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Record W2589094117 · doi:10.1001/jamacardio.2016.5501

Identification of Patients With Stable Chest Pain Deriving Minimal Value From Noninvasive Testing

2017· article· en· W2589094117 on OpenAlexaff
Christopher B. Fordyce, Pamela S. Douglas, Rhonda Roberts, Udo Hoffmann, Hussein R. Al‐Khalidi, Manesh R. Patel, Christopher B. Granger, John B. Kostis, Daniel B. Mark, Kerry L. Lee, James E. Udelson

Bibliographic record

VenueJAMA Cardiology · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of British Columbia
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineChest painInternal medicinePre- and post-test probabilityLogistic regressionRandomized controlled trialClinical trialCoronary artery diseaseCohort studyCohortProspective cohort studyCardiology

Abstract

fetched live from OpenAlex

Importance: Guidelines recommend noninvasive testing for patients with stable chest pain, although many subsequently have normal test results and no adverse clinical events. Objective: To describe a risk tool developed to use only pretest clinical data to identify patients with chest pain with normal coronary arteries and no clinical events during follow-up (minimal-risk cohort). Design, Setting, and Participants: This secondary analysis of a randomized, pragmatic comparative effectiveness trial (Prospective Multicenter Imaging Study for Evaluation of Chest Pain [PROMISE]) includes stable, symptomatic outpatients without known coronary artery disease referred for noninvasive testing at 193 sites in North America. Interventions: Patients were randomized to receive coronary computed tomography angiography (CCTA) vs functional testing. Main Outcomes and Measures: A low-risk tool was developed and internally validated from July 27, 2010, to September 19, 2013, in 4631 patients receiving CCTA as their initial test, with a median follow-up of 25 months. Logistic regression analysis was used to evaluate pretest variables to determine factors associated with minimal risk using a two-thirds random sample for model derivation (n = 3087) and a one-third sample for testing and validation (n = 1544). The model was then applied to the CCTA and functional testing arms, and test results and event rates were ascertained. Results: A total of 1243 of 4631 patients (26.8%) were in the minimal-risk cohort. The final minimal-risk model included 10 clinical variables that together were correlated with normal CCTA results and no clinical events (C statistic = 0.725 for the derivation and validation subsets; 95% CI, 0.705-0.746): younger age; female sex; racial or ethnic minority; no history of hypertension, diabetes, or dyslipidemia; family history of premature coronary artery disease; never smoking; symptoms unrelated to physical or mental stress; and higher high-density lipoprotein cholesterol level. Across the entire PROMISE cohort, this model was associated with the lowest rates of severely abnormal test results (1.3% for CCTA; 5.6% for functional) and cardiovascular death or myocardial infarction (0.5% for a median of 25 months) among patients at the highest probability (10th decile) of minimal risk. Conclusions and Relevance: In contemporary practice, more than 25% of patients with stable chest pain referred for noninvasive testing will have normal coronary arteries and no long-term clinical events. A clinical tool using readily available pretest variables discriminates such minimal-risk patients, for whom deferred testing may be considered. Trial Registration: clinicaltrials.gov Identifier: NCT01174550.

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)0.012
metaresearch head score (Gemma)0.034
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 score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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

Citations101
Published2017
Admission routes1
Has abstractyes

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