Screening Entire Healthcare System ECG Database: Association of Deep Terminal Negativity of P wave in lead V1 and ECG Referral with Mortality

Abstract

Background: Each encounter of asymptomatic individuals with the healthcare system presents an opportunity for improvement of cardiovascular disease (CVD) awareness and sudden cardiac death (SCD) risk assessment. ECG sign deep terminal negativity of the P wave in V1 (DTNPV1) was shown to be associated with an increased risk of SCD in the general population.

Objective: To evaluate association of DTNPV1 with all-cause mortality and newly diagnosed atrial fibrillation (AFib) in the large tertiary healthcare system patient population.

Methods: Retrospective double cohort study compared two levels of exposure (automatically measured amplitude of P-prime (Pp) in V1): DTNPV1 (Pp from −100μV to −200μV) and ZeroPpV1 (Pp=0). An entire healthcare system (2010–2014) ECG database was screened. Medical records of children and patients with previously diagnosed AFib/atrial flutter (AFl), implanted pacemaker or cardioverter-defibrillator were excluded. DTNPV1 (n=3,413) and ZeroPpV1 (n=3,405) cohorts were matched by age and sex. Primary outcome was all-cause mortality. Secondary outcomes were newly diagnosed AFib/AFl. Median follow-up was 2.5 y.

Results: DTNPV1 was associated with all-cause mortality (HR 1.95(1.64–2.31); P<0.0001) and newly diagnosed AFib (HR 1.29(1.04–1.59); P=0.021) after adjustment for CVD, comorbidities, other ECG parameters, medications, and index ECG referral. Index ECG referral by a cardiologist was independently associated with 34% relative risk reduction of mortality (HR 0.66(0.52–0.84); P=0.001), as compared to ECG referral by a non-cardiologist.

Conclusion: DTNPV1 is independently associated with twice higher risk of all-cause death, as compared to patients without P prime in V1. Life-saving effect of the index ECG referral by a cardiologist requires further study.

Jason A. Thomas

PhD
Medical Data & AI Scientist | Strategist | Informatician | Tech lead - Senior Data & AI Scientist - Philips

My research interests include 1) building foundational layers (data, infrastructure, knowledge representation, talent, culture) to support biomedical data science and 2) applying data science & AI methods on data to drive business value and improve patient outcomes.