Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest

[EN] Optimal defibrillation timing guided by ventricular fibrillation (VF) waveform analysis would contribute to improved survival of out-of-hospital cardiac arrest (OHCA) patients by minimizing myocardial damage caused by futile defibrillation shocks and minimizing interruptions to cardiopulmonary resuscitation. Recently, fuzzy entropy (FuzzyEn) tailored to jointly measure VF amplitude and regularity has been shown to be an efficient defibrillation success predictor. In this study, 734 shocks from 296 OHCA patients (50 survivors) were analyzed, and the embedding dimension (m) and matching tolerance (r) for FuzzyEn and sample entropy (SampEn) were adjusted to predict defibrillation success and patient survival. Entropies were significantly larger in successful shocks and in survivors, and when compared to the available methods, FuzzyEn presented the best prediction results, marginally outperforming SampEn. The sensitivity and specificity of FuzzyEn were 83.3% and 76.7% when predicting defibrillation success, and 83.7% and 73.5% for patient survival. Sensitivities and specificities were two points above those of the best available methods, and the prediction accuracy was kept even for VF intervals as short as 2s. These results suggest that FuzzyEn and SampEn may be promising tools for optimizing the defibrillation time and predicting patient survival in OHCA patients presenting VF.This work received financial support from Spanish Ministerio de Economia y Competitividad and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), projects TEC2015-64678-R and DPI2017-83952-C3; from UPV/EHU through the grant PIF15/190 and through project GIU17/031; from the Basque Government through grant PRE-2016-1-0012; and from Junta de Comunidades de Castilla-La Mancha through SBPLY/17/180501/000411.Chicote, B.; Irusta, U.; Aramendi, E.; Alcaraz, R.; Rieta, JJ.; Isasi, I.; Alonso, D.... (2018). Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest. Entropy. 20(8):1-25. https://doi.org/10.3390/e20080591125208Gräsner, J.-T., Lefering, R., Koster, R. W., Masterson, S., Böttiger, B. W., Herlitz, J., … Maurer, H. (2016). EuReCa ONE⿿27 Nations, ONE Europe, ONE Registry. Resuscitation, 105, 188-195. doi:10.1016/j.resuscitation.2016.06.004Benjamin, E. J., Virani, S. S., Callaway, C. W., Chamberlain, A. M., Chang, A. R., Cheng, S., … Deo, R. (2018). Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association. Circulation, 137(12). doi:10.1161/cir.0000000000000558Rubart, M. (2005). Mechanisms of sudden cardiac death. Journal of Clinical Investigation, 115(9), 2305-2315. doi:10.1172/jci26381Zoll, P. M. (1952). Resuscitation of the Heart in Ventricular Standstill by External Electric Stimulation. New England Journal of Medicine, 247(20), 768-771. doi:10.1056/nejm195211132472005Cobb, L. A. (1999). Influence of Cardiopulmonary Resuscitation Prior to Defibrillation in Patients With Out-of-Hospital Ventricular Fibrillation. JAMA, 281(13), 1182. doi:10.1001/jama.281.13.1182Wik, L., Hansen, T. B., Fylling, F., Steen, T., Vaagenes, P., Auestad, B. H., & Steen, P. A. (2003). Delaying Defibrillation to Give Basic Cardiopulmonary Resuscitation to Patients With Out-of-Hospital Ventricular Fibrillation. JAMA, 289(11), 1389. doi:10.1001/jama.289.11.1389Link, M. S., Atkins, D. L., Passman, R. S., Halperin, H. R., Samson, R. A., White, R. D., … Kerber, R. E. (2010). Part 6: Electrical Therapies: Automated External Defibrillators, Defibrillation, Cardioversion, and Pacing * 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation, 122(18_suppl_3), S706-S719. doi:10.1161/circulationaha.110.970954Takata, T. S., Page, R. L., & Joglar, J. A. (2001). Automated External Defibrillators: Technical Considerations and Clinical Promise. Annals of Internal Medicine, 135(11), 990. doi:10.7326/0003-4819-135-11-200112040-00011Figuera, C., Irusta, U., Morgado, E., Aramendi, E., Ayala, U., Wik, L., … Alonso-Atienza, F. (2016). Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators. PLOS ONE, 11(7), e0159654. doi:10.1371/journal.pone.0159654Telesz, B. J., Hess, E. P., Atkinson, E., & White, R. D. (2015). Recurrent ventricular fibrillation: Experience with first responders prior to advanced life support interventions. Resuscitation, 88, 138-142. doi:10.1016/j.resuscitation.2014.10.010Xie, J., Weil, M. H., Sun, S., Tang, W., Sato, Y., Jin, X., & Bisera, J. (1997). High-Energy Defibrillation Increases the Severity of Postresuscitation Myocardial Dysfunction. Circulation, 96(2), 683-688. doi:10.1161/01.cir.96.2.683Cheskes, S., Schmicker, R. H., Christenson, J., Salcido, D. D., Rea, T., Powell, J., … Morrison, L. (2011). Perishock Pause. Circulation, 124(1), 58-66. doi:10.1161/circulationaha.110.010736Reed, M. J., Clegg, G. R., & Robertson, C. E. (2003). Analysing the ventricular fibrillation waveform. Resuscitation, 57(1), 11-20. doi:10.1016/s0300-9572(02)00441-0Firoozabadi, R., Nakagawa, M., Helfenbein, E. D., & Babaeizadeh, S. (2013). Predicting defibrillation success in sudden cardiac arrest patients. Journal of Electrocardiology, 46(6), 473-479. doi:10.1016/j.jelectrocard.2013.06.007Ristagno, G., Li, Y., Fumagalli, F., Finzi, A., & Quan, W. (2013). Amplitude spectrum area to guide resuscitation—A retrospective analysis during out-of-hospital cardiopulmonary resuscitation in 609 patients with ventricular fibrillation cardiac arrest. Resuscitation, 84(12), 1697-1703. doi:10.1016/j.resuscitation.2013.08.017Callaway, C. W., & Menegazzi, J. J. (2005). Waveform analysis of ventricular fibrillation to predict defibrillation. Current Opinion in Critical Care, 11(3), 192-199. doi:10.1097/01.ccx.0000161725.71211.42He, M., Gong, Y., Li, Y., Mauri, T., Fumagalli, F., Bozzola, M., … Ristagno, G. (2015). Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests. Critical Care, 19(1). doi:10.1186/s13054-015-1142-zBrown, C. G., & Dzwonczyk, R. (1996). Signal Analysis of the Human Electrocardiogram During Ventricular Fibrillation: Frequency and Amplitude Parameters as Predictors of Successful Countershock. Annals of Emergency Medicine, 27(2), 184-188. doi:10.1016/s0196-0644(96)70346-3Sherman, L. D., Callaway, C. W., & Menegazzi, J. J. (2000). Ventricular fibrillation exhibits dynamical properties and self-similarity. Resuscitation, 47(2), 163-173. doi:10.1016/s0300-9572(00)00229-xWEAVER, W. D. (1985). Amplitude of Ventricular Fibrillation Waveform and Outcome After Cardiac Arrest. Annals of Internal Medicine, 102(1), 53. doi:10.7326/0003-4819-102-1-53Jekova, I., Mougeolle, F., & Valance, A. (2004). Defibrillation shock success estimation by a set of six parameters derived from the electrocardiogram. Physiological Measurement, 25(5), 1179-1188. doi:10.1088/0967-3334/25/5/008Wu, X., Bisera, J., & Tang, W. (2013). Signal integral for optimizing the timing of defibrillation. Resuscitation, 84(12), 1704-1707. doi:10.1016/j.resuscitation.2013.08.005Hamprecht, F. A., Jost, D., Rüttimann, M., Calamai, F., & Kowalski, J. J. (2001). Preliminary results on the prediction of countershock success with fibrillation power. Resuscitation, 50(3), 297-299. doi:10.1016/s0300-9572(01)00360-4Neurauter, A., Eftestøl, T., Kramer-Johansen, J., Abella, B. S., Sunde, K., Wenzel, V., … Strohmenger, H.-U. (2007). Prediction of countershock success using single features from multiple ventricular fibrillation frequency bands and feature combinations using neural networks. Resuscitation, 73(2), 253-263. doi:10.1016/j.resuscitation.2006.10.002Ristagno, G., Mauri, T., Cesana, G., Li, Y., Finzi, A., Fumagalli, F., … Pesenti, A. (2015). Amplitude Spectrum Area to Guide Defibrillation. Circulation, 131(5), 478-487. doi:10.1161/circulationaha.114.010989Eftestøl, T., Sunde, K., Ole Aase, S., Husøy, J. H., & Steen, P. A. (2000). Predicting Outcome of Defibrillation by Spectral Characterization and Nonparametric Classification of Ventricular Fibrillation in Patients With Out-of-Hospital Cardiac Arrest. Circulation, 102(13), 1523-1529. doi:10.1161/01.cir.102.13.1523Povoas, H. P., & Bisera, J. (2000). Electrocardiographic waveform analysis for predicting the success of defibrillation. Critical Care Medicine, 28(Supplement), N210-N211. doi:10.1097/00003246-200011001-00010Podbregar, M., Kovačič, M., Podbregar-Marš, A., & Brezocnik, M. (2003). Predicting defibrillation success by ‘genetic’ programming in patients with out-of-hospital cardiac arrest. Resuscitation, 57(2), 153-159. doi:10.1016/s0300-9572(03)00030-3Callaway, C. W., Sherman, L. D., Mosesso, V. N., Dietrich, T. J., Holt, E., & Clarkson, M. C. (2001). Scaling Exponent Predicts Defibrillation Success for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest. Circulation, 103(12), 1656-1661. doi:10.1161/01.cir.103.12.1656Sherman, L. D., Rea, T. D., Waters, J. D., Menegazzi, J. J., & Callaway, C. W. (2008). Logarithm of the absolute correlations of the ECG waveform estimates duration of ventricular fibrillation and predicts successful defibrillation. Resuscitation, 78(3), 346-354. doi:10.1016/j.resuscitation.2008.04.009Lin, L.-Y., Lo, M.-T., Ko, P. C.-I., Lin, C., Chiang, W.-C., Liu, Y.-B., … Ma, M. H.-M. (2010). Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest. Resuscitation, 81(3), 297-301. doi:10.1016/j.resuscitation.2009.12.003Gong, Y., Lu, Y., Zhang, L., Zhang, H., & Li, Y. (2015). Predict Defibrillation Outcome Using Stepping Increment of Poincare Plot for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest. BioMed Research International, 2015, 1-7. doi:10.1155/2015/493472Watson, J. N., Uchaipichat, N., Addison, P. S., Clegg, G. R., Robertson, C. E., Eftestol, T., & Steen, P. A. (2004). Improved prediction of defibrillation success for out-of-hospital VF cardiac arrest using wavelet transform methods. Resuscitation, 63(3), 269-275. doi:10.1016/j.resuscitation.2004.06.012Gundersen, K., Kvaløy, J. T., Kramer-Johansen, J., & Eftestøl, T. (2008). Identifying approaches to improve the accuracy of shock outcome prediction for out-of-hospital cardiac arrest. Resuscitation, 76(2), 279-284. doi:10.1016/j.resuscitation.2007.07.019Howe, A., Escalona, O. J., Di Maio, R., Massot, B., Cromie, N. A., Darragh, K. M., … McEneaney, D. J. (2014). A support vector machine for predicting defibrillation outcomes from waveform metrics. Resuscitation, 85(3), 343-349. doi:10.1016/j.resuscitation.2013.11.021Indik, J. H., Conover, Z., McGovern, M., Silver, A. E., Spaite, D. W., Bobrow, B. J., & Kern, K. B. (2014). Association of Amplitude Spectral Area of the Ventricular Fibrillation Waveform With Survival of Out-of-Hospital Ventricular Fibrillation Cardiac Arrest. Journal of the American College of Cardiology, 64(13), 1362-1369. doi:10.1016/j.jacc.2014.06.1196Coult, J., Sherman, L., Kwok, H., Blackwood, J., Kudenchuk, P. J., & Rea, T. D. (2016). Short ECG segments predict defibrillation outcome using quantitative waveform measures. Resuscitation, 109, 16-20. doi:10.1016/j.resuscitation.2016.09.020Endoh, H., Hida, S., Oohashi, S., Hayashi, Y., Kinoshita, H., & Honda, T. (2010). Prompt prediction of successful defibrillation from 1-s ventricular fibrillation waveform in patients with out-of-hospital sudden cardiac arrest. Journal of Anesthesia, 25(1), 34-41. doi:10.1007/s00540-010-1043-xChicote, B., Irusta, U., Alcaraz, R., Rieta, J., Aramendi, E., Isasi, I., … Ibarguren, K. (2016). Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest. Entropy, 18(9), 313. doi:10.3390/e18090313Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049. doi:10.1152/ajpheart.2000.278.6.h2039Weiting Chen, Zhizhong Wang, Hongbo Xie, & Wangxin Yu. (2007). Characterization of Surface EMG Signal Based on Fuzzy Entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(2), 266-272. doi:10.1109/tnsre.2007.897025Xiao-Feng, L., & Yue, W. (2009). Fine-grained permutation entropy as a measure of natural complexity for time series. Chinese Physics B, 18(7), 2690-2695. doi:10.1088/1674-1056/18/7/011Fadlallah, B., Chen, B., Keil, A., & Príncipe, J. (2013). Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information. Physical Review E, 87(2). doi:10.1103/physreve.87.022911Eftestøl, T., Sunde, K., & Steen, P. A. (2002). Effects of Interrupting Precordial Compressions on the Calculated Probability of Defibrillation Success During Out-of-Hospital Cardiac Arrest. Circulation, 105(19), 2270-2273. doi:10.1161/01.cir.0000016362.42586.feEdelson, D. P., Abella, B. S., Kramer-Johansen, J., Wik, L., Myklebust, H., Barry, A. M., … Becker, L. B. (2006). Effects of compression depth and pre-shock pauses predict defibrillation failure during cardiac arrest. Resuscitation, 71(2), 137-145. doi:10.1016/j.resuscitation.2006.04.008Ibarguren, K., Unanue, J. M., Alonso, D., Vaqueriza, I., Irusta, U., Aramendi, E., & Chicote, B. (2015). Difference in survival from pre-hospital cardiac arrest between cities and villages in the Basque Autonomous Community. Resuscitation, 96, 114. doi:10.1016/j.resuscitation.2015.09.269Jacobs, I., Nadkarni, V., Bahr, J., Berg, R. A., Billi, J. E., Bossaert, L., … Zideman, D. (2004). Cardiac arrest and cardiopulmonary resuscitation outcome reports: update and simplification of the Utstein templates for resuscitation registries. Resuscitation, 63(3), 233-249. doi:10.1016/j.resuscitation.2004.09.008Rittenberger, J. C., Raina, K., Holm, M. B., Kim, Y. J., & Callaway, C. W. (2011). Association between Cerebral Performance Category, Modified Rankin Scale, and discharge disposition after cardiac arrest. Resuscitation, 82(8), 1036-1040. doi:10.1016/j.resuscitation.2011.03.034Marn-Pernat, A., Weil, M. H., Tang, W., Pernat, A., & Bisera, J. (2001). Optimizing timing of ventricular defibrillation. Critical Care Medicine, 29(12), 2360-2365. doi:10.1097/00003246-200112000-00019Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6), 2297-2301. doi:10.1073/pnas.88.6.2297Chen, W., Zhuang, J., Yu, W., & Wang, Z. (2009). Measuring complexity using FuzzyEn, ApEn, and SampEn. Medical Engineering & Physics, 31(1), 61-68. doi:10.1016/j.medengphy.2008.04.005Alcaraz, R., Abásolo, D., Hornero, R., & Rieta, J. J. (2010). Optimal parameters study for sample entropy-based atrial fibrillation organization analysis. Computer Methods and Programs in Biomedicine, 99(1), 124-132. doi:10.1016/j.cmpb.2010.02.009Zou, K. H., O’Malley, A. J., & Mauri, L. (2007). Receiver-Operating Characteristic Analysis for Evaluating Diagnostic Tests and Predictive Models. Circulation, 115(5), 654-657. doi:10.1161/circulationaha.105.594929Perkins, N. J., & Schisterman, E. F. (2006). The Inconsistency of «Optimal» Cutpoints Obtained using Two Criteria based on the Receiver Operating Characteristic Curve. American Journal of Epidemiology, 163(7), 670-675. doi:10.1093/aje/kwj063Monsieurs, K. G., Nolan, J. P., Bossaert, L. L., Greif, R., Maconochie, I. K., Nikolaou, N. I., … Wyllie, J. (2015). European Resuscitation Council Guidelines for Resuscitation 2015. Resuscitation, 95, 1-80. doi:10.1016/j.resuscitation.2015.07.038Ruiz, J., Ayala, U., de Gauna, S. R., Irusta, U., González-Otero, D., Alonso, E., … Eftestøl, T. (2013). Feasibility of automated rhythm assessment in chest compression pauses during cardiopulmonary resuscitation. Resuscitation, 84(9), 1223-1228. doi:10.1016/j.resuscitation.2013.01.034Ayala, U., Irusta, U., Ruiz, J., Ruiz de Gauna, S., González-Otero, D., Alonso, E., … Eftestøl, T. (2015). Fully automatic rhythm analysis during chest compression pauses. Resuscitation, 89, 25-30. doi:10.1016/j.resuscitation.2014.11.022Singh, A., Saini, B. S., & Singh, D. (2015). An alternative approach to approximate entropy threshold value (r) selection: application to heart rate variability and systolic blood pressure variability under postural challenge. Medical & Biological Engineering & Computing, 54(5), 723-732. doi:10.1007/s11517-015-1362-zNeurauter, A., Eftestøl, T., Kramer-Johansen, J., Abella, B. S., Wenzel, V., Lindner, K. H., … Strohmenger, H.-U. (2008). Improving countershock success prediction during cardiopulmonary resuscitation using ventricular fibrillation features from higher ECG frequency bands. Resuscitation, 79(3), 453-459. doi:10.1016/j.resuscitation.2008.07.024Rosso, O. A., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., Schürmann, M., & Başar, E. (2001). Wavelet entropy: a new tool for analysis of short duration brain electrical signals. Journal of Neuroscience Methods, 105(1), 65-75. doi:10.1016/s0165-0270(00)00356-3Weaver, B., & Wuensch, K. L. (2013). SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients. Behavior Research Methods, 45(3), 880-895. doi:10.3758/s13428-012-0289-7Sherman, L. D. (2006). The frequency ratio: An improved method to estimate ventricular fibrillation duration based on Fourier analysis of the waveform. Resuscitation, 69(3), 479-486. doi:10.1016/j.resuscitation.2005.09.024Weisfeldt, M. L., & Becker, L. B. (2002). Resuscitation After Cardiac Arrest. JAMA, 288(23), 3035. doi:10.1001/jama.288.23.3035Gazmuri, R. J., Berkowitz, M., & Cajigas, H. (1999). Myocardial effects of ventricular fibrillation in the isolated rat heart. Critical Care Medicine, 27(8), 1542-1550. doi:10.1097/00003246-199908000-00023JARDETZKY, O., GREENE, E. A., & LORBER, V. (1956). Oxygen Consumption of the Completely Isolated Dog Heart In Fibrillation. Circulation Research, 4(2), 144-147. doi:10.1161/01.res.4.2.144Hoogendijk, M. G., Schumacher, C. A., Belterman, C. N. W., Boukens, B. J., Berdowski, J., de Bakker, J. M. T., … Coronel, R. (2012). Ventricular fibrillation hampers the restoration of creatine-phosphate levels during simulated cardiopulmonary resuscitations. EP Europace, 14(10), 1518-1523. doi:10.1093/europace/eus078Neumar, R. W., Brown, C. G., Van Ligten, P., Hoekstra, J., Altschuld, R. A., & Baker, P. (1991). Estimation of myocardial ischemic injury during ventricular fibrillation with total circulatory arrest using high-energy phosphates and lactate as metabolic markers. Annals of Emergency Medicine, 20(3), 222-229. doi:10.1016/s0196-0644(05)80927-8Kern, K. B., Garewal, H. S., Sanders, A. B., Janas, W., Nelson, J., Sloan, D., … Ewy, G. A. (1990). Depletion of myocardial adenosine triphosphate during prolonged untreated ventricular fibrillation: effect on defibrillation success. Resuscitation, 20(3), 221-229. doi:10.1016/0300-9572(90)90005-yChoi, H. J., Nguyen, T., Park, K. S., Cha, K. C., Kim, H., Lee, K. H., & Hwang, S. O. (2013). Effect of cardiopulmonary resuscitation on restoration of myocardial ATP in prolonged ventricular fibrillation. Resuscitation, 84(1), 108-113. doi:10.1016/j.resuscitation.2012.06.006Salcido, D. D., Menegazzi, J. J., Suffoletto, B. P., Logue, E. S., & Sherman, L. D. (2009). Association of intramyocardial high energy phosphate concentrations with quantitative measures of the ventricular fibrillation electrocardiogram waveform. Resuscitation, 80(8), 946-950. doi:10.1016/j.resuscitation.2009.05.002Reynolds, J. C., Salcido, D. D., & Menegazzi, J. J. (2012). Correlation between coronary perfusion pressure and quantitative ECG waveform measures during resuscitation of prolonged ventricular fibrillation. Resuscitation, 83(12), 1497-1502. doi:10.1016/j.resuscitation.2012.04.013Didon, J.-P., Krasteva, V., Ménétré, S., Stoyanov, T., & Jekova, I. (2011). Shock advisory system with minimal delay triggering after end of chest compressions: Accuracy and gained hands-off time. Resuscitation, 82, S8-S15. doi:10.1016/s0300-9572(11)70145-9Ruiz de Gauna, S., Irusta, U., Ruiz, J., Ayala, U., Aramendi, E., & Eftestøl, T. (2014). Rhythm Analysis during Cardiopulmonary Resuscitation: Past, Present, and Future. BioMed Research International, 2014, 1-13. doi:10.1155/2014/386010Manis, G., Aktaruzzaman, M., & Sassi, R. (2018). Low Computational Cost for Sample Entropy. Entropy, 20(1), 61. doi:10.3390/e20010061Snyder, D., & Morgan, C. (2004). Wide variation in cardiopulmonary resuscitation interruption intervals among commercially available automated external defibrillators may affect survival despite high defibrillation efficacy. Critical Care Medicine, 32(Supplement), S421-S424. doi:10.1097/01.ccm.0000134265.35871.2bMenegazzi, J. J., Callaway, C. W., Sherman, L. D., Hostler, D. P., Wang, H. E., Fertig, K. C., & Logue, E. S. (2004). Ventricular Fibrillation Scaling Exponent Can Guide Timing of Defibrillation and Other Therapies. Circulation, 109(7), 926-931. doi:10.1161/01.cir.0000112606.41127.d2Lombardi, F. (2001). Sudden cardiac death: role of heart rate variability to identify patients at risk. Cardiovascular Research, 50(2), 210-217. doi:10.1016/s0008-6363(01)00221-8Moorman, J. R., Carlo, W. A., Kattwinkel, J., Schelonka, R. L., Porcelli, P. J., Navarrete, C. T., … Michael O’Shea, T. (2011). Mortality Reduction by Heart Rate Characteristic Monitoring in Very Low Birth Weight Neonates: A Randomized Trial. The Journal of Pediatrics, 159(6), 900-906.e1. doi:10.1016/j.jpeds.2011.06.044Sessa, F., Anna, V., Messina, G., Cibelli, G., Monda, V., Marsala, G., … Salerno, M. (2018). Heart rate variability as predictive factor for sudden cardiac death. Aging, 10(2), 166-17