Sudden cardiac death and pump failure death prediction in chronic heart failure by combining ECG and clinical markers in an integrated risk model

PLoS One. 2017 Oct 11;12(10):e0186152. doi: 10.1371/journal.pone.0186152. eCollection 2017.

Abstract

Background: Sudden cardiac death (SCD) and pump failure death (PFD) are common endpoints in chronic heart failure (CHF) patients, but prevention strategies are different. Currently used tools to specifically predict these endpoints are limited. We developed risk models to specifically assess SCD and PFD risk in CHF by combining ECG markers and clinical variables.

Methods: The relation of clinical and ECG markers with SCD and PFD risk was assessed in 597 patients enrolled in the MUSIC (MUerte Súbita en Insuficiencia Cardiaca) study. ECG indices included: turbulence slope (TS), reflecting autonomic dysfunction; T-wave alternans (TWA), reflecting ventricular repolarization instability; and T-peak-to-end restitution (ΔαTpe) and T-wave morphology restitution (TMR), both reflecting changes in dispersion of repolarization due to heart rate changes. Standard clinical indices were also included.

Results: The indices with the greatest SCD prognostic impact were gender, New York Heart Association (NYHA) class, left ventricular ejection fraction, TWA, ΔαTpe and TMR. For PFD, the indices were diabetes, NYHA class, ΔαTpe and TS. Using a model with only clinical variables, the hazard ratios (HRs) for SCD and PFD for patients in the high-risk group (fifth quintile of risk score) with respect to patients in the low-risk group (first and second quintiles of risk score) were both greater than 4. HRs for SCD and PFD increased to 9 and 11 when using a model including only ECG markers, and to 14 and 13, when combining clinical and ECG markers.

Conclusion: The inclusion of ECG markers capturing complementary pro-arrhythmic and pump failure mechanisms into risk models based only on standard clinical variables substantially improves prediction of SCD and PFD in CHF patients.

MeSH terms

  • Aged
  • Biomarkers / metabolism*
  • Chronic Disease
  • Death, Sudden, Cardiac / pathology*
  • Electrocardiography*
  • Female
  • Heart Failure / diagnostic imaging*
  • Heart Failure / physiopathology
  • Heart-Assist Devices / adverse effects*
  • Humans
  • Male
  • Middle Aged
  • Models, Cardiovascular*
  • Multivariate Analysis
  • Probability
  • Prognosis
  • ROC Curve
  • Stroke Volume

Substances

  • Biomarkers

Grants and funding

This work was supported by projects TIN2013-41998-R to EP, PL, and JR, and DPI2016-75458-R to JPM, EP, and PL from the Spanish Ministry of Economy and Competitiveness (MINECO), Spain, the MULTITOOLS2HEART from CIBER-BBN through Instituto de Salud Carlos III, Spain to JPM, EP, and PL, the European Social Fund (EU) and Aragón Government through BSICoS group (T96) to JPM, EP, and PL, and by the European Research Council (ERC) through project ERC-2014-StG 638284 to EP.