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3. Cardiac arrest teams and time of day: effects on surviving in-hospital resuscitation

Author

Christ M, Dierschke W, von Auenmueller KI, van Bracht M, Grett M, and Trappe HJ

Subjects
Medicine (General), R5-920
Abstract

Martin Christ, Wolfgang Dierschke, Katharina Isabel von Auenmueller, Marc van Bracht, Martin Grett, Hans-Joachim Trappe Department of Cardiology and Angiology, Marienhospital Herne, Ruhr – University Bochum, Herne, Germany Objectives: Little is known about the factors that influence survival following in-hospital resuscitation, but previous investigations have suggested that in-hospital resuscitations outside of regular working hours are associated with worse survival rates. Material and methods: In-hospital cardiac arrest teams at our hospital were instructed to complete a questionnaire following every emergency call between July 2011 and June 2013. Data on all resuscitation attempts were collected and analyzed. Results: A total of 65 in-hospital resuscitations were recorded in 42 males (64.6%) and 23 females (35.4%) (mean age 72.0±14.3 years). A total of 54 (83.1%) cardiac arrests were witnessed; seven (10.8%) showed a shockable rhythm at the time of the first ECG. Resuscitation attempts lasted 29.3±41.3 minutes, and 4.1±3.1 mg epinephrine was given. Return of spontaneous circulation could be achieved in 38 patients (58.5%); 29 (44.6%) survived the first day, 23 (35.4%) the seventh day, and 15 patients (23.1%) were discharged alive. Significantly more in-hospital resuscitations were obtained for those performed during non-regular working hours (P

Published
2014

5. GBDTMO: as new option for early-stage breast cancer detection and classification using machine learning

Author

Vibith A. S. and Jobin Christ M C

Subjects
Breast cancer, machine learning, accuracy, classification, ensemble, Mayfly search, Control engineering systems. Automatic machinery (General), TJ212-225, Automation, T59.5
Abstract

Breast cancer is the second leading cause of disease death in women, after lung and bronchus cancer. According to measurements, mammography misses breast cancer in 10% to 15% of cases for women aged 50 to 69 years. In the current study, we used the Wisconsin breast cancer dataset to develop a two-stage model for breast cancer diagnosis. The main goal of this study effort is to effectively carry out feature selection and classification tasks. Gradient Boosting Decision Tree-based Mayfly Optimisation (GBDTMO), an innovative and efficient breast cancer diagnostic machine learning system, is provided. In the second stage, we employ a Mayfly search to determine which subset of traits is the best. Two more well-known datasets on breast cancer, the ICCR and the Cancer Corpus, were also compared for classification accuracy. The accuracy of the suggested GBDTMO model was higher than that of the existing GBDT and Practical Federated Gradient Boosting Decision Tree (PFGBDT), which had accuracy values of 93.25% and 94.25%, respectively. Similarly, the recall, F-measure, and ROC area values were 98.52%, 97.52%, and 96.32%, respectively. Furthermore, it demonstrated a lower RMSE of 0.98 than the existing GBDT and PFGBDT.

Published
2023
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18. Chest pain in patients with cancer: prevalence of myocardial infarction and diagnostic performance of high-sensitivity cardiac troponin T/I

Author

Bima, P, primary, Lopez-Ayala, P, additional, Koechlin, L, additional, Boeddinghaus, J, additional, Nestelberger, T, additional, Okamura, B, additional, Muench-Gerber, T S, additional, Rubini Gimenez, M, additional, Miro, O, additional, Martin-Sanchez, F J, additional, Christ, M, additional, Keller, D, additional, Lindhal, B, additional, Giannitsis, E, additional, and Mueller, C, additional

Published
2023
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19. Production of Sigma{\pm}pi?pK+ in p+p reactions at 3.5 GeV beam energy

Author

Agakishiev, G., Balanda, A., Belver, D., Belyaev, A., Berger-Chen, J. C., Blanco, A., Boehmer, M., Boyard, J. L., Cabanelas, P., Castro, E., Chernenko, S., Destefanis, T. Christ M., Dohrmann, F., Dybczak, A., Epple, E., Fabbietti, L., Fateev, O., Finocchiaro, P., Fonte, P., Friese, J., Fröhlich, I., Galatyuk, T., Garzon, J. A., Gernhäuser, R., Gilardi, C., Golubeva, M., Gonza'lez-Di'az, D., Guber, F., Gumberidze, M., Heinz, T., Hennino, T., Holzmann, R., Ierusalimov, A., Iori, I., Ivashkin, A., Jurkovic, M., Kämpfer, B., Kanaki, K., Karavicheva, T., Koenig, I., Koenig, W., Kolb, B. W., Kotte, R., Kr'asa, A., Krizek, F., Krücken, R., Kuc, H., Kuehn, W., Kugler, A., Kurepin, A., Lalik, R., Lang, S., Lange, J. S., Lapidus, K., Liu, T., Lopes, L., Lorenz, M., Maier, L., Mangiarotti, A., Markert, J., Metag, V., Michalska, B., Michel, J., Morinie're, E., Mousa, J., Müntz, C., Naumann, L., Otwinowski, J., Pachmayer, Y. C., Palka, M., Parpottas, Y., Pechenov, V., Pechenova, O., Pietraszko, J., Przygoda, W., Ramstein, B., Reshetin, A., Rustamov, A., Sadovsky, A., Salabura, P., Schmah, A., Schwab, E., Siebenson, J., Sobolev, Yu. G., Spataro, S., Spruck, B., Ströbele, H., Stroth, J., Sturm, C., Tarantola, A., Teilab, K., Tlusty, P., Traxler, M., Trebacz, R., Tsertos, H., Wagner, V., Weber, M., Wendisch, C., Wüstenfeld, J., Yurevich, S., and Zanevsky, Y.

Subjects
Nuclear Experiment
Abstract

We study the production of Sigma^+-pi^+-pK^+ particle quartets in p+p reactions at 3.5 GeV kinetic beam energy. The data were taken with the HADES experiment at GSI. This report evaluates the contribution of resonances like Lambda(1405$, Sigma(1385)^0, Lambda(1520), Delta(1232), N^* and K^*0 to the Sigma^+- pi^-+ p K+ final state. The resulting simulation model is compared to the experimental data in several angular distributions and it shows itself as suitable to evaluate the acceptance corrections properly., Comment: 15 pages, 5 figures

Published
2012
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20. External Validation of the SMART Medical Clearance Form for Emergency Patients With Psychiatric Manifestations.

Author

Festini, D., Wthrich, F., and Christ, M.

Abstract

Background: The SMART Medical Clearance Form, developed in the USA, is used to standardize the medical evaluation of emergency patients with primarily psychiatric manifestations. The goal of this study was external validation of the use of this form. Methods: Data were collected retrospectively on emergency patients with psychiatric manifestations. The combined primary clinical endpoint consisted of hospitalization, repeated presentation to the emergency room, and/or death within 30 days. Results: From September 2019 to June 2022, 2404 patients presented with psychiatric manifestations to the emergency room of the Cantonal Hospital of Lucerne, Switzerland, of whom 674 were included in the study. 134 did not satisfy any of the parameters of the SMART Medical Clearance Form (the nSMART group), while 540 satisfied at least one parameter (the pSMART group). In the nSMART group, there were no hospitalizations for a medical indication, no repeated presentations for medical reasons, and no deaths within 30 days. In the pSMART group, there were 90 hospitalizations, 4 repeated presentations, and 4 deaths within 30 days. Although 44% of the patients in the nSMART group underwent further diagnostic studies, such as imaging or laboratory tests, none of these studies led to any change in these patients further clinical management. Conclusion: Use of the SMART Medical Clearance Form apparently enables safe standardized processing of patients with psychiatric manifestations in the emergency room. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Absolutely continuous spectrum of Stark operators

Author

Christ, M. and Kiselev, A.

Subjects
Mathematics - Spectral Theory, Mathematical Physics, 34L40, 81Q10
Abstract

We prove several new results on the absolutely continuous spectra of perturbed one-dimensional Stark operators. First, we find new classes of perturbations, characterized mainly by smoothness conditions, which preserve purely absolutely continuous spectrum. Then we establish stability of the absolutely continuous spectrum in more general situations, where imbedded singular spectrum may occur. We present two kinds of optimal conditions for the stability of absolutely continuous spectrum: decay and smoothness. In the decay direction, we show that a sufficient (in the power scale) condition is $|q(x)| \leq C(1+|x|)^{-{1/4}-\epsilon};$ in the smoothness direction, a sufficient condition in H\"older classes is $q \in C^{{1/2}+\epsilon}(\reals)$. On the other hand, we show that there exist potentials which both satisfy $|q(x)| \leq C(1+|x|)^{-1/4}$ and belong to $C^{1/2}(\reals)$ for which the spectrum becomes purely singular on the whole real axis, so that the above results are optimal within the scales considered., Comment: 29 pages

Published
2001

28. Scattering and wave operators for one-dimensional Schr\'odinger operators with slowly decaying nonsmooth potentials

Author

Christ, M. and Kiselev, A.

Subjects
Mathematics - Spectral Theory, Mathematical Physics, 34L40, 35P25, 42B20, 81Q10
Abstract

We prove existence of modified wave operators for one-dimensional Schr\"odinger equations with potential in $L^p(\reals)$, $p<2$. If in addition the potential is conditionally integrable, then the usual M\"oller wave operators exist. We also prove asymptotic completeness of these wave operators for some classes of random potentials, and for almost every boundary condition for any given potential., Comment: 46 pages

Published
2001

29. Periportale Steatose bei Mäusen beeinflusst verschiedene Parameter des perizentralen Arzneimittelstoffwechsels

Author

Albadry, M, additional, Höpfl, S, additional, Ehteshamzad, N, additional, König, M, additional, Böttcher, M, additional, Neumann, J, additional, Lupp, A, additional, Dirsch, O, additional, Radde, N, additional, Christ, B, additional, Christ, M, additional, Schwen, L, additional, Laue, H, additional, Klopfleisch, R, additional, and Dahmen, U, additional

Published
2023
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30. GBDTMO: as new option for early-stage breast cancer detection and classification using machine learning.

Author

A. S., Vibith and Christ M C, Jobin

Subjects
MACHINE learning, BREAST cancer, TUMOR classification, EARLY detection of cancer, CANCER diagnosis, FEATURE selection
Abstract

Breast cancer is the second leading cause of disease death in women, after lung and bronchus cancer. According to measurements, mammography misses breast cancer in 10% to 15% of cases for women aged 50 to 69 years. In the current study, we used the Wisconsin breast cancer dataset to develop a two-stage model for breast cancer diagnosis. The main goal of this study effort is to effectively carry out feature selection and classification tasks. Gradient Boosting Decision Tree-based Mayfly Optimisation (GBDTMO), an innovative and efficient breast cancer diagnostic machine learning system, is provided. In the second stage, we employ a Mayfly search to determine which subset of traits is the best. Two more well-known datasets on breast cancer, the ICCR and the Cancer Corpus, were also compared for classification accuracy. The accuracy of the suggested GBDTMO model was higher than that of the existing GBDT and Practical Federated Gradient Boosting Decision Tree (PFGBDT), which had accuracy values of 93.25% and 94.25%, respectively. Similarly, the recall, F-measure, and ROC area values were 98.52%, 97.52%, and 96.32%, respectively. Furthermore, it demonstrated a lower RMSE of 0.98 than the existing GBDT and PFGBDT. [ABSTRACT FROM AUTHOR]

Published
2023
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31. On multilinear oscillatory integrals, nonsingular and singular

Author

Christ, M, Li, X C, Tao, T, and Thiele, C

Abstract

Basic questions concerning nonsingular multilinear operators with oscillatory factors are posed and partially answered. L-p norm inequalities are established for multilinear integral operators of Calderon-Zygmund type which incorporate oscillatory factors e(iP), where P is a real-valued polynomial.

Published
2005

33. Correction to: The Intensive Care Global Study on Severe Acute Respiratory Infection (IC-GLOSSARI): a multicenter, multinational, 14-day inception cohort study

Author

Sakr, Yasser, Ferrer, Ricard, Reinhart, Konrad, Beale, Richard, Rhodes, Andrew, Moreno, Rui, Timsit, Jean Francois, Brochard, Laurent, Thompson, B. Taylor, Rezende, Ederlon, Chiche, Jean Daniel, Auer, J., Schatzl, G., Mach, K., Gruber, H., Schreurs, E., Vander Laenen, M., Ceunen, H., Wauters, J., Francois, G., Deschamps, P., Castanares, D., Debels, D., Pierrakos, C., Vincent, J. L., Taccone, F., Vymazal, T., Gornik, I., Vujiaklija Brajkovic, A., Medici, R., Nielsen, J., Bendtsen, A., Siegel, H., Suonsyrjä, T., Hraech, S., Daviaux, F., Guillot, M., Castelain, V., Losser, R-R., Novy, E., Bouadma, L., Misset, B., Philippart, F., Mallat, J., Zogheib, E., Miclo, M., Teboul, J-L., Anguel, N., Darmon, M., Pham, T., Barberet, G., Plantefeve, G., Floccard, B., Kheladze, Z., Bloos, F., Faltlhauser, A., Helmes, T., Zacharowski, K., Meybohm, P., Schwarzkopf, K., Christ, M., Baumgaertel, M., John, S., Nentwich, J., Deja, M., Goldmann, A., Gottschalk, A., Honig, F., Siepe, B., Goebel, U., Lehmke, J., Behrens, S., Fiedler, K., Sagoschen, I., Riessen, R., Haap, M., Simon, Ph., Kaisers, U., Behrens, S., Niesen, M., Jaschinski, U., Hoersch, S., Jung, A., Allgaeuer, S., Haake, H., Lange, A., Papanikolaou, M., Balla, M., Giannakou, M., Soultati, I., Nikos, G., Koulouras, V., Kyriazopoulos, G., Gkika, D., Vlachogianni, G., Psaroulis, K., Mouloudi, E., Massa, E., Nichol, A., Meany, E., Motherway, C., Bellani, G., Pota, V., Schiavone, V., Girardis, M., Busani, S., Petrucci, N., Di Pasquale, R., Mazzini, P., Molin, A., Pellerano, G., Volta, C., Spadaro, S., Guarracino, F., Savioli, M., Pellis, T., Chinellato, N., Gatta, A., Cecchini, F., Raineri, S. M., Cortegiani, A., Kekstas, G., Karosas, V., Anguseva, T., Mitrev, Z., Beck, O., Cimic, N., Janssen, G., Bormans, L., Kuiper, M., Koopmans, K., Den Boer, S., de Groot, M., Dennesen, P., van den Bosch, J., Kluge, G., Mikaszewska-Sokolewicz, M., Lazowski, T., Chruscikowski, M., Machon, J., Adamik, B., Kübler, A., Wieczorek, A., Afonso, S., Matos, R., Catorze, N., Araujo, A., Costa, Z., Pais-de-Lacerda, A., Martins, I., Cardiga, R., Fernandes, L., Serra, I., Martinho, A., Tomescu, D., Popescu, M., Scarlatescu, E., Stoica, R., Macri, A., Filipescu, D., Rupnik, E., Tomic, V., Sifrer, F., Sole Violan, J., Ferrer Agüero, J. M., Izura, J., Monedero, P., de Cabo, C. Muños, Aguilar, G., Belda, F. J., Blanquer, J., Nives Carbonell, E., Lopez-Delgado, J-C., Aragon, C., Joya, C., Ortiz-Leyba, C., Fernandez Gonzalez, C. J., de la Torre-Prados, M-V., Puerto-Morlan, A., Araujo Aguilar, P., Tomás Marsilla, J. I., Vera Aratcoz, P., Olmo, A., Ferrer Roca, R., Catalan, R. M., Garcia Olivares, P., Albis, A., Alvarez, M., Corcoles Gonzalez, V., Gutierrez Rubio, J. M., Montoiro Allue, R., Rubio Mateo-Sidron, J., Hobrok, M., Cecconi, M., Di Tomasso, N., Raj, A., Szakmany, T., Srinivasa, L., Mathew, S., Ferguson, A., Blahut-Zugaj, M., Watters, M., Henderson, S., Sim, M., Csabi, P., O’Neill, O., Nutt, C., Humphreys, S., Bhowmick, K., Donnelly, A., O’Kane, S., Garfield, M., Jha, R., Unni, N., Gordon, A., Rubulotta, F., Ravi, K., Lunch, G., Franco, F., Higgs, D., Strandvik, G., Jonas, A., Hopkins, Ph., Hurst, T., Bellini, A., Balogun, O., Srinivasan, R., Ostermann, M., Alexander, P., McCalman, K., Bedford, J., Fulop, M., Brescia, G., Strachan, J., Meyer, J., Stotz, M., Brett, S., Zand, F., Nikandish, R., Hashemian, S., Jamaati, H., Alsheikhly, A. S., Almekhlafi, G., Albarrak, M., Maghrabi, A., Salahuddin, N., Aisa, T., Atalan, H. K., Sungur, M., Hegazi, M., Bauer, P., Mukkera, S., Fried, J., Barger, M., Gueret, R., Gonzalez, C., Lovesio, C., Dellera, Ch., Barrios, D., Leite Mendes, C., Gottardo, P., Caser, E., Santos, C., Carvalho, A., Teixeira, C., Samaniego, W., Whittle, S., Molano, D., Rojas, A., Guerra, K., Villamagua, B., Salgado-Yepez, E., Morocho, D., Remache-Vargas, N., Ñamendys-Silva, S., Rodriguez, D., Dominguez, G., Barraza, G., Bermudez-Aceves, E., Sanchez-Hurtado, L. A., Baltazar-Torres, J. A., Quispe Sierra, R., Chavez, C., von Osten, I., Van Haren, C., Smalley, N., Kol, M., Wong, H., Smith, R., Yu, L., Wu, X., Chao, L., Zhai, Q., Wu, D., Zhang, X., Jing, X., Bigornia, R., Ikeda-Maquiling, Y., Robles, J., Palo, J. E., Nguyen, T., Dao, C., Dixit, S., Gurjar, M., Reddy, P., Pravin, A., Simran, S., Ramakrishnan, N., Shetty, R., Udwadia, F., Faraz, M., Indraratna, K., Rajasinhe, J., and IC-GLOSSARI Investigators and ESICM Trials Group

Published
2017
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34. GBDTMO: as new option for early-stage breast cancer detection and classification using machine learning

Author

Vibith, A. S., Jobin Christ, M. C., Vibith, A. S., and Jobin Christ, M. C.

Abstract

Breast cancer is the second leading cause of disease death in women, after lung and bronchus cancer. According to measurements, mammography misses breast cancer in 10% to 15% of cases for women aged 50 to 69 years. In the current study, we used the Wisconsin breast cancer dataset to develop a two-stage model for breast cancer diagnosis. The main goal of this study effort is to effectively carry out feature selection and classification tasks. Gradient Boosting Decision Tree-based Mayfly Optimisation (GBDTMO), an innovative and efficient breast cancer diagnostic machine learning system, is provided. In the second stage, we employ a Mayfly search to determine which subset of traits is the best. Two more well-known datasets on breast cancer, the ICCR and the Cancer Corpus, were also compared for classification accuracy. The accuracy of the suggested GBDTMO model was higher than that of the existing GBDT and Practical Federated Gradient Boosting Decision Tree (PFGBDT), which had accuracy values of 93.25% and 94.25%, respectively. Similarly, the recall, F-measure, and ROC area values were 98.52%, 97.52%, and 96.32%, respectively. Furthermore, it demonstrated a lower RMSE of 0.98 than the existing GBDT and PFGBDT.

Published
2023

44. 0/1h-algorithm using a new high-sensitivity cardiac troponin I assay for early diagnosis of myocardial infarction

Author

Koechlin, L, primary, Boeddinghaus, J, additional, Lopez-Ayala, P, additional, Nestelberger, T, additional, Wussler, D, additional, Twerenbold, R, additional, Zimmermann, T, additional, Wildi, K, additional, Miro, O, additional, Martin-Sanchez, J, additional, Keller, D, additional, Christ, M, additional, Buser, A, additional, Rubini Gimenez, M, additional, and Mueller, C, additional

Published
2022
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47. Performance of high-sensitivity cardiac troponin T versus I for the early diagnosis of myocardial infarction

Author

Koechlin, L, primary, Boeddinghaus, J, additional, Lopez-Ayala, P, additional, Nestelberger, T, additional, Miro, O, additional, Wussler, D, additional, Zimmermann, T, additional, Strebel, I, additional, Christ, M, additional, Wildi, K, additional, Rubini Gimenez, M, additional, Martin-Sanchez, J, additional, Keller, D, additional, Twerenbold, R, additional, and Mueller, C, additional

Published
2022
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48. Clinical risk scores versus simple integrated clinical judgment in patients with suspected acute coronary syndromes

Author

Boeddinghaus, J, primary, Meier, M, additional, Nestelberger, T, additional, Koechlin, L, additional, Lopez-Ayala, P, additional, Rubini Gimenez, M, additional, Miro, O, additional, Martin-Sanchez, J, additional, Kawecki, D, additional, Keller, D, additional, Nickel, C, additional, Bingisser, R, additional, Christ, M, additional, and Mueller, C, additional

Published
2022
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49. Extending the no objective testing rules to patients triaged by the ESC 0/1-hour algorithms

Author

Boeddinghaus, J, primary, Ratmann, P D, additional, Nestelberger, T, additional, Lopez-Ayala, P, additional, Koechlin, L, additional, Wildi, K, additional, Miro, O, additional, Martin-Sanchez, J, additional, Christ, M, additional, Gualandro, D, additional, Rubini Gimenez, M, additional, Keller, D, additional, Buser, A, additional, and Mueller, C, additional

Published
2022
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