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Outcomes of Participants With Diabetes in the ISCHEMIA Trials

2021· article· en· W3199769651 on OpenAlexaff
Jonathan Newman, Rebecca Anthopolos, G.B. John Mancini, Sripal Bangalore, Harmony R. Reynolds, Dennis Kunichoff, Roxy Senior, Jesús Peteiro, Balram Bhargava, Pallav Garg, Jorge Escobedo, Rolf Doerr, Tomasz Mazurek, José Ramón González‐Juanatey, Grzegorz Gajos, Carlo Briguori, Hong Cheng, András Vértes, Sandeep Mahajan, Luis A. Guzmán, Mátyás Keltai, Aldo P. Maggioni, Gregg W. Stone, Jeffrey S. Berger, Yves Rosenberg, William E. Boden, Bernard Chaitman, Jerome L. Fleg, Judith S. Hochman, David J. Maron

Bibliographic record

VenueCirculation · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsLondon Health Sciences CentreWestern UniversitySpinal Cord Injury BCUniversity of British Columbia
FundersNational Center for Advancing Translational SciencesNational Heart, Lung, and Blood Institute
KeywordsMedicineInterquartile rangeDiabetes mellitusInternal medicineHazard ratioRevascularizationKidney diseaseMyocardial infarctionCoronary artery diseaseCardiologySurgeryConfidence intervalEndocrinology

Abstract

fetched live from OpenAlex

Background: Among patients with diabetes and chronic coronary disease, it is unclear if invasive management improves outcomes when added to medical therapy. Methods: The ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) trials (ie, ISCHEMIA and ISCHEMIA–Chronic Kidney Disease) randomized chronic coronary disease patients to an invasive (medical therapy + angiography and revascularization if feasible) or a conservative approach (medical therapy alone with revascularization if medical therapy failed). Cohorts were combined after no trial-specific effects were observed. Diabetes was defined by history, hemoglobin A1c ≥6.5%, or use of glucose-lowering medication. The primary outcome was all-cause death or myocardial infarction (MI). Heterogeneity of effect of invasive management on death or MI was evaluated using a Bayesian approach to protect against random high or low estimates of treatment effect for patients with versus without diabetes and for diabetes subgroups of clinical (female sex and insulin use) and anatomic features (coronary artery disease severity or left ventricular function). Results: Of 5900 participants with complete baseline data, the median age was 64 years (interquartile range, 57–70), 24% were female, and the median estimated glomerular filtration was 80 mL·min −1 ·1.73 −2 (interquartile range, 64–95). Among the 2553 (43%) of participants with diabetes, the median percent hemoglobin A1c was 7% (interquartile range, 7–8), and 30% were insulin-treated. Participants with diabetes had a 49% increased hazard of death or MI (hazard ratio, 1.49 [95% CI, 1.31–1.70]; P <0.001). At median 3.1-year follow-up the adjusted event-free survival was 0.54 (95% bootstrapped CI, 0.48–0.60) and 0.66 (95% bootstrapped CI, 0.61–0.71) for patients with diabetes versus without diabetes, respectively, with a 12% (95% bootstrapped CI, 4%–20%) absolute decrease in event-free survival among participants with diabetes. Female and male patients with insulin-treated diabetes had an adjusted event-free survival of 0.52 (95% bootstrapped CI, 0.42–0.56) and 0.49 (95% bootstrapped CI, 0.42–0.56), respectively. There was no difference in death or MI between strategies for patients with diabetes versus without diabetes, or for clinical (female sex or insulin use) or anatomic features (coronary artery disease severity or left ventricular function) of patients with diabetes. Conclusions: Despite higher risk for death or MI, chronic coronary disease patients with diabetes did not derive incremental benefit from routine invasive management compared with initial medical therapy alone. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01471522.

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)0.032
metaresearch head score (Gemma)0.048
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 score0.032
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.048
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.077
GPT teacher head0.328
Teacher spread0.251 · 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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Citations33
Published2021
Admission routes1
Has abstractyes

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