7 research outputs found

    Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics

    Full text link
    [EN] This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could be of great clinical impact. Although the advent of new blood glucose monitoring technologies may reduce the incidence of the problems stated above, incorrect device or sensor manipulation, patient adherence, sensor detachment, time constraints, adoption barriers or affordability can still result in relatively short and artifacted records, as the ones analyzed in this paper or in other similar works. This study is aimed at characterizing the changes induced by such artifacts, enabling the arrangement of countermeasures in advance when possible. Despite the presence of these disturbances, results demonstrate that SampEn and FuzzyEn are sufficiently robust to achieve a significant classification performance, using records obtained from patients with duodenal-jejunal exclusion. The classification results, in terms of area under the ROC of up to 0.9, with several tests yielding AUC values also greater than 0.8, and in terms of a leave-one-out average classification accuracy of 80%, confirm the potential of these measures in this context despite the presence of artifacts, with SampEn having slightly better performance than FuzzyEn.The Czech partners were supported by DROIKEM000023001 and RVOVFN64165. No funding was received to support this research work by the Spanish partners.Cuesta Frau, D.; Novák, D.; Burda, V.; Molina Picó, A.; Vargas-Rojo, B.; Mraz, M.; Kavalkova, P.... (2018). Characterization of Artifact Influence on the Classification of Glucose Time Series Using Sample Entropy Statistics. Entropy. 20(11):1-18. https://doi.org/10.3390/e20110871S118201

    Serum lactate in refractory out-of-hospital cardiac arrest:Post-hoc analysis of the Prague OHCA study

    Get PDF
    Background: The severity of tissue hypoxia is routinely assessed by serum lactate. We aimed to determine whether early lactate levels predict outcomes in refractory out-of-hospital cardiac arrest (OHCA) treated by conventional and extracorporeal cardiopulmonary resuscitation (ECPR). Methods: This study is a post-hoc analysis of a randomized Prague OHCA study (NCT01511666) assessing serum lactate levels in refractory OHCA treated by ECPR (the ECPR group) or conventional resuscitation with prehospital achieved return of spontaneous circulation (the ROSC group). Lactate concentrations measured on admission and every 4 hours (h) during the first 24 h were used to determine their relationship with the neurological outcome (the best Cerebral Performance Category score within 180 days post-cardiac arrest). Results:In the ECPR group (92 patients, median age 58.5 years, 83% male) 26% attained a favorable neurological outcome. In the ROSC group (82 patients, median age 55 years, 83% male) 59% achieved a favorable neurological outcome. In ECPR patients lactate concentrations could discriminate favorable outcome patients, but not consistently in the ROSC group. On admission, serum lactate &gt;14.0 mmol/L for ECPR (specificity 87.5%, sensitivity 54.4%) and &gt;10.8 mmol/L for the ROSC group (specificity 83%, sensitivity 41.2%) predicted an unfavorable outcome. Conclusion: In refractory OHCA serum lactate concentrations measured anytime during the first 24 h after admission to the hospital were found to correlate with the outcome in patients treated by ECPR but not in patients with prehospital ROSC. A single lactate measurement is not enough for a reliable outcome prediction and cannot be used alone to guide treatment.</p

    Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods

    Get PDF
    [EN] Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal¿jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonlinear algorithms (Lempel¿Ziv Complexity, Sample Entropy, Permutation Entropy, and modified Permutation Entropy), well suited to the characterization of biomedical time series. We applied them to glucose records drawn from two extreme cases available of DJBL implantation: before and after 10 months. The results confirmed the hypothesis and an accuracy of 86.4% was achieved with modified Permutation Entropy. Other metrics also yielded significant classification accuracy results, all above 70%, provided a suitable parameter configuration was chosen. With the Leave¿One¿Out method, the results were very similar, between 72% and 82% classification accuracy. There was also a decrease in entropy of glycaemia records during the time interval studied. These findings provide a solid foundation to assess how glucose metabolism may be influenced by DJBL implantation and opens a new line of research in this field.The Czech clinical partners were supported by DRO IKEM 000023001 and RVO VFN 64165. The Czech technical partners were supported by Research Centre for Informatics grant numbers CZ.02.1.01/0.0/16 - 019/0000765 and SGS16/231/OHK3/3T/13-Support of interactive approaches to biomedical data acquisition and processing. No funding was received to support this research work by the Spanish and British partnersCuesta Frau, D.; Novák, D.; Burda, V.; Abasolo, D.; Adjei, T.; Varela, M.; Vargas, B.... (2019). Influence of Duodenal-Jejunal Implantation on Glucose Dynamics: A Pilot Study Using Different Nonlinear Methods. Complexity. 2019. https://doi.org/10.1155/2019/6070518S2019Kassirer, J. P., & Angell, M. (1998). Losing Weight — An Ill-Fated New Year’s Resolution. New England Journal of Medicine, 338(1), 52-54. doi:10.1056/nejm199801013380109Van Gaal, L., & Dirinck, E. (2016). Pharmacological Approaches in the Treatment and Maintenance of Weight Loss. Diabetes Care, 39(Supplement 2), S260-S267. doi:10.2337/dcs15-3016De Jonge, C., Rensen, S. S., Verdam, F. J., Vincent, R. P., Bloom, S. R., Buurman, W. A., … Greve, J. W. M. (2015). Impact of Duodenal-Jejunal Exclusion on Satiety Hormones. Obesity Surgery, 26(3), 672-678. doi:10.1007/s11695-015-1889-yMuñoz, R., Dominguez, A., Muñoz, F., Muñoz, C., Slako, M., Turiel, D., … Escalona, A. (2013). Baseline glycated hemoglobin levels are associated with duodenal-jejunal bypass liner-induced weight loss in obese patients. Surgical Endoscopy, 28(4), 1056-1062. doi:10.1007/s00464-013-3283-yOgata, H., Tokuyama, K., Nagasaka, S., Ando, A., Kusaka, I., Sato, N., … Yamamoto, Y. (2007). Long-range Correlated Glucose Fluctuations in Diabetes. Methods of Information in Medicine, 46(02), 222-226. doi:10.1055/s-0038-1625411Rodríguez de Castro, C., Vigil, L., Vargas, B., García Delgado, E., García Carretero, R., Ruiz-Galiana, J., & Varela, M. (2016). Glucose time series complexity as a predictor of type 2 diabetes. Diabetes/Metabolism Research and Reviews, 33(2), e2831. doi:10.1002/dmrr.2831DeFronzo, R. A. (2004). Pathogenesis of type 2 diabetes mellitus. Medical Clinics of North America, 88(4), 787-835. doi:10.1016/j.mcna.2004.04.013Zhang, 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:10.1109/10.966601Bandt, C., & Pompe, B. (2002). Permutation Entropy: A Natural Complexity Measure for Time Series. Physical Review Letters, 88(17). doi:10.1103/physrevlett.88.174102Bian, C., Qin, C., Ma, Q. D. Y., & Shen, Q. (2012). Modified permutation-entropy analysis of heartbeat dynamics. Physical Review E, 85(2). doi:10.1103/physreve.85.021906Zhao, L., Wei, S., Zhang, C., Zhang, Y., Jiang, X., Liu, F., & Liu, C. (2015). Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects. Entropy, 17(12), 6270-6288. doi:10.3390/e17096270Weinstein, R. L., Schwartz, S. L., Brazg, R. L., Bugler, J. R., Peyser, T. A., & McGarraugh, G. V. (2007). Accuracy of the 5-Day FreeStyle Navigator Continuous Glucose Monitoring System: Comparison with frequent laboratory reference measurements. Diabetes Care, 30(5), 1125-1130. doi:10.2337/dc06-1602Weber, C., & Schnell, O. (2009). The Assessment of Glycemic Variability and Its Impact on Diabetes-Related Complications: An Overview. Diabetes Technology & Therapeutics, 11(10), 623-633. doi:10.1089/dia.2009.0043Cuesta-Frau, D., Miró-Martínez, P., Oltra-Crespo, S., Jordán-Núñez, J., Vargas, B., González, P., & Varela-Entrecanales, M. (2018). Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures. Entropy, 20(11), 853. doi:10.3390/e20110853Cuesta–Frau, D., Miró–Martínez, P., Oltra–Crespo, S., Jordán–Núñez, J., Vargas, B., & Vigil, L. (2018). Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm. Computer Methods and Programs in Biomedicine, 165, 197-204. doi:10.1016/j.cmpb.2018.08.018Cuesta–Frau, D., Varela–Entrecanales, M., Molina–Picó, A., & Vargas, B. (2018). Patterns with Equal Values in Permutation Entropy: Do They Really Matter for Biosignal Classification? Complexity, 2018, 1-15. doi:10.1155/2018/1324696Mayer, C. C., Bachler, M., Hörtenhuber, M., Stocker, C., Holzinger, A., & Wassertheurer, S. (2014). Selection of entropy-measure parameters for knowledge discovery in heart rate variability data. BMC Bioinformatics, 15(S6). doi:10.1186/1471-2105-15-s6-s2Sheng Lu, Xinnian Chen, Kanters, J. K., Solomon, I. C., & Chon, K. H. (2008). Automatic Selection of the Threshold Value rr for Approximate Entropy. IEEE Transactions on Biomedical Engineering, 55(8), 1966-1972. doi:10.1109/tbme.2008.919870Crenier, L., Lytrivi, M., Van Dalem, A., Keymeulen, B., & Corvilain, B. (2016). Glucose Complexity Estimates Insulin Resistance in Either Nondiabetic Individuals or in Type 1 Diabetes. The Journal of Clinical Endocrinology & Metabolism, 101(4), 1490-1497. doi:10.1210/jc.2015-4035Cuesta, D., Varela, M., Miró, P., Galdós, P., Abásolo, D., Hornero, R., & Aboy, M. (2007). Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy. Medical & Biological Engineering & Computing, 45(7), 671-678. doi:10.1007/s11517-007-0200-3Chen, 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.2008.04.005Xiao-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.87.02291

    Growing Evidence for LV Unloading in VA ECMO

    No full text
    Impressively increasing availability of mechanical circulatory/cardiac support systems (MCSs) worldwide, together with the deepening of the knowledge of critical care medical practitioners, has inevitably led to the discussion about further improvements of intensive care associated to MCS. An appealing topic of the left ventricle (LV) overload related to VA ECMO support endangering myocardial recovery is being widely discussed within the scientific community. Unloading of LV leads to the reduction in LV end-diastolic pressure, reduction in pressure in the left atrium, and decrease in the LV thrombus formation risk. Consequently, better conditions for myocardial recovery, with comfortable filling pressures and a better oxygen delivery/demand ratio, are achieved. The combination of VA ECMO and Impella device, also called ECPELLA, seems to be a promising strategy that may bring the improvement of CS mortality rates. The series of presented trials and meta-analyses clearly showed the potential benefits of this strategy. However, the ongoing research has brought a series of new questions, such as whether Impella itself is the only appropriate unloading modality, or any other approach to unload LV would be beneficial in the same way. Benefits and potential risks of LV unloading and its timing are being discussed in this current review

    Intraarrest transport, extracorporeal cardiopulmonary resuscitation, and early invasive management in refractory out-of-hospital cardiac arrest: an individual patient data pooled analysis of two randomised trialsResearch in context

    No full text
    Summary: Background: Refractory out-of-hospital cardiac arrest (OHCA) treated with standard advanced cardiac life support (ACLS) has poor outcomes. Transport to hospital followed by in-hospital extracorporeal cardiopulmonary resuscitation (ECPR) initiation may improve outcomes. We performed a pooled individual patient data analysis of two randomised controlled trials evaluating ECPR based approach in OHCA. Methods: The individual patient data from two published randomised controlled trials (RCTs) were pooled: ARREST (enrolled Aug 2019–June 2020; NCT03880565) and PRAGUE-OHCA (enrolled March 1, 2013–Oct 25, 2020; NCT01511666). Both trials enrolled patients with refractory OHCA and compared: intra-arrest transport with in-hospital ECPR initiation (invasive approach) versus continued standard ACLS. The primary outcome was 180-day survival with favourable neurological outcome (defined as Cerebral Performance Category 1–2). Secondary outcomes included: cumulative survival at 180 days, 30-day favourable neurological survival, and 30-day cardiac recovery. Risk of bias in each trial was assessed by two independent reviewers using the Cochrane risk-of-bias tool. Heterogeneity was assessed via Forest plots. Findings: The two RCTs included 286 patients. Of those randomised to the invasive (n = 147) and standard (n = 139) groups, respectively: the median age was 57 (IQR 47–65) and 58 years (IQR 48–66), and the median duration of resuscitation was 58 (IQR 43–69) and 49 (IQR 33–71) minutes (p = 0.17). In a modified intention to treat analysis, 45 (32.4%) in the invasive and 29 (19.7%) patients in the standard arm survived to 180 days with a favourable neurological outcome [absolute difference (AD), 95% CI: 12.7%, 2.6–22.7%, p = 0.015]. Forty-seven (33.8%) and 33 (22.4%) patients survived to 180 days [HR 0.59 (0.43–0.81); log rank test p = 0.0009]. At 30 days, 44 (31.7%) and 24 (16.3%) patients had favourable neurological outcome (AD 15.4%, 5.6–25.1%, p = 0.003), 60 (43.2%), and 46 (31.3%) patients had cardiac recovery (AD: 11.9%, 0.7–23%, p = 0.05), in the invasive and standard arms, respectively. The effect was larger in patients presenting with shockable rhythms (AD 18.8%, 7.6–29.4; p = 0.01; HR 2.26 [1.23–4.15]; p = 0.009) and prolonged CPR (>45 min; HR 3.99 (1.54–10.35); p = 0.005). Interpretation: In patients with refractory OHCA, the invasive approach significantly improved 30- and 180-day neurologically favourable survival. Funding: None

    ECPR for refractory OHCA - lessons from 3 randomized controlled trials. The trialists´view

    No full text
    Extracorporeal cardiopulmonary resuscitation is a promising treatment for refractory out-of-hospital cardiac arrest. Three recent randomized trials (ARREST-trial, Prague OHCA study, and INCEPTION-trial) that addressed the clinical benefit of ECPR in out-of-hospital cardiac arrest, yielded seemingly diverging results. The evidence for extracorporeal cardiopulmonary resuscitation in out-of-hospital cardiac arrest, derived from three recent RCT's, is not contradictory but rather complementary. Excellent results can be achieved with a very high level of dedication, provided that strict selection criteria are applied. However, pragmatic implementation of extracorporeal cardiopulmonary resuscitation does not necessarily lead to improved outcome of refractory out-of-hospital cardiac arrest. Centers that are performing extracorporeal cardiopulmonary resuscitation for out-of-hospital cardiac arrest or aspire to do so, should critically evaluate whether they are able to meet the prerequisites that are needed to conduct an effective extracorporeal cardiopulmonary resuscitation program
    corecore