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Mortality after cardiovascular surgery and percutaneous coronary intervention prior to versus during the COVID-19 pandemic

2023· article· en· W4388596281 on OpenAlexaffabout
Antony Chu, X Wang, J. Michael Paterson, Michael Hillmer, Kamil Malikov, Douglas S. Lee

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMinistry of Health and Long Term CareInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsMedicinePercutaneous coronary interventionConventional PCIPandemicRetrospective cohort studyMortality ratePopulationEmergency medicineLogistic regressionCohortInternal medicineCoronavirus disease 2019 (COVID-19)Myocardial infarctionEnvironmental healthDisease

Abstract

fetched live from OpenAlex

Abstract Background The COVID-19 pandemic disrupted the usual delivery of health care services globally. However, little is known about its specific impact on cardiovascular procedure rates and outcomes. Purpose To compare rates of isolated coronary artery bypass graft (CABG) surgery, isolated open valve surgery (iValve) and percutaneous coronary intervention (PCI), and 30-day all-cause mortality among patients undergoing these procedures before versus during the COVID-19 pandemic. Methods We undertook a retrospective cohort study of patients aged 18 years and older in Ontario, Canada, comparing procedure and outcome event rates between April 1, 2018 and March 31, 2020 (pre-pandemic) with those between April 1, 2020 and March 31, 2022 (pandemic). Procedures were identified from hospital records, available for all patients hospitalized in the province, and were calculated per 100,000 population. Deaths were identified from the Ontario Health Insurance Plan Registered Persons Database. Multivariable logistic regression models were used to compute mortality rates adjusted for relevant sociodemographic and clinical characteristics. Additionally, we estimated the number of procedures needed to result in one excess death as the difference between the two periods divided by the pre-pandemic rate. Results During the study period, 24,823, 7,768 and 86,549 patients underwent CABG, iValve and PCI respectively. Between the pre-pandemic and pandemic periods, CABG, iValve and PCI rates declined 18.8%, 19.3% and 14.7%, respectively (Figure). Overall, the 30-day mortality rates were 1.9%, 2.4% and 2.6% following CABG, iValve and PCI, respectively. Adjusted 30-day mortality after CABG increased from 1.92 per 100 procedures (95% CI 1.69-2.15) pre-pandemic to 2.25 (2.01-2.49) during the pandemic (p=0.043). Adjusted 30-day mortality also increased among patients receiving iValve [from 2.25 (1.78, 2.71) to 3.29 (2.76, 3.82); p=0.016] and PCI [from 2.41 (2.28, 2.54) to 2.74 (2.60, 2.88); p<0.001]. These differences translate to an estimated excess of one death per 303 CABG surgeries, 96 iValve surgeries and 303 PCIs during the pandemic period. Conclusions In Ontario, Canada, CABG, iValve and PCI rates decreased during the COVID-19 pandemic and had not fully returned to pre-pandemic levels by two years following its initiation. While 30-day mortality rates following cardiovascular procedures significantly increased during the pandemic, the incremental number of additional deaths was small.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.636

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.197
GPT teacher head0.410
Teacher spread0.213 · 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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Citations1
Published2023
Admission routes2
Has abstractyes

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