Prevalence of Metabolic Syndrome and Its Clinical and Angiographic Profile in Patients With Naive Acute Coronary Syndrome in North Indian Population
Bibliographic record
Abstract
BACKGROUND: Data of isolated metabolic syndrome as risk factor in patients presenting with acute coronary syndrome (ACS) especially in context to Indian subcontinent are sparse. Therefore, we studied the prevalence of metabolic syndrome (MetS), and its clinical and angiographic profile in naive ACS patients in North Indian population. METHODS: A single-center, prospective, observational study of 324 patients was conducted at LPS Institute of Cardiology, G.S.V.M. Medical College, Kanpur, India with newly diagnosed ACS patients with MetS, as per modified NCEP-ATP III criteria. They were divided into two groups with and without MetS, and their clinical and angiographic profiles were studied. RESULTS: Prevalence of MetS in our study was 37.65%. Patients with MetS were significantly older than without MetS (60.3 ± 8.4 vs. 57.6 ± 7.9), and had females preponderance (35.24% vs. 24.25%), less tobacco abuse (30.32% vs. 42.57%), more non-ST-segment elevation ACS (58.19% vs. 36.14%), less ST-segment elevation myocardial infarction (STEMI) (41.80% vs. 63.86%), more cardiogenic shock (27.04% vs. 17.32%), recurrent ischemia (14.75% vs. 7.42%) and on angiogram, lesser single vessel disease (21.13% vs. 53.96%), more double vessel disease (39.34 vs. 24.26%), triple vessel disease (19.67% vs. 10.39%), left main (13.11% vs. 4.45%) and complex coronary lesions (tubular 40.98% vs. 31.68%; diffuse 26.23% vs. 18.32%). However, there was a trend of lower but insignificant mortality with MetS (5.44% vs. 6.55%). CONCLUSION: There was high prevalence of MetS among patients with ACS in North Indian population with more advanced coronary artery disease. To the best of our knowledge, this is the first study from North India documenting clinical and angiographic profile of patients with MetS and ACS.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".