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Record W2981379697 · doi:10.1093/eurheartj/ehz746.0425

P5471Baseline characteristics, healthcare resource use and clinical outcomes of stable post-myocardial infarction patients with diabetes: insights from the global prospective TIGRIS study

2019· article· en· W2981379697 on OpenAlexaff
José Carlos Nicolau, David Brieger, Shaun G. Goodman, Mauricio G. Cohen, Tabassome Simon, Dirk Westermann, Christopher B. Granger, Richard Grieve, J Y Chen, Katarina Hedman, Carl Mellström, Gunnar Brandrup‐Wognsen, Ruth Owen, Stuart Pocock

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineDiabetes mellitusMyocardial infarctionInternal medicineCoronary artery diseasePopulationBody mass indexHeart failureKidney diseaseStroke (engine)Observational studyAnginaType 2 diabetesCardiologyEmergency medicineEndocrinology

Abstract

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Abstract Background There is a growing prevalence of diabetes worldwide in patients in the general population, including those with prior myocardial infarction (MI). Purpose To describe the characteristics, health status, resource utilization and clinical adverse events of stable post-MI patients with diabetes. Methods The long-Term rIsk, clinical manaGement and healthcare Resource utilization of stable coronary artery dISease (TIGRIS) prospective observational study (NCT01866904) obtained data from 8985 stable patients 1–3 years post-MI from 369 centres in 25 countries, who provided diabetes status (no, yes, insulin-treated) and follow-up. Diabetes status, other patient characteristics, medications, medical history and healthcare resource utilization were recorded at enrolment. Health status was assessed at enrolment, 1 and 2 years by EQ-5D-3L and converted to an EQ-5D score. Deaths, cardiovascular (CV) events, bleeding events and related hospitalizations were recorded during 2 years of follow-up. Results Diabetes mellitus (DM) was prevalent at enrolment in 2966 (33%) patients of whom 872 (29%) were insulin-treated. Compared to patients without DM, those with DM had a higher mean body mass index (28.2 vs 26.6kg/m2) and heart rate (71 vs 67bpm), were more likely to have had ≥2 prior MIs (12% vs 10%), chronic kidney disease (10% vs 6%), peripheral artery disease (10% vs 5%), heart failure (15% vs 10%), anaemia (4% vs 2%), angina (12% vs 9%), stroke (6% vs 4%) and chronic obstructive pulmonary disease (9% vs 7%). Patients with DM reported more problems for each domain of the EQ-5D (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression), which resulted in a lower mean EQ-5D utility score at enrolment (0.83±0.22 for no-diabetes vs 0.86±0.19 for diabetes). Moreover, they also had higher CV hospitalization rates in the 6 months prior to enrolment (6.4% vs 5%). All these measures were more marked in insulin-dependent diabetics. The incidences of all-cause death, CV death and the composite of CV death, MI and stroke were all significantly higher in patients with DM, especially those on insulin (see Figure). For CV death, MI and stroke the 2-year risk ratios, compared to patients without DM, were 2.64 (P<0.001) and 1.48 (P<0.001) respectively for those with insulin-treated DM and non-insulin treated. Figure 1 Conclusions Within a global population of stable post-MI patients, those with DM (especially those on insulin) have poorer health status and EQ-5D utility score, higher hospitalization rates and worse clinical outcomes compared with those without DM. Thus, in cardiac clinics worldwide, patients with DM require particularly close attention. Acknowledgement/Funding The study was funded by AstraZeneca

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)0.001
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 score0.011
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.021
GPT teacher head0.289
Teacher spread0.268 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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