PMID- 29020031
OWN - NLM
STAT- MEDLINE
DCOM- 20171020
LR  - 20181113
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 12
IP  - 10
DP  - 2017
TI  - Sudden cardiac death and pump failure death prediction in chronic heart failure 
      by combining ECG and clinical markers in an integrated risk model.
PG  - e0186152
LID - 10.1371/journal.pone.0186152 [doi]
LID - e0186152
AB  - 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 Subita 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 (DeltaalphaTpe) 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, DeltaalphaTpe and TMR. For PFD, the 
      indices were diabetes, NYHA class, DeltaalphaTpe 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.
FAU - Ramirez, Julia
AU  - Ramirez J
AUID- ORCID: 0000-0003-4130-5866
AD  - Clinical Pharmacology Department, William Harvey Research Institute, John Vane 
      Science Centre, Queen Mary University of London, Charterhouse Square, London, 
      United Kingdom.
FAU - Orini, Michele
AU  - Orini M
AD  - Institute of Cardiovascular Science, University College London, London, United 
      Kingdom.
AD  - Barts Heart Centre, St Bartholomeus Hospital, London, United Kingdom.
FAU - Minchole, Ana
AU  - Minchole A
AD  - Department of Computer Science, University of Oxford, Oxford, United Kingdom.
FAU - Monasterio, Violeta
AU  - Monasterio V
AD  - Universidad San Jorge, Campus Universitario, Villanueva de Gallego, Spain.
FAU - Cygankiewicz, Iwona
AU  - Cygankiewicz I
AD  - Department of Electrocardiology, Medical University of Lodz, Sterling Regional 
      Center for Heart Diseases, Lodz, Poland.
FAU - Bayes de Luna, Antonio
AU  - Bayes de Luna A
AD  - Catalan Institute of Cardiovascular Sciences, Santa Creu I Sant Pau Hospital, 
      Barcelona, Spain.
FAU - Martinez, Juan Pablo
AU  - Martinez JP
AD  - Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, 
      Aragon Institute of Engineering Research, IIS Aragon, University of Zaragoza, 
      Zaragoza, Spain.
AD  - Biomedical Research Networking Center in Bioengineering, Biomaterials and 
      Nanomedicine (CIBER-BBN), Zaragoza, Spain.
FAU - Laguna, Pablo
AU  - Laguna P
AD  - Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, 
      Aragon Institute of Engineering Research, IIS Aragon, University of Zaragoza, 
      Zaragoza, Spain.
AD  - Biomedical Research Networking Center in Bioengineering, Biomaterials and 
      Nanomedicine (CIBER-BBN), Zaragoza, Spain.
FAU - Pueyo, Esther
AU  - Pueyo E
AD  - Biomedical Signal Interpretation and Computational Simulation (BSICoS) group, 
      Aragon Institute of Engineering Research, IIS Aragon, University of Zaragoza, 
      Zaragoza, Spain.
AD  - Biomedical Research Networking Center in Bioengineering, Biomaterials and 
      Nanomedicine (CIBER-BBN), Zaragoza, Spain.
LA  - eng
PT  - Journal Article
DEP - 20171011
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
RN  - 0 (Biomarkers)
SB  - IM
MH  - Aged
MH  - Biomarkers/*metabolism
MH  - Chronic Disease
MH  - Death, Sudden, Cardiac/*pathology
MH  - *Electrocardiography
MH  - Female
MH  - Heart Failure/*diagnostic imaging/physiopathology
MH  - Heart-Assist Devices/*adverse effects
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - *Models, Cardiovascular
MH  - Multivariate Analysis
MH  - Probability
MH  - Prognosis
MH  - ROC Curve
MH  - Stroke Volume
PMC - PMC5636125
COIS- Competing Interests: The authors have declared that no competing interests exist.
EDAT- 2017/10/12 06:00
MHDA- 2017/10/21 06:00
PMCR- 2017/10/11
CRDT- 2017/10/12 06:00
PHST- 2017/06/07 00:00 [received]
PHST- 2017/09/26 00:00 [accepted]
PHST- 2017/10/12 06:00 [entrez]
PHST- 2017/10/12 06:00 [pubmed]
PHST- 2017/10/21 06:00 [medline]
PHST- 2017/10/11 00:00 [pmc-release]
AID - PONE-D-17-21818 [pii]
AID - 10.1371/journal.pone.0186152 [doi]
PST - epublish
SO  - PLoS One. 2017 Oct 11;12(10):e0186152. doi: 10.1371/journal.pone.0186152. 
      eCollection 2017.