterize any associations with clinically important outcomes.
Collapse
Affiliation(s)
- Lida P Hariri
- Department of Pathology, Massachusetts General Hospital, Boston, MA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA.
| | - Crystal M North
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Angela R Shih
- Department of Pathology, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Rebecca A Israel
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Jason H Maley
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | | | - Vladimir Vinarsky
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Jonah Rubin
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Daniel A Okin
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Alyssa Sclafani
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Jehan W Alladina
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Jason W Griffith
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Michael A Gillette
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Yuval Raz
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Christopher J Richards
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Alexandra K Wong
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Amy Ly
- Department of Pathology, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Yin P Hung
- Department of Pathology, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Raghu R Chivukula
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Whitehead Institute for Biomedical Research, Cambridge, MA; Harvard Medical School, Boston, MA
| | - Camille R Petri
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Tiara F Calhoun
- Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Laura N Brenner
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Kathryn A Hibbert
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Benjamin D Medoff
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - C Corey Hardin
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - James R Stone
- Department of Pathology, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| | - Mari Mino-Kenudson
- Department of Pathology, Massachusetts General Hospital, Boston, MA; Harvard Medical School, Boston, MA
| |
Collapse
|
2
|
Zhang H, Jiang XJ, Liu XH, Ma H, Zhang YH, Rao Y, Li L, Xu HY, Lyu FJ. Chest computed tomography (CT) findings and semiquantitative scoring of 60 patients with coronavirus disease 2019 (COVID-19): A retrospective imaging analysis combining anatomy and pathology. PLoS One 2020; 15:e0238760. [PMID: 32886711 PMCID: PMC7473568 DOI: 10.1371/journal.pone.0238760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 08/22/2020] [Indexed: 12/24/2022] Open
Abstract
In this study, we ascertained the chest CT data of 60 patients admitted to 3 hospitals in Chongqing with confirmed COVID-19. We conducted anatomical and pathological analyses to elucidate the possible reasons for the distribution, morphology, and characteristics of COVID-19 in chest CT. We also shared a semiquantitative scoring of affected lung segments, which was recommended by our local medical association. This scoring system was applied to quantify the severity of the disease. The most frequent imaging findings of COVID-19 were subpleural ground glass opacities and consolidation; there was a significant difference in semiquantitative scores between the early, progressive, and severe stages of the disease. We conclude that the chest CT findings of COVID-19 showed certain characteristics because of the anatomical features of the human body and pathological changes caused by the virus. Therefore, chest CT is a valuable tool for facilitating the diagnosis of COVID-19 and semiquantitative scoring of affected lung segments may further elucidate diagnosis and assessment of disease severity. This will assist healthcare workers in diagnosing COVID-19 and assessing disease severity, facilitate the selection of appropriate treatment options, which is important for reducing the spread of the virus, saving lives, and controlling the pandemic.
Collapse
Affiliation(s)
- Hao Zhang
- Department of Radiology, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| | - Xu-jing Jiang
- Department of General Surgery, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| | - Xiao-hua Liu
- Department of Radiology, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| | - Hong Ma
- Department of Oncology, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| | - Ya-hong Zhang
- Department of Radiology, Changshou People’s Hospital of Chongqing, Chongqing, China
| | - Yue Rao
- Department of Radiology, Zhongxian People’s Hospital of Chongqing, Chongqing, China
| | - Lin Li
- Department of Pharmacy, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| | - Hai-yan Xu
- Department of Gastroenterology, Dianjiang People’s Hospital of Chongqing, Chongqing, China
- * E-mail: (HX); (FL)
| | - Fa-jin Lyu
- Department of Radiology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- * E-mail: (HX); (FL)
| |
Collapse
|
3
|
Xu TL, Ao MY, Zhou X, Zhu WF, Nie HY, Fang JH, Sun X, Zheng B, Chen XF. China's practice to prevent and control COVID-19 in the context of large population movement. Infect Dis Poverty 2020; 9:115. [PMID: 32814591 PMCID: PMC7435224 DOI: 10.1186/s40249-020-00716-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 07/07/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The emerging infectious disease, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a serious threat in China and worldwide. Challenged by this serious situation, China has taken many measures to contain its transmission. This study aims to systematically review and record these special and effective practices, in hope of benefiting for fighting against the ongoing worldwide pandemic. METHODS The measures taken by the governments was tracked and sorted on a daily basis from the websites of governmental authorities (e.g. National Health Commission of the People's Republic of China). And the measures were reviewed and summarized by categorizations, figures and tables, showing an ever-changing process of combating with an emerging infectious disease. The population shift levels, daily local new diagnosed cases, daily mortality and daily local new cured cases were used for measuring the effect of the measures. RESULTS The practices could be categorized into active case surveillance, rapid case diagnosis and management, strict follow-up and quarantine of persons with close contacts, and issuance of guidance to help the public understand and adhere to control measures, plus prompt and effective high-level policy decision, complete activation of the public health system, and full involvement of the society. Along with the measures, the population shift levels, daily local new diagnosed cases, and mortality were decreased, and the daily local new cured cases were increased in China. CONCLUSIONS China's practices are effective in controlling transmission of SARS-CoV-2. Considering newly occurred situations (e.g. imported cases, work resumption), the control measures may be adjusted.
Collapse
Affiliation(s)
- Tie-Long Xu
- Evidence-based Medicine Research Center, Jiangxi University of Traditional Chinese Medicine, Nanchang City, Jiangxi Province, P. R. China
| | - Mei-Ying Ao
- Evidence-based Medicine Research Center, Jiangxi University of Traditional Chinese Medicine, Nanchang City, Jiangxi Province, P. R. China
| | - Xu Zhou
- Evidence-based Medicine Research Center, Jiangxi University of Traditional Chinese Medicine, Nanchang City, Jiangxi Province, P. R. China
| | - Wei-Feng Zhu
- Evidence-based Medicine Research Center, Jiangxi University of Traditional Chinese Medicine, Nanchang City, Jiangxi Province, P. R. China
| | - He-Yun Nie
- Evidence-based Medicine Research Center, Jiangxi University of Traditional Chinese Medicine, Nanchang City, Jiangxi Province, P. R. China
| | - Jian-He Fang
- Evidence-based Medicine Research Center, Jiangxi University of Traditional Chinese Medicine, Nanchang City, Jiangxi Province, P. R. China
| | - Xin Sun
- Evidence-based Medicine Research Center, Jiangxi University of Traditional Chinese Medicine, Nanchang City, Jiangxi Province, P. R. China. .,Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu City, Sichuan Province, P. R. China.
| | - Bin Zheng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, and National Center for Tropical Diseases Research, Shanghai, People's Republic of China.
| | - Xiao-Fan Chen
- Evidence-based Medicine Research Center, Jiangxi University of Traditional Chinese Medicine, Nanchang City, Jiangxi Province, P. R. China.
| |
Collapse
|
4
|
Deeks JJ, Dinnes J, Takwoingi Y, Davenport C, Spijker R, Taylor-Phillips S, Adriano A, Beese S, Dretzke J, Ferrante di Ruffano L, Harris IM, Price MJ, Dittrich S, Emperador D, Hooft L, Leeflang MM, Van den Bruel A. Antibody tests for identification of current and past infection with SARS-CoV-2. Cochrane Database Syst Rev 2020; 6:CD013652. [PMID: 32584464 PMCID: PMC7387103 DOI: 10.1002/14651858.cd013652] [Citation(s) in RCA: 432] [Impact Index Per Article: 108.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and resulting COVID-19 pandemic present important diagnostic challenges. Several diagnostic strategies are available to identify current infection, rule out infection, identify people in need of care escalation, or to test for past infection and immune response. Serology tests to detect the presence of antibodies to SARS-CoV-2 aim to identify previous SARS-CoV-2 infection, and may help to confirm the presence of current infection. OBJECTIVES To assess the diagnostic accuracy of antibody tests to determine if a person presenting in the community or in primary or secondary care has SARS-CoV-2 infection, or has previously had SARS-CoV-2 infection, and the accuracy of antibody tests for use in seroprevalence surveys. SEARCH METHODS We undertook electronic searches in the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 27 April 2020. SELECTION CRITERIA We included test accuracy studies of any design that evaluated antibody tests (including enzyme-linked immunosorbent assays, chemiluminescence immunoassays, and lateral flow assays) in people suspected of current or previous SARS-CoV-2 infection, or where tests were used to screen for infection. We also included studies of people either known to have, or not to have SARS-CoV-2 infection. We included all reference standards to define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction tests (RT-PCR) and clinical diagnostic criteria). DATA COLLECTION AND ANALYSIS We assessed possible bias and applicability of the studies using the QUADAS-2 tool. We extracted 2x2 contingency table data and present sensitivity and specificity for each antibody (or combination of antibodies) using paired forest plots. We pooled data using random-effects logistic regression where appropriate, stratifying by time since post-symptom onset. We tabulated available data by test manufacturer. We have presented uncertainty in estimates of sensitivity and specificity using 95% confidence intervals (CIs). MAIN RESULTS We included 57 publications reporting on a total of 54 study cohorts with 15,976 samples, of which 8526 were from cases of SARS-CoV-2 infection. Studies were conducted in Asia (n = 38), Europe (n = 15), and the USA and China (n = 1). We identified data from 25 commercial tests and numerous in-house assays, a small fraction of the 279 antibody assays listed by the Foundation for Innovative Diagnostics. More than half (n = 28) of the studies included were only available as preprints. We had concerns about risk of bias and applicability. Common issues were use of multi-group designs (n = 29), inclusion of only COVID-19 cases (n = 19), lack of blinding of the index test (n = 49) and reference standard (n = 29), differential verification (n = 22), and the lack of clarity about participant numbers, characteristics and study exclusions (n = 47). Most studies (n = 44) only included people hospitalised due to suspected or confirmed COVID-19 infection. There were no studies exclusively in asymptomatic participants. Two-thirds of the studies (n = 33) defined COVID-19 cases based on RT-PCR results alone, ignoring the potential for false-negative RT-PCR results. We observed evidence of selective publication of study findings through omission of the identity of tests (n = 5). We observed substantial heterogeneity in sensitivities of IgA, IgM and IgG antibodies, or combinations thereof, for results aggregated across different time periods post-symptom onset (range 0% to 100% for all target antibodies). We thus based the main results of the review on the 38 studies that stratified results by time since symptom onset. The numbers of individuals contributing data within each study each week are small and are usually not based on tracking the same groups of patients over time. Pooled results for IgG, IgM, IgA, total antibodies and IgG/IgM all showed low sensitivity during the first week since onset of symptoms (all less than 30.1%), rising in the second week and reaching their highest values in the third week. The combination of IgG/IgM had a sensitivity of 30.1% (95% CI 21.4 to 40.7) for 1 to 7 days, 72.2% (95% CI 63.5 to 79.5) for 8 to 14 days, 91.4% (95% CI 87.0 to 94.4) for 15 to 21 days. Estimates of accuracy beyond three weeks are based on smaller sample sizes and fewer studies. For 21 to 35 days, pooled sensitivities for IgG/IgM were 96.0% (95% CI 90.6 to 98.3). There are insufficient studies to estimate sensitivity of tests beyond 35 days post-symptom onset. Summary specificities (provided in 35 studies) exceeded 98% for all target antibodies with confidence intervals no more than 2 percentage points wide. False-positive results were more common where COVID-19 had been suspected and ruled out, but numbers were small and the difference was within the range expected by chance. Assuming a prevalence of 50%, a value considered possible in healthcare workers who have suffered respiratory symptoms, we would anticipate that 43 (28 to 65) would be missed and 7 (3 to 14) would be falsely positive in 1000 people undergoing IgG/IgM testing at days 15 to 21 post-symptom onset. At a prevalence of 20%, a likely value in surveys in high-risk settings, 17 (11 to 26) would be missed per 1000 people tested and 10 (5 to 22) would be falsely positive. At a lower prevalence of 5%, a likely value in national surveys, 4 (3 to 7) would be missed per 1000 tested, and 12 (6 to 27) would be falsely positive. Analyses showed small differences in sensitivity between assay type, but methodological concerns and sparse data prevent comparisons between test brands. AUTHORS' CONCLUSIONS The sensitivity of antibody tests is too low in the first week since symptom onset to have a primary role for the diagnosis of COVID-19, but they may still have a role complementing other testing in individuals presenting later, when RT-PCR tests are negative, or are not done. Antibody tests are likely to have a useful role for detecting previous SARS-CoV-2 infection if used 15 or more days after the onset of symptoms. However, the duration of antibody rises is currently unknown, and we found very little data beyond 35 days post-symptom onset. We are therefore uncertain about the utility of these tests for seroprevalence surveys for public health management purposes. Concerns about high risk of bias and applicability make it likely that the accuracy of tests when used in clinical care will be lower than reported in the included studies. Sensitivity has mainly been evaluated in hospitalised patients, so it is unclear whether the tests are able to detect lower antibody levels likely seen with milder and asymptomatic COVID-19 disease. The design, execution and reporting of studies of the accuracy of COVID-19 tests requires considerable improvement. Studies must report data on sensitivity disaggregated by time since onset of symptoms. COVID-19-positive cases who are RT-PCR-negative should be included as well as those confirmed RT-PCR, in accordance with the World Health Organization (WHO) and China National Health Commission of the People's Republic of China (CDC) case definitions. We were only able to obtain data from a small proportion of available tests, and action is needed to ensure that all results of test evaluations are available in the public domain to prevent selective reporting. This is a fast-moving field and we plan ongoing updates of this living systematic review.
Collapse
Affiliation(s)
- Jonathan J Deeks
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Jacqueline Dinnes
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Yemisi Takwoingi
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Clare Davenport
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - René Spijker
- Medical Library, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Amsterdam, Netherlands
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Sian Taylor-Phillips
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Ada Adriano
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Sophie Beese
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Janine Dretzke
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Lavinia Ferrante di Ruffano
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Isobel M Harris
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Malcolm J Price
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | | | | | - Lotty Hooft
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Mariska Mg Leeflang
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
- Biomarker and Test Evaluation Programme (BiTE), Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Ann Van den Bruel
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| |
Collapse
|