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Record W3155775888 · doi:10.1097/ccm.0000000000005026

Acute Cardiac Injury in Coronavirus Disease 2019 and Other Viral Infections—A Systematic Review and Meta-Analysis

2021· review· en· W3155775888 on OpenAlexaff
Matthew P. Cheng, Alessandro Cau, Todd C. Lee, Daniel Brodie, Arthur S. Slutsky, John C. Marshall, Srinivas Murthy, Terry Lee, Joel Singer, Koray Demir, John H. Boyd, Hyejee Ohm, David M. Maslove, Alberto Goffi, Isaac I. Bogoch, David Sweet, Keith R. Walley, James A. Russell

Bibliographic record

VenueCritical Care Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsVancouver General HospitalQueen's UniversitySt. Paul's HospitalUniversity Health NetworkBC Children's HospitalUniversity of TorontoSt. Michael's HospitalCentre for Advancing Health OutcomesUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsMedicineCoronavirusInternal medicineAngiotensin-converting enzyme 2Angiotensin-converting enzymeCardiologyDiseaseBlood pressureCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)

Abstract

fetched live from OpenAlex

OBJECTIVES: Severe acute respiratory syndrome-related coronavirus-2 binds and inhibits angiotensin-converting enzyme-2. The frequency of acute cardiac injury in patients with coronavirus disease 2019 is unknown. The objective was to compare the rates of cardiac injury by angiotensin-converting enzyme-2-binding viruses from viruses that do not bind to angiotensin-converting enzyme-2. DATA SOURCES: We performed a systematic review of coronavirus disease 2019 literature on PubMed and EMBASE. STUDY SELECTION: We included studies with ten or more hospitalized adults with confirmed coronavirus disease 2019 or other viral pathogens that described the occurrence of acute cardiac injury. This was defined by the original publication authors or by: 1) myocardial ischemia, 2) new cardiac arrhythmia on echocardiogram, or 3) new or worsening heart failure on echocardiogram. DATA EXTRACTION: We compared the rates of cardiac injury among patients with respiratory infections with viruses that down-regulate angiotensin-converting enzyme-2, including H1N1, H5N1, H7N9, and severe acute respiratory syndrome-related coronavirus-1, to those with respiratory infections from other influenza viruses that do not bind angiotensin-converting enzyme-2, including Influenza H3N2 and influenza B. DATA SYNTHESIS: Of 57 studies including 34,072 patients, acute cardiac injury occurred in 50% (95% CI, 44-57%) of critically ill patients with coronavirus disease 2019. The overall risk of acute cardiac injury was 21% (95% CI, 18-26%) among hospitalized patients with coronavirus disease 2019. In comparison, 37% (95% CI, 26-49%) of critically ill patients with other respiratory viruses that bind angiotensin-converting enzyme-2 (p = 0.061) and 12% (95% CI, 7-22%) of critically ill patients with other respiratory viruses that do not bind angiotensin-converting enzyme-2 (p < 0.001) experienced a cardiac injury. CONCLUSIONS: Acute cardiac injury may be associated with whether the virus binds angiotensin-converting enzyme-2. Acute cardiac injury occurs in half of critically ill coronavirus disease 2019 patients, but only 12% of patients infected by viruses that do not bind to angiotensin-converting enzyme-2.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.071
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.071
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0170.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.178
GPT teacher head0.555
Teacher spread0.377 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations37
Published2021
Admission routes1
Has abstractyes

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