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1. Differentiating Viral and Bacterial Infections: A Machine Learning Model Based on Routine Blood Test Values

Author

Gunčar, Gregor, Kukar, Matjaž, Smole, Tim, Moškon, Sašo, Vovko, Tomaž, Podnar, Simon, Černelč, Peter, Brvar, Miran, Notar, Mateja, Köster, Manca, Jelenc, Marjeta Tušek, and Notar, Marko

Subjects
Computer Science - Machine Learning, I.2.6, J.3
Abstract

The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a Brier score of 0.129, and an area under the ROC curve (AUC) of 0.905, outperforming a CRP-based decision rule. Notably, the machine learning model enhanced accuracy within the CRP range of 10-40 mg/L, a range where CRP alone is less informative. These results highlight the advantage of integrating multiple blood parameters in diagnostics. The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management., Comment: 16 pages

Published
2023
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3. COVID-19 diagnosis by routine blood tests using machine learning

Author

Kukar, Matjaž, Gunčar, Gregor, Vovko, Tomaž, Podnar, Simon, Černelč, Peter, Brvar, Miran, Zalaznik, Mateja, Notar, Mateja, Moškon, Sašo, and Notar, Marko

Subjects
Physics - Medical Physics, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning, I.2.6, J.3
Abstract

Physicians taking care of patients with coronavirus disease (COVID-19) have described different changes in routine blood parameters. However, these changes, hinder them from performing COVID-19 diagnosis. We constructed a machine learning predictive model for COVID-19 diagnosis. The model was based and cross-validated on the routine blood tests of 5,333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected operational ROC point at a sensitivity of 81.9% and specificity of 97.9%. The cross-validated area under the curve (AUC) was 0.97. The five most useful routine blood parameters for COVID19 diagnosis according to the feature importance scoring of the XGBoost algorithm were MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. tSNE visualization showed that the blood parameters of the patients with severe COVID-19 course are more like the parameters of bacterial than viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results present a significant contribution to improvements in COVID-19 diagnosis., Comment: 11 pages, 4 figures, 2 tables

Published
2020
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4. Application of machine learning for hematological diagnosis

Author

Gunčar, Gregor, Kukar, Matjaž, Notar, Mateja, Brvar, Miran, Černelč, Peter, Notar, Manca, and Notar, Marko

Subjects
Statistics - Machine Learning
Abstract

Quick and accurate medical diagnosis is crucial for the successful treatment of a disease. Using machine learning algorithms, we have built two models to predict a hematologic disease, based on laboratory blood test results. In one predictive model, we used all available blood test parameters and in the other a reduced set, which is usually measured upon patient admittance. Both models produced good results, with a prediction accuracy of 0.88 and 0.86, when considering the list of five most probable diseases, and 0.59 and 0.57, when considering only the most probable disease. Models did not differ significantly from each other, which indicates that a reduced set of parameters contains a relevant fingerprint of a disease, expanding the utility of the model for general practitioner's use and indicating that there is more information in the blood test results than physicians recognize. In the clinical test we showed that the accuracy of our predictive models was on a par with the ability of hematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone, can be successfully applied to predict hematologic diseases and could open up unprecedented possibilities in medical diagnosis., Comment: 15 pages, 6 figures

Published
2017
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5. Multiple Myeloma Treatment in Real-world Clinical Practice: Results of a Prospective, Multinational, Noninterventional Study

Author

Bekadja, Mohamed, Hamladji, Rose-Marie, Ali, Hocine Ait, Hamdi, Selma, Touhami, Hadj, Mansour, Nourredine Sidi, Willenbacher, Wolfgang, Linkesch, Werner, Nemet, Damir, Pedersen, Robert Shou, Abildgaard, Niels, Laane, Edward, Hein, Marju, Mohty, Mohamad, Eveillard, Jean Richard, Yamani, Abderrazak el, Moreau, Philippe, Sanhes, Laurence, Lepeu, Gérard, Laribi, Kamel, Jourdan, Eric, Fitoussi, Olivier, Allangba, Olivier, Fleury, Joël, Escoffre, Martine, Benramdane, Riad, Cartron, Guillaume, Dine, Gérard, Legouffe, Eric, Harich, Hanns-Detlev, Illmer, Thomas, Dörfel, Steffen, Hannig, Carla Verena, Koenigsmann, Michael, Prange-Krex, Gabriele, Salwender, Hans, Tamm, Ingo, Zeller, Wolfgang, Maasberg, Michael, Schlag, Rudolf, Klausmann, Martine, Uhlig, Jens, Alkemper, Burkhard, Schütz, Stefan, Tessen, Hans-Werner, Mohr, Benno, Schmidt, Peter, Heinrich, Bernhard, Hebart, Holger, Seipelt, Gernot, Zoeller, Thomas, Heits, Frank, Müller-Naendrup, Clemens, Hansen, Richard, Repp, Roland, Von Weikersthal, Ludwig Fischer, Schmits, Rudolf, Heßling, Jörg, Krammer-Steiner, B., Janzen, Viktor, Schauer, Michael, Grüner, Marcus W., Kisro, Jens, Denzlinger, Claudio, Freier, Werner, Junghanss, Christian, Görner, Martin, Laichinger, Katharina, Ostermann, Helmut, Dürk, Heinz, Hess, Georg, Reich, Gernot, Terpos, Evangelos, Dimopoulos, Meletios, Matsouka, Panagiota, Pouli, Anastasia, Anagnostopoulos, Achilles, Masszi, Tamas, Borbényi, Zita, Ivanyi, Janos, Szomor, Arpad, Abadi, Uri, Nagler, Arnon, Magen, Hila, Avivi, Irit, Quitt, Miriam, Palumbo, Antonio, Boccadoro, Mario, De Stefano, Valerio, Za, Tommaso, Vallisa, Daniele, Foa, Roberto, Corso, Alessandro, Bosi, Alberto, Vacca, Angelo, Lanza, Francesco, Palazzo, Giulia, Avvisati, Giuseppe, Cavo, Michele, Ferrara, Felicetto, Consoli, Ugo, Cantonetti, Maria, Angelucci, Emanuele, Califano, Catello, Di Raimondo, Francesco, Guarini, Attilio, Musso, Maurizio, Pizzuti, Michele, Giuliani, Nicola, Ardizzoia, Antonio, Di Renzo, Nicola, Gaidano, Gianluca, Gozzetti, Alessandro, Pitini, Vincenzo, Farina, Gabriella, Centurioni, Riccardo, De Fabritiis, Paolo, Iuliano, Francesco, La Nasa, Giorgio, La Verde, Giacinto, Pane, Fabrizio, Recine, Umberto, La Targia, Maria, Mineo, Giuseppe, Cangialosi, Clotilde, Fagnani, Daniele, Federici, Augusto, Romano, Atelda, Specchia, Giorgina, Storti, Sergio, Bongarzoni, Velia, Bacigalupo, Andrea, Gobbi, Marco, Latte, Giancarlo, Mannina, Donato, Capalbo, Silvana, Lejniece, Sandra, Pečeliūnas, Valdas, Jurgutis, Mindaugas, Stankovic, Svetlana, Legiec, Wojciech, Woszczyk, Dariusz, Hołojda, Jadwiga, Gornik, Slawomir, Pluta, Andrzej, Morawiec-Szymonik, Elzbieta, Kyrcz-Krzemien, Slawomira, Homenda, Wojciech, Grosicki, Sebastian, Sulek, Kazimierz, Lange, Andrzej, Kloczko, Janusz, Starzak-Gwozdz, Jolanta, Hellmann, Andrzej, Komarnicki, Mieczyslaw, Kuliczkowski, Kazimierz, Viveiros, Carolina, Gonçalves, Cristina, Esefyeva, Natalia, Kochkareva, Julia, Kaplanov, Kamil, Volodicheva, Elena, Laricheva, Elena, Dergacheva, Valentina, Chukavina, Marina, Volchenko, Natalia, Nazarova, Irina, Anchukova, Ludmila, Ovanesova, Elena, Gritsenko, Taras, Salogub, Galina, Magomedova, Ludmila, Kuznetsova, Irina, Osyunikhina, Svetlana, Serdyuk, Olga, Karyagina, Elena, Ivanova, Valentina, Černelč, Slovenia Peter, Louw, Vernon, Coetzee, Corlia, Gunther, Karen, Moodley, Dhayanithi, Duran, Soledad, Gutiérrez, Asunción Echeveste, De Oteyza, Jaime Perez, Capote, Francisco Javier, Casanova, Maria, Sanchez, Jesus Martin, Rios-Herranz, Eduardo, Ibañez-Garcia, Jeronima, Herranz, Maria Jose, Hernandez, Belen, Sanchez, Sara Sanchez, Escalante, Fernando, Carnicero, Fernando, Lleonart, Joan Bargay, Gironella, Mercedes, Martínez, Rafael, De La Guia, Ana Lopez, Palomera, Luis, Iglesias, Rebeca, Ramos, Fernando Solano, De La Serna, Javier, Sanchez, Pedro Garcia, Vidal, Juan Besalduch, Mateos, Maria-Victoria, Morfa, Miguel Diaz, Beksac, Turkey – Meral, Vural, Filiz, Aydin, Yildiz, Unal, Ali, Goker, Hakan, Bilgir, Oktay, Guvenc, Birol, Turgut, Mehmet, Ozet, Gulsum Gulistan, Ali, Ridvan, Masliak, Zvenyslava, Kyselyova, Maryna, Glushko, Nataliia, Vybyrana, Renata, Skrypnyk, Igor, Tretyak, Natalya, Kharchevska, Tetiana, Dyagil, Iryna, Popovs'ka, Tetiana, Shimanskiy, Vadim, Lysa, Tamila, Oliynyk, Hanna, Vilchevskaya, Kateryna, Kryachok, Iryna, Popovych, Yuriy, Romanyuk, Natalia, Yushchenko, Natalia, Kaplan, Polina, Rekhtman, Grygoriy, Pylypenko, Halyna, Kozlov, Viktor, Drach, Johannes, Harousseau, Jean-Luc, Einsele, Hermann, Goldschmidt, Hartmut, Facon, Thierry, Michalet, Mauricette, Savchenko, Valery G., De la Rubia, Javier, Cook, Gordon, Mellqvist, Ulf-Henrik, Ludwig, Heinz, Beksac, Meral, Bekadja, Mohamed Amine, Durán, Maria Soledad, Kochkareva, Yulia, Berthou, Christian, Silva, João, Pedersen, Robert Schou, Černelč, Peter, Potamianou, Anna, Couturier, Catherine, Feys, Caroline, and Thoret-Bauchet, Florence

Published
2018
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7. Mobilization with cyclophosphamide reduces the number of lymphocyte subpopulations in the leukapheresis product and delays their reconstitution after autologous hematopoietic stem cell transplantation in patients with multiple myeloma

Author

Skerget Matevz, Skopec Barbara, Zontar Darja, and Cernelc Peter

Subjects
mobilization, myeloma, cyclophosphamide, nk cell, stem cell transplantation, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Autologous hematopoietic stem cell transplantation is considered the standard of care for younger patients with multiple myeloma. Several mobilization regimens are currently used, most commonly growth factors alone or in combination with chemotherapy. The aim of our study was to investigate the differences in lymphocyte subpopulation counts between three different mobilization regimens on collection day, in the leukapheresis product and on day 15 after autologous hematopoietic stem cell transplantation.

Published
2016
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25. Frontline induction therapy for multiple myeloma (MM) in real-world clinical practice: Third interim analysis of the multinational, observational EMMOS study (NCT01241396)

Author

Mohty, Mohamad, Terpos, Evangelos, Mateos, Maria-Victoria, Palumbo, Antonio, Legiec, Wojciech, Beksac, Meral, Laane, Edward, Amine Bekadja, Mohamed, Nemet, Damir, Peceliunas, Valdas, Cavo, Michele, Moreau, Philippe, Masszi, Tamas, Cernelc, Peter, Perez de Oteyza, Jaime, Salwender, Hans, Abadi, Uri, Dimopoulos, Meletios, Stankovic, Svetlana, Salogub, Galina Nikolaevna, Willenbacher, Wolfgang, Abildgaard , Niels, Gunther, Karen, Silva, Joao, Lejniece, Sandra, Dyagil, Irina, Potamianou, Anna, Couturier, Catherine, Olie, Robert, Feys, Caroline, Thoret-Bauchet, Florence, and Boccadoro, Mario

Published
2015

26. Frontline induction therapy for multiple myeloma (MM) in real-world clinical practice:Third interim analysis of the multinational, observational EMMOS study (NCT01241396)

Author

Mohty, Mohamad, Terpos, Evangelos, Mateos, Maria-Victoria, Palumbo, Antonio, Legiec, Wojciech, Beksac, Meral, Laane, Edward, Amine Bekadja, Mohamed, Nemet, Damir, Peceliunas, Valdas, Cavo, Michele, Moreau, Philippe, Masszi, Tamas, Cernelc, Peter, Perez de Oteyza, Jaime, Salwender, Hans, Abadi, Uri, Dimopoulos, Meletios, Stankovic, Svetlana, Salogub, Galina Nikolaevna, Willenbacher, Wolfgang, Abildgaard , Niels, Gunther, Karen, Silva, Joao, Lejniece, Sandra, Dyagil, Irina, Potamianou, Anna, Couturier, Catherine, Olie, Robert, Feys, Caroline, Thoret-Bauchet, Florence, and Boccadoro, Mario

Published
2015

27. First-line therapy for multiple myeloma(MM): results from the second interim analysis of the real-world, international, non-interventional EMMOS study

Author

Mohty, Mohamad, terpos, Evangelos, Mateos, maria-Victoria, Palumbo, Antonio, Lejniece, Sandra, beksac, Meral, Bakadja, Mohamed Amine, Legiec, ojchiech, Dimopoulos, Maletios, Stankovic, Svetlana, Duran, Maria Soledad, De tefano, Valerio, Kochkareva, Yulia, Laane, Edward, Berthou, Christian, Salwender, Hans, Masliak, Zvenyslava, Peculiunas, Valdas, Willenbacher, Wolfgang, Silva, Joao, Louw, Vernon, Nemet, Damir, Borbenyi, Zita, Abadi, Uri, Schou Pedersen, Robert Cernelc, Peter, Potamianou, Anna, Couturier, Catherine, Olie, Robert, Feys, Caroline, and horet-Bauchet, Florence Boccadoro, Mario

Subjects
education, multiple myeloma, therapy, health care economics and organizations
Abstract

First-line therapy for multiple myeloma(MM): results from the second interim analysis of the real-world, international, non-interventional EMMOS study

Published
2014

29. Efficacy of CD34+ Stem Cell Therapy in Nonischemic Dilated Cardiomyopathy Is Absent in Patients With Diabetes but Preserved in Patients With Insulin Resistance

Author

Vrtovec, Bojan, primary, Sever, Matjaz, additional, Jensterle, Mojca, additional, Poglajen, Gregor, additional, Janez, Andrej, additional, Kravos, Nika, additional, Zemljic, Gregor, additional, Cukjati, Marko, additional, Cernelc, Peter, additional, Haddad, François, additional, Wu, Joseph C., additional, and Jorde, Ulrich P., additional

Published
2016
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30. Analysis of Final Data from the Multinational, Non-Interventional, Observational Emmos Study (NCT01241396) in Patients (Pts) with Multiple Myeloma (MM) in Real-World Clinical Practice

Author

Mohty, Mohamad, primary, Terpos, Evangelos, additional, Mateos, Maria-Victoria, additional, Palumbo, Antonio, additional, Lejniece, Sandra, additional, Beksac, Meral, additional, Bekadja, Mohamed Amine, additional, Legiec, Wojciech, additional, Dimopoulos, Meletios, additional, Stankovic, Svetlana, additional, Durán, Maria Soledad, additional, De Stefano, Valerio, additional, Corso, Alessandro, additional, Kochkareva, Yulia, additional, Laane, Edward, additional, Berthou, Christian, additional, Salwender, Hans-Juergen, additional, Masliak, Zvenyslava, additional, Pečeliūnas, Valdas, additional, Willenbacher, Wolfgang, additional, Da Silva, João, additional, Louw, Vernon, additional, Nemet, Damir, additional, Borbényi, Zita, additional, Abadi, Uri, additional, Pedersen, Robert Schou, additional, Cernelc, Peter, additional, Potamianou, Anna, additional, Couturier, Catherine, additional, Olie, Robert, additional, Feys, Caroline, additional, Thoret-Bauchet, Florence, additional, and Boccadoro, Mario, additional

Published
2015
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34. EFFICACY OF CD34+ STEM CELL THERAPY IN NON-ISCHEMIC DILATED CARDIOMYOPATHY IS ABSENT IN PATIENTS WITH DIABETES BUT PRESERVED IN PATIENTS WITH INSULIN RESISTANCE

Author

Vrtovec, Bojan, primary, Sever, Matjaz, additional, Jensterle, Mojca, additional, Poglajen, Gregor, additional, Janez, Andrej, additional, Kravos, Nika, additional, Zemljic, Gregor, additional, Cukjati, Marko, additional, Cernelc, Peter, additional, Haddad, Francois, additional, Wu, Joseph C., additional, and Jorde, Ulrich P., additional

Published
2015
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44. Effects of Transendocardial CD34+Cell Transplantation on Diastolic Parameters in Patients with Nonischemic Dilated Cardiomyopathy

Author

Bervar, Mojca, Kozelj, Mirta, Poglajen, Gregor, Sever, Matjaz, Zemljic, Gregor, Frljak, Sabina, Cukjati, Marko, Cernelc, Peter, Haddad, François, and Vrtovec, Bojan

Abstract

We sought to evaluate the physiological background and the effects of CD34+cell transplantation on diastolic parameters in nonischemic dilated cardiomyopathy patients (DCM). We enrolled 38 DCM patients with NYHA class III and LVEF < 40% who underwent transendocardial CD34+cell transplantation. Peripheral blood CD34+cells were mobilized by G‐CSF, collected via apheresis, and injected transendocardially in the areas of myocardial hibernation. Patients were followed for 1 year. At baseline, estimated filling pressures were significantly elevated (E/e′ ≥ 15) in 18 patients (Group A), and moderately elevated (E/e ′< 15) in 20 patients (Group B). The groups did not differ in age (54 ± 9 years vs. 52 ± 10 years; p= .62), gender (male: 85% vs. 78%; p= .57), or LVEF (31 ± 7% vs. 34 ± 6%; p= .37). When compared to Group B patients in Group A had more segments with myocardial scar (4.9 ± 2.7 vs. 2.7 ± 2.9; p= .03), myocardial hibernation (2.2 ± 1.6 vs. 0.9 ± 1.1; p= .02), and longer average local relaxation time on electroanatomical mapping (378 ± 41 ms vs. 333 ± 34 ms, p= .01). During follow‐up there was an improvement in diastolic parameters in Group A (E/e′: from 24.3 ± 12.1 to 16.3 ± 8.0; p= .005), but not in Group B (E/e′: from 10.2 ± 3.7 to 13.2 ± 9.1; p= .19). Accordingly, in Group A, we found an increase in 6‐minute walk distance (from 463 ± 83 m to 546 ± 91 m; p= .03), and a decrease in NT‐proBNP (from 2140 ± 1743 pg/ml to 863 ± 836 pg/ml; p= .02). In nonischemic DCM, diastolic dysfunction appears to correlate with areas of myocardial scar and hibernation. Transendocardial CD34+cell transplantation may improve diastolic parameters in this patient cohort. StemCellsTranslationalMedicine2017;6:1515–1521

Published
2017
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48. Intracoronary Transplantation of CD34+ Cells Is Associated With Improved Myocardial Perfusion in Patients With Nonischemic Dilated Cardiomyopathy.

Author

LEZAIC, LUKA, SOCAN, ALJAZ, POGLAJEN, GREGOR, KOLENC PEITL, PETRA, SEVER, MATJAZ, CUKJATI, MARKO, CERNELC, PETER, WU, JOSEPH C., HADDAD, FRANÇOIS, and VRTOVEC, BOJAN

Abstract

Background: We investigated the effects of intracoronary transplantation of CD34+ cells on myocardial perfusion in patients with nonischemic dilated cardiomyopathy (DCM). Methods and Results: We enrolled 21 patients with DCM (left ventricular ejection fraction [LVEF] <40%, New York Heart Association functional class III) who underwent peripheral stem cell mobilization with granulocyte-colony stimulating factor (G-CSF). CD34+ cells were collected by means of apheresis. Patients underwent myocardial perfusion imaging, and CD34+ cells were injected in the coronary artery supplying viable segments with reduced myocardial perfusion and regional dysfunction. Myocardial perfusion imaging was repeated 6 months later. Clinical response to stem cell therapy was predefined as a change in LVEF >5%. The majority of patients were men (81%) with an overall mean age 53 ± 9 years, LVEF 25 ± 5%, and 6-minute walking distance 354 ± 71 m. Myocardial perfusion defects at rest were observed in 86% of patients and were more common in the left anterior descending territory (50%). At 6 months' follow-up, there was a significant improvement in rest myocardial perfusion scores (6.3 ± 5.8 vs 3.1 ± 4.3; P < .001), LVEF (25 ± 7% vs 29 ± 8%; P = .005), and 6-minute walking distance (354 ± 71 m vs 404 ± 91 m; P < .001). Responders to stem cell therapy had lower summed rest perfusion score at both baseline (3.2 ± 3.0 vs 9.1 ± 6.3; P = .015) and follow-up (1.0 ± 1.5 vs 5.0 ± 5.1; P = .028). Conclusions: CD34+ cell transplantation may lead to improved myocardial perfusion in patients with nonischemic DCM. Patients with less severe myocardial perfusion defects at baseline may have an increased likelihood to respond to intracoronary CD34+ cell transplantation. [ABSTRACT FROM AUTHOR]

Published
2015
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