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Record W4310239154 · doi:10.1161/jaha.122.026025

Automated Quantification of Abnormal QRS Peaks From High‐Resolution ECGs Predicts Late Ventricular Arrhythmias in Hypertrophic Cardiomyopathy: A 5‐Year Prospective Multicenter Study

2022· article· en· W4310239154 on OpenAlexafffund
Adrian Suszko, Praloy Chakraborty, Karthik Viswanathan, Scott Barichello, John L. Sapp, Mario Talajic, Zachary Laksman, Raymond Yee, Anna Woo, Danna Spears, Arnon Adler, Harry Rakowski, Vijay S. Chauhan

Bibliographic record

VenueJournal of the American Heart Association · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsLondon Health Sciences CentreSt. Paul's HospitalAdlerMontreal Heart InstituteQueen Elizabeth II Health Sciences CentreUniversity Health Network
FundersHeart and Stroke Foundation of Canada
KeywordsMedicineCardiologyInternal medicineHypertrophic cardiomyopathyQRS complexEjection fractionProspective cohort studyHazard ratioReceiver operating characteristicImplantable cardioverter-defibrillatorHeart failureCardiomyopathySudden cardiac deathAmbulatoryProportional hazards modelConfidence interval

Abstract

fetched live from OpenAlex

Background Patients with hypertrophic cardiomyopathy (HCM) are at risk of ventricular arrhythmia (VA) attributed to abnormal electrical activation arising from myocardial fibrosis and myocyte disarray. We sought to quantify intra‐QRS peaks (QRSp) in high‐resolution ECGs as a measure of abnormal activation to predict late VA in patients with HCM. Methods and Results Prospectively enrolled patients with HCM (n=143, age 53±14 years) with prophylactic implantable cardioverter‐defibrillators had 3‐minute, high‐resolution (1024 Hz), digital 12‐lead ECGs recorded during intrinsic rhythm. For each precordial lead, QRSp was defined as the total number of peaks detected in the QRS complex that deviated from a smoothing filtered version of the QRS. The VA end point was appropriate implantable cardioverter‐defibrillator therapy during 5‐year prospective follow‐up. After 5 years, 21 (16%) patients had VA. Patients who were VA positive had greater QRSp (6.0 [4.0–7.0] versus 4.0 [2.0–5.0]; P <0.01) and lower left ventricular ejection fraction (57±11 versus 62±9; P =0.038) compared with patients who were VA negative, but had similar established HCM risk metrics. Receiver operating characteristic analysis revealed that QRSp discriminated VA (area under the curve=0.76; P <0.001), with a QRSp ≥4 achieving 91% sensitivity and 39% specificity. The annual VA rate was greater in patients with QRSp ≥4 versus QRSp <4 (4.4% versus 0.98%; P =0.012). In multivariable Cox regression, age <50 years (hazard ratio [HR], 2.53; P =0.009) and QRSp (HR per QRS peak, 1.41; P =0.009) predicted VA after adjusting for established HCM risk metrics. In patients aged <50 years, the annual VA rate was 0.0% for QRSp <4 compared with 6.9% for QRSp ≥4 ( P =0.012). Conclusions QRSp predicted VA in patients with HCM who were eligible for an implantable cardioverter‐defibrillator after adjusting for established HCM risk metrics, such that each additional QRS peak increases VA risk by 40%. QRSp <4 was associated with a <1% annual VA risk in all patients, and no VA risk among those aged <50 years. This novel ECG metric may improve patient selection for prophylactic implantable cardioverter‐defibrillator therapy by identifying those with low VA risk. These findings require further validation in a lower risk HCM cohort. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02560844.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

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