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Record W2766162546 · doi:10.14740/cr612w

Occurrence and Impact of Time Delay to Primary Percutaneous Coronary Intervention in Patients With ST-Segment Elevation Myocardial Infarction

2017· article· en· W2766162546 on OpenAlexvenueno aff
Mohammady Shahin, Slayman Obeid, Lotfy Hamed, Christian Templin, Oliver Gämperli, Fabian Nietlispach, Willbald Maier, Nooraldaem Yousif, François Mach, Marco Roffi, Stephan Windecker, Lorenz Räber, Christian M. Matter, Thomas F. Lüscher

Bibliographic record

VenueCardiology Research · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsnot available
FundersNational Research FoundationSanofiAstraZenecaEli Lilly and Company
KeywordsMedicineDoor-to-balloonPercutaneous coronary interventionMyocardial infarctionCardiologyST segmentInternal medicineArrival timeCardiac catheterizationCohortProspective cohort studyEmergency medicinePrimary angioplasty

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of the study was to evaluate the occurrence, duration and impact of time delays to primary percutaneous coronary intervention (pPCI) in ST-segment elevation myocardial infarction (STEMI). METHODS: A total of 357 consecutive STEMI patients enrolled in the prospective Special Program University Medicine ACS (SPUM-ACS) cohort were included. In order to identify the causes behind a possible treatment delay, we constructed four different time points which included: 1) symptom onset to hospital arrival, 2) hospital arrival to arrival in the catheterization laboratory, 3) hospital arrival to first balloon inflation, and 4) time from arrival in the catheterization laboratory to first balloon inflation in addition to total ischemic time. Patients were stratified according to a delay > 3 h, > 30 min, > 90 min and > 1 h, respectively and major adverse events at 0, 30 and 365 days were analyzed. RESULTS: Resuscitated STEMI patients (23 patients) and STEMI patients presenting at weekends (101 patients) and to lesser extent at night hours (100 patients) experienced more time delays than stable patients and those presenting at office hours. Median door-to-balloon time averaged 93 min in resuscitated, but 65 min in stable patients. Median door-to-balloon time at weekends and public holidays was 89 min, but 68 min at office hours. Median time from hospital arrival to cathlab arrival at weekends and public holidays was 30 min, but 15 min during office hours. Corresponding times for resuscitated patients was 45 and 15 min in stable patients. Of note, resuscitated patients were late presenters as regards time from symptoms onset to hospital arrival with a median time of 180 min compared to 155 min in stable patients. Median total ischemic time was 225 min for all patients, 223 min at day hours, 239 at night hours, 244 min at weekends, 233 min at office days, 220 min in stable patients and 273 min in resuscitated patients. Patients with STEMI who arrived > 3 h after symptom onset had a higher rate of myocardial infarction (MI) at 1 year (1.6% vs. 9% in < 3 h; P = 0.008). Furthermore, STEMI patients who had a delay of > 1 h from cathlab arrival to first balloon inflation had a higher rate of in hospital reinfarction at 0 day (0.6% vs. 0% in < 1 h; P = 0.007), MI at 30 days (0.8% vs. 0% in < 1 h; P = 0.001) and MI at 1 year (1.4% vs. 1.1% in < 1 h; P = 0.012). Similarly, in these patients, cardiac deaths at 0 day (0.8% vs. 0.6% in < 1 h; P = 0.035) and at 30 days (0.8% vs. 0.6% in < 1 h; P = 0.035) were higher as were major adverse cardiovascular events (MACCE) at 0 day (1.4% vs. 0.8% in < 1 h; P = 0.004). CONCLUSION: Resuscitated STEMI patients and those presenting at weekends and to lesser extent at night hours experienced more time delays and longer ischemic time than stable patients and those presenting at office hours. In STEMI patients, any delay in treatment increased their risk of MACCE. Efforts should focus on improving patient's awareness along with minimizing in-hospital transfer to the catheterization laboratory especially at weekends and in resuscitated patients.

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)0.002
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 score0.030
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.034
GPT teacher head0.372
Teacher spread0.338 · 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".

Quick stats

Citations19
Published2017
Admission routes1
Has abstractyes

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