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1. Managing Pandemic Responses with Health Informatics - Challenges for Assessing Digital Health Technologies.

Author

Magrabi F, Ammenwerth E, Craven CK, Cresswell K, De Keizer NF, Medlock SK, Scott PJ, Wong ZS, and Georgiou A

Subjects
Humans, COVID-19, Medical Informatics, Technology Assessment, Biomedical
Abstract

Objectives: To highlight the role of technology assessment in the management of the COVID-19 pandemic., Method: An overview of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems., Results: Evaluation of digital health technologies for COVID-19 should be based on their technical maturity as well as the scale of implementation. For mature technologies like telehealth whose efficacy has been previously demonstrated, pragmatic, rapid evaluation using the complex systems paradigm which accounts for multiple sociotechnical factors, might be more suitable to examine their effectiveness and emerging safety concerns in new settings. New technologies, particularly those intended for use on a large scale such as digital contract tracing, will require assessment of their usability as well as performance prior to deployment, after which evaluation should shift to using a complex systems paradigm to examine the value of information provided. The success of a digital health technology is dependent on the value of information it provides relative to the sociotechnical context of the setting where it is implemented., Conclusion: Commitment to evaluation using the evidence-based medicine and complex systems paradigms will be critical to ensuring safe and effective use of digital health technologies for COVID-19 and future pandemics. There is an inherent tension between evaluation and the imperative to urgently deploy solutions that needs to be negotiated., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Published
2021
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2. Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse

Author

Fleuren, Lucas M., de Bruin, Daan P., Tonutti, Michele, Lalisang, Robbert C.A., Elbers, Paul W.G., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Vonk, Sebastiaan J.J., Fornasa, Mattia, Machado, Tomas, Dam, Tariq, de Keizer, Nicolet F., Raeissi, Masoume, van der Meer, Nardo J.M., Rigter, Sander, Wils, Evert Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco, Kamps, Marlijn J.A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G.C.A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G.M., Mehagnoul-Schipper, Jannet, Dormans, Tom, Houwert, Taco, Hovenkamp, Hidde, Londono, Roberto Noorduijn, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Ercole, Ari, van der Schaar, Mihaela, Beudel, Martijn, Hoogendoorn, Mark, Girbes, Armand R.J., Herter, Willem E., Thoral, Patrick J., Roggeveen, Luca, van Diggelen, Fuda, el Hassouni, Ali, Guzman, David Romero, Bhulai, Sandjai, Ouweneel, Dagmar, Driessen, Ronald, Peppink, Jan, de Grooth, H. J., Zijlstra, G. J., van Tienhoven, A. J., van der Heiden, Evelien, Spijkstra, Jan Jaap, van der Spoel, Hans, de Man, Angelique, Klausch, Thomas, de Vries, Heder, Neree tot Babberich, Michael de, Thijssens, Olivier, Wagemakers, Lot, Berend, Julie, Silva, Virginia Ceni, Kullberg, Bob, Heunks, Leo, Juffermans, Nicole, Slooter, Arjan, Rettig, Thijs C.D., Reuland, M. C., van Manen, Laura, Montenij, Leon, van Bommel, Jasper, van den Berg, Roy, van Geest, Ellen, Hana, Anisa, Simsek, Suat, van den Bogaard, B., Pickkers, Peter, van der Heiden, Pim, van Gemeren, Claudia, Meinders, Arend Jan, de Bruin, Martha, Rademaker, Emma, van Osch, Frits, de Kruif, Martijn, Hendriks, Stefaan H.A., Schroten, Nicolas, Boelens, Age D., Arnold, Klaas Sierk, Karakus, A., Fijen, J. W., Festen-Spanjer, Barbara, Achterberg, Sefanja, Lens, Judith, van Koesveld, Jacomar, van den Tempel, Walter, Simons, Koen S., de Jager, Cornelis P.C., Oostdijk, Evelien, Labout, Joost, van der Gaauw, Bart, Reidinga, Auke C., Koetsier, Peter, Kuiper, Michael, Cornet, Alexander D., Beishuizen, Albertus, de Jong, Paul, Geutjes, Dennis, Faber, Harald J., Lutisan, Johan, Brunnekreef, Gert, Gemert, Ankie W.M.M.Koopman van, Entjes, Robert, van den Akker, Remko, Simons, Bram, Rijkeboer, A. A., Arbous, Sesmu, Aries, Marcel, van den Oever, Niels C.Gritters, van Tellingen, Martijn, Intensive Care, Medical Informatics, APH - Methodology, APH - Quality of Care, Intensive Care Medicine, Neurology, ANS - Neurodegeneration, AII - Inflammatory diseases, APH - Digital Health, Artificial intelligence, Network Institute, Computational Intelligence, Artificial Intelligence (section level), Mathematics, Intensive care medicine, VU University medical center, ACS - Microcirculation, ACS - Diabetes & metabolism, Epidemiologie, RS: NUTRIM - R3 - Respiratory & Age-related Health, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, and MUMC+: MA Medische Staf IC (9)

Subjects
2019-20 coronavirus outbreak, Letter, Coronavirus disease 2019 (COVID-19), Critically ill, business.industry, Information Dissemination, Critical Illness, MEDLINE, lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], COVID-19, Critical Care and Intensive Care Medicine, Data warehouse, Data sharing, Intensive Care Units, SDG 3 - Good Health and Well-being, Data Warehousing, Scale (social sciences), Medicine, Humans, Operations management, business, Netherlands
Abstract

Contains fulltext : 238662.pdf (Publisher’s version ) (Closed access)

Published
2021
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3. Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

Author

Fleuren, Lucas M., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C.A., Dam, Tariq A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Vonk, Sebastiaan J.J., Fornasa, Mattia, Machado, Tomas, van der Meer, Nardo J.M., Rigter, Sander, Wils, Evert Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco, Kamps, Marlijn J.A., Ramnarain, Dharmanand, Nowitzky, Ralph, Nooteboom, Fleur G.C.A., de Ruijter, Wouter, Urlings-Strop, Louise C., Smit, Ellen G.M., Mehagnoul-Schipper, D. Jannet, Dormans, Tom, de Jager, Cornelis P.C., Hendriks, Stefaan H.A., Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert, Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Achterberg, Sefanja, Faber, Harald J., Karakus, A., Beukema, Menno, Entjes, Robert, de Jong, Paul, Houwert, Taco, Hovenkamp, Hidde, Noorduijn Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cinà, Giovanni, Beudel, Martijn, de Keizer, Nicolet F., Hoogendoorn, Mark, Girbes, Armand R.J., Herter, Willem E., Elbers, Paul W.G., Thoral, Patrick J., Rettig, Thijs C.D., Reuland, M. C., van Manen, Laura, Montenij, Leon, van Bommel, Jasper, van den Berg, Roy, van Geest, Ellen, Hana, Anisa, Boersma, W. G., van den Bogaard, B., Pickkers, Peter, van der Heiden, Pim, van Gemeren, Claudia C.W., Meinders, Arend Jan, de Bruin, Martha, Rademaker, Emma, van Osch, Frits H.M., de Kruif, Martijn, Schroten, Nicolas, Arnold, Klaas Sierk, Fijen, J. W., van Koesveld, Jacomar J.M., Simons, Koen S., Labout, Joost, van de Gaauw, Bart, Kuiper, Michael, Beishuizen, Albertus, Geutjes, Dennis, Lutisan, Johan, Grady, Bart P.X., van den Akker, Remko, Simons, Bram, Rijkeboer, A. A., Arbous, Sesmu, Aries, Marcel, van den Oever, Niels C.Gritters, van Tellingen, Martijn, Dijkstra, Annemieke, van Raalte, Rutger, Roggeveen, Luca, van Diggelen, Fuda, Hassouni, Ali el, Guzman, David Romero, Bhulai, Sandjai, Ouweneel, Dagmar, Driessen, Ronald, Peppink, Jan, de Grooth, H. J., Zijlstra, G. J., van Tienhoven, A. J., van der Heiden, Evelien, Spijkstra, Jan Jaap, van der Spoel, Hans, de Man, Angelique, Klausch, Thomas, de Vries, Heder, de Neree tot Babberich, Michael, Thijssens, Olivier, Wagemakers, Lot, van der Pol, Hilde G.A., Hendriks, Tom, Berend, Julie, Silva, Virginia Ceni, Kullberg, Bob, Heunks, Leo, Juffermans, Nicole, Slooter, Arjan, Intensive care medicine, ACS - Diabetes & metabolism, ACS - Microcirculation, Amsterdam Cardiovascular Sciences, Neurology, AII - Infectious diseases, AII - Cancer immunology, CCA - Cancer biology and immunology, AII - Inflammatory diseases, Epidemiology and Data Science, APH - Methodology, ACS - Pulmonary hypertension & thrombosis, Intensive Care Medicine, APH - Quality of Care, Medical Informatics, Graduate School, Nephrology, Cardiology, Gastroenterology and Hepatology, Amsterdam Gastroenterology Endocrinology Metabolism, APH - Digital Health, Artificial intelligence, Network Institute, Computational Intelligence, Artificial Intelligence (section level), Mathematics, Intensive Care, Epidemiologie, RS: NUTRIM - R3 - Respiratory & Age-related Health, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, MUMC+: MA Medische Staf IC (9), and Internal medicine

Subjects
Icu patients, Coronavirus disease 2019 (COVID-19), Adverse outcomes, medicine.medical_treatment, Critical Care and Intensive Care Medicine, Machine learning, computer.software_genre, law.invention, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, law, SCORE, medicine, 030212 general & internal medicine, Risk factor, Research Articles, Mechanical ventilation, business.industry, RC86-88.9, Other Research Radboud Institute for Health Sciences [Radboudumc 0], COVID-19, 030208 emergency & critical care medicine, Medical emergencies. Critical care. Intensive care. First aid, Intensive care unit, Data warehouse, Data extraction, Mortality prediction, Risk factors, Artificial intelligence, business, computer
Abstract

Background The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. Methods The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. Results A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. Conclusion Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.

Published
2021
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