Estimation of cut-off points under complex-sampling design data

In the context of logistic regression models, a cut-off point is usually selected to dichotomize the estimated predicted probabilities based on the model. The techniques proposed to estimate optimal cut-off points in the literature, are commonly developed to be applied in simple random samples and their applicability to complex sampling designs could be limited. Therefore, in this work we propose a methodology to incorporate sampling weights in the estimation process of the optimal cut-off points, and we evaluate its performance using a real data-based simulation study. The results suggest the convenience of considering sampling weights for estimating optimal cut-off points.IT1294-19 BERC 2018-2021 KK-2020/00049 PIF18/21

Relative Age Effect in Elite Sports: Methodological Bias or Real Discrimination?

Sport sciences researchers talk about a relative age effect when they observe a biased distribution of elite athletes' birthdates, with an over-representation of those born at the beginning of the competitive year and an under-representation of those born at the end. Using the whole sample of the French male licensed soccer players (n = 1,831,524), our study suggests that there could be an important bias in the statistical test of this effect. This bias could in turn lead to falsely conclude to a systemic discrimination in the recruitment of professional players. Our findings question the accuracy of past results concerning the existence of this effect at the elite level

Ventricular fibrillation detection in ventricular fibrillation signals corrupted by cardiopulmonary resuscitation artifact

This study is focused on the removal of artifacts due to Cardio Pulmonary Resuscitation (CPR) on Ventricular Fibrillation ECG signals. The aim is to allow a reliable analysis of the cardiac rhythm by an AED or the defibrillation success analysis during CPR episodes. The research is based on a human model for the CPR artifact and the VF ECG signals. The test signals were generated adding the CPR artifact (noise) to the VF (signal), with a known Signal-to-Noise Ratio (SNR). The results of the adaptive Kalman filtering have been obtained according to three different levels: SNR improvement; Sensitivity improvement in the AED algorithm for the detection of shockable rhythm; and Variations of the significant frequencies, compared to the values obtained with the original VF signals. In all cases, remarkable results have been achieved regarding to the efficiency in the artifact removal. 1

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/e20080591S125208Gräsner, 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), 166-1

Transformación de los roles de las mujeres colonas y cambio socioambiental : el caso de la penetración de la minería transnacional en un agroecosistema de la comunidad de Junín, Ecuador

Este artículo trata de analizar el rol de las mujeres en una comunidad campesina de la Sierra norte del Ecuador, en función de las transformaciones socioambientales sufridas en base a dos procesos que se están dando hoy en día en el panorama local de Junín. Se trata por un lado de la tendencia de la comunidad hacia un modelo agroindustrial, y por el otro del conflicto socioambiental minero, latente desde hace poco más de una década en la realidad de la comunidad. Ambos procesos se encuentran fuertemente vinculados e influyen de manera directa sobre las estrategias de vida de la comunidad, puesto que ambos acaban transformando, debido a la aparición de nuevas actividades productivas, el uso que se hace de los recursos, de esta manera se observa una evolución del agroecosistema que conforma la comunidad de Junín. La discusión del presente estudio gira entorno a las repercusiones que estos dos procesos han tenido entorno al rol de la mujer, es decir se pretende estudiar un cambio social relacionado con el rol de la mujer en la comunidad, mediante un cambio ambiental que se ha dado entorno a estos dos procesos. De esta manera se ha observado que el conflicto minero ha sido el punto de partida que ha hecho salir a la mujer a la esfera pública, sobretodo por las alternativas productivas que ha creado, concretamente el ecoturismo por medio del cual la mujer ha pasado a realizar actividades productivas. Por otra parte, la modernización agrícola ha dejado patente la relación existente entre la mujer y la unidad doméstica. Esta tendencia hacia el modelo agroindustrial ha hecho desplazar a los hombres de la comunidad a realizar actividades agrícolas relacionadas con la comercialización, mientras que la mujer se ha mantenido en el sector de la agricultura de subsistencia

Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survivalof out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrilla-tors (AED). AED algorithms for VF-detection are customarily assessed using Holter record-ings from public electrocardiogram (ECG) databases, which may be different from the ECGseen during OHCA events. This study evaluates VF-detection using data from both OHCApatients and public Holter recordings. ECG-segments of 4-s and 8-s duration were ana-lyzed. For each segment 30 features were computed and fed to state of the art machinelearning (ML) algorithms. ML-algorithms with built-in feature selection capabilities wereused to determine the optimal feature subsets for both databases. Patient-wise bootstraptechniques were used to evaluate algorithm performance in terms of sensitivity (Se), speci-ficity (Sp) and balanced error rate (BER). Performance was significantly better for publicdata with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times morefeatures than the data from public databases for an accurate detection (6 vs 3). No signifi-cant differences in performance were found for different segment lengths, the BER differ-ences were below 0.5-points in all cases. Our results show that VF-detection is morechallenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s

VETTONIA PROJECT: A VIRTUAL ENVIRONMENT FOR THE EDUCATIONAL DISSEMINATION OF THE IRON AGE

The VETTONIA project aims to disseminate the rich heritage from the Iron Age of the western Iberian Peninsula and the archaeological investigations carried out on this topic in recent years. The project utilizes new technologies such as virtual tours, 3D models, and impressions to create interactive and stimulating ways to access the results of the most recent archaeological research. Using these resources, lectures and seminars are being given in various forums with diverse types of audiences to present the virtual tours and the rest of the dissemination initiatives. In addition, the project presents its different initiatives during the annual archaeological interventions developed in the oppidum of Ulaca (Solosancho, Ávila, Spain), with good reception by the attending public. The VETTONIA project represents a pioneering dissemination experience that takes advantage of the educational opportunities offered by new technologies. In the future, tools such as virtual tours to archaeological sites may prove essential in classroom teaching at different levels and could promote sustainable tourism in fragile natural environments such as those that constitute the major settlements of the Late Iron Age (ca. 400–50 BC)

Oxigenación con membrana extracorpórea en el paciente COVID-19: resultados del Registro Español ECMO-COVID de la Sociedad Española de Cirugía Cardiovascular y Endovascular (SECCE)

Background and aim: COVID-19 patients with severe heart or respiratory failure are potential candidates for extracorporeal membrane oxygenation (ECMO). Indications and management of these patients are unclear. Our aim is to describe the results of a prospective registry of COVID-19 patients treated with ECMO. Methods: An anonymous prospective registry of COVID-19 patients treated with veno-arterial (V-A) or veno-venous (V-V) ECMO was created on march 2020. Clinical, analytical and respiratory preimplantation variables, implantation data and post-implantation course data were recorded. The primary endpoint was all cause in-hospital mortality. Secondary events were functional recovery and the combined endpoint of mortality and functional recovery in patients followed at least 3 months after discharge. Results: Three hundred and sixty-six patients from 25 hospitals were analyzed, 347 V-V ECMO and 18 V-A ECMO patients (mean age 52.7 and 49.5 years respectively). Patients with V-V ECMO were more obese, had less frequently organ damage other than respiratory failure and needed less inotropic support; Thirty three percent of V-A ECMO and 34.9% of V-A ECMO were discharged (P = NS). Hospital mortality was non-significantly different, 56.2% versus 50.9% respectively, mainly during ECMO therapy and mostly due to multiorgan failure. Other 51 patients (14%) remained admitted. Mean follow-up was 196 +/- 101.7 days (95%CI: 170.8-221.6). After logistic regression, body weight (OR 0.967, 95%CI: 0.95-0.99, P = 0.004) and ECMO implantation in the own centre (OR 0.48, 95%CI: 0.27-0.88, P = 0.018) were protective for hospital mortality. Age (OR 1.063, 95%CI: 1.005-1.12, P = 0.032), arterial hypertension (3.593, 95%CI: 1.06-12.19, P = 0.04) and global (2.44, 95%CI: 0.27-0.88, P = 0.019), digestive (OR 4,23, 95%CI: 1.27-14.07, P = 0.019) and neurological (OR 4.66, 95%CI: 1.39-15.62, P = 0.013) complications during ECMO therapy were independent predictors of primary endpoint occurrence. Only the post-discharge day at follow-up was independent predictor of both secondary endpoints occurrence. Conclusions: Hospital survival of severely ill COVID-19 patients treated with ECMO is near 50%. Age, arterial hypertension and ECMO complications are predictors of hospital mortality, and body weight and implantation in the own centre are protective. Functional recovery is only predicted by the follow-up time after discharge. A more homogeneous management of these patients is warranted for clinical results and future research optimization. (C) 2022 Sociedad Espanola de Cirugia Cardiovascular y Endovascular. Published by Elsevier Espana, S.L.U