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Establishment and Management of Mechanical Circulatory Support During the COVID-19 Pandemic

2020· article· en· W3023621058 on OpenAlexaff
Duc Thinh Pham, Hadi Toeg, Ruggero De Paulis, Pavan Atluri

Bibliographic record

VenueCirculation · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsMontreal Heart InstituteUniversity of Ottawa
FundersNorthwestern University
KeywordsMedicinePandemicCoronavirus disease 2019 (COVID-19)Cardiac surgeryMedical emergencySurgeryInternal medicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

ith >3 million cases worldwide, the novel coronavirus disease 2019 (COVID-19) pandemic presents a public health crisis that challenges the availability of healthcare practitioners and resources.Most patients who have COVID-19 present with no or mild symptoms.However, of 44 672 confirmed and presumed (attributable to exposure) cases reported in China, 14% had severe illness, and 5% were in critical respiratory failure, shock, and multisystem organ failure. 1 Twenty percent of patients present with some degree of cardiac injury, manifested by ECG abnormalities, arrhythmias, elevated troponin levels, and cardiac dysfunction. 2Although the prognosis of these abnormalities in stable patients remains poorly understood, myocardial collapse occurs in a small subset of patients.Extracorporeal membrane oxygenation (ECMO) represents a treatment option for COVID-19-related respiratory, cardiac, or combined cardiopulmonary failure.Current knowledge on ECMO use in COVID-19 infection remains limited to small series and registry data.The Italian experience provides preliminary data of critically ill patients.The majority of hospitalized patients were admitted for hypoxemic respiratory failure with intubation rates varying by center (30% to 88%).The mortality rate in critically ill patients was 26% to 84%. 3,4There was a varied presentation of cardiac manifestations, similar to those described earlier, often presumed secondary to myocardial injury. 3A webinar of the Japanese experience with venovenous ECMO (VV-ECMO for isolated respiratory failure) in patients with CO-VID-19 (n=32) demonstrated a successful wean rate from ECMO (67%), with low mortality (2%), with the remaining patients still supported.Two patients required conversion to venoarterial ECMO (VA-ECMO) because of cardiac dysfunction.Similarly, an April 28, 2020 review of the Extra-corporeal Life Support Organization registry denoted 604 COVID-19-related patients supported with ECMO (67% North America, 26% Europe), of whom 92% were initiated as VV-ECMO, with the remainder supported on VA-ECMO platforms.To date, 24% had been successfully weaned, of whom 43% were discharged alive.First-line therapy for critically ill patients with COVID-19 remains supportive, including early intubation with ventilatory support, neuromuscular blockade, pulmonary vasodilators, and prone positioning. 3In cases with COVID-19-related myocardial injury, initial support also remains medical, with inotropic and vasoconstrictor therapy.However, for scenarios with refractory pulmonary and myocardial compromise, strong consideration of early mechanical circulatory support, mainly ECMO, is warranted.Patient selection, treatment algorithm, inflow/outflow configuration, resource allocation, and effectiveness of ECMO have yet to be fully defined in COVID-19.We propose the following algorithm: (1) VV-ECMO for isolated respiratory failure; (2) mechanical circulatory support (including VA-ECMO) for isolated cardiogenic shock; or (3) VA-ECMO for cardiorespiratory failure (Figure).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.037
GPT teacher head0.293
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations15
Published2020
Admission routes1
Has abstractyes

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