I read with interest the Nordic myocarditis cohort study by Husby et al (1) on clinical outcomes of three different types of myocarditis: COVID-19 mRNA vaccination-associated (“vaccine-associated”), COVID-19 infection-associated (“infection-associated”) and “conventional” myocarditis. I would like to make several comments and request response from the authors.
A significant limitation of the Nordic and other similar studies is the under-diagnosis of COVID-19 vaccine-associated myocarditis and possibly COVID-19 infection-associated myocarditis. To be included in the Nordic myocarditis cohort study, an individual had to develop symptom(s) with myocarditis, present to a medical provider with such symptom(s), and the medical provider had to consider myocarditis and then order at least ECG or cardiac enzyme(s), and the individual had to be admitted to the hospital as an inpatient. Individuals with myocarditis who did not satisfy all the above conditions would not have been included in the Nordic study. Only a prospective study with continuous screening for myocardial injury/inflammation would be able to provide complete information on the incidence and outcomes of myocarditis associated with COVID-19 vaccine or infection. Limited prospective data on myocardial injury post-vaccine is currently available, suggesting significantly more common occurrence (2,3) than proclaimed but with unknown long-term prognosis.
During the COVID-19 pandemic, there were many more pat...
I read with interest the Nordic myocarditis cohort study by Husby et al (1) on clinical outcomes of three different types of myocarditis: COVID-19 mRNA vaccination-associated (“vaccine-associated”), COVID-19 infection-associated (“infection-associated”) and “conventional” myocarditis. I would like to make several comments and request response from the authors.
A significant limitation of the Nordic and other similar studies is the under-diagnosis of COVID-19 vaccine-associated myocarditis and possibly COVID-19 infection-associated myocarditis. To be included in the Nordic myocarditis cohort study, an individual had to develop symptom(s) with myocarditis, present to a medical provider with such symptom(s), and the medical provider had to consider myocarditis and then order at least ECG or cardiac enzyme(s), and the individual had to be admitted to the hospital as an inpatient. Individuals with myocarditis who did not satisfy all the above conditions would not have been included in the Nordic study. Only a prospective study with continuous screening for myocardial injury/inflammation would be able to provide complete information on the incidence and outcomes of myocarditis associated with COVID-19 vaccine or infection. Limited prospective data on myocardial injury post-vaccine is currently available, suggesting significantly more common occurrence (2,3) than proclaimed but with unknown long-term prognosis.
During the COVID-19 pandemic, there were many more patients in the Nordic study with myocarditis post-vaccine than post-infection (530 vs 109, ratio 4.9), especially among individuals 12-39 years old (340 vs 48, ratio 7.1). Regrettably, unlike their earlier study (4), the authors in the current study did not provide the population denominators to allow calculation of incidences of diagnosed post-vaccine vs post-infection myocarditis. It would be useful to know if in the current Nordic study, the incidence of post-vaccine myocarditis was higher than that of post-infection myocarditis at least in some subgroups, as clearly shown for males aged 13-39 years in a UK National Database study (5) and suggested in the same authors’ previous study (4).
In their analyses, the authors presented data on only 3 comorbidities: malignancy, cardiovascular disease, or autoimmune diseases. They recognized age difference, with the infection-associated myocarditis group being significantly older (median age 17-20 years older, except for Finland) than the vaccine-associated myocarditis group (Table S4). They then presented data on individuals aged 12-39 years (mean not available, to exclude persistent effect of age difference) without the above 3 comorbidities, but ignoring other significant comorbidities (Table 4). It is unclear why the authors did not report on other comorbidities that they had presented in their previous paper (4), including chronic pulmonary disease, diabetes, and renal disease, and on body mass index (6), especially knowing that outcomes after COVID-19 infection are affected by multiple comorbidities other than the 3 they selected. Their finding of nearly similar percentage of “any” predisposing comorbidity in post-vaccination vs post-infection myocarditis (Table 1) may, therefore, be misleading, since that comparison does not account for all known comorbidities, which may be more prevalent in hospitalized COVID-19 infected individuals (compared to vaccine-associated myocarditis patients) and which, if present, would result in worse clinical outcomes in these infected patients, including the subgroup aged 12-39 years.
When comparing outcomes of heart failure and death in post-vaccine vs post-infection myocarditis patients, it is important to recognize that the post-infection patients may be “sicker” patients, like other hospitalized COVID-19 infected patients. Additionally, clinical outcomes of post-infection patients including heart failure or/and death and hospital length of stay are influenced by COVID-19 infection with all its manifestations, not just by COVID-19 infection-associated myocarditis. In other words, COVID-19 infection-associated myocarditis may not be inherently/alone accountable for worse clinical outcomes compared to COVID-19 mRNA vaccine-associated myocarditis.
The authors found significantly more hospital readmissions among vaccine-associated myocarditis patients vs infection-associated myocarditis patients, 62 vs 9 (relative risk 0.76 vs 0.49, admitted Jan 1, 2020 or later). The authors attempt to explain this finding, which is not consistent with their other observations, as follows: “This finding could reflect increased clinical interest in patients with myocarditis associated with vaccination, however, which could have resulted in increased rates of readmission for further clinical evaluation. Also, the higher risk of death among patients with myocarditis after covid-19 disease could potentially bias the risk of readmission downward for this patient group.” The authors do not provide evidence to support their speculation on the clinical interest being higher for vaccine-associated myocarditis vs infection-associated myocarditis. Furthermore, since the absolute number of deaths in both groups was very small & the same, i.e., 6, that would not be expected to have significant effect on relative risk in the 2 groups.
In the Discussion section, the authors state that: “The overall findings on outcomes of myocarditis associated with vaccination by us and others are reassuring …” That statement is not evidently supported by the available data. Even without considering the multiple limitations in the study detailed above, a greater number of vaccine-associated myocarditis patients developed heart failure (22 vs 12) and underwent hospital readmission (62 vs 9) than infection-associated myocarditis patients, in the 1st 90 days after myocarditis onset. More importantly, among the many more patients with vaccine-associated myocarditis (compared to those with infection-associated myocarditis), the majority would be expected to demonstrate persistent cardiac MRI abnormalities (7, 8, 9), as the authors partly acknowledge. Most practicing physicians would not find it reassuring that there are significantly higher numbers of vaccine-associated myocarditis and numerically higher numbers of vaccine-associated myocarditis-related heart failure and hospital readmissions, compared to infection-associated numbers.
The above discussion is particularly relevant for decisions on COVID-19 vaccination in healthy adolescents and healthy young adults, where the occurrence of vaccine-associated myocarditis would likely result in late gadolinium enhancement with its potential adverse prognosis (10), namely increased risk of combined end point comprised of all-cause mortality, cardiac mortality, and major adverse cardiovascular events. Vaccine-associated myocarditis would effectively scar, for life, healthy young individuals who are at minimal to low risk of mortality* (and other serious adverse outcomes) from COVID-19 infection itself. Decisions on COVID-19 vaccination should utilize individual risk-benefit assessment, which is the cornerstone of individualized evidence-based healthcare.
* Age-based analysis of COVID-19 infection mortality risk in children & young individuals (irrespective of individual state of health):
US Census age statistics (11):
Children, age 0-17 years: Population: 72,777,000 = 22.31% of total population (326,195,000), as of 2021 US Census.
Young individuals, age 0-39 years: = 51.66% of total population (326,195,000), as of 2021 US Census.
CDC COVID-19 infection statistics (12):
Age 0-17 years: COVID-19 deaths: 1,582 = 0.14% of all deaths (1,125,044), as of Apr 12, 2023 CDC data.
Therefore, children, age 0-17 years, are 159 times less likely to die from COVID-19 infection, than would be expected based on their % of the US population.
Age 0-39 years: COVID-19 deaths: 44,516 = 3.96% of all deaths (1,125,044), as of Apr 12, 2023 CDC data.
Therefore, young individuals, age 0-39 years, are 13 times less likely to die from COVID-19 infection, than would be expected based on their % of the US population.
References:
1. Husby A, Gulseth H, Hovi P, et al. Clinical outcomes of myocarditis after SARS-CoV-2 mRNA vaccination in four Nordic countries: population based cohort study. BMJ Med. 2023;2(1):e000373. doi: 10.1136/bmjmed-2022-000373.
2. Mansanguan S, Charunwatthana P, Piyaphanee W, et al. Cardiovascular Manifestation of the BNT162b2 mRNA COVID-19 Vaccine in Adolescents. Trop Med Infect Dis. 2022;7(8):196. doi: 10.3390/tropicalmed7080196.
3. Chiu S, Chen Y, Hsu C, et al. Changes of ECG parameters after BNT162b2 vaccine in the senior high school students. Eur J Pediatr. 2023;182(3):1155-1162. doi: 10.1007/s00431-022-04786-0.
4. Karlstad O, Hovi P, Husby A, et al. SARS-CoV-2 Vaccination and Myocarditis in a Nordic Cohort Study of 23 Million Residents. JAMA Cardiol. 2022;7(6):600-612. doi: 10.1001/jamacardio.2022.0583.
5. Patone M, Mei X, Handunnetthi L, et al. Risk of Myocarditis After Sequential Doses of
COVID-19 Vaccine and SARS-CoV-2 Infection by Age and Sex. Circulation. 2022;146:743–754. doi: 10.1161/CIRCULATIONAHA.122.059970.
6. Gao M, Piernas C, Astbury N, et al. Lancet Diabetes Endocrinol. 2021;9(6):350-359. doi: 10.1016/S2213-8587(21)00089-9. Associations between body-mass index and COVID-19 severity in 6·9 million people in England: a prospective, community-based, cohort study.
7. Schauer J, Buddhe S, Gulhane A, et al. Persistent Cardiac Magnetic Resonance Imaging Findings in a Cohort of Adolescents with Post-Coronavirus Disease 2019 mRNA Vaccine Myopericarditis. J Pediatr. 2022;245:233-237. doi: 10.1016/j.jpeds.2022.03.032.
8. Kracalik I, Oster M, Broder K, et al. Lancet Child Adolesc Health. 2022;6(11):788-798. Outcomes at least 90 days since onset of myocarditis after mRNA COVID-19 vaccination in adolescents and young adults in the USA: a follow-up surveillance study. doi: 10.1016/S2352-4642(22)00244-9.
9. Amir G, Rotstein A, Razon Y, et al. CMR Imaging 6 Months After Myocarditis Associated with the BNT162b2 mRNA COVID-19 Vaccine. Pediatr Cardiol. 2022;43(7):1522-1529. doi: 10.1007/s00246-022-02878-0.
10. Georgiopoulos G, Figliozzi S, Sanguineti F, et al. Prognostic Impact of Late Gadolinium Enhancement by Cardiovascular Magnetic Resonance in Myocarditis: A Systematic Review and Meta-Analysis. Circ Cardiovasc Imaging. 2021 Jan;14(1):e011492. doi: 10.1161/CIRCIMAGING.120.011492.
11. US Census. Age and Sex Composition in the United States 2021. https://www.census.gov/data/tables/2021/demo/age-and-sex/2021-age-sex-co.... Accessed April 16, 2023.
12. CDC. Provisional COVID-19 Deaths by Sex and Age. https://data.cdc.gov/NCHS/Provisional-COVID-19-Deaths-by-Sex-and-Age/9bh.... Accessed April 16, 2023.
We read with interest the Nordic registry study by Husby et al on clinical outcomes of three different types of myocarditis: SARS-CoV-2 mRNA vaccination-associated, COVID-19 associated and “conventional” myocarditis. We appreciate the information the authors provided on patient characteristics by myocarditis type. We note >50% patients with COVID-19-associated and conventional myocarditis were over 40, those with vaccine-associated myocarditis were younger on average and 38% were 12-24 years v.s <25% for the other two types. Further, the proportion of men was higher in all three categories, and the proportion of patients with underlying comorbidities similar; however, we wonder, given the relatively small number of patients, if the authors could disclose which comorbidities and how many were present (in total) among the patients with COVID-19 vs. vaccination-associated myocarditis, perhaps as a summary table.
Second, do the authors have any information on the vaccination status (number of doses and dates given in relation to the myocarditis diagnosis) among those categorized as having COVID-associated myocarditis? If so, could this information be broken down by age group?
Related, the most recent exposure defined the type of myocarditis in patients who received both an mRNA vaccine and had a positive test for SARS-CoV-2 in the last 28 days. We therefore wonder if the authors could provide the number of times both were diagnosed with...
We read with interest the Nordic registry study by Husby et al on clinical outcomes of three different types of myocarditis: SARS-CoV-2 mRNA vaccination-associated, COVID-19 associated and “conventional” myocarditis. We appreciate the information the authors provided on patient characteristics by myocarditis type. We note >50% patients with COVID-19-associated and conventional myocarditis were over 40, those with vaccine-associated myocarditis were younger on average and 38% were 12-24 years v.s <25% for the other two types. Further, the proportion of men was higher in all three categories, and the proportion of patients with underlying comorbidities similar; however, we wonder, given the relatively small number of patients, if the authors could disclose which comorbidities and how many were present (in total) among the patients with COVID-19 vs. vaccination-associated myocarditis, perhaps as a summary table.
Second, do the authors have any information on the vaccination status (number of doses and dates given in relation to the myocarditis diagnosis) among those categorized as having COVID-associated myocarditis? If so, could this information be broken down by age group?
Related, the most recent exposure defined the type of myocarditis in patients who received both an mRNA vaccine and had a positive test for SARS-CoV-2 in the last 28 days. We therefore wonder if the authors could provide the number of times both were diagnosed within the last 28 days and the proportion of attributions to SARS-CoV-2 versus vaccination for these occurrences.
Finally, was there any evidence of genetic predisposition to vaccine-associated myocarditis with twins, siblings or related family members being disproportionately affected?
We thank the authors for this study and for their responses to the above questions, which we feel could both help inform vaccination recommendations and provide insights into underlying pathophysiology of mRNA vaccine- and COVID-19-associated myocarditis.
Dr Conroy and colleagues investigated 469,095 adults aged 40-69, to find 40687 incident events (9 per 1000 person years) of cardiovascular disease in 2083 coeliac sufferers giving a hazard ration of 1.27 (1.11- 1.45) (1). What are the mechanisms for these clinical associations ?
The pathophysiological mechanism in DP Burkitt”s “high fibre” hypothesis to explain chronic diseases is autonomic injury caused by straining on the toilet (2, 3). Coordinated straining may injure branches of sympathetic segments T10-L2 with the potential to injure pelvic organs including kidneys and adrenals. These may extend to contiguous segments at T8-9r supplying small bowel (cf nulliparous endometriosis, inflammatory bowel disease and hypertension). In DP Barkers “fetal origins of adult diseases” hypothesis the pathophysiological mechanism is an in utero injury to autonomic vasomotor nerves in small babies caused by involuntary “fetal hypertension” (4, 5). Kidneys and pancreas seem to bear the brunt of this assault that may result in type 1 diabetes mellitus and hypertension in later life.
On this simple analysis there are, therefore, potential primary neurological mechanisms to account for both cardiovascular and autoimmune diseases within these data-driven hypotheses for chronic diseases. Are autoantibodies secondary consequences of primary denervatory injuries in these chronic “autoimmune” conditions ?. If the BioBank held details of birthweights or bowel habits (...
Dr Conroy and colleagues investigated 469,095 adults aged 40-69, to find 40687 incident events (9 per 1000 person years) of cardiovascular disease in 2083 coeliac sufferers giving a hazard ration of 1.27 (1.11- 1.45) (1). What are the mechanisms for these clinical associations ?
The pathophysiological mechanism in DP Burkitt”s “high fibre” hypothesis to explain chronic diseases is autonomic injury caused by straining on the toilet (2, 3). Coordinated straining may injure branches of sympathetic segments T10-L2 with the potential to injure pelvic organs including kidneys and adrenals. These may extend to contiguous segments at T8-9r supplying small bowel (cf nulliparous endometriosis, inflammatory bowel disease and hypertension). In DP Barkers “fetal origins of adult diseases” hypothesis the pathophysiological mechanism is an in utero injury to autonomic vasomotor nerves in small babies caused by involuntary “fetal hypertension” (4, 5). Kidneys and pancreas seem to bear the brunt of this assault that may result in type 1 diabetes mellitus and hypertension in later life.
On this simple analysis there are, therefore, potential primary neurological mechanisms to account for both cardiovascular and autoimmune diseases within these data-driven hypotheses for chronic diseases. Are autoantibodies secondary consequences of primary denervatory injuries in these chronic “autoimmune” conditions ?. If the BioBank held details of birthweights or bowel habits (and many do not) then one or two more keystrokes may shed further aetiological insights ?
References
(1) Conroy M, Allen N ,Lacey B, Soilleux E, Littlejohns T.
Association between coeliac disease and cardiovascular disease: prospective analysis of UK Biobank data. BMJ Med. 2023 Jan 4;2(1):e000371. doi: 10.1136/bmjmed-2022-000371.
(2) Quinn MJ.
Autonomic denervation and Western diseases.
Am J Med 2014; 127:3-4.
(3) Burkitt, D P. “Some diseases characteristic of modern Western civilization.”
British Medical Journal vol. 1,5848 (1973): 274-8.
(4) Barker DJP.
The fetal and infant origins of adult disease.
BMJ. 1990 Nov 17;301(6761):1111.
(5) Wang YQ, Zhang HJ, Quinn MJ.
Fetal Hypertension and the "Barker Hypothesis".
Angiology. 2020 Jan;71(1):92-9.
As authors of the QFracture papers1-3, we read this article by Livingstone et al with interest. They stated that the had externally validated the QFracture-2016 algorithm using CPRD4. The authors report that whilst there was very good to excellent discrimination, calibration was poor. The authors attributed an apparent under-prediction to their outcome definition using the CPRD validation dataset since this included GP data linked to hospital data. However, we think this under-prediction is due to the authors using the wrong algorithm – the authors have confirmed that they had used a previous version (QFracture-2012) which is based on unlinked data. The QFracture-2016 algorithm is the version which is currently recommended and used in the NHS and is derived from the QResearch database including GP data linked to hospital and mortality data3. Therefore, the authors need to correct their paper and update their conclusions accordingly. We would also like to highlight that the code groups for QFracture are available here https://www.qresearch.org/data/qcode-group-library/
References
1. Hippisley-Cox J, Coupland C. Predicting risk of osteoporotic fracture in men and women in England and Wales: Prospective derivation and validation of QFractureScores. BMJ (Online) 2009;339(7733):1291-95. doi: 10.1136/bmj.b4229
2. Hippisley-Cox J, Coupland C. Derivation and validation of updated QFracture algorith...
As authors of the QFracture papers1-3, we read this article by Livingstone et al with interest. They stated that the had externally validated the QFracture-2016 algorithm using CPRD4. The authors report that whilst there was very good to excellent discrimination, calibration was poor. The authors attributed an apparent under-prediction to their outcome definition using the CPRD validation dataset since this included GP data linked to hospital data. However, we think this under-prediction is due to the authors using the wrong algorithm – the authors have confirmed that they had used a previous version (QFracture-2012) which is based on unlinked data. The QFracture-2016 algorithm is the version which is currently recommended and used in the NHS and is derived from the QResearch database including GP data linked to hospital and mortality data3. Therefore, the authors need to correct their paper and update their conclusions accordingly. We would also like to highlight that the code groups for QFracture are available here https://www.qresearch.org/data/qcode-group-library/
References
1. Hippisley-Cox J, Coupland C. Predicting risk of osteoporotic fracture in men and women in England and Wales: Prospective derivation and validation of QFractureScores. BMJ (Online) 2009;339(7733):1291-95. doi: 10.1136/bmj.b4229
2. Hippisley-Cox J, Coupland C. Derivation and validation of updated QFracture algorithm to predict risk of osteoporotic fracture in primary care in the United Kingdom: Prospective open cohort study. BMJ (Online) 2012;345(7864) doi: 10.1136/bmj.e3427
3. Hippisley-Cox J CC. QFracture-2016Annual update informaiton 2016 [Available from: https://www.qresearch.org/media/vh3hekdi/qfracture-2016-annual-update-in....
4. Livingstone SJ, Morales DR, McMinn M, et al. Effect of competing mortality risks on predictive performance of the QFracture risk prediction tool for major osteoporotic fracture and hip fracture: external validation cohort study in a UK primary care population. BMJ Medicine 2022;1(1) doi: 10.1136/bmjmed-2022-000316
We read with great interest the article on ‘Conducting umbrella reviews’. In our view, it is important to highlight that what is described as ‘umbrella reviews’ in this article is often referred to as ‘overviews (of reviews)’, and shares similar definitions and goals, namely to synthesise evidence at the systematic review-level. The term ‘overviews of reviews’ is employed by Cochrane, a leading international organization for evidence synthesis. The corresponding chapter in the Cochrane handbook (1) was revised a few years ago and provides a summary of methods research for this type of evidence synthesis along with recommendations for conducting overviews of reviews. Although this has been prepared for Cochrane, we think that the vast majority of the content can also be used outside Cochrane and for a range of research questions. It provides guidance for topics not mentioned in your article such as dealing with overlapping primary studies across reviews on the same topic, decision tools supporting the inclusion of reviews, and updating reviews by conducting supplemental searches for primary studies. Most importantly, in the article by Belbasis et al. there is no explicit mention of assessing the quality or risk of bias of the included reviews and there is no mention of the recently published reporting guideline for overviews of reviews of healthcare interventions (2).
A large body of research by many evidence synthesis groups over the past 10+ years exists to advan...
We read with great interest the article on ‘Conducting umbrella reviews’. In our view, it is important to highlight that what is described as ‘umbrella reviews’ in this article is often referred to as ‘overviews (of reviews)’, and shares similar definitions and goals, namely to synthesise evidence at the systematic review-level. The term ‘overviews of reviews’ is employed by Cochrane, a leading international organization for evidence synthesis. The corresponding chapter in the Cochrane handbook (1) was revised a few years ago and provides a summary of methods research for this type of evidence synthesis along with recommendations for conducting overviews of reviews. Although this has been prepared for Cochrane, we think that the vast majority of the content can also be used outside Cochrane and for a range of research questions. It provides guidance for topics not mentioned in your article such as dealing with overlapping primary studies across reviews on the same topic, decision tools supporting the inclusion of reviews, and updating reviews by conducting supplemental searches for primary studies. Most importantly, in the article by Belbasis et al. there is no explicit mention of assessing the quality or risk of bias of the included reviews and there is no mention of the recently published reporting guideline for overviews of reviews of healthcare interventions (2).
A large body of research by many evidence synthesis groups over the past 10+ years exists to advance the methods for umbrella/overview of reviews. We understand that it is not possible to cover all methodological details in a short article. However, we would like to highlight important literature that is not included in this article (e.g. on including systematic reviews (3, 4) or overlapping primary studies (5, 6, 7). Some articles provide a comprehensive view of available methods and literature specific for umbrella/overviews of reviews (8, 9, 10). Inconsistent nomenclature and lack of references to previous work may be misleading to researchers and policymakers new to umbrella reviews and can create confusion for readers and authors of evidence syntheses.
References:
1. Pollock M, Fernandes RM, Becker LA, Pieper D, Hartling L. Chapter V: Overviews of Reviews. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). Cochrane, 2022. Available from www.training.cochrane.org/handbook
2. Gates M, Gates A, Pieper D, Fernandes R M, Tricco A C, Moher D et al. Reporting guideline for overviews of reviews of healthcare interventions: development of the PRIOR statement. BMJ 2022; 378 :e070849 doi:10.1136/bmj-2022-070849
3. Pollock M, Fernandes RM, Newton AS, Scott SD, Hartling L. A decision tool to help researchers make decisions about including systematic reviews in overviews of reviews of healthcare interventions. Systematic Reviews2019; 8: 29.
4. Pollock M, Fernandes RM, Newton AS, Scott SD, Hartling L. The impact of different inclusion decisions on the comprehensiveness and complexity of overviews of reviews of healthcare interventions. Syst Rev. 2019 Jan 11;8(1):18. doi: 10.1186/s13643-018-0914-3.
5. Lunny C, Pieper D, Thabet P, Kanji S. Managing overlap of primary study results across systematic reviews: practical considerations for authors of overviews of reviews. BMC Med Res Methodol. 2021 Jul 7;21(1):140.
doi: 10.1186/s12874-021-01269-y.
6. Hennessy EA, Johnson BT. Examining overlap of included studies in meta-reviews: Guidance for using the corrected covered area index. Res Synth Methods. 2020 Jan;11(1):134-145. doi: 10.1002/jrsm.1390. Epub 2019 Dec 10.
7. Bougioukas KI, Diakonidis T, Mavromanoli AC, Haidich AB. ccaR: A package for assessing primary study overlap across systematic reviews in overviews. Res Synth Methods. 2022 Nov 12. doi: 10.1002/jrsm.1610. Online ahead of print.
8. Gates M, Gates A, Guitard S, Pollock M, Hartling L. Guidance for overviews of reviews continues to accumulate, but important challenges remain: a scoping review. Syst Rev2020;9:254. doi:10.1186/s13643-020-01509-0
9. Lunny, C., Brennan, S.E., McDonald, S. and McKenzie, J.E., 2018. Toward a comprehensive evidence map of overview of systematic review methods: paper 2—risk of bias assessment; synthesis, presentation and summary of the findings; and assessment of the certainty of the evidence. Systematic reviews, 7(1), pp.1-31.
10. Lunny C, Brennan SE, McDonald S, McKenzie JE. Toward a comprehensive evidence map of overview of systematic review methods: paper 1—purpose, eligibility, search and data extraction. Systematic reviews. 2017 Dec;6(1):1-27.
I read with interest the Nordic myocarditis cohort study by Husby et al (1) on clinical outcomes of three different types of myocarditis: COVID-19 mRNA vaccination-associated (“vaccine-associated”), COVID-19 infection-associated (“infection-associated”) and “conventional” myocarditis. I would like to make several comments and request response from the authors.
A significant limitation of the Nordic and other similar studies is the under-diagnosis of COVID-19 vaccine-associated myocarditis and possibly COVID-19 infection-associated myocarditis. To be included in the Nordic myocarditis cohort study, an individual had to develop symptom(s) with myocarditis, present to a medical provider with such symptom(s), and the medical provider had to consider myocarditis and then order at least ECG or cardiac enzyme(s), and the individual had to be admitted to the hospital as an inpatient. Individuals with myocarditis who did not satisfy all the above conditions would not have been included in the Nordic study. Only a prospective study with continuous screening for myocardial injury/inflammation would be able to provide complete information on the incidence and outcomes of myocarditis associated with COVID-19 vaccine or infection. Limited prospective data on myocardial injury post-vaccine is currently available, suggesting significantly more common occurrence (2,3) than proclaimed but with unknown long-term prognosis.
During the COVID-19 pandemic, there were many more pat...
Show MoreTo the Editor:
We read with interest the Nordic registry study by Husby et al on clinical outcomes of three different types of myocarditis: SARS-CoV-2 mRNA vaccination-associated, COVID-19 associated and “conventional” myocarditis. We appreciate the information the authors provided on patient characteristics by myocarditis type. We note >50% patients with COVID-19-associated and conventional myocarditis were over 40, those with vaccine-associated myocarditis were younger on average and 38% were 12-24 years v.s <25% for the other two types. Further, the proportion of men was higher in all three categories, and the proportion of patients with underlying comorbidities similar; however, we wonder, given the relatively small number of patients, if the authors could disclose which comorbidities and how many were present (in total) among the patients with COVID-19 vs. vaccination-associated myocarditis, perhaps as a summary table.
Second, do the authors have any information on the vaccination status (number of doses and dates given in relation to the myocarditis diagnosis) among those categorized as having COVID-associated myocarditis? If so, could this information be broken down by age group?
Show MoreRelated, the most recent exposure defined the type of myocarditis in patients who received both an mRNA vaccine and had a positive test for SARS-CoV-2 in the last 28 days. We therefore wonder if the authors could provide the number of times both were diagnosed with...
Dr Conroy and colleagues investigated 469,095 adults aged 40-69, to find 40687 incident events (9 per 1000 person years) of cardiovascular disease in 2083 coeliac sufferers giving a hazard ration of 1.27 (1.11- 1.45) (1). What are the mechanisms for these clinical associations ?
The pathophysiological mechanism in DP Burkitt”s “high fibre” hypothesis to explain chronic diseases is autonomic injury caused by straining on the toilet (2, 3). Coordinated straining may injure branches of sympathetic segments T10-L2 with the potential to injure pelvic organs including kidneys and adrenals. These may extend to contiguous segments at T8-9r supplying small bowel (cf nulliparous endometriosis, inflammatory bowel disease and hypertension). In DP Barkers “fetal origins of adult diseases” hypothesis the pathophysiological mechanism is an in utero injury to autonomic vasomotor nerves in small babies caused by involuntary “fetal hypertension” (4, 5). Kidneys and pancreas seem to bear the brunt of this assault that may result in type 1 diabetes mellitus and hypertension in later life.
On this simple analysis there are, therefore, potential primary neurological mechanisms to account for both cardiovascular and autoimmune diseases within these data-driven hypotheses for chronic diseases. Are autoantibodies secondary consequences of primary denervatory injuries in these chronic “autoimmune” conditions ?. If the BioBank held details of birthweights or bowel habits (...
Show MoreAs authors of the QFracture papers1-3, we read this article by Livingstone et al with interest. They stated that the had externally validated the QFracture-2016 algorithm using CPRD4. The authors report that whilst there was very good to excellent discrimination, calibration was poor. The authors attributed an apparent under-prediction to their outcome definition using the CPRD validation dataset since this included GP data linked to hospital data. However, we think this under-prediction is due to the authors using the wrong algorithm – the authors have confirmed that they had used a previous version (QFracture-2012) which is based on unlinked data. The QFracture-2016 algorithm is the version which is currently recommended and used in the NHS and is derived from the QResearch database including GP data linked to hospital and mortality data3. Therefore, the authors need to correct their paper and update their conclusions accordingly. We would also like to highlight that the code groups for QFracture are available here https://www.qresearch.org/data/qcode-group-library/
Show MoreReferences
1. Hippisley-Cox J, Coupland C. Predicting risk of osteoporotic fracture in men and women in England and Wales: Prospective derivation and validation of QFractureScores. BMJ (Online) 2009;339(7733):1291-95. doi: 10.1136/bmj.b4229
2. Hippisley-Cox J, Coupland C. Derivation and validation of updated QFracture algorith...
We read with great interest the article on ‘Conducting umbrella reviews’. In our view, it is important to highlight that what is described as ‘umbrella reviews’ in this article is often referred to as ‘overviews (of reviews)’, and shares similar definitions and goals, namely to synthesise evidence at the systematic review-level. The term ‘overviews of reviews’ is employed by Cochrane, a leading international organization for evidence synthesis. The corresponding chapter in the Cochrane handbook (1) was revised a few years ago and provides a summary of methods research for this type of evidence synthesis along with recommendations for conducting overviews of reviews. Although this has been prepared for Cochrane, we think that the vast majority of the content can also be used outside Cochrane and for a range of research questions. It provides guidance for topics not mentioned in your article such as dealing with overlapping primary studies across reviews on the same topic, decision tools supporting the inclusion of reviews, and updating reviews by conducting supplemental searches for primary studies. Most importantly, in the article by Belbasis et al. there is no explicit mention of assessing the quality or risk of bias of the included reviews and there is no mention of the recently published reporting guideline for overviews of reviews of healthcare interventions (2).
A large body of research by many evidence synthesis groups over the past 10+ years exists to advan...
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