Development of an Algorithm for Early Detection of Pneumonia in Ventilated Patients

Igal Bar-Ilan, Respiration Scan Ltd, NESS ZIONA, Israel (igal@bioforum.co.il)
Yamit Naor, Respiration Scan Ltd, Ness Ziona, Israel
Irit Segalovich, Respiration Scan Ltd, Ness Ziona, Israel

Ventilator-associated pneumonia (VAP) is a severe and life-threatening complication in mechanically ventilated patients, particularly in intensive care units (ICUs). It is associated with high morbidity, mortality, and significant healthcare costs. Early and accurate detection is critical for timely intervention, reducing complications, and improving patient outcomes. Traditional diagnostic methods, such as microbial cultures, are time-consuming and lack sensitivity and specificity, emphasizing the need for innovative, non-invasive, and rapid diagnostic solutions.

This study focuses on the development of a novel algorithm for the early detection of VAP by analyzing volatile organic compounds (VOCs) in exhaled breath. The algorithm is based on a comprehensive database of thousands of GC-MS runs derived from both In Vitro bacterial culture experiments and In Vivo breath samples collected from mechanically ventilated patients. These analyses identified VOC biomarkers indicative of bacterial activity and inflammation.

A smart alignment method was developed to enhance data accuracy and reproducibility, integrating retention time correction and mass spectrum matching across GC-MS runs. This approach minimized variability and ensured precise identification of VOC biomarkers. Advanced statistical tools and machine learning techniques were applied to construct a predictive model capable of distinguishing VAP cases from non-infected controls with high sensitivity and specificity.

Preliminary results demonstrate that the algorithm significantly reduces diagnostic turnaround times compared to traditional methods, offering a rapid, non-invasive, and reliable approach to VAP detection. The integration of advanced analytical techniques with machine learning shows potential to revolutionize ICU patient care by enabling earlier diagnosis and more targeted therapeutic interventions.

Further clinical validation is ongoing to optimize the algorithm and prepare it for broader implementation in hospital settings.

Keywords: Ventilator-associated pneumonia, volatile organic compounds, GC-MS, machine learning, smart alignment, early detection.

Short Biography of Presenting Author

Dr. Igal Bar-Ilan – Analytical Chemist and CTO of Respiration Scan Ltd

Dr. Igal Bar-Ilan is a leading analytical chemist with over 40 years of expertise in chromatography, spectroscopy, mass spectrometry, and wet chemistry. He is one of the founders of Tel-Hai Academic College and Respiration Scan Ltd, where he served as CTO and a board member for 7 years. At Respiration Scan Ltd, Dr. Bar-Ilan developed advanced medical sampling techniques, analytical methods, and algorithms to process thousands of GCMS data sets. These efforts supported both InVitro and InVivo studies, including two clinical trials involving approximately 120 patients. He trained and managed a dedicated team to implement the methods he developed, ensuring operational excellence.

For 32 years, Dr. Bar-Ilan headed the Analytical Chemistry and Special Projects Department at MIGAL – Galilee Research Institute. During this time, he made significant contributions to biotechnology, environmental, and agricultural research. His work includes the development and application of innovative analytical techniques such as gas chromatography (GC), liquid chromatography (HPLC), thin-layer chromatography (TLC), UV-Vis spectroscopy, FTIR, ICP-MS, and mass spectrometry (GCMS and LCMS). He is widely recognized for solving complex analytical challenges and implementing robust analytical methods.

In addition to his research and industry contributions, Dr. Bar-Ilan is an experienced educator, lecturing at Bioforum, Tel-Hai Academic College, and the Weizmann Institute. He is passionate about mentoring the next generation of chemists and providing consultancy services to various industries.

Dr. Bar-Ilan has been a member of the Israeli Society of Analytical Chemistry (ISAC) for 20 years and serves on the organizing committee of the ISRANALYTICA conference, Israel’s leading analytical chemistry event. He has chaired the conference three times, demonstrating his leadership and commitment to advancing the field.

Dr. Bar-Ilan’s expertise spans a wide range of fields, including the development and application of analytical methods in chromatography, mass spectrometry, spectroscopy, and wet chemistry. His scientific rigor and leadership have earned him recognition as a pioneer in analytical chemistry, contributing to innovations in both research and industrial applications.


Development of an Algorithm for Early Detection of Pneumonia in Ventilated Patients Igal Bar-Ilan, Respiration Scan Ltd, NESS ZIONA, Israel (igal@bioforum.co.il) Yamit Naor, Respiration Scan Ltd, Ness Ziona, Israel Irit Segalovich, Respiration Scan Ltd, Ness Ziona, Israel Ventilator-associated pneumonia (VAP) is a severe and life-threatening complication in mechanically ventilated patients, particularly in intensive care units (ICUs). It is associated with high morbidity, mortality, and significant healthcare costs. Early and accurate detection is critical for timely intervention, reducing complications, and improving patient outcomes. Traditional diagnostic methods, such as microbial cultures, are time-consuming and lack sensitivity and specificity, emphasizing the need for innovative, non-invasive, and rapid diagnostic solutions. This study focuses on the development of a novel algorithm for the early detection of VAP by analyzing volatile organic compounds (VOCs) in exhaled breath. The algorithm is based on a comprehensive database of thousands of GC-MS runs derived from both In Vitro bacterial culture experiments and In Vivo breath samples collected from mechanically ventilated patients. These analyses identified VOC biomarkers indicative of bacterial activity and inflammation. A smart alignment method was developed to enhance data accuracy and reproducibility, integrating retention time correction and mass spectrum matching across GC-MS runs. This approach minimized variability and ensured precise identification of VOC biomarkers. Advanced statistical tools and machine learning techniques were applied to construct a predictive model capable of distinguishing VAP cases from non-infected controls with high sensitivity and specificity. Preliminary results demonstrate that the algorithm significantly reduces diagnostic turnaround times compared to traditional methods, offering a rapid, non-invasive, and reliable approach to VAP detection. The integration of advanced analytical techniques with machine learning shows potential to revolutionize ICU patient care by enabling earlier diagnosis and more targeted therapeutic interventions. Further clinical validation is ongoing to optimize the algorithm and prepare it for broader implementation in hospital settings. Keywords: Ventilator-associated pneumonia, volatile organic compounds, GC-MS, machine learning, smart alignment, early detection. Short Biography of Presenting Author Dr. Igal Bar-Ilan – Analytical Chemist and CTO of Respiration Scan Ltd Dr. Igal Bar-Ilan is a leading analytical chemist with over 40 years of expertise in chromatography, spectroscopy, mass spectrometry, and wet chemistry. He is one of the founders of Tel-Hai Academic College and Respiration Scan Ltd, where he served as CTO and a board member for 7 years. At Respiration Scan Ltd, Dr. Bar-Ilan developed advanced medical sampling techniques, analytical methods, and algorithms to process thousands of GCMS data sets. These efforts supported both InVitro and InVivo studies, including two clinical trials involving approximately 120 patients. He trained and managed a dedicated team to implement the methods he developed, ensuring operational excellence. For 32 years, Dr. Bar-Ilan headed the Analytical Chemistry and Special Projects Department at MIGAL – Galilee Research Institute. During this time, he made significant contributions to biotechnology, environmental, and agricultural research. His work includes the development and application of innovative analytical techniques such as gas chromatography (GC), liquid chromatography (HPLC), thin-layer chromatography (TLC), UV-Vis spectroscopy, FTIR, ICP-MS, and mass spectrometry (GCMS and LCMS). He is widely recognized for solving complex analytical challenges and implementing robust analytical methods. In addition to his research and industry contributions, Dr. Bar-Ilan is an experienced educator, lecturing at Bioforum, Tel-Hai Academic College, and the Weizmann Institute. He is passionate about mentoring the next generation of chemists and providing consultancy services to various industries. Dr. Bar-Ilan has been a member of the Israeli Society of Analytical Chemistry (ISAC) for 20 years and serves on the organizing committee of the ISRANALYTICA conference, Israel’s leading analytical chemistry event. He has chaired the conference three times, demonstrating his leadership and commitment to advancing the field. Dr. Bar-Ilan’s expertise spans a wide range of fields, including the development and application of analytical methods in chromatography, mass spectrometry, spectroscopy, and wet chemistry. His scientific rigor and leadership have earned him recognition as a pioneer in analytical chemistry, contributing to innovations in both research and industrial applications.

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