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Cardiogenic shock is more prevalent in racialised and newcomer populations and associated with higher mortality

2024· article· en· W4403807817 on OpenAlexaffabout
Darshan H. Brahmbhatt, Radhiana Hassan, Francesco Scolari, Victoria Wang, Narmin Ibrahimova, M Nissar, Kristine A. Keon, Eun Ji Shin, Ava Isabella De Pellegrin Overgaard, Nick L. S. Fung, Filio Billia, Charlotte Overgaard, Andrea O. Y. Luk

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsThe Scarborough HospitalSouthlake Regional Health CenterUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineCardiogenic shockShock (circulatory)Intensive care medicineInternal medicineCardiologyMyocardial infarction

Abstract

fetched live from OpenAlex

Abstract Background The World Health Organization (WHO) has recognised social determinants of health (SDH) as non-medical factors that affect health outcomes. These SDH have a significant effect on health inequities, whereby more marginalised populations often have worse outcomes due to unfair and avoidable differences in health status. The Ontario Marginalization index (ON-Marg) is an area-level index derived from 42 census variables identifying differences in marginalisation, allowing identification of inequities between population groups and geographical areas. The ‘racialised and newcomer populations’ dimension of the ON-Marg characterises the proportions of recent immigrants and/or people belonging to ‘visible minority’ groups in geographical locales. These data can be used as a surrogate for individual patient data to investigate health outcomes related to this dimension. This study investigated the prevalence and mortality associated with racial marginalisation in patients admitted with cardiogenic shock at a quaternary cardiac referral centre. Methods A single-centre registry of CS admissions from 2014-2023 at a quaternary referral centre cardiac intensive care unit (CICU) in Ontario, Canada was studied. Patient postal codes were identified and mapped to ON-Marg ‘racialised and newcomer populations’ data using the Postal Code Conversion File. The ON-Marg data are categorised into equal quintiles, which was used to determine differences in distribution of CS cases and inpatient survival using the Chi-squared test and Kaplan-Meier methods. Only the index CS hospitalisation was included in the analysis. Results We identified 1513 patients, including 456 (30.1%) females, aged 60.2±16.1 years, with 333 (24.8%) due to acute myocardial infarction CS. The majority of patients were SCAI stage D (69.2%) with 488 (32.3%) dying and 186 (12.3%) receiving a heart transplant or durable ventricular assist device (VAD) before discharge. CS patients were more likely to be in the higher quintiles for marginalisation racialised and newcomer populations and these individuals had higher mortality (p=0.005, figure 1). In a survival analysis, with right-censoring for transplant or VAD, there were significant differences between marginalisation quintiles (figure 2). Conclusions Patients admitted with CS to a large cardiac centre were more likely to be from populations with higher levels of racialised and newcomer individuals. Increasing marginalisation was associated with higher mortality. This identifies a need for delineating whether these differences are aetiological or whether there are barriers in accessing high-quality care in a timely fashion. This could allow an improvement in outcomes for these patient groups. While census-based area-level marginalisation indices can be helpful for identifying possible barriers to equitable care, individualised patient-level data are needed to confirm these findings.

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 score0.230
Threshold uncertainty score0.457

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.065
GPT teacher head0.351
Teacher spread0.286 · 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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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