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Record W2781196962 · doi:10.1111/acem.13371

Sex Differences in Diagnoses, Treatment, and Outcomes for Emergency Department Patients With Chest Pain and Elevated Cardiac Troponin

2017· article· en· W2781196962 on OpenAlexafffundabout
Karin H. Humphries, May K. Lee, Mona Izadnegahdar, Min Gao, Daniel T. Holmes, Frank Scheuermeyer, Martha Mackay, André Mattman, Eric Grafstein

Bibliographic record

VenueAcademic Emergency Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineEmergency departmentChest painMedical diagnosisTroponinEmergency medicineTroponin IInternal medicineCardiologyMedical emergencyMyocardial infarctionRadiologyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: While sex differences in the treatment and outcomes of subjects with acute coronary syndromes are well documented, little is known about the impact of cardiac troponin (cTn) levels obtained in the emergency department (ED) on the observed sex differences. We sought to determine whether cTn levels by chest pain features modify sex differences in diagnosis, treatment, and outcomes in patients presenting with chest pain suggestive of ischemia. METHODS: All adults presenting to two hospitals in Vancouver, Canada, between May 2008 and March 2013 with ischemic chest pain and with cTn testing were included in the study. Outcomes were obtained through data linkage with population-based administrative data sets, including Vital Statistics (death), Discharge Abstract Database (hospitalizations), and PharmaNet (medications). Cumulative event rates for the composite major adverse cardiac event (MACE) endpoint (death, myocardial infarction [MI], incident admission for heart failure or for angina requiring diagnostic catheterization or revascularization) were estimated for each sex and cTn level using the Kaplan-Meier method; Cox models were used to estimate hazard ratios and 95% confidence interval (CIs) for 1-year MACE and 7-day catheterization. Logistic models were used to estimate odds ratios (ORs) and 95% CI for 90-day medication use. RESULTS: Over the 5-year study period, 25,539 patients presented to the ED with chest pain of which 7,272 (2,933 females and 4,339 males) met the inclusion criteria. Among patients with chest pain with cardiac features/history and cTn > 99th percentile, females were less likely to be diagnosed with MI (46.4% vs. 57.5%). Females in the cTnI > 99th percentile group had the worst outcomes with a 1-year MACE rate of 22.7% (95% CI = 18.5-27.7) versus 18.8% (95% CI = 16.2-21.6), although this difference was attenuated and not statistically significant after adjustment for baseline differences. Overall, females underwent fewer diagnostic catheterizations than males within 7 days of admission to the ED. Even when cTn was above the 99th percentile and the chest pain was cardiac in nature, 48.4% of females underwent a diagnostic catheterization compared to 64.3% of males (p < 0.001). Within 90 days of discharge, females were less likely to use the evidence-based cardiac medications. The most striking sex differences were noted when cTnI levels were > 99th percentile and when the chest pain was cardiac in nature; males filled 25% more prescriptions for statins than their female counterparts. Adjustment for baseline differences did not attenuate this difference. CONCLUSIONS: Sex differences in diagnosis and treatment after presentation to the ED with chest pain are not explained by differences in chest pain features or levels of cTn. Even when females have cardiac chest pain and cTn levels > 99th percentile, they are less likely to be diagnosed with MI, less likely to undergo diagnostic cardiac catheterization within 7 days, and less likely to use evidence-based cardiac medications, but they have the highest 1-year MACE rate. The higher MACE rate appears to be driven by the higher burden of comorbid conditions.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.030
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.364
Teacher spread0.313 · 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.

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

Citations54
Published2017
Admission routes3
Has abstractyes

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