Rapid diagnosis of infection etiology in febrile pediatric oncology patients using infrared spectroscopy of leukocytes
Adam H. Agbaria
Department of Physics, Ben-Gurion University, Beer-Sheva, Israel
Search for more papers by this authorGuy Beck Rosen
Department of Hematology, Soroka University Medical Center, Beer-Sheva, Israel
Search for more papers by this authorItshak Lapidot
Department of Electrical and Electronics Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv, Israel
Search for more papers by this authorDaniel H. Rich
Department of Physics, Ben-Gurion University, Beer-Sheva, Israel
Search for more papers by this authorShaul Mordechai
Department of Physics, Ben-Gurion University, Beer-Sheva, Israel
Search for more papers by this authorJoseph Kapelushnik
Department of Hematology, Soroka University Medical Center, Beer-Sheva, Israel
Search for more papers by this authorMahmoud Huleihel
Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Search for more papers by this authorCorresponding Author
Ahmad Salman
Department of Physics, SCE-Sami Shamoon College of Engineering, Beer-Sheva, Israel
Correspondence
Prof Ahmad Salman, Department of Physics, SCE-Sami Shamoon College of Engineering, Beer-Sheva 84100, Israel.
Email: [email protected]
Search for more papers by this authorAdam H. Agbaria
Department of Physics, Ben-Gurion University, Beer-Sheva, Israel
Search for more papers by this authorGuy Beck Rosen
Department of Hematology, Soroka University Medical Center, Beer-Sheva, Israel
Search for more papers by this authorItshak Lapidot
Department of Electrical and Electronics Engineering, ACLP-Afeka Center for Language Processing, Afeka Tel-Aviv Academic College of Engineering, Tel-Aviv, Israel
Search for more papers by this authorDaniel H. Rich
Department of Physics, Ben-Gurion University, Beer-Sheva, Israel
Search for more papers by this authorShaul Mordechai
Department of Physics, Ben-Gurion University, Beer-Sheva, Israel
Search for more papers by this authorJoseph Kapelushnik
Department of Hematology, Soroka University Medical Center, Beer-Sheva, Israel
Search for more papers by this authorMahmoud Huleihel
Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Search for more papers by this authorCorresponding Author
Ahmad Salman
Department of Physics, SCE-Sami Shamoon College of Engineering, Beer-Sheva, Israel
Correspondence
Prof Ahmad Salman, Department of Physics, SCE-Sami Shamoon College of Engineering, Beer-Sheva 84100, Israel.
Email: [email protected]
Search for more papers by this authorAbstract
Rapid diagnosis of the etiology of infection is highly important for an effective treatment of the infected patients. Bacterial and viral infections are serious diseases that can cause death in many cases. The human immune system deals with many viral and bacterial infections that cause no symptoms and pass quietly without treatment. However, oncology patients undergoing chemotherapy have a very weak immune system caused by leukopenia, and even minor pathogen infection threatens their lives. For this reason, physicians tend to prescribe immediately several types of antibiotics for febrile pediatric oncology patients (FPOPs). Uncontrolled use of antibiotics is one of the major contributors to the development of resistant bacteria. Therefore, for oncology patients, a rapid and objective diagnosis of the etiology of the infection is extremely critical. Current identification methods are time-consuming (>24 h). In this study, the potential of midinfrared spectroscopy in tandem with machine learning algorithms is evaluated for rapid and objective diagnosis of the etiology of infections in FPOPs using simple peripheral blood samples. Our results show that infrared spectroscopy enables the diagnosis of the etiology of infection as bacterial or viral within 70 minutes after the collection of the blood sample with 93% sensitivity and 88% specificity.
Supporting Information
Filename | Description |
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jbio201900215-sup-0002-Supinfo.docxWord 2007 document , 220.5 KB | Appendix S1 Author bio |
jbio201900215-sup-0001-Supinfo.docxWord 2007 document , 605.1 KB | Table S1 Details of the patients' samples included in this study. Table S2: Performance of the different classifiers in percentage using three different feature vectors, raw data (RD), second derivative (SD) and selected features of the SD spectra (SFSD), in the 900 to 1800 cm−1 region for the classification between control and infected patients (first step) and viral and bacterial patients (second step) for plasma samples. Figure S1: Average infrared absorption spectra of plasma of (a) control vs infected patients (combined bacterial and viral) and (b) bacterial vs viral infected patients in the 900–1800 cm−1 region after preprocessing. The inset expands the region where the major spectral differences exist. Figure S2: ROC curves of the classification between the different classes of plasma samples based on extreme gradient boosting, Naïve Bayes and Random Forest using 120 selected features in the SD spectra of plasma for (a) first step (infected patients-controls) and (b) bacterial-viral patients. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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