Tom Van Steenkiste1, Joeri Ruyssinck2, Leen De Baets3, Johan Decruyenaere4, Filip De Turck5, Femke Ongenae6, Tom Dhaene7. 1. Ghent University - imec, IDLab, Department of Information Technology, Technologiepark 15, B-9052, Ghent, Belgium. Electronic address: tomd.vansteenkiste@ugent.be. 2. Ghent University - imec, IDLab, Department of Information Technology, Technologiepark 15, B-9052, Ghent, Belgium. Electronic address: joeri.ruyssinck@ugent.be. 3. Ghent University - imec, IDLab, Department of Information Technology, Technologiepark 15, B-9052, Ghent, Belgium. Electronic address: leen.debaets@ugent.be. 4. Ghent University Hospital, Department of Internal Medicine, De Pintelaan 185, B-9050 Ghent, Belgium. Electronic address: johan.Decruyenaere@ugent.be. 5. Ghent University - imec, IDLab, Department of Information Technology, Technologiepark 15, B-9052, Ghent, Belgium. Electronic address: filip.deturck@ugent.be. 6. Ghent University - imec, IDLab, Department of Information Technology, Technologiepark 15, B-9052, Ghent, Belgium. Electronic address: femke.ongenae@ugent.be. 7. Ghent University - imec, IDLab, Department of Information Technology, Technologiepark 15, B-9052, Ghent, Belgium. Electronic address: tom.dhaene@ugent.be.
Abstract
INTRODUCTION: Blood cultures are often performed in the intensive care unit (ICU) to detect bloodstream infections and identify pathogen type, further guiding treatment. Early detection is essential, as a bloodstream infection can give cause to sepsis, a severe immune response associated with an increased risk of organ failure and death. PROBLEM STATEMENT: The early clinical detection of a bloodstream infection is challenging but rapid targeted treatment, within the first place antimicrobials, substantially increases survival chances. As blood cultures require time to incubate, early clinical detection using physiological signals combined with indicative lab values is pivotal. OBJECTIVE: In this work, a novel method is constructed and explored for the potential prediction of the outcome of a blood culture test. The approach is based on a temporal computational model which uses nine clinical parameters measured over time. METHODOLOGY: We use a bidirectional long short-term memory neural network, a type of recurrent neural network well suited for tasks where the time lag between a predictive event and outcome is unknown. Evaluation is performed using a novel high-quality database consisting of 2177 ICU admissions at the Ghent University Hospital located in Belgium. RESULTS: The network achieves, on average, an area under the receiver operating characteristic curve of 0.99 and an area under the precision-recall curve of 0.82. In addition, our results show that predicting several hours upfront is possible with only a small decrease in predictive power. In this setting, it outperforms traditional non-temporal, machine learning models. CONCLUSION: Our proposed computational model accurately predicts the outcome of blood culture tests using nine clinical parameters. Moreover, it can be used in the ICU as an early warning system to detect patients at risk of blood stream infection.
INTRODUCTION: Blood cultures are often performed in the intensive care unit (ICU) to detect bloodstream infections and identify pathogen type, further guiding treatment. Early detection is essential, as a bloodstream infection can give cause to sepsis, a severe immune response associated with an increased risk of organ failure and death. PROBLEM STATEMENT: The early clinical detection of a bloodstream infection is challenging but rapid targeted treatment, within the first place antimicrobials, substantially increases survival chances. As blood cultures require time to incubate, early clinical detection using physiological signals combined with indicative lab values is pivotal. OBJECTIVE: In this work, a novel method is constructed and explored for the potential prediction of the outcome of a blood culture test. The approach is based on a temporal computational model which uses nine clinical parameters measured over time. METHODOLOGY: We use a bidirectional long short-term memory neural network, a type of recurrent neural network well suited for tasks where the time lag between a predictive event and outcome is unknown. Evaluation is performed using a novel high-quality database consisting of 2177 ICU admissions at the Ghent University Hospital located in Belgium. RESULTS: The network achieves, on average, an area under the receiver operating characteristic curve of 0.99 and an area under the precision-recall curve of 0.82. In addition, our results show that predicting several hours upfront is possible with only a small decrease in predictive power. In this setting, it outperforms traditional non-temporal, machine learning models. CONCLUSION: Our proposed computational model accurately predicts the outcome of blood culture tests using nine clinical parameters. Moreover, it can be used in the ICU as an early warning system to detect patients at risk of blood stream infection.
Authors: Ali A Rabaan; Saad Alhumaid; Abbas Al Mutair; Mohammed Garout; Yem Abulhamayel; Muhammad A Halwani; Jeehan H Alestad; Ali Al Bshabshe; Tarek Sulaiman; Meshal K AlFonaisan; Tariq Almusawi; Hawra Albayat; Mohammed Alsaeed; Mubarak Alfaresi; Sultan Alotaibi; Yousef N Alhashem; Mohamad-Hani Temsah; Urooj Ali; Naveed Ahmed Journal: Antibiotics (Basel) Date: 2022-06-08
Authors: Anneroos W Boerman; Michiel Schinkel; Lotta Meijerink; Eva S van den Ende; Lara Ca Pladet; Martijn G Scholtemeijer; Joost Zeeuw; Anuschka Y van der Zaag; Tanca C Minderhoud; Paul W G Elbers; W Joost Wiersinga; Robert de Jonge; Mark Hh Kramer; Prabath W B Nanayakkara Journal: BMJ Open Date: 2022-01-04 Impact factor: 2.692
Authors: Daniela Dambroso-Altafini; Thatiany C Menegucci; Bruno B Costa; Rafael R B Moreira; Sheila A B Nishiyama; Josmar Mazucheli; Maria C B Tognim Journal: Sci Rep Date: 2022-09-14 Impact factor: 4.996