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Abstract 4138477: Small Vessels, Big Challenges: Clinical and Demographic Correlates of Failure to Respond to Optimal Medical Therapy in Patients with Microvascular Angina

2024· article· en· W4404341274 on OpenAlexaboutno aff
Diya Karwa, Avraj Virdi, Tim Kinnaird, Sean Gallagher, Omar Aldalati, Rhodri Davies, Abbas Zaidi, Richard Wheeler, Peter O’Callaghan, Zaheer Yousef, Vasim Farooq

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnginaMedical therapyIntensive care medicineHeart failureStable anginaCardiologyInternal medicineCoronary artery diseaseMyocardial infarction

Abstract

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Background: Microvascular angina (MVA), resulting from coronary microvascular dysfunction, affects 40-50% of patients with exertional chest pain or dyspnea and a normal coronary angiogram. This condition significantly impairs quality of life and elevates the risk of major cardiac events, including myocardial infarction, stroke and death. MVA is classified as 'structural' (coronary flow reserve [CFR] <2.5&index of microcirculatory resistance [IMR] >25) or 'functional' (CFR <2.5&IMR <25). Current treatments are empirical, necessitating further investigation. Methods: This single-centre, retrospective study conducted in South Wales from 2019-24 examined MVA cases confirmed via national electronic health records. The Canadian Cardiovascular Society (CCS) angina grade was assessed at diagnosis and post optimal medical therapy (OMT) during clinic visits. Ordinal logistic regression was used to identify independent correlates of higher CCS grade, considering baseline LDL, age, diffuse epicardial disease, sex, hypertension, kidney function, smoking status and HbA1c levels as covariates. Results: Among 64 identified MVA cases (median age 69 yrs, IQR 61.3-77.8; 68.8% male), 81.3% had structural MVA, 6.3% functional MVA and 12.5% mixed disease. Coronary vessel testing was performed on 1, 2 or 3 vessels in 29.7% (n=19), 53.1% (n=34; 21 positive [+ve] in 2 vessels, 13 +ve in 1 vessel) and 17.2% (n=11; 5 +ve in 3 vessels, 5 +ve in 2 vessels, 1 +ve in 1 vessel) of cases, respectively. The LAD was most commonly affected vessel (82.8%). At diagnosis, 79.7% had CCS grade III or higher (Fig 1). Post OMT, 45.3% achieved CCS grade 0-I, 31.8% CCS grade II and 15.6% remained at CCS grade III (refractory angina). Medications included ranolazine (84.4%), beta-blockers (70.3%) and calcium channel blockers (28.1%). Ordinal logistic regression analysis (X 2 =0.27, p=0.033) identified baseline LDL (95% CI [0.16, 1.26]; p=0.011), age (95% CI [-0.12, -0.001]; p=0.045), diffuse epicardial disease (95% CI [0.016, 2.13]; p=0.047) and male sex (95% CI [0.066, 2.36]; p=0.038) as independent correlates of higher CCS grade, signifying poor response to OMT. Conclusion: MVA manifests regionally, underscoring the need for multi-territory testing. Although tailored OMT proved effective for many, 15.6% of patients continued to experience refractory symptoms (CCS grade III). This highlights the necessity for alternative therapeutic strategies to improve patient outcomes in this challenging condition.

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)0.001
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.280
Teacher spread0.252 · 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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Citations0
Published2024
Admission routes1
Has abstractyes

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