Next Article in Journal
Self-Assessment of Competence and Referral Behavior for Musculoskeletal Injections among Dutch General Practitioners
Next Article in Special Issue
Role of Cardiac Computed Tomography for Etiology Evaluation of Newly Diagnosed Heart Failure with Reduced Ejection Fraction
Previous Article in Journal
Improving the Prognostic Performance of SUVmax in 18F-Fluorodeoxyglucose Positron-Emission Tomography/Computed Tomography Using Tumor-to-Liver and Tumor-to-Blood Standard Uptake Ratio for Locally Advanced Cervical Cancer Treated with Concurrent Chemoradiotherapy
Previous Article in Special Issue
Impact of Acute Hemoglobin Falls in Heart Failure Patients: A Population Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 9
Issue 6
10.3390/jcm9061879
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Short-Period Temporal Dispersion Repolarization Markers Predict 30-Days Mortality in Decompensated Heart Failure

by
Gianfranco Piccirillo
1,
Federica Moscucci
1,*,
Gaetano Bertani
1,
Ilaria Lospinuso
1,
Fabiola Mastropietri
1,
Marcella Fabietti
1,
Teresa Sabatino
1,
Giulia Zaccagnini
1,
Davide Crapanzano
1,
Ilaria Di Diego
1,
Andrea Corrao
1,
Pietro Rossi
2 and
Damiano Magrì
3
1
Anestesiologiche e Cardiovascolari, Dipartimento di Scienze Cliniche Internistiche, Policlinico Umberto I, La Sapienza University of Rome, 00185 Rome, Italy
2
Cardiology Division, Arrhythmology Unit, S. Giovanni Calibita, Isola Tiberina, 00186 Rome, Italy
3
Dipartimento di Medicina Clinica e Molecolare, S. Andrea Hospital, Sapienza University of Rome, 00185 Rome, Italy
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(6), 1879; https://doi.org/10.3390/jcm9061879
Submission received: 29 May 2020 / Accepted: 11 June 2020 / Published: 16 June 2020
(This article belongs to the Special Issue The Management of Chronic Heart Failure and Hypertension)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background and Objectives: Electrocardiographic (ECG) markers of the temporal dispersion of the myocardial repolarization phase have been shown able to identify chronic heart failure (CHF) patients at high mortality risk. The present prospective single-center study sought to investigate in a well-characterized cohort of decompensated heart failure (HF) patients the ability of short-term myocardial temporal dispersion ECG variables in predicting the 30-day mortality, as well as their relationship with N-terminal Pro Brain Natriuretic Peptide (NT-proBNP) plasmatic values. Method: One hundred and thirteen subjects (male: 59, 67.8%) with decompensated CHF underwent 5 min of ECG recording, via a mobile phone. We obtained QT end (QTe), QT peak (QTp) and T peak to T end (Te) and calculated the mean, standard deviation (SD), and normalized index (VN). Results: Death occurred for 27 subjects (24%) within 30 days after admission. Most of the repolarization indexes (QTe mean (p < 0.05), QTeSD (p < 0.01), QTpSD (p < 0.05), mean Te (p < 0.05), TeSD (p < 0.001) QTeVN (p < 0.05) and TeVN (p < 0.01)) were significantly higher in those CHF patients with the highest NT-proBNP (>75th percentile). In all the ECG data, only TeSD was significantly and positively related to the NT-proBNP levels (r: 0.471; p < 0.001). In the receiver operating characteristic (ROC) analysis, the highest accuracy for 30-day mortality was found for QTeSD (area under curve, AUC: 0.705, p < 0.01) and mean Te (AUC: 0.680, p < 0.01), whereas for the NT-proBNP values higher than the 75th percentile, the highest accuracy was found for TeSD (AUC: 0.736, p < 0.001) and QTeSD (AUC: 0.696, p < 0.01). Conclusion: Both mean Te and TeSD could be considered as reliable markers of worsening HF and of 30-day mortality. Although larger and possibly interventional studies are needed to confirm our preliminary finding, these non-invasive and transmissible ECG parameters could be helpful in the remote monitoring of advanced HF patients and, possibly, in their clinical management. (ClinicalTrials.gov number, NCT04127162).

1. Introduction

The markers of the temporal dispersion of the myocardial repolarization phase have been shown to able to identify those heart failure (HF) patients at high risk, either in terms of all-cause mortality [1,2] or sudden cardiac death due to malignant ventricular arrhythmias [2,3,4]. Indeed, it is widely accepted that myocardial repolarization might suffer from a number of possible conditions due to the complex interplay between ionic membrane channels, membranes’ transporter mechanisms and many cardiac and extra-cardiac multi-organ regulatory systems.
Of note, besides the direct myocardial structural damages (i.e., ischemia, necrosis, fibrosis, hypertrophy, disarray, etc.) [5,6], drugs [7,8,9], aging [9,10,11,12], mental stress [13,14,15] and neurohumoral activation [4,10,12,16,17,18,19,20,21], as well as the immune–inflammatory response [22,23] are all factors able to worsen the myocardial repolarization phase. Thus, due to the abovementioned high susceptibility to different possible stressors, there is growing interest in the analysis of myocardial repolarization phase dispersion as a possible marker of global cardiovascular risk rather than a mere non-invasive electrophysiological marker of electrical instability. Particularly, changes in the electrocardiographic (ECG)-derived variables associated with the myocardial repolarization phase might be extremely useful as immediate markers of complex structural and molecular mechanisms leading to clinical decompensation, as well as possibly lethal arrhythmic complications [24]. These non-invasive and relatively easy to obtain ECG-derived data, in a setting of increasingly ageing population with many comorbidities, could represent a useful tool in the management of chronic patients in the contexts of non-intensive care, institutionalization or even at home. In such a context, the short-term evaluation of the interval between the T peak and T end (Te) segment represents one of the most promising approaches in myocardial repolarization dispersion analysis [25].
One of the clinical settings which could benefit from this approach is undoubtedly represented by patients with chronic heart failure (CHF) syndrome, which is the HF decompensation, a frequent event characterized by a high risk of mortality and morbidity. Accordingly, a prompt detection of a worsening myocardial repolarization dispersion might enable us to more strictly manage the patients by modifying their therapeutic regimen or by reducing the intervals between their medical examinations.
Therefore, the present prospective single-center study sought to investigate in a well-characterized cohort of decompensated HF patients the ability of a number of short-term myocardial temporal dispersion ECG variables and, particularly of those derived from the analysis of the Te interval, of predicting the 30-day cardiovascular and total mortality. Furthermore, due to the well-known role of the plasmatic levels of N-terminal Pro Brain Natriuretic Peptide (NT-proBNP) in identifying those HF patients at high risk, we also evaluated the ability of the temporal dispersion of myocardial repolarization to identify those decompensated HF patients with the highest NT-proBNP values defined according to an arbitrary cutoff value corresponding to the 75th percentile.

2. Methods

2.1. Patients and Protocol

We enrolled a total 117 consecutive HF patients admitted to our Geriatric Medicine Department from January 2019 to January 2020 due to a decompensated CHF condition, the latter defined according to the 2016 European Society of Cardiology HF guidelines [26]. All patients had stable previous clinical conditions at home with NYHA class II-III and all of them were in the fourth NYHA functional class at the time of enrollment.
At the study run-in, each patient underwent a full clinical history, physical examination, standard ECG and standard transthoracic echocardiogram. Furthermore, a 5-min single lead (II lead) ECG recording (MiocardioEventTM, Rome, Italy) and a blood sample for NT-proBNP dosage (Alere Triage Analyzer, Alere, San Diego, CA, USA) and other serum variables were obtained. The Cockcroft–Gault formula was used to assess the creatinine clearance.
All patients provided informed consent for the use of their records for research purposes and the study was in accordance with good clinical practice and the principles of the Declaration of Helsinki for clinical research involving human patients. The study underwent Ethical Committee of Policlinico Umberto I approbation. The ClinicalTrials.gov number is NCT04127162.

2.2. Offline Data Analysis

The 5-min single lead (D II) ECG signals (Miocardio Event™, Rome, Italy) were acquired, digitized at a sampling frequency of 500 Hz and wirelessly transmitted to a cloud platform for data storage via a mobile phone. Subsequently, all digitized signal recordings were downloaded by the cloud platform and analyzed by a single physician (G.P.) blinded to the subjects’ circumstances. Thereafter, the following ECG intervals from the respective time series were obtained: RR, QT end (QTe), QT peak (QTp) and T peak to T end (Te). Briefly, the QTe was obtained by measuring the interval from the onset of the Q-wave to the T-wave end; the QTp was obtained by measuring the interval from the onset of the Q-wave to the T-wave peak; the Te was obtained from the T-wave peak to the T-wave end. To identify the abovementioned ECG segments, software originally proposed by Berger [27] and validated in other subsequent studies was used [2,9,10,11,12,13,16,17,18,19,28,29,30,31]. We therefore calculated the mean, variance and standard deviation (SD) values for each of these repolarization phase intervals and, finally, we calculated the variance normalized for the mean (VN), according to the following Formulas (1)–(3) [2,4,9,10,11,12,13,16,17,18,19,28,29,30,31]:
QTeVN = QTe variance/(QTemean)2
QTpVN = QTp variance/(QTp mean)2
TeVI = Te variance/(Te mean)2
Software for data analysis was designed and produced by our research group with the LabView program (National Instruments, Austin, TX, USA).

2.3. Statistical Analysis

All variables with a normal distribution were expressed as mean ± standard deviation, except for non-normally distributed variables, such as as median and inter-quartile range (i.r.). Two distinct subgroups (Group 1 and 2) were identified according to an arbitrarily defined value of NT-proBNP corresponding to the 75th percentile value obtained in the overall study sample (i.e., <75th and ≥75th percentile).
A one-way analysis of variance (ANOVA)and Bonferroni test were used to compare data for the normally distributed variables; Kruskal–Wallis and Mann–Whitney tests were used to compare non-normally distributed variables (as evaluated by a Kolmogorov–Smirnov test). Univariable Cox proportional-hazards regression analysis was used to test the association between continuous and dichotomized variables with 30-day mortality. The temporal myocardial repolarization dispersion variables with the highest χ2 were used for Kaplan–Meier curves [2,32].
Receiver operating characteristic (ROC) curves were used to determine the sensitivity and specificity of the studied parameters predictive of mortality and areas under curves (AUCs) and 95% confidence intervals (CIs) were calculated to compare the diagnostic accuracy.
All data were evaluated by use of database SPSS-PC+ (SPSS-PC+ Inc, Chicago, IL, USA). A p-value less than or equal to 0.05 were considered statistically significant.

3. Results

Starting from 117 eligible patients, four patients were excluded because of poor quality ECGs. Accordingly, a total of 113 decompensated HF patients were analyzed and prospectively followed. The 75th percentile value of NT-proBNP in the overall population was equal to 6.660 pg/mL and, accordingly, two distinct subgroups were defined (Group 1 and Group 2).
Table 1 shows the detailed comparison between the two study groups. Group 1 consisted of 85 patients, while Group 2 consisted of 28 subjects (Table 1). Except for the left ventricular ejection fraction (LVEF) and, obviously, the NT-proBNP values, the two groups showed similar clinical characteristics. At the study run-in, all patients were in NYHA functional class IV.
As reported in detail in Table 2, most of the ECG-derived data dealing with the temporal dispersion of the repolarization data were significantly lower in Group 1 than in the counterpart. Of note, with respect to specific patients’ subsets, those with atrial fibrillation showed higher values of QTpSD (p < 0.01), TeSD (p < 0.05), QTpVN (p < 0.05) and TeVN (p < 0.05), but no differences with respect to QTe, QTp and Te means. Conversely, in the 26 patients with a paced rhythm, a significantly longer QTe (479 ± 58 vs. 424 ± 58 ms, p < 0.001), QTp (366 ± 76 vs. 322 ± 55 ms, p < 0.01) and Te (112 ± 24 vs. 102 ± 22 ms, p < 0.05) were observed.
A positive relationship was found between NT-proBNP and TeSD, the latter expressed on a logarithmic scale (r: 0.471; p < 0.001) (Figure 1).
A total of 27 (24%) patients died within 30 days: 18 patients died from respiratory failure, six from terminal heart failure, two from fatal acute myocardial infarction and one patient from arrhythmic sudden cardiac death (sustained ventricular tachycardia and ventricular fibrillation). Thus, we classified 27 patients as total mortality and nine as cardiovascular mortality. Specifically, 17 patients among Group 1 died (20%) and ten (36%) in Group 2.
Comparing surviving and deceased patients (Figure 2A,B), mean Te (p < 0.001), QTeSD (p < 0.05) and Te SD (p < 0,05) were shown to be highly predictive forpoor prognosis.
We also obtained ROC curves for the NT-proBNP values higher than the 75th percentile and the following were the ECG variables with the best accuracy: TeSD (AUC: 0.736, p < 0.001), QTeSD (AUC: 0.696, p < 0.01), TeVN (AUC: 0.674, p < 0.01), QTpSD (AUC: 0.655, p < 0.05), QTeVN (AUC: 0.650, p < 0.05) and mean QTe (AUC: 0.639, p < 0.05) (Figure 3B).
The survival curve constructed for the 75th percentile value of mean Te exhibited a good significance for total mortality (Figure 4A) and cardiovascular mortality (Figure 4B).

4. Discussion

The major finding of the present study was the significant relationship between some short-period ECG markers of the repolarization phase with worsening HF, as well as 30-day mortality. Particularly, the TESD was linearly related to the NTproBNP plasma level and the simple mean Te values was an independent predictor of both all-cause and cardiovascular mortality in our study sample.
Our data showed a 30-day mortality of 24%, higher than that reported in another similar study, where a mortality risk ranging from 2.1 to 21.9% was found [32]. However, it should be noted that the abovementioned study evaluated patients significantly younger (<75 years old) than those we enrolled (i.e., mean age of 83 years old).
During CHF, ventricular repolarization is deeply involved, and the more advanced this clinical syndrome is, the longer the duration and the more the temporal homogeneity of repolarization is affected [4,16,25]. The pathological basis of these changes includes the morphological remodeling of the histological substrate (myocyte hypertrophy, disarray, fibrosis, etc.), especially ion channel remodeling. In fact, in the last twenty years, many authors highlighted potassium (IKto, IKs, IKr and IK1), sodium channels (INa) and calcium handling alterations. In particular, CHF was associated with the downregulation of IKto, IKs, IKr and IK1; on the contrary, INa inactivation occurs later in comparison with normal subjects and finally, the release and storage of calcium in the sarcoplasmic reticulum were found to be abnormal [33]. The results of these alterations, as already mentioned above, is an increase in the duration and the temporal inhomogeneity of repolarization.
Although Te was studied as non-invasive marker of sudden cardiac risk, a recent meta-analysis [34] of more than 150,000 subjects evidenced that it might act, not only as a marker of ventricular arrhythmic or sudden cardiac death risk, but also as a risk marker of total and cardiac death. Our data are consistent with this meta-analysis, the most accurate mean Te cut-off value for mortality risk in our study was higher than 13 ms, in comparison with the one found in the meta-analysis. However, in our opinion, the novel and interesting datum was that a dynamic parameter, TeSD, was associated with a well-known biomarker of HF severity, NT-proBNP, thus enabling us to speculate that this dynamic ECG repolarization marker could be used for the remote monitoring of CHF patients. In fact, this non-invasive, repeatable, inexpensive and transmissible ECG parameter could be helpful in monitoring those HF patients at high risk of worsening or mortality [35].
Up to now, the pathophysiologic basis of the relation between NT-proBNP and TeSD remains to be clarified in decompensated CHF, but it is likely that both markers suffer from neuro-humoral activation [36] in terms of sympathetic and renin–angiotensin–aldosterone system overstimulation [16,37]. Accordingly, it was well known that catecholamines are able to decrease the current density of IKto, IK1, IKs and L-type Ca2+ channels and to upregulate T-type Ca2+ channels [38,39] and, contextually, it has been reported that angiotesin II decreases the current density of IK1, IKto and IKur [40,41]. Aldosterone also induces an upregulation of L-type Ca2+ and Na+ channels [42].
Finally, we would remark that, although most of the decompensated HF patients were not in sinus rhythm (atrial fibrillation, frequent premature ventricular or supraventricular contractions, electrostimulated subjects, etc.), the non-invasive ECG markers we found predictive in our study, namely Te and TeSD, do not need a sinus rhythm condition to be calculated. Mean Te and TeSD provided reliable data in this subject category.

5. Conclusions

In conclusion, mean Te and TeSD could be considered as reliable markers of worsening HF and of 30-day mortality. Although the small number of patients enrolled and the observational single-center nature of the study, our data might be useful from a clinical viewpoint. Indeed, having a non-invasive marker, predictive of clinical worsening and mortality in a complex syndrome such as the advanced HF, could guarantee prompt intervention by the clinician and immediate and targeted therapeutic choices. The small number of patients did not allow a multivariate Cox analysis. Obviously, larger and possibly interventional studies are needed to confirm our preliminary finding.

Author Contributions

G.P.: conceptualization, data curation, formal analysis, project administration, writing original draft., F.M.: methodology, data curation, writing original draft, validation, G.B.: data collection, validation, I.L.: data collection, validation, F.M.: data collection, final validation, M.F.: data collection, validation, T.S.: data collection, validation, G.Z.: data collection, validation, D.C.: data collection, validation, I.D.D.: data collection, validation, A.C.: data collection, validation, P.R.: conceptualization, project administration, software, resources, validation and D.M.: methodology, validation, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The Authors deny personal or financial conflicts of interest regarding this paper.

References

  1. Tereshchenko, L.G.; Cygankiewicz, I.; McNitt, S.; Bayes-Genis, A.; Han, L.; Sur, S.; Couderc, J.P.; Berger, R.D.; de Luna, A.B.; Zareba, W. Predictive value of beat-to-beat QT variability index across the continuum of left ventricular dysfunction: Competing risks of noncardiac or cardiovascular death and sudden or nonsudden cardiac death. Circ. Arrhythmia Electrophysiol. 2012, 5, 719–727. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Piccirillo, G.; Magrì, D.; Matera, S.; Magnanti, M.; Torrini, A.; Pasquazzi, E.; Schifano, E.; Velitti, S.; Marigliano, V.; Quaglione, R.; et al. QT variability strongly predicts sudden cardiac death in asymptomatic subjects with mild or moderate left ventricular systolic dysfunction: A prospective study. Eur. Heart J. 2007, 28, 1344–1350. [Google Scholar] [CrossRef] [PubMed]
  3. Haigney, M.C.; Zareba, W.; Gentlesk, P.J.; Goldstein, R.E.; Illovsky, M.; McNitt, S.; Andrews, M.L.; Moss, A.J.; Multicenter Automatic Defibrillator Implantation Trial II investigators. QT interval variability and spontaneous ventricular tachycardia or fibrillation in the Multicenter Automatic Defibrillator Implantation Trial (MADIT) II patients. J. Am. Coll. Cardiol. 2004, 44, 1481–1487. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Baumert, M.; Porta, A.; Vos, M.A.; Malik, M.; Couderc, J.P.; Laguna, P.; Piccirillo, G.; Smith, G.L.; Tereshchenko, L.G.; Volders, P.G. QT interval variability in body surface ECG: Measurement, physiological basis, and clinical value: Position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESC Working Group on Cardiac Cellular Electrophysiology. Europace 2016, 18, 925–944. [Google Scholar]
  5. Tyack, P.L.; Calambokidis, J.; Friedlaender, A.; Goldbogen, J.; Southall, B. First Long-Term Behavioral Records from Cuvier’s Beaked Whales (Ziphiuscavirostris) Reveal Record-Breaking Dives. PLoS ONE 2014, 9, e92633. [Google Scholar]
  6. Disertori, M.; Rigoni, M.; Pace, N.; Casolo, G.; Masè, M.; Gonzini, L.; Lucci, D.; Nollo, G.; Ravelli, F. Myocardial Fibrosis Assessment by LGE Is a Powerful Predictor of Ventricular Tachyarrhythmias in Ischemic and Nonischemic LV Dysfunction: A Meta-Analysis. JACC Cardiovasc. Imaging 2016, 9, 1046–1055. [Google Scholar] [CrossRef]
  7. Antzelevitch, C. Drug-induced spatial dispersion of repolarization. Cardiol. J. 2008, 15, 100–121. [Google Scholar]
  8. Li, M.; Ramos, L.G. Drug-Induced QT Prolongation and Torsades de Pointes. Pharm. Ther. 2017, 42, 473–477. [Google Scholar]
  9. Piccirillo, G.; Moscucci, F.; Fabietti, M.; Di Iorio, C.; Mastropietri, F.; Sabatino, T.; Crapanzano, D.; Bertani, G.; Zaccagnini, G.; Lospinuso, I.; et al. Age, gender and drug therapy influences on Tpeak-tendinterval and on electrical risk score. J. Electrocardiol. 2020, 59, 88–92. [Google Scholar] [CrossRef]
  10. Piccirillo, G.; Magnanti, M.; Matera, S.; Di Carlo, S.; De Laurentis, T.; Torrini, A.; Marchitto, N.; Ricci, R.; Magrí, D. Age and QT variability index during free breathing, controlled breathing and tilt in patients with chronic heart failure and healthy control subjects. Transl. Res. 2006, 148, 72–78. [Google Scholar] [CrossRef]
  11. Piccirillo, G.; Cacciafesta, M.; Lionetti, M.; Nocco, M.; Di Giuseppe, V.; Moisè, A.; Naso, C.; Marigliano, V. Influence of age, the autonomic nervous system and anxiety on QT-interval variability. Clin. Sci. (Lond.) 2001, 101, 429–438. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Piccirillo, G.; Moscucci, F.; Pascucci, M.; Pappadà, M.A.; D’Alessandro, G.; Rossi, P.; Quaglione, R.; Di Barba, D.; Barillà, F.; Magrì, D. Influence of aging and chronic heart failure on temporal dispersion of myocardial repolarization. Clin. Interv. Aging 2013, 8, 293–300. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Piccirillo, G.; Moscucci, F.; Fabietti, M.; Parrotta, I.; Mastropietri, F.; Di Iorio, C.; Sabatino, T.; Crapanzano, D.; Vespignani, G.; Mariani, M.V.; et al. Arrhythmic Risk in Elderly Patients Candidates to Transcatheter Aortic Valve Replacement: Predictive Role of Repolarization Temporal Dispersion. Front. Physiol. 2019, 10, 991. [Google Scholar] [CrossRef] [Green Version]
  14. Child, N.; Hanson, B.; Bishop, M.; Rinaldi, C.A.; Bostock, J.; Western, D.; Cooklin, M.; O’Neil, M.; Wright, M.; Razavi, R.; et al. Effect of mental challenge induced by movie clips on action potential duration in normal human subjects independent of heart rate. Circ. Arrhythmia Electrophysiol. 2014, 7, 518–523. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Taggart, P.; Critchley, H.; van Duijvendoden, S.; Lambiase, P.D. Significance of neuro-cardiac control mechanisms governed by higher regions of the brain. Auton. Neurosci. 2016, 199, 54–65. [Google Scholar] [CrossRef] [PubMed]
  16. Piccirillo, G.; Magrì, D.; Ogawa, M.; Song, J.; Chong, V.J.; Han, S.; Joung, B.; Choi, E.K.; Hwang, S.; Chen, L.S.; et al. Autonomic nervous system activity measured directly and QT interval variability in normal and pacing-induced tachycardia heart failure dogs. J. Am. Coll. Cardiol. 2009, 54, 840–850. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Piccirillo, G.; Rossi, P.; Mitra, M.; Quaglione, R.; Dell’Armi, A.; Di Barba, D.; Maisto, D.; Lizio, A.; Barillà, F.; Magrì, D. Indexes of temporal myocardial repolarization dispersion and sudden cardiac death in heart failure: Any difference? Ann. Noninvasive Electrocardiol. 2013, 18, 130–139. [Google Scholar] [CrossRef]
  18. Piccirillo, G.; Magrì, D.; Pappadà, M.A.; Maruotti, A.; Ogawa, M.; Han, S.; Joung, B.; Rossi, P.; Nguyen, B.L.; Lin, S.F.; et al. Autonomic nerve activity and the short-term variability of the Tpeak-Tend interval in dogs with pacing-induced heart failure. Heart Rhythm 2012, 9, 2044–2050. [Google Scholar] [CrossRef]
  19. Piccirillo, G.; Moscucci, F.; D’Alessandro, G.; Pascucci, M.; Rossi, P.; Han, S.; Chen, L.S.; Lin, S.F.; Chen, P.S.; Magrì, D. Myocardial repolarization dispersion and autonomic nerve activity in a canine experimental acute myocardial infarction model. Heart Rhythm 2014, 11, 110–118. [Google Scholar] [CrossRef] [Green Version]
  20. Yehya, Y.M.; Hussein, A.M.; Ezam, K.; Eid, E.A.; Ibrahim, E.M.; Sarhan, M.A.F.E.; Elsayed, A.; Sarhan, M.E. Blockade of Renin Angiotensin System Ameliorates the Cardiac Arrhythmias and Sympathetic Neural Remodeling in Hearts of Type 2 DM Rat Model. Endocr. Metab. Immune Disord. Drug Targets 2020, 20, 464–478. [Google Scholar] [CrossRef]
  21. Tamargo, J.; Caballero, R.; Gómez, R.; Delpón, E. Cardiac electrophysiological effects of nitric oxide. Cardiovasc. Res. 2010, 87, 593–600. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Sordillo, P.P.; Sordillo, D.C.; Helson, L. Review: The Prolonged QT Interval: Role of Pro-inflammatory Cytokines, Reactive Oxygen Species and the Ceramide and Sphingosine-1 Phosphate Pathways. In Vivo 2015, 29, 619–636. [Google Scholar] [PubMed]
  23. Lazzerini, P.E.; Capecchi, P.L.; Laghi-Pasini, F. Long QT Syndrome: An Emerging Role for Inflammation and Immunity. Front. Cardiovasc. Med. 2015, 2, 26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Ramírez, J.; Orini, M.; Mincholé, A.; Monasterio, V.; Cygankiewicz, I.; Bayés de Luna, A.; Martínez, J.P.; Laguna, P.; Pueyo, E. 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, 12, e0186152. [Google Scholar] [CrossRef]
  25. Elitok, A.; Emet, S.; Karaayvaz, E.B.; Erdogan, O.; Aydogan, M.; Engin, B.; Cevik, E.; Orta, H.; Okumus, G.; Bilge, A.K. The relationship between t-wave peak-to-end interval and hemodynamic parameters in patients with pulmonary arterial hypertension. Ann. Noninvasive Electrocardiol. 2020, 18, e12764. [Google Scholar] [CrossRef] [Green Version]
  26. Ponikowski, P.; Voors, A.A.; Anker, S.D.; Bueno, H.; Cleland, J.G.; Coats, A.J.; Falk, V.; González-Juanatey, J.R.; Harjola, V.P.; Jankowska, E.A.; et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur. J. Heart Fail. 2016, 18, 891–975. [Google Scholar] [PubMed]
  27. Berger, R.D.; Kasper, E.K.; Baughman, K.L.; Marban, E.; Calkins, H.; Tomaselli, G.F. Beat-to-beat QT interval variability: Novel evidence for repolarization lability in ischemic and nonischemic dilated cardiomyopathy. Circulation 1997, 96, 1557–1565. [Google Scholar] [CrossRef] [PubMed]
  28. Piccirillo, G.; Moscucci, F.; Persi, A.; Di Barba, D.; Pappadà, M.A.; Rossi, P.; Quaglione, R.; Nguyen, B.L.; Barillà, F.; Casenghi, M.; et al. Intra-QT spectral coherence as a possible noninvasive marker of sustained ventricular tachycardia. Biomed. Res. Int. 2014, 2014, 583035. [Google Scholar] [CrossRef] [PubMed]
  29. Piccirillo, G.; Ottaviani, C.; Fiorucci, C.; Petrocchi, N.; Moscucci, F.; Di Iorio, C.; Mastropietri, F.; Parrotta, I.; Pascucci, M.; Magrì, D. Transcranial direct current stimulation improves the QT variability index and autonomic cardiac control in healthy subjects older than 60 years. Clin. Interv. Aging 2016, 11, 1687–1695. [Google Scholar] [CrossRef] [Green Version]
  30. Piccirillo, G.; Moscucci, F.; Pofi, R.; D’Alessandro, G.; Minnetti, M.; Isidori, A.M.; Francomano, D.; Lenzi, A.; Puddu, P.E.; Alexandre, J.; et al. Changes in left ventricular repolarization after short-term testosterone replacement therapy in hypogonadal males. J. Endocrinol. Investig. 2019, 42, 1051–1065. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. La Rovere, M.T.; Pinna, G.D.; Maestri, R.; Mortara, A.; Capomolla, S.; Febo, O.; Ferrari, R.; Franchini, M.; Gnemmi, M.; Opasich, C.; et al. Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients. Circulation 2003, 107, 565–570. [Google Scholar] [CrossRef] [PubMed]
  32. Adams, K.F., Jr.; Uddin, N.; Patterson, J.H. Clinical predictors of in-hospital mortality in acutely decompensated heart failure-piecing together the outcome puzzle. Congest. Heart Fail. 2008, 14, 127–134. [Google Scholar] [CrossRef] [PubMed]
  33. Rahm, A.K.; Lugenbiel, P.; Schweizer, P.A.; Katus, H.A.; Thomas, D. Role of ion channels in heart failure and channelopathies. Biophys. Rev. 2018, 10, 1097–1106. [Google Scholar] [CrossRef] [PubMed]
  34. Tse, G.; Gong, M.; Wong, W.T.; Georgopoulos, S.; Letsas, K.P.; Vassiliou, V.S.; Chan, Y.S.; Yan, B.P.; Wong, S.H.; Wu, W.K.K.; et al. The Tpeak-Tend interval as an electrocardiographic risk marker of arrhythmic and mortality outcomes: A systematic review and meta-analysis. Heart Rhythm 2017, 14, 1131–1137. [Google Scholar] [CrossRef] [Green Version]
  35. Gronda, E.; Vanoli, E.; Sacchi, S.; Grassi, G.; Ambrosio, G.; Napoli, C. Risk of heart failure progression in patients with reduced ejection fraction: Mechanisms and therapeutic options. Heart Fail. Rev. 2020, 25, 295–303. [Google Scholar] [CrossRef]
  36. Kurokawa, J.; Abriel, H. Neurohormonal regulation of cardiac ion channels in chronic heart failure. J. Cardiovasc. Pharmacol. 2009, 54, 98–105. [Google Scholar] [CrossRef]
  37. Chan, T.O.; Zhang, X.Q.; Gao, E.; Song, J.; Koch, W.J.; Feldman, A.M.; Cheung, J.Y. Induced overexpression of Na+/Ca2+ exchanger transgene: Altered myocyte contractility, [Ca2+]i transients, SR Ca2+ contents, and action potential duration. Am. J. Physiol. Heart Circ. Physiol. 2009, 297, 590–601. [Google Scholar]
  38. Rossow, C.F.; Dilly, K.W.; Yuan, C.; Nieves-Cintrón, M.; Cabarrus, J.L.; Santana, L.F. NFATc3-dependent loss of I(to) gradient across the left ventricular wall during chronic beta adrenergic stimulation. J. Mol. Cell. Cardiol. 2009, 46, 249–256. [Google Scholar] [CrossRef] [Green Version]
  39. Domenighetti, A.A.; Boixel, C.; Cefai, D.; Abriel, H.; Pedrazzini, T. Chronic angiotensin II stimulation in the heart produces an acquired long QT syndrome associated with IK1 potassium current downregulation. J. Mol. Cell. Cardiol. 2007, 42, 63–70. [Google Scholar] [CrossRef]
  40. Rivard, K.; Paradis, P.; Nemer, M.; Fiset, C. Cardiac-specific overexpression of the human type 1 angiotensin II receptor causes delayed repolarization. Cardiovasc. Res. 2008, 78, 53–62. [Google Scholar] [CrossRef] [Green Version]
  41. Rougier, J.S.; Muller, O.; Berger, S.; Centeno, G.; Schütz, G.; Firsov, D.; Abriel, H. Mineralocorticoid receptor is essential for corticosteroid-induced up-regulation of L-type calcium currents in cultured neonatal cardiomyocytes. Pflugers Arch. 2008, 456, 407–412. [Google Scholar] [CrossRef] [PubMed]
  42. Boixel, C.; Gavillet, B.; Rougier, J.S.; Abriel, H. Aldosterone increases voltage-gated sodium current in ventricular myocytes. Am. J. Physiol. Heart Circ. Physiol. 2006, 290, 2257–2266. [Google Scholar] [CrossRef] [PubMed]
Jcm 09 01879 g001 550
Figure 1. Linear regression between log TeSD and NT-proBNP. NT-proBNP: N-terminal Pro Brain Natriuretic Peptide; SD: standard deviation.
Figure 1. Linear regression between log TeSD and NT-proBNP. NT-proBNP: N-terminal Pro Brain Natriuretic Peptide; SD: standard deviation.
Jcm 09 01879 g001
Jcm 09 01879 g002 550
Figure 2. (A) QTe, QTp and Te means in the cardiovascular and non-cardiovascular deceased subjects. In the box plots, the central line represents the median distribution. Each box spans from the 25th to 75th percentile points, and error bars extend from the 10th to 90th percentile points. (B) QTe, QTp and Te standard deviations (SD) in the cardiovascular and non-cardiovascular deceased subjects. In the box plots, the central line represents the median distribution. Each box spans from the 25th to 75th percentile points, and error bars extend from the 10th to 90th percentile points.In the ROC analysis, the highest accuracy for 30-day mortality was found for the following variables: QTeSD (AUC: 0.705, p < 0.01), mean Te (AUC: 0.680, p < 0.01), QTeVN (AUC: 0.686, p < 0.01) and TeSD (AUC: 0.648, p < 0.05) (Figure 3A). ROC, receiver operating characteristic; AUC, area under curve; NS, not significant. QTe, QTp, Te, QTeVN (explained in the text).
Figure 2. (A) QTe, QTp and Te means in the cardiovascular and non-cardiovascular deceased subjects. In the box plots, the central line represents the median distribution. Each box spans from the 25th to 75th percentile points, and error bars extend from the 10th to 90th percentile points. (B) QTe, QTp and Te standard deviations (SD) in the cardiovascular and non-cardiovascular deceased subjects. In the box plots, the central line represents the median distribution. Each box spans from the 25th to 75th percentile points, and error bars extend from the 10th to 90th percentile points.In the ROC analysis, the highest accuracy for 30-day mortality was found for the following variables: QTeSD (AUC: 0.705, p < 0.01), mean Te (AUC: 0.680, p < 0.01), QTeVN (AUC: 0.686, p < 0.01) and TeSD (AUC: 0.648, p < 0.05) (Figure 3A). ROC, receiver operating characteristic; AUC, area under curve; NS, not significant. QTe, QTp, Te, QTeVN (explained in the text).
Jcm 09 01879 g002
Jcm 09 01879 g003 550
Figure 3. (A,B) ROC curve of statistically significant examined variables. Sensitivity–specificity of different variables for mortality (A) and higher NT-proBNP levels (≥75th percentile) (B). Univariable Cox regression analysis reported a significant relationship between 30-day mortality and QTeSD (hazard ratio: 1.10, 95% confidence limit: 1.03–1.18, p < 0.05), mean Te (hazard ratio 1.02, 95% confidence limit: 1.01–1.04, p < 0.001) and TeSD (hazard ratio 1.12, 95% confidence limit: 1.01–1.23, p < 0.001) (Table 3). All number value in the table of the figure should be intended as follow: .526 means 0.526.
Figure 3. (A,B) ROC curve of statistically significant examined variables. Sensitivity–specificity of different variables for mortality (A) and higher NT-proBNP levels (≥75th percentile) (B). Univariable Cox regression analysis reported a significant relationship between 30-day mortality and QTeSD (hazard ratio: 1.10, 95% confidence limit: 1.03–1.18, p < 0.05), mean Te (hazard ratio 1.02, 95% confidence limit: 1.01–1.04, p < 0.001) and TeSD (hazard ratio 1.12, 95% confidence limit: 1.01–1.23, p < 0.001) (Table 3). All number value in the table of the figure should be intended as follow: .526 means 0.526.
Jcm 09 01879 g003
Jcm 09 01879 g004 550
Figure 4. (A) Kaplan–Meier survival curve for total mortality by subdividing the study subjects with a cutoff at the 75th percentile of mean Te. (B) Kaplan–Meier survival curve for cardiovascular mortality by subdividing the study subjects with a cutoff at the 75th percentile of mean Te.
Figure 4. (A) Kaplan–Meier survival curve for total mortality by subdividing the study subjects with a cutoff at the 75th percentile of mean Te. (B) Kaplan–Meier survival curve for cardiovascular mortality by subdividing the study subjects with a cutoff at the 75th percentile of mean Te.
Jcm 09 01879 g004
Table
Table 1. General characteristics of the study subjects.
Table 1. General characteristics of the study subjects.
<75th≥75th
of NT-proBNP
Group 1Group 2
n: 85n: 28p
Age, years82 ± 1085 ± 11ns
Male/Female, n44/4115/13ns
BMI, kg/m226 ± 526 ± 5ns
SBP, mm Hg127 ± 21124 ± 20ns
DBP, mm Hg70 ± 1165 ± 11ns
Heart Rate, b/m74 ± 1476 ± 14ns
Left Ventricular Ejection Fraction, %46 ± 935 ± 9<0.001
NT-proBNP, pg/mL1530 (2225)13,100 (11,780)<0.001
Serum Potassium, mmol/L4.1 ± 0.54.3 ± 0.6ns
Serum Calcium, mmol/L2.2 ± 0.22.1 ± 0.4ns
Creatinine Clearance, mL/m49 ± 2738 ± 30ns
CHF with Depressed Systolic Function, n (%)44 (52)24 (86)ns
CHF with Preserved Systolic Function, n (%)59 (69)13 (46)ns
Hypertension, n (%)64 (75)19 (68)ns
Hypercholesterolemia, n (%)33 (39)17 (61)ns
Diabetes, n (%)31 (36)13 (46)ns
Known Myocardial Ischemia History, n (%)24 (28)10 (36)ns
Valve Diseases12 (14)4 (14)ns
Premature Supraventricular Complexes, n (%)12 (14)5 (18)ns
Premature Ventricular Complexes, n (%)22 (26)6 (21)ns
Permanent Atrial fibrillation, n (%)29 (34)9 (32)ns
Left Bundle Branch Block, n (%)11 (13)7 (25)ns
Right Bundle Branch Block, n (%)15 (18)7 (25)ns
Pacemaker-ICD, n (%)16 (19)10 (36)ns
β-blockers, n (%)49 (58)24 (86)ns
Furosemide, n (%)61 (72)26 (93)ns
ACEi/ARB36 (42)9 (32)ns
Aldosterone antagonists, n (%)11 (13)3 (11)ns
Potassium, n (%)5 (6)2 (7)ns
Nitrates, n (%)10 (12)4 (14)ns
Ivabradine, n (%)3 (4)2 (7)ns
Digoxin, n (%)4 (5)2 (7)ns
Statins, n (%)22 (26)12 (43)ns
Antiplatelet drugs, n (%)32 (38)6 (21)ns
Oral Anticoagulants, n (%)21 (25)9 (32)ns
Diltiazem or Verapamil, n (%)6 (7)0 (0)ns
Dihydropyridine Calcium channel blockers, n (%)13 (15)2 (7)ns
Propafenone, n (%)1 (1)0 (0)ns
Amiodarone, n (%)1 (1)4 (14)<0.05
Ranolazine, n (%)5 (6)0 (0)ns
Sacubitril/Valsartan, n (%)1 (1)0 (0)ns
BMI: body mass index, SBP: systolic blood pressure, DBP: diastolic blood pressure; CHF depressed ejection fraction: ejection fraction <50%; CHF preserved ejection fraction: ejection fraction >50%; ACEi: angiotensin converting enzyme inhibitors; ARB: angiotensin receptors blockers; NT-proBNP: N-terminal Pro Brain Natriuretic Peptide; SD: standard deviation; ICD: implantable cardioverter defibrillator. Data are expressed as mean ± SD, or median (interquartile range), or number of patients (%).
Table
Table 2. Short period repolarization temporal dispersion variables in study patients.
Table 2. Short period repolarization temporal dispersion variables in study patients.
<75th≥75th
of NT-proBNP
Group 1Group 2
n: 85n: 28p
QTe mean, ms428 ± 62463 ± 850.020
QTe SD, ms5 (4)8 (5)0.002
QTe SD, log ms0.69 ± 0.290.87 ± 0.240.003
QTp mean, ms326 ± 60351 ± 69ns
QTp SD, ms5 (3)7 (4)0.014
QTp SD, log ms0.74 ± 0.200.85 ± 0.160.009
Te mean, ms101 ± 18113 ± 320.023
Te SD, ms6 (4)9 (5)<0.001
Te SD, ms0.80 ± 0.160.96 ± 0.18<0.001
QTeVN0.15 (0.32)0.30 (0.41)0.017
QTpVN0.29 (0.49)0.36 (0.56)ns
TeVN4 (4)9 (11)0.006
QTe, QTp, QTeVN, QTpVN, TeVN (explained in the text). Data are expressed as mean ± SD, or median (interquartile range), or number of patients (%).
Table
Table 3. Prediction of mortality in study patients by Cox regression (continuous variables).
Table 3. Prediction of mortality in study patients by Cox regression (continuous variables).
VariablesΧ2Univariable Analysis Hazard Ratio (95% CI)p-Values
QTe SD7.601.10 (1.03–1.18)0.006
Mean Te12.181.02 (1.01–1.04)< 0.001
Te SD4.651.12 (1.01–1.23)0.026
Data are presented as: hazard ratio (95% confidence limit).

Share and Cite

MDPI and ACS Style

Piccirillo, G.; Moscucci, F.; Bertani, G.; Lospinuso, I.; Mastropietri, F.; Fabietti, M.; Sabatino, T.; Zaccagnini, G.; Crapanzano, D.; Di Diego, I.; et al. Short-Period Temporal Dispersion Repolarization Markers Predict 30-Days Mortality in Decompensated Heart Failure. J. Clin. Med. 2020, 9, 1879. https://doi.org/10.3390/jcm9061879

AMA Style

Piccirillo G, Moscucci F, Bertani G, Lospinuso I, Mastropietri F, Fabietti M, Sabatino T, Zaccagnini G, Crapanzano D, Di Diego I, et al. Short-Period Temporal Dispersion Repolarization Markers Predict 30-Days Mortality in Decompensated Heart Failure. Journal of Clinical Medicine. 2020; 9(6):1879. https://doi.org/10.3390/jcm9061879

Chicago/Turabian Style

Piccirillo, Gianfranco, Federica Moscucci, Gaetano Bertani, Ilaria Lospinuso, Fabiola Mastropietri, Marcella Fabietti, Teresa Sabatino, Giulia Zaccagnini, Davide Crapanzano, Ilaria Di Diego, and et al. 2020. "Short-Period Temporal Dispersion Repolarization Markers Predict 30-Days Mortality in Decompensated Heart Failure" Journal of Clinical Medicine 9, no. 6: 1879. https://doi.org/10.3390/jcm9061879

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop