Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios

[EN] Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel-Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and the Generalitat Valenciana (AICO/2019/220 & GV/2018/104)Mas-Cabo, J.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; Perales-Marin, A.; Monfort-Ortiz, R.; Roca-Prats, A.... (2020). Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios. Entropy. 22(7):1-15. https://doi.org/10.3390/e22070743S115227Wagura, P., Wasunna, A., Laving, A., Wamalwa, D., & Ng’ang’a, P. (2018). Prevalence and factors associated with preterm birth at kenyatta national hospital. BMC Pregnancy and Childbirth, 18(1). doi:10.1186/s12884-018-1740-2Liu, L., Johnson, H. L., Cousens, S., Perin, J., Scott, S., Lawn, J. E., … Black, R. E. (2012). Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. The Lancet, 379(9832), 2151-2161. doi:10.1016/s0140-6736(12)60560-1Howson, C. P., Kinney, M. V., McDougall, L., & Lawn, J. E. (2013). Born Too Soon: Preterm birth matters. Reproductive Health, 10(S1). doi:10.1186/1742-4755-10-s1-s1Euliano, T. Y., Nguyen, M. T., Darmanjian, S., McGorray, S. P., Euliano, N., Onkala, A., & Gregg, A. R. (2013). Monitoring uterine activity during labor: a comparison of 3 methods. American Journal of Obstetrics and Gynecology, 208(1), 66.e1-66.e6. doi:10.1016/j.ajog.2012.10.873Devedeux, D., Marque, C., Mansour, S., Germain, G., & Duchêne, J. (1993). Uterine electromyography: A critical review. American Journal of Obstetrics and Gynecology, 169(6), 1636-1653. doi:10.1016/0002-9378(93)90456-sChkeir, A., Fleury, M.-J., Karlsson, B., Hassan, M., & Marque, C. (2013). Patterns of electrical activity synchronization in the pregnant rat uterus. BioMedicine, 3(3), 140-144. doi:10.1016/j.biomed.2013.04.007Fele-Žorž, G., Kavšek, G., Novak-Antolič, Ž., & Jager, F. (2008). A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Medical & Biological Engineering & Computing, 46(9), 911-922. doi:10.1007/s11517-008-0350-yMas-Cabo, J., Prats-Boluda, G., Perales, A., Garcia-Casado, J., Alberola-Rubio, J., & Ye-Lin, Y. (2018). Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Medical & Biological Engineering & Computing, 57(2), 401-411. doi:10.1007/s11517-018-1888-yVinken, M. P. G. C., Rabotti, C., Mischi, M., & Oei, S. G. (2009). Accuracy of Frequency-Related Parameters of the Electrohysterogram for Predicting Preterm Delivery. Obstetrical & Gynecological Survey, 64(8), 529-541. doi:10.1097/ogx.0b013e3181a8c6b1Hassan, M., Terrien, J., Marque, C., & Karlsson, B. (2011). Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Engineering & Physics, 33(8), 980-986. doi:10.1016/j.medengphy.2011.03.010Lemancewicz, A., Borowska, M., Kuć, P., Jasińska, E., Laudański, P., Laudański, T., & Oczeretko, E. (2016). Early diagnosis of threatened premature labor by electrohysterographic recordings – The use of digital signal processing. Biocybernetics and Biomedical Engineering, 36(1), 302-307. doi:10.1016/j.bbe.2015.11.005Garcia-Casado, J., Ye-Lin, Y., Prats-Boluda, G., Mas-Cabo, J., Alberola-Rubio, J., & Perales, A. (2018). Electrohysterography in the diagnosis of preterm birth: a review. Physiological Measurement, 39(2), 02TR01. doi:10.1088/1361-6579/aaad56Most, O., Langer, O., Kerner, R., Ben David, G., & Calderon, I. (2008). Can myometrial electrical activity identify patients in preterm labor? American Journal of Obstetrics and Gynecology, 199(4), 378.e1-378.e6. doi:10.1016/j.ajog.2008.08.003Verdenik, I., Pajntar, M., & Leskošek, B. (2001). Uterine electrical activity as predictor of preterm birth in women with preterm contractions. European Journal of Obstetrics & Gynecology and Reproductive Biology, 95(2), 149-153. doi:10.1016/s0301-2115(00)00418-8Horoba, K., Jezewski, J., Matonia, A., Wrobel, J., Czabanski, R., & Jezewski, M. (2016). Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals. Biocybernetics and Biomedical Engineering, 36(4), 574-583. doi:10.1016/j.bbe.2016.06.004Lucovnik, M., Maner, W. L., Chambliss, L. R., Blumrick, R., Balducci, J., Novak-Antolic, Z., & Garfield, R. E. (2011). Noninvasive uterine electromyography for prediction of preterm delivery. American Journal of Obstetrics and Gynecology, 204(3), 228.e1-228.e10. doi:10.1016/j.ajog.2010.09.024Smrdel, A., & Jager, F. (2015). Separating sets of term and pre-term uterine EMG records. Physiological Measurement, 36(2), 341-355. doi:10.1088/0967-3334/36/2/341Maner, W. (2003). Predicting term and preterm delivery with transabdominal uterine electromyography. Obstetrics & Gynecology, 101(6), 1254-1260. doi:10.1016/s0029-7844(03)00341-7Leman, H., Marque, C., & Gondry, J. (1999). Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Transactions on Biomedical Engineering, 46(10), 1222-1229. doi:10.1109/10.790499Mischi, M., Chen, C., Ignatenko, T., de Lau, H., Ding, B., Oei, S. G. G., & Rabotti, C. (2018). Dedicated Entropy Measures for Early Assessment of Pregnancy Progression From Single-Channel Electrohysterography. IEEE Transactions on Biomedical Engineering, 65(4), 875-884. doi:10.1109/tbme.2017.2723933Garfield, R. E., Maner, W. L., MacKay, L. B., Schlembach, D., & Saade, G. R. (2005). Comparing uterine electromyography activity of antepartum patients versus term labor patients. American Journal of Obstetrics and Gynecology, 193(1), 23-29. doi:10.1016/j.ajog.2005.01.050Maner, W. L., & Garfield, R. E. (2007). Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data. Annals of Biomedical Engineering, 35(3), 465-473. doi:10.1007/s10439-006-9248-8DIMITROV, G. V., ARABADZHIEV, T. I., MILEVA, K. N., BOWTELL, J. L., CRICHTON, N., & DIMITROVA, N. A. (2006). Muscle Fatigue during Dynamic Contractions Assessed by New Spectral Indices. Medicine & Science in Sports & Exercise, 38(11), 1971-1979. doi:10.1249/01.mss.0000233794.31659.6dNagarajan, R., Eswaran, H., Wilson, J. D., Murphy, P., Lowery, C., & Preißl, H. (2003). Analysis of uterine contractions: a dynamical approach. The Journal of Maternal-Fetal & Neonatal Medicine, 14(1), 8-21. doi:10.1080/jmf.14.1.8.21Zhang, X.-S., Roy, R. J., & Jensen, E. W. (2001). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601Garfield, R. E., Maner, W. L., Maul, H., & Saade, G. R. (2005). Use of uterine EMG and cervical LIF in monitoring pregnant patients. BJOG: An International Journal of Obstetrics & Gynaecology, 112, 103-108. doi:10.1111/j.1471-0528.2005.00596.xGrotegut, C. A., Paglia, M. J., Johnson, L. N. C., Thames, B., & James, A. H. (2011). Oxytocin exposure during labor among women with postpartum hemorrhage secondary to uterine atony. American Journal of Obstetrics and Gynecology, 204(1), 56.e1-56.e6. doi:10.1016/j.ajog.2010.08.02

A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity

[EN] Postpartum hemorrhage (PPH) is one of the major causes of maternal mortality and morbidity worldwide, with uterine atony being the most common origin. Currently there are no obstetrical techniques available for monitoring postpartum uterine dynamics, as tocodynamometry is not able to detect weak uterine contractions. In this study, we explored the feasibility of monitoring postpartum uterine activity by non-invasive electrohysterography (EHG), which has been proven to outperform tocodynamometry in detecting uterine contractions during pregnancy. A comparison was made of the temporal, spectral, and non-linear parameters of postpartum EHG characteristics of vaginal deliveries and elective cesareans. In the vaginal delivery group, EHG obtained a significantly higher amplitude and lower kurtosis of the Hilbert envelope, and spectral content was shifted toward higher frequencies than in the cesarean group. In the non-linear parameters, higher values were found for the fractal dimension and lower values for Lempel-Ziv, sample entropy and spectral entropy in vaginal deliveries suggesting that the postpartum EHG signal is extremely non-linear but more regular and predictable than in a cesarean. The results obtained indicate that postpartum EHG recording could be a helpful tool for earlier detection of uterine atony and contribute to better management of prophylactic uterotonic treatment for PPH prevention.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and the Generalitat Valenciana (GV/2018/104 and AICO/2019/220).Díaz-Martínez, MDA.; Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Cardona-Urrego, K.; Monfort-Ortiz, R.; Lopez-Corral, A.... (2020). A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity. Sensors. 20(11):1-14. https://doi.org/10.3390/s20113023S1142011Ngwenya, S. (2016). Postpartum hemorrhage: incidence, risk factors, and outcomes in a low-resource setting. International Journal of Women’s Health, Volume 8, 647-650. doi:10.2147/ijwh.s119232Carroli, G., Cuesta, C., Abalos, E., & Gulmezoglu, A. M. (2008). Epidemiology of postpartum haemorrhage: a systematic review. Best Practice & Research Clinical Obstetrics & Gynaecology, 22(6), 999-1012. doi:10.1016/j.bpobgyn.2008.08.004Souza, J. P., Gülmezoglu, A. M., Carroli, G., Lumbiganon, P., & Qureshi, Z. (2011). The world health organization multicountry survey on maternal and newborn health: study protocol. BMC Health Services Research, 11(1). doi:10.1186/1472-6963-11-286Knight, M., Callaghan, W. M., Berg, C., Alexander, S., Bouvier-Colle, M.-H., Ford, J. B., … Walker, J. (2009). Trends in postpartum hemorrhage in high resource countries: a review and recommendations from the International Postpartum Hemorrhage Collaborative Group. BMC Pregnancy and Childbirth, 9(1). doi:10.1186/1471-2393-9-55Callaghan, W. M., Kuklina, E. V., & Berg, C. J. (2010). Trends in postpartum hemorrhage: United States, 1994–2006. American Journal of Obstetrics and Gynecology, 202(4), 353.e1-353.e6. doi:10.1016/j.ajog.2010.01.011Marshall, A. L., Durani, U., Bartley, A., Hagen, C. E., Ashrani, A., Rose, C., … Pruthi, R. K. (2017). The impact of postpartum hemorrhage on hospital length of stay and inpatient mortality: a National Inpatient Sample–based analysis. American Journal of Obstetrics and Gynecology, 217(3), 344.e1-344.e6. doi:10.1016/j.ajog.2017.05.004Prick, B. W., Duvekot, J. J., van der Moer, P. E., van Gemund, N., van der Salm, P. C. M., Jansen, A. J. G., … Uyl-de Groot, C. A. (2014). Cost-effectiveness of red blood cell transfusion vs. non-intervention in women with acute anaemia after postpartum haemorrhage. Vox Sanguinis, 107(4), 381-388. doi:10.1111/vox.12181Castiel, D., Bréchat, P.-H., Benoît, B., Nguon, B., Gayat, E., Soyer, P., … Barranger, E. (2008). Coût total des actes chirurgicaux dans la prise en charge des hémorragies de la délivrance. Gynécologie Obstétrique & Fertilité, 36(5), 507-515. doi:10.1016/j.gyobfe.2008.03.009Fukami, T., Koga, H., Goto, M., Ando, M., Matsuoka, S., Tohyama, A., … Tsujioka, H. (2019). Incidence and risk factors for postpartum hemorrhage among transvaginal deliveries at a tertiary perinatal medical facility in Japan. PLOS ONE, 14(1), e0208873. doi:10.1371/journal.pone.0208873Vogel, J. P., Williams, M., Gallos, I., Althabe, F., & Oladapo, O. T. (2019). WHO recommendations on uterotonics for postpartum haemorrhage prevention: what works, and which one? BMJ Global Health, 4(2), e001466. doi:10.1136/bmjgh-2019-001466Lutomski, J., Byrne, B., Devane, D., & Greene, R. (2012). Increasing trends in atonic postpartum haemorrhage in Ireland: an 11-year population-based cohort study. BJOG: An International Journal of Obstetrics & Gynaecology, 119(9), 1150-1151. doi:10.1111/j.1471-0528.2012.03370.xWilmink, F. A., Wilms, F. F., Heydanus, R., Mol, B. W. J., & Papatsonis, D. N. M. (2008). Fetal complications after placement of an intrauterine pressure catheter: A report of two cases and review of the literature. The Journal of Maternal-Fetal & Neonatal Medicine, 21(12), 880-883. doi:10.1080/14767050802220508Hadar, E., Biron-Shental, T., Gavish, O., Raban, O., & Yogev, Y. (2014). A comparison between electrical uterine monitor, tocodynamometer and intra uterine pressure catheter for uterine activity in labor. The Journal of Maternal-Fetal & Neonatal Medicine, 28(12), 1367-1374. doi:10.3109/14767058.2014.954539Alberola-Rubio, J., Prats-Boluda, G., Ye-Lin, Y., Valero, J., Perales, A., & Garcia-Casado, J. (2013). Comparison of non-invasive electrohysterographic recording techniques for monitoring uterine dynamics. Medical Engineering & Physics, 35(12), 1736-1743. doi:10.1016/j.medengphy.2013.07.008Euliano, T. Y., Nguyen, M. T., Darmanjian, S., McGorray, S. P., Euliano, N., Onkala, A., & Gregg, A. R. (2013). Monitoring uterine activity during labor: a comparison of 3 methods. American Journal of Obstetrics and Gynecology, 208(1), 66.e1-66.e6. doi:10.1016/j.ajog.2012.10.873Euliano, T. Y., Nguyen, M. T., Marossero, D., & Edwards, R. K. (2007). Monitoring Contractions in Obese Parturients. Obstetrics & Gynecology, 109(5), 1136-1140. doi:10.1097/01.aog.0000258799.24496.93Benalcazar Parra, C., Tendero, A. I., Ye-Lin, Y., Alberola-Rubio, J., Perales Marin, A., Garcia-Casado, J., & Prats-Boluda, G. (2018). Feasibility of Labor Induction Success Prediction based on Uterine Myoelectric Activity Spectral Analysis. Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies. doi:10.5220/0006649400700077Euliano, T., Skowronski, M., Marossero, D., Shuster, J., & Edwards, R. (2006). Prediction of intrauterine pressure waveform from transabdominal electrohysterography. The Journal of Maternal-Fetal & Neonatal Medicine, 19(12), 803-808. doi:10.1080/14767050601023657Benalcazar-Parra, C., Garcia-Casado, J., Ye-Lin, Y., Alberola-Rubio, J., Lopez, Á., Perales-Marin, A., & Prats-Boluda, G. (2019). New electrohysterogram-based estimators of intrauterine pressure signal, tonus and contraction peak for non-invasive labor monitoring. Physiological Measurement, 40(8), 085003. doi:10.1088/1361-6579/ab37dbRooijakkers, M. J., Rabotti, C., Oei, S. G., Aarts, R. M., & Mischi, M. (2014). Low-complexity intrauterine pressure estimation using the Teager energy operator on electrohysterographic recordings. Physiological Measurement, 35(7), 1215-1228. doi:10.1088/0967-3334/35/7/1215Schlembach, D., Maner, W. L., Garfield, R. E., & Maul, H. (2009). Monitoring the progress of pregnancy and labor using electromyography. European Journal of Obstetrics & Gynecology and Reproductive Biology, 144, S33-S39. doi:10.1016/j.ejogrb.2009.02.016Fele-Žorž, G., Kavšek, G., Novak-Antolič, Ž., & Jager, F. (2008). A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Medical & Biological Engineering & Computing, 46(9), 911-922. doi:10.1007/s11517-008-0350-yHassan, M., Terrien, J., Marque, C., & Karlsson, B. (2011). Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Engineering & Physics, 33(8), 980-986. doi:10.1016/j.medengphy.2011.03.010Mas-Cabo, J., Prats-Boluda, G., Perales, A., Garcia-Casado, J., Alberola-Rubio, J., & Ye-Lin, Y. (2018). Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Medical & Biological Engineering & Computing, 57(2), 401-411. doi:10.1007/s11517-018-1888-yLemancewicz, A., Borowska, M., Kuć, P., Jasińska, E., Laudański, P., Laudański, T., & Oczeretko, E. (2016). Early diagnosis of threatened premature labor by electrohysterographic recordings – The use of digital signal processing. Biocybernetics and Biomedical Engineering, 36(1), 302-307. doi:10.1016/j.bbe.2015.11.005Mas-Cabo, J., Prats-Boluda, G., Garcia-Casado, J., Alberola-Rubio, J., Perales, A., & Ye-Lin, Y. (2019). Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records. Journal of Sensors, 2019, 1-13. doi:10.1155/2019/5373810Acharya, U. R., Sudarshan, V. K., Rong, S. Q., Tan, Z., Lim, C. M., Koh, J. E., … Bhandary, S. V. (2017). Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Computers in Biology and Medicine, 85, 33-42. doi:10.1016/j.compbiomed.2017.04.013Fergus, P., Cheung, P., Hussain, A., Al-Jumeily, D., Dobbins, C., & Iram, S. (2013). Prediction of Preterm Deliveries from EHG Signals Using Machine Learning. PLoS ONE, 8(10), e77154. doi:10.1371/journal.pone.0077154Ren, P., Yao, S., Li, J., Valdes-Sosa, P. A., & Kendrick, K. M. (2015). Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals. PLOS ONE, 10(7), e0132116. doi:10.1371/journal.pone.0132116Benalcazar-Parra, C., Ye-Lin, Y., Garcia-Casado, J., Monfort-Ortiz, R., Alberola-Rubio, J., Perales, A., & Prats-Boluda, G. (2019). Prediction of Labor Induction Success from the Uterine Electrohysterogram. Journal of Sensors, 2019, 1-12. doi:10.1155/2019/6916251Sammali, F., Kuijsters, N. P. M., Schoot, B. C., Mischi, M., & Rabotti, C. (2018). Feasibility of Transabdominal Electrohysterography for Analysis of Uterine Activity in Nonpregnant Women. Reproductive Sciences, 25(7), 1124-1133. doi:10.1177/1933719118768700Ye-Lin, Y., Bueno-Barrachina, J. M., Prats-boluda, G., Rodriguez de Sanabria, R., & Garcia-Casado, J. (2017). Wireless sensor node for non-invasive high precision electrocardiographic signal acquisition based on a multi-ring electrode. Measurement, 97, 195-202. doi:10.1016/j.measurement.2016.11.009Maul, H., Maner, W., Olson, G., Saade, G., & Garfield, R. (2004). Non-invasive transabdominal uterine electromyography correlates with the strength of intrauterine pressure and is predictive of labor and delivery. The Journal of Maternal-Fetal & Neonatal Medicine, 15(5), 297-301. doi:10.1080/14767050410001695301Shukla, S., Singh, S. K., & Mitra, D. (2020). An efficient heart sound segmentation approach using kurtosis and zero frequency filter features. Biomedical Signal Processing and Control, 57, 101762. doi:10.1016/j.bspc.2019.101762Ye-Lin, Y., Alberola-Rubio, J., Prats-boluda, G., Perales, A., Desantes, D., & Garcia-Casado, J. (2014). Feasibility and Analysis of Bipolar Concentric Recording of Electrohysterogram with Flexible Active Electrode. Annals of Biomedical Engineering, 43(4), 968-976. doi:10.1007/s10439-014-1130-5Vrhovec, J., Macek-Lebar, A., & Rudel, D. (s. f.). Evaluating Uterine Electrohysterogram with Entropy. IFMBE Proceedings, 144-147. doi:10.1007/978-3-540-73044-6_36Zhang, X.-S., Roy, R. J., & Jensen, E. W. (2001). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601Aboy, M., Hornero, R., Abasolo, D., & Alvarez, D. (2006). Interpretation of the Lempel-Ziv Complexity Measure in the Context of Biomedical Signal Analysis. IEEE Transactions on Biomedical Engineering, 53(11), 2282-2288. doi:10.1109/tbme.2006.883696Katz, M. J. (1988). Fractals and the analysis of waveforms. Computers in Biology and Medicine, 18(3), 145-156. doi:10.1016/0010-4825(88)90041-8De Lau, H., Yang, K. T., Rabotti, C., Vlemminx, M., Bajlekov, G., Mischi, M., & Oei, S. G. (2016). Toward a new modality for detecting a uterine rupture: electrohysterogram propagation analysis during trial of labor after cesarean. The Journal of Maternal-Fetal & Neonatal Medicine, 30(5), 574-579. doi:10.1080/14767058.2016.1178227Benalcazar-Parra, C., Ye-Lin, Y., Garcia-Casado, J., Monfort-Orti, R., Alberola-Rubio, J., Perales, A., & Prats-Boluda, G. (2018). Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs. Medical Engineering & Physics, 56, 27-35. doi:10.1016/j.medengphy.2018.04.002Garcia-Casado, J., Ye-Lin, Y., Prats-Boluda, G., Mas-Cabo, J., Alberola-Rubio, J., & Perales, A. (2018). Electrohysterography in the diagnosis of preterm birth: a review. Physiological Measurement, 39(2), 02TR01. doi:10.1088/1361-6579/aaad56Fisch, G. S., Cohen, I. L., Jenkins, E. C., & Brown, W. T. (1988). Screening developmentally disabled male populations for fragile X: The effect of sample size. American Journal of Medical Genetics, 30(1-2), 655-663. doi:10.1002/ajmg.1320300166Ye-Lin, Y., Garcia-Casado, J., Prats-Boluda, G., Alberola-Rubio, J., & Perales, A. (2014). Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions. Computational and Mathematical Methods in Medicine, 2014, 1-11. doi:10.1155/2014/470786Alberola-Rubio, J., Garcia-Casado, J., Prats-Boluda, G., Ye-Lin, Y., Desantes, D., Valero, J., & Perales, A. (2017). Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography? Computer Methods and Programs in Biomedicine, 144, 127-133. doi:10.1016/j.cmpb.2017.03.018Maner, W. L., MacKay, L. B., Saade, G. R., & Garfield, R. E. (2006). Characterization of abdominally acquired uterine electrical signals in humans, using a non-linear analytic method. Medical & Biological Engineering & Computing, 44(1-2), 117-123. doi:10.1007/s11517-005-0011-3Marchini, G., Lagercrantz, H., Winberg, J., & Uvnäs-Moberg, K. (1988). Fetal and maternal plasma levels of gastrin, somatostatin and oxytocin after vaginal delivery and elective cesarean section. Early Human Development, 18(1), 73-79. doi:10.1016/0378-3782(88)90044-8Pickering, K., Gallos, I. D., Williams, H., Price, M. J., Merriel, A., Lissauer, D., … Roberts, T. E. (2018). Uterotonic Drugs for the Prevention of Postpartum Haemorrhage: A Cost-Effectiveness Analysis. PharmacoEconomics - Open, 3(2), 163-176. doi:10.1007/s41669-018-0108-xMorfaw, F., Fundoh, M., Pisoh, C., Ayaba, B., Mbuagbaw, L., Anderson, L. N., & Thabane, L. (2019). Misoprostol as an adjunct to oxytocin can reduce postpartum-haemorrhage: a propensity score–matched retrospective chart review in Bamenda-Cameroon, 2015–2016. BMC Pregnancy and Childbirth, 19(1). doi:10.1186/s12884-019-2407-3Grotegut, C. A., Paglia, M. J., Johnson, L. N. C., Thames, B., & James, A. H. (2011). Oxytocin exposure during labor among women with postpartum hemorrhage secondary to uterine atony. American Journal of Obstetrics and Gynecology, 204(1), 56.e1-56.e6. doi:10.1016/j.ajog.2010.08.023Shen, Y., Oda, T., Tamura, N., Kohmura‐Kobayashi, Y., Furuta‐Isomura, N., Yaguchi, C., … Kanayama, N. (2019). Elevated bradykinin receptor type 1 expression in postpartum acute myometritis: Possible involvement in augmented interstitial edema of the atonic gravid uterus. Journal of Obstetrics and Gynaecology Research, 45(8), 1553-1561. doi:10.1111/jog.1401

COVID-19 and stem cell transplantation; results from an EBMT and GETH multicenter prospective survey

Altres ajuts: British Society for Blood and Marrow Transplantation and Cellular Therapy (BSBMTCT); UK NIHR Imperial College Biomedical Research Centre.This study reports on 382 COVID-19 patients having undergone allogeneic (n = 236) or autologous (n = 146) hematopoietic cell transplantation (HCT) reported to the European Society for Blood and Marrow Transplantation (EBMT) or to the Spanish Group of Hematopoietic Stem Cell Transplantation (GETH). The median age was 54.1 years (1.0-80.3) for allogeneic, and 60.6 years (7.7-81.6) for autologous HCT patients. The median time from HCT to COVID-19 was 15.8 months (0.2-292.7) in allogeneic and 24.6 months (−0.9 to 350.3) in autologous recipients. 83.5% developed lower respiratory tract disease and 22.5% were admitted to an ICU. Overall survival at 6 weeks from diagnosis was 77.9% and 72.1% in allogeneic and autologous recipients, respectively. Children had a survival of 93.4%. In multivariate analysis, older age (p = 0.02), need for ICU (p < 0.0001) and moderate/high immunodeficiency index (p = 0.04) increased the risk while better performance status (p = 0.001) decreased the risk for mortality. Other factors such as underlying diagnosis, time from HCT, GVHD, or ongoing immunosuppression did not significantly impact overall survival. We conclude that HCT patients are at high risk of developing LRTD, require admission to ICU, and have increased mortality in COVID-19

COVID-19 and stem cell transplantation; results from an EBMT and GETH multicenter prospective survey

This study reports on 382 COVID-19 patients having undergone allogeneic (n = 236) or autologous (n = 146) hematopoietic cell transplantation (HCT) reported to the European Society for Blood and Marrow Transplantation (EBMT) or to the Spanish Group of Hematopoietic Stem Cell Transplantation (GETH). The median age was 54.1 years (1.0-80.3) for allogeneic, and 60.6 years (7.7-81.6) for autologous HCT patients. The median time from HCT to COVID-19 was 15.8 months (0.2-292.7) in allogeneic and 24.6 months (-0.9 to 350.3) in autologous recipients. 83.5% developed lower respiratory tract disease and 22.5% were admitted to an ICU. Overall survival at 6 weeks from diagnosis was 77.9% and 72.1% in allogeneic and autologous recipients, respectively. Children had a survival of 93.4%. In multivariate analysis, older age (p = 0.02), need for ICU (p < 0.0001) and moderate/high immunodeficiency index (p = 0.04) increased the risk while better performance status (p = 0.001) decreased the risk for mortality. Other factors such as underlying diagnosis, time from HCT, GVHD, or ongoing immunosuppression did not significantly impact overall survival. We conclude that HCT patients are at high risk of developing LRTD, require admission to ICU, and have increased mortality in COVID-19

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Background The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae. Methods Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months. Findings Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01). Interpretation Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at root(NN)-N-S=2.76 TeV

Peer reviewe