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1. A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients

Author

Izdebski, Adam, Thoral, Patrick J., Lalisang, Robbert C. A., McHugh, Dean M., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco A. A., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., Tempel, Walter van den, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Elbers, Paul W. G., and Cinà, Giovanni

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data. The consequence on clinical practice is that, when lacking results from a randomized trial, medical personnel is left without guidance on what seems to be effective in a real-world scenario. This article proposes a pragmatic methodology to obtain preliminary but robust estimation of treatment effect from observational studies, to provide front-line clinicians with a degree of confidence in their treatment strategy. Our study design is applied to an open problem, the estimation of treatment effect of the proning maneuver on COVID-19 Intensive Care patients.

Published
2021

2. Biomarker Analysis Provides Evidence for Host Response Homogeneity in Patients With COVID-19

Author

van Agtmael, Michiel A., Algera, Anne G., Appelman, Brent, van Baarle, Floor E.H.P., van de Beek, Diederik, Beudel, Martijn, Bogaard, Harm J., Bos, Lieuwe D.J., Botta, Michela, de Brabander, Justin, de Bree, Godelieve J., Brouwer, Matthijs C., de Bruin, Sanne, Bugiani, Marianna, Bulle, Esther B., Chouchane, Osoul, Cloherty, Alex P.M., Buis, David, de Rotte, Maurtis C.F.J., Dijkstra, Mirjam, Dongelmans, Dave A., Dujardin, Romein W.G., Elbers, Paul E., Fleuren, Lucas M., Geerlings, Suzanne E., Geijtenbeek, Theo B.H., Girbes, Armand R.J., Goorhuis, Bram, Grobusch, Martin P., Hagens, Laura A., Hamann, Jorg, Harris, Vanessa C., Hemke, Robert, Hermans, Sabine M., Heunks, Leo M.A., Hollmann, Markus W., Horn, Janneke, Hovius, Joppe W., de Jong, Menno D., Koning, Rutger, Lim, Endry H.T., van Mourik, Niels, Nellen, Jeannine F., Nossent, Esther J., Paulus, Frederique, Peters, Edgar, Piña-Fuentes, Dan, van der Poll, Tom, Preckel, Bennedikt, Raasveld, Jorinde, Reijnders, Tom D.Y., Schinkel, Michiel, Schrauwen, Femke A.P., Schultz, Marcus J., Schuurman, Alex R., Schuurmans, Jaap, Sigaloff, Kim, Slim, Marleen A., Smeele, Patrick, Smit, Marry R., Stijnis, Cornelis, Stilma, Willemke, Teunissen, Charlotte E., Thoral, Patrick, Tsonas, Anissa M., Tuinman, Pieter R., van der Valk, Marc, Veelo, Denise P., Vlaar, Alexander P.J., Volleman, Carolien, de Vries, Heder, van Vught, Lonneke A., van Vugt, Michèle, Wiersinga, Joost, Wouters, Dorien, Zwinderman, Koos, van Amstel, Rombout B.E., Michels, Erik H.A., and Smeele, Patrick J.

Published
2024
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3. Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression

Author

Qian, Zhaozhi, Zame, William R., Fleuren, Lucas M., Elbers, Paul, and van der Schaar, Mihaela

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Modeling a system's temporal behaviour in reaction to external stimuli is a fundamental problem in many areas. Pure Machine Learning (ML) approaches often fail in the small sample regime and cannot provide actionable insights beyond predictions. A promising modification has been to incorporate expert domain knowledge into ML models. The application we consider is predicting the progression of disease under medications, where a plethora of domain knowledge is available from pharmacology. Pharmacological models describe the dynamics of carefully-chosen medically meaningful variables in terms of systems of Ordinary Differential Equations (ODEs). However, these models only describe a limited collection of variables, and these variables are often not observable in clinical environments. To close this gap, we propose the latent hybridisation model (LHM) that integrates a system of expert-designed ODEs with machine-learned Neural ODEs to fully describe the dynamics of the system and to link the expert and latent variables to observable quantities. We evaluated LHM on synthetic data as well as real-world intensive care data of COVID-19 patients. LHM consistently outperforms previous works, especially when few training samples are available such as at the beginning of the pandemic.

Published
2021

6. Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records

Author

Vagliano, Iacopo, Schut, Martijn C., Abu-Hanna, Ameen, Dongelmans, Dave A., de Lange, Dylan W., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, de Jong, Remko, Peters, Marco A.A., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C.D., Reuland, M.C., Arbous, Sesmu, Fleuren, Lucas M., Dam, Tariq A., Thoral, Patrick J., Lalisang, Robbert C.A., Tonutti, Michele, de Bruin, Daan P., Elbers, Paul W.G., and de Keizer, Nicolette F.

Published
2022
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8. Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning

Author

Dam, Tariq A., Roggeveen, Luca F., van Diggelen, Fuda, Fleuren, Lucas M., Jagesar, Ameet R., Otten, Martijn, de Vries, Heder J., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco A. A., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Machado, Tomas, Herter, Willem E., de Grooth, Harm-Jan, Thoral, Patrick J., Girbes, Armand R. J., Hoogendoorn, Mark, and Elbers, Paul W. G.

Published
2022
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9. Racial Disparities in Pulse Oximetry, in COVID-19 and ICU Settings

Author

Reep, Carmen A.T., Fleuren, Lucas M., Heunks, Leo, Wils, Evert Jan, Reep, Carmen A.T., Fleuren, Lucas M., Heunks, Leo, and Wils, Evert Jan

Abstract

OBJECTIVES (BACKGROUND): This study aimed to assess the impact of race on pulse oximetry reliability, taking into account Spo2 ranges, COVID-19 diagnosis, and ICU admission. DESIGN: Retrospective cohort study covering admissions from January 2020 to April 2024. SETTING: National COVID Cohort Collaborative (N3C) database, consisting of electronic health records from 80 U.S. institutions.PATIENTS/SUBJECTS: Patients were selected from the N3C database based on the availability of data on self-identified race and both pulse oximetry estimated Spo2 and Sao2. Subgroups included patients in ICU and non-ICU settings, with or without a diagnosis of COVID-19 disease. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The agreement between Spo2 and Sao2 was assessed across racial groups (American Indian or Alaska Native, Asian, Black, Hispanic or Latino, Pacific Islander, and White). Each patient’s initial Sao2 measurement was matched with the closest Spo2 values recorded within the preceding 10-minute time frame. The risk of hidden hypoxemia (Spo2 ≥ 88% but Sao2 < 88%) was determined for various Spo2 ranges, races, and clinical scenarios. We used a generalized logistic mixed-effects model to evaluate the impact of relevant variables, such as COVID-19, ICU admission, age, sex, race, and Spo2, on the risk of hidden hypoxemia, while accounting for the random effects within each hospital. A total of 80,541 patients were included, consisting of 596 American Indian or Alaska Native, 2,729 Asian, 11,889 Black, 13,154 Hispanic or Latino, 221 Pacific Islander, and 51,952 White individuals. Discrepancies between Spo2 and Sao2 were observed across all racial groups, with the most pronounced bias in Black patients. Hidden hypoxemia rates were h

Published
2024

10. Predictors for extubation failure in COVID-19 patients using a machine learning approach

Author

Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, Houwert, Taco, Hovenkamp, Hidde, Noorduijn Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cinà, Giovanni, Kantorik, Adam, de Ruijter, Tom, Herter, Willem E., Beudel, Martijn, Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.

Published
2021
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11. The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Author

Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, Houwert, Taco, Hovenkamp, Hidde, Noorduijn-Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cina, Giovanni, Beudel, Martijn, Herter, Willem E., Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.

Published
2021
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12. Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome

Author

Dam, Tariq A., de Grooth, Harm-Jan, Klausch, Thomas, Fleuren, Lucas M., de Bruin, Daan P., Entjes, Robert, Rettig, Thijs C. D., Dongelmans, Dave A., Boelens, Age D., Rigter, Sander, Hendriks, Stefaan H. A., de Jong, Remko, Kamps, Marlijn J. A, Peters, Marco, Karakus, A., Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert-Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P. C., Nooteboom, Fleur G. C. A., Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D., Reidinga, Auke C., de Ruijter, Wouter, Bosman, Rob J., Frenzel, Tim, Urlings-Strop, Louise C., de Jong, Paul, Smit, Ellen G. M., Cremer, Olaf L., Mehagnoul-Schipper, D. Jannet, Faber, Harald J., Lens, Judith, Brunnekreef, Gert B., Festen-Spanjer, Barbara, Dormans, Tom, Dijkstra, Annemieke, Simons, Bram, Rijkeboer, A. A., Arbous, Sesmu, Aries, Marcel, Beukema, Menno, Pretorius, Daniël, van Raalte, Rutger, van Tellingen, Martijn, Gritters van den Oever, Niels C., Lalisang, Robbert C. A., Tonutti, Michele, Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.

Published
2021
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16. 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., Fleuren, Lucas M., de Bruin, Daan P., Tonutti, Michele, Lalisang, Robbert C. A., Elbers, Paul W. G., Dutch ICU Data Sh, van Osch, Frits, Aries, Marcel, Fleuren, Lucas M., Fleuren, Lucas M., de Bruin, Daan P., Tonutti, Michele, Lalisang, Robbert C. A., Elbers, Paul W. G., Dutch ICU Data Sh, van Osch, Frits, and Aries, Marcel

Published
2021

17. Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients:A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records

Author

Vagliano, Iacopo, Schut, Martijn C., Abu-Hanna, Ameen, Dongelmans, Dave A., de Lange, Dylan W., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert Jan, Frenzel, Tim, de Jong, Remko, Peters, Marco A.A., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C.D., Reuland, M. C., Arbous, Sesmu, Fleuren, Lucas M., Dam, Tariq A., Thoral, Patrick J., Lalisang, Robbert C.A., Tonutti, Michele, de Bruin, Daan P., Elbers, Paul W.G., de Keizer, Nicolette F., Vagliano, Iacopo, Schut, Martijn C., Abu-Hanna, Ameen, Dongelmans, Dave A., de Lange, Dylan W., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert Jan, Frenzel, Tim, de Jong, Remko, Peters, Marco A.A., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C.D., Reuland, M. C., Arbous, Sesmu, Fleuren, Lucas M., Dam, Tariq A., Thoral, Patrick J., Lalisang, Robbert C.A., Tonutti, Michele, de Bruin, Daan P., Elbers, Paul W.G., and de Keizer, Nicolette F.

Abstract

Purpose: To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular Electronic Health Records (EHR) data versus less-granular registry data. Methods: Observational study of all COVID-19 patients admitted to 19 Dutch ICUs participating in both the national quality registry National Intensive Care Evaluation (NICE) and the EHR-based Dutch Data Warehouse (hereafter EHR). Multiple models were developed on data from the first 24 h of ICU admissions from February to June 2020 (first COVID-19 wave) and validated on prospective patients admitted to the same ICUs between July and December 2020 (second COVID-19 wave). We assessed model discrimination, calibration, and the degree of relatedness between development and validation population. Coefficients were used to identify relevant risk factors. Results: A total of 1533 patients from the EHR and 1563 from the registry were included. With high granular EHR data, the average AUROC was 0.69 (standard deviation of 0.05) for the internal validation, and the AUROC was 0.75 for the temporal validation. The registry model achieved an average AUROC of 0.76 (standard deviation of 0.05) in the internal validation and 0.77 in the temporal validation. In the EHR data, age, and respiratory-system related variables were the most important risk factors identified. In the NICE registry data, age and chronic respiratory insufficiency were the most important risk factors. Conclusion: In our study, prognostic models built on less-granular but readily-available registry data had similar performance to models built on high-granular EHR data and showed similar transportability to a prospective COVID-19 population. Future research is needed to verify whether this finding can be confirmed for upcoming waves.

Published
2022

18. Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care:Development and validation of a prognostic tool for in-hospital mortality

Author

Plečko, Drago, Bennett, Nicolas, Mårtensson, Johan, Dam, Tariq A., Entjes, Robert, Rettig, Thijs C.D., Dongelmans, Dave A., Boelens, Age D., Rigter, Sander, Hendriks, Stefaan H.A., de Jong, Remko, Kamps, Marlijn J.A., Peters, Marco, Karakus, Attila, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P.C., Nooteboom, Fleur G.C.A., Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D., Reidinga, Auke C., de Ruijter, Wouter, Bosman, Rob J., Frenzel, Tim, Urlings-Strop, Louise C., de Jong, Paul, Smit, Ellen G.M., Cremer, Olaf L., Mehagnoul-Schipper, D. Jannet, Faber, Harald J., Lens, Judith, Brunnekreef, Gert B., Festen-Spanjer, Barbara, Dormans, Tom, de Bruin, Daan P., Lalisang, Robbert C.A., Vonk, Sebastiaan J.J., Haan, Martin E., Fleuren, Lucas M., Thoral, Patrick J., Elbers, Paul W.G., Bellomo, Rinaldo, Plečko, Drago, Bennett, Nicolas, Mårtensson, Johan, Dam, Tariq A., Entjes, Robert, Rettig, Thijs C.D., Dongelmans, Dave A., Boelens, Age D., Rigter, Sander, Hendriks, Stefaan H.A., de Jong, Remko, Kamps, Marlijn J.A., Peters, Marco, Karakus, Attila, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P.C., Nooteboom, Fleur G.C.A., Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D., Reidinga, Auke C., de Ruijter, Wouter, Bosman, Rob J., Frenzel, Tim, Urlings-Strop, Louise C., de Jong, Paul, Smit, Ellen G.M., Cremer, Olaf L., Mehagnoul-Schipper, D. Jannet, Faber, Harald J., Lens, Judith, Brunnekreef, Gert B., Festen-Spanjer, Barbara, Dormans, Tom, de Bruin, Daan P., Lalisang, Robbert C.A., Vonk, Sebastiaan J.J., Haan, Martin E., Fleuren, Lucas M., Thoral, Patrick J., Elbers, Paul W.G., and Bellomo, Rinaldo

Abstract

Background: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction. Methods: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores. Results: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/−24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71–0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64–0.71], 0.61 [CI 0.58–0.66], 0.67 [CI 0.63–0.70], 0.70 [CI 0.67–0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively). Conclusions: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far.

Published
2022

19. Rapid evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality

Author

Medische Staf Intensive Care, Infection & Immunity, Plečko, Drago, Bennett, Nicolas, Mårtensson, Johan, Dam, Tariq A, Entjes, Robert, Rettig, Thijs C D, Dongelmans, Dave A, Boelens, Age D, Rigter, Sander, Hendriks, Stefaan H A, de Jong, Remko, Kamps, Marlijn J A, Peters, Marco, Karakus, Attila, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert-Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P C, Nooteboom, Fleur G C A, Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D, Reidinga, Auke C, de Ruijter, Wouter, Bosman, Rob J, Frenzel, Tim, Urlings-Strop, Louise C, de Jong, Paul, Smit, Ellen G M, Cremer, Olaf L, Mehagnoul-Schipper, D Jannet, Faber, Harald J, Lens, Judith, Brunnekreef, Gert B, Festen-Spanjer, Barbara, Dormans, Tom, de Bruin, Daan P, Lalisang, Robbert C A, Vonk, Sebastiaan J J, Haan, Martin E, Fleuren, Lucas M, Thoral, Patrick J, Elbers, Paul W G, Bellomo, Rinaldo, Medische Staf Intensive Care, Infection & Immunity, Plečko, Drago, Bennett, Nicolas, Mårtensson, Johan, Dam, Tariq A, Entjes, Robert, Rettig, Thijs C D, Dongelmans, Dave A, Boelens, Age D, Rigter, Sander, Hendriks, Stefaan H A, de Jong, Remko, Kamps, Marlijn J A, Peters, Marco, Karakus, Attila, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert-Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P C, Nooteboom, Fleur G C A, Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D, Reidinga, Auke C, de Ruijter, Wouter, Bosman, Rob J, Frenzel, Tim, Urlings-Strop, Louise C, de Jong, Paul, Smit, Ellen G M, Cremer, Olaf L, Mehagnoul-Schipper, D Jannet, Faber, Harald J, Lens, Judith, Brunnekreef, Gert B, Festen-Spanjer, Barbara, Dormans, Tom, de Bruin, Daan P, Lalisang, Robbert C A, Vonk, Sebastiaan J J, Haan, Martin E, Fleuren, Lucas M, Thoral, Patrick J, Elbers, Paul W G, and Bellomo, Rinaldo

Published
2022

20. Additional file 1 of Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning

Author

Dam, Tariq A., Roggeveen, Luca F., van Diggelen, Fuda, Fleuren, Lucas M., Jagesar, Ameet R., Otten, Martijn, de Vries, Heder J., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Dongelmans, Dave A., de Jong, Remko, Peters, Marco A. A., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Machado, Tomas, Herter, Willem E., de Grooth, Harm-Jan, Thoral, Patrick J., Girbes, Armand R. J., Hoogendoorn, Mark, and Elbers, Paul W. G.

Abstract

Additional file 1: Table S1. Parameter bounds. Table S2. Parameter categories. Table S3. Feature derivations. Table S4. Included features. Table S5. Hyper parameter optimization. Table S6. Feature Importance Metrics. Table S7. Patient characteristics. Table S8. ROC AUC score. Table S9. F1-score. Table S10. Sensitivity Analysis Minimal Prone Duration. Table S11. Sensitivity Analysis Cut-off Values. Table S12. Ranked Feature Importance. Table S13. Feature Importance Values. Figure S1. PaO2/FiO2 ratio difference in mmHg over time after starting prone position. Prone positions which continued for longer than 24 hours were filtered out.

Published
2022
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21. Additional file 1 of Right dose, right now: bedside, real-time, data-driven, and personalised antibiotic dosing in critically ill patients with sepsis or septic shock—a two-centre randomised clinical trial

Author

Roggeveen, Luca F., Guo, Tingjie, Fleuren, Lucas M., Driessen, Ronald, Thoral, Patrick, van Hest, Reinier M., Mathot, Ron A. A., Swart, Eleonora L., de Grooth, Harm-Jan, van den Bogaard, Bas, Girbes, Armand R. J., Bosman, Rob J., and Elbers, Paul W. G.

Abstract

Additional file 1. Electronic supplementary material.

Published
2022
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22. 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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23. Klinisch beloop van covid-19 in Nederland: Een overzicht van 2607 ziekenhuispatiënten uit de eerste golf

Author

Ariës, Marcel J H, van den Bergh, Joop P, Beudel, Martijn, Boersma, Wim, Dormans, Tom, Douma, Renee A, Eerens, Annet, Elbers, Paul W G, Fleuren, Lucas M, Gritters van den Oever, Niels C, de Haan, Lianne, van der Horst, Iwan J C C, Hu, Shi, Hubers, Deborah, Janssen, Marcus L F, de Kruif, Martijn, Kubben, Pieter L, van Kuijk, Sander M J, Noordzij, Peter G, Ottenhoff, Maarten, Piña-Fuentes, Dan A I, Potters, Wouter V, Reidinga, Auke C, Renckens, Roos S C, Rigter, Sander, Rusch, Daisy, Schinkel, Michiel, Sigaloff, Kim C E, Simsek, Suat, Stassen, Patricia, Stassen, Robert, Thomas, Rajat M, van Wingen, Guido A, Vonk Noordegraaf, Anton, Welling, Max, Wiersinga, W Joost, Wolvers, Marije D J, Wyers, Caroline E, Intensive Care, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, MUMC+: MA Medische Staf IC (9), MUMC+: MA Intensive Care (3), RS: Carim - V04 Surgical intervention, Klinische Neurowetenschappen, MUMC+: HZC Med Staf Spec Klinische Neurofys (9), RS: MHeNs - R3 - Neuroscience, Neurochirurgie, MUMC+: MA Med Staf Spec Neurochirurgie (9), MUMC+: KIO Kemta (9), Epidemiologie, RS: CAPHRI - R2 - Creating Value-Based Health Care, RS: CAPHRI - R5 - Optimising Patient Care, MUMC+: MA Alg Interne Geneeskunde (9), Interne Geneeskunde, and Family Medicine

Subjects
Male, Cardiovascular Diseases/epidemiology, SARS-CoV-2/isolation & purification, Age Factors, Critical Care/methods, Netherlands/epidemiology, Comorbidity, Kaplan-Meier Estimate, Severity of Illness Index, Risk Factors, Humans, Diagnostic Tests, Routine/methods, Female, Hospital Mortality, Aged, COVID-19/epidemiology
Abstract

OBJECTIVE: To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands. DESIGN: Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported. METHODS: Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission. RESULTS: Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels. CONCLUSION: Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission.

Published
2021

24. Additional file 1 of 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., and Thoral, Patrick J.

Abstract

Additional file 1: Figure S1. Patient selection. Figure S2. Selection of observations throughout the course of IMV. Figure S3. Nested cross-validation. Figure S4. Importance of the top 10 predictors for the prediction of ventilator free days, as well as the difference for predictors over time. Figure S5. SHAP plot ICU mortality (XGBoost). Figure S6. SHAP plot for ICU free days (XGBoost). Figure S7. SHAP plot for ventilator free days (XGBoost). Figure S8. PDPs. Table S1. Overview of all predictors used in the model with a definition where applicable. Table S2. Overall algorithm performance for each of the different outcomes. Table S3. Statistical results for a regression model per outcome. Table S4. Predictor correlations.

Published
2021
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25. Additional file 3 of The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Author

Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, Houwert, Taco, Hovenkamp, Hidde, Noorduijn-Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cina, Giovanni, Beudel, Martijn, Herter, Willem E., Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.

Abstract

Additional file 3: Table S1. Overview of derived clinical score.

Published
2021
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26. Additional file 1 of Predictors for extubation failure in COVID-19 patients using a machine learning approach

Author

Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, Houwert, Taco, Hovenkamp, Hidde, Noorduijn Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cin��, Giovanni, Kantorik, Adam, de Ruijter, Tom, Herter, Willem E., Beudel, Martijn, Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.

Abstract

Additional file 1. Table S1 Overview of machine learning studies that investigate extubation readiness. Table S2 Model performance. Figure S1 PD-plot for BMI

Published
2021
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27. Additional file 2 of The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Author

Fleuren, Lucas M., Dam, Tariq A., Tonutti, Michele, de Bruin, Daan P., Lalisang, Robbert C. A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., 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., Achterberg, Sefanja, Oostdijk, Evelien, Reidinga, Auke C., Festen-Spanjer, Barbara, Brunnekreef, Gert B., Cornet, Alexander D., van den Tempel, Walter, Boelens, Age D., Koetsier, Peter, Lens, Judith, Faber, Harald J., Karakus, A., Entjes, Robert, de Jong, Paul, Rettig, Thijs C. D., Arbous, Sesmu, Vonk, Sebastiaan J. J., Fornasa, Mattia, Machado, Tomas, Houwert, Taco, Hovenkamp, Hidde, Noorduijn-Londono, Roberto, Quintarelli, Davide, Scholtemeijer, Martijn G., de Beer, Aletta A., Cina, Giovanni, Beudel, Martijn, Herter, Willem E., Girbes, Armand R. J., Hoogendoorn, Mark, Thoral, Patrick J., and Elbers, Paul W. G.

Subjects
ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Data_FILES
Abstract

Additional file 2. Institutional review board documentation.

Published
2021
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28. Predicting mortality of individual patients with COVID-19: a multicentre Dutch cohort

Author

Ottenhoff, Maarten C, primary, Ramos, Lucas A, additional, Potters, Wouter, additional, Janssen, Marcus L F, additional, Hubers, Deborah, additional, Hu, Shi, additional, Fridgeirsson, Egill A, additional, Piña-Fuentes, Dan, additional, Thomas, Rajat, additional, van der Horst, Iwan C C, additional, Herff, Christian, additional, Kubben, Pieter, additional, Elbers, Paul W G, additional, Marquering, Henk A, additional, Welling, Max, additional, Simsek, Suat, additional, de Kruif, Martijn D, additional, Dormans, Tom, additional, Fleuren, Lucas M, additional, Schinkel, Michiel, additional, Noordzij, Peter G, additional, van den Bergh, Joop P, additional, Wyers, Caroline E, additional, Buis, David T B, additional, Wiersinga, W Joost, additional, van den Hout, Ella H C, additional, Reidinga, Auke C, additional, Rusch, Daisy, additional, Sigaloff, Kim C E, additional, Douma, Renee A, additional, de Haan, Lianne, additional, Gritters van den Oever, Niels C, additional, Rennenberg, Roger J M W, additional, van Wingen, Guido A, additional, Aries, Marcel J H, additional, and Beudel, Martijn, additional

Published
2021
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29. Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome

Author

Dam, Tariq A, Dam, Tariq A, de Grooth, Harm-Jan, Klausch, Thomas, Fleuren, Lucas M, de Bruin, Daan P, Entjes, Robert, Rettig, Thijs C D, Dongelmans, Dave A, Boelens, Age D, Rigter, Sander, Hendriks, Stefaan H A, de Jong, Remko, Kamps, Marlijn J A, Peters, Marco, Karakus, A, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert-Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P C, Nooteboom, Fleur G C A, Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D, Reidinga, Auke C, de Ruijter, Wouter, Bosman, Rob J, Frenzel, Tim, Urlings-Strop, Louise C, de Jong, Paul, Smit, Ellen G M, Cremer, Olaf L, Mehagnoul-Schipper, D Jannet, Faber, Harald J, Lens, Judith, Brunnekreef, Gert B, Festen-Spanjer, Barbara, Dormans, Tom, Dijkstra, Annemieke, Simons, Bram, Rijkeboer, A A, Arbous, Sesmu, Aries, Marcel, Beukema, Menno, Pretorius, Daniël, van Raalte, Rutger, van Tellingen, Martijn, Gritters van den Oever, Niels C, Lalisang, Robbert C A, Tonutti, Michele, Girbes, Armand R J, Hoogendoorn, Mark, Thoral, Patrick J, Elbers, Paul W G, Dam, Tariq A, Dam, Tariq A, de Grooth, Harm-Jan, Klausch, Thomas, Fleuren, Lucas M, de Bruin, Daan P, Entjes, Robert, Rettig, Thijs C D, Dongelmans, Dave A, Boelens, Age D, Rigter, Sander, Hendriks, Stefaan H A, de Jong, Remko, Kamps, Marlijn J A, Peters, Marco, Karakus, A, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert-Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P C, Nooteboom, Fleur G C A, Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D, Reidinga, Auke C, de Ruijter, Wouter, Bosman, Rob J, Frenzel, Tim, Urlings-Strop, Louise C, de Jong, Paul, Smit, Ellen G M, Cremer, Olaf L, Mehagnoul-Schipper, D Jannet, Faber, Harald J, Lens, Judith, Brunnekreef, Gert B, Festen-Spanjer, Barbara, Dormans, Tom, Dijkstra, Annemieke, Simons, Bram, Rijkeboer, A A, Arbous, Sesmu, Aries, Marcel, Beukema, Menno, Pretorius, Daniël, van Raalte, Rutger, van Tellingen, Martijn, Gritters van den Oever, Niels C, Lalisang, Robbert C A, Tonutti, Michele, Girbes, Armand R J, Hoogendoorn, Mark, Thoral, Patrick J, and Elbers, Paul W G

Abstract

OBJECTIVES: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients.DESIGN: Multicenter retrospective cohort study.SETTING: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020.PATIENTS: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded.MEASUREMENTS AND MAIN RESULTS: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio2, set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao2/Fio2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant (p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves.CONCLUSIONS: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under dire

Published
2021

30. 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, 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, and de Beer, Aletta A

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, I

Published
2021

31. Some Patients Are More Equal Than Others:Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome

Author

Dam, Tariq A, de Grooth, Harm-Jan, Klausch, Thomas, Fleuren, Lucas M, de Bruin, Daan P, Entjes, Robert, Rettig, Thijs C D, Dongelmans, Dave A, Boelens, Age D, Rigter, Sander, Hendriks, Stefaan H A, de Jong, Remko, Kamps, Marlijn J A, Peters, Marco, Karakus, A, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert-Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P C, Nooteboom, Fleur G C A, Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D, Reidinga, Auke C, de Ruijter, Wouter, Bosman, Rob J, Frenzel, Tim, Urlings-Strop, Louise C, de Jong, Paul, Smit, Ellen G M, Cremer, Olaf L, Mehagnoul-Schipper, D Jannet, Faber, Harald J, Lens, Judith, Brunnekreef, Gert B, Festen-Spanjer, Barbara, Dormans, Tom, Dijkstra, Annemieke, Simons, Bram, Rijkeboer, A A, Arbous, Sesmu, Aries, Marcel, Beukema, Menno, Pretorius, Daniël, van Raalte, Rutger, van Tellingen, Martijn, Gritters van den Oever, Niels C, Lalisang, Robbert C A, Tonutti, Michele, Girbes, Armand R J, Hoogendoorn, Mark, Thoral, Patrick J, Elbers, Paul W G, Dam, Tariq A, de Grooth, Harm-Jan, Klausch, Thomas, Fleuren, Lucas M, de Bruin, Daan P, Entjes, Robert, Rettig, Thijs C D, Dongelmans, Dave A, Boelens, Age D, Rigter, Sander, Hendriks, Stefaan H A, de Jong, Remko, Kamps, Marlijn J A, Peters, Marco, Karakus, A, Gommers, Diederik, Ramnarain, Dharmanand, Wils, Evert-Jan, Achterberg, Sefanja, Nowitzky, Ralph, van den Tempel, Walter, de Jager, Cornelis P C, Nooteboom, Fleur G C A, Oostdijk, Evelien, Koetsier, Peter, Cornet, Alexander D, Reidinga, Auke C, de Ruijter, Wouter, Bosman, Rob J, Frenzel, Tim, Urlings-Strop, Louise C, de Jong, Paul, Smit, Ellen G M, Cremer, Olaf L, Mehagnoul-Schipper, D Jannet, Faber, Harald J, Lens, Judith, Brunnekreef, Gert B, Festen-Spanjer, Barbara, Dormans, Tom, Dijkstra, Annemieke, Simons, Bram, Rijkeboer, A A, Arbous, Sesmu, Aries, Marcel, Beukema, Menno, Pretorius, Daniël, van Raalte, Rutger, van Tellingen, Martijn, Gritters van den Oever, Niels C, Lalisang, Robbert C A, Tonutti, Michele, Girbes, Armand R J, Hoogendoorn, Mark, Thoral, Patrick J, and Elbers, Paul W G

Abstract

OBJECTIVES: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients.DESIGN: Multicenter retrospective cohort study.SETTING: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020.PATIENTS: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded.MEASUREMENTS AND MAIN RESULTS: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio2, set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao2/Fio2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant (p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves.CONCLUSIONS: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under dire

Published
2021

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

Author

Infection & Immunity, Medische Staf Intensive Care, Epi Infectieziekten Team 1, Brain, Fleuren, Lucas M, de Bruin, Daan P, Tonutti, Michele, Lalisang, Robbert C A, Elbers, Paul W G, Dutch ICU Data Sharing Collaborators, Infection & Immunity, Medische Staf Intensive Care, Epi Infectieziekten Team 1, Brain, Fleuren, Lucas M, de Bruin, Daan P, Tonutti, Michele, Lalisang, Robbert C A, Elbers, Paul W G, and Dutch ICU Data Sharing Collaborators

Published
2021

33. Why we should sample sparsely and aim for a higher target: Lessons from model‐based therapeutic drug monitoring of vancomycin in intensive care patients

Author

Guo, Tingjie, primary, Hest, Reinier M., additional, Fleuren, Lucas M., additional, Roggeveen, Luca F., additional, Bosman, Rob J., additional, Voort, Peter H.J., additional, Girbes, Armand R.J., additional, Mathot, Ron A.A., additional, Hasselt, Johan G.C., additional, and Elbers, Paul W.G., additional

Published
2020
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34. Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients

Author

Roggeveen, Luca F., primary, Guo, Tingjie, additional, Driessen, Ronald H., additional, Fleuren, Lucas M., additional, Thoral, Patrick, additional, van der Voort, Peter H. J., additional, Girbes, Armand R. J., additional, Bosman, Rob J., additional, and Elbers, Paul, additional

Published
2020
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36. Right dose, right now

Author

Roggeveen, Luca F., Guo, Tingjie, Fleuren, Lucas M., Driessen, Ronald, Thoral, Patrick, van Hest, Reinier M., Mathot, Ron A. A., Swart, Eleonora L., de Grooth, Harm-Jan, van den Bogaard, Bas, Girbes, Armand R. J., Bosman, Rob J., Elbers, Paul W. G., Supporting clinical sciences, and Intensive Care

Subjects
medicine.medical_specialty, PHARMACOKINETICS, Critical Care, medicine.drug_class, Big data, Antibiotics, Decision Support, Critical Care and Intensive Care Medicine, autokinetics, Intensive care, medicine, critical illness, Humans, Dosing, Precision Medicine, Intensive care medicine, intensive care, drug monitoring, Medicine(all), electronic patient record, medicine.diagnostic_test, Critically ill, business.industry, General Medicine, Precision medicine, Anti-Bacterial Agents, Anesthesiology and Pain Medicine, Therapeutic drug monitoring, Drug dosing, business
Abstract

Antibiotics save lives and are essential for the practice of intensive care medicine. Adequate antibiotic treatment is closely related to outcome. However this is challenging in the critically ill, as their pharmacokinetic profile is markedly altered. Therefore, it is surprising that critical care physicians continue to rely on standard dosing regimens for every patient, regardless of the actual clinical situation. This review outlines the pharmacokinetic and pharmacodynamic principles that underlie the need for individualized and personalized drug dosing. At present, although therapeutic drug monitoring may be of help, it has major disadvantages, remains unavailable for most antibiotics and has produced mixed results. We therefore propose the AutoKinetics concept, taking decision support for antibiotic dosing back to the bedside. By direct interaction with electronic patient records, this opens the way for the use of big data for providing the right dose at the right time in each patient.

Published
2015
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37. Why we should sample sparsely and aim for a higher target: Lessons from model‐based therapeutic drug monitoring of vancomycin in intensive care patients.

Author

Guo, Tingjie, Hest, Reinier M., Fleuren, Lucas M., Roggeveen, Luca F., Bosman, Rob J., Voort, Peter H.J., Girbes, Armand R.J., Mathot, Ron A.A., Hasselt, Johan G.C., and Elbers, Paul W.G.

Subjects
DRUG monitoring, INTENSIVE care patients, STANDARD deviations, VANCOMYCIN
Abstract

Aims: To explore the optimal data sampling scheme and the pharmacokinetic (PK) target exposure on which dose computation is based in the model‐based therapeutic drug monitoring (TDM) practice of vancomycin in intensive care (ICU) patients. Methods: We simulated concentration data for 1 day following four sampling schemes, Cmin, Cmax + Cmin, Cmax + Cmid‐interval + Cmin, and rich sampling where a sample was drawn every hour within a dose interval. The datasets were used for Bayesian estimation to obtain PK parameters, which were used to compute the doses for the next day based on five PK target exposures: AUC24 = 400, 500, and 600 mg·h/L and Cmin = 15 and 20 mg/L. We then simulated data for the next day, adopting the computed doses, and repeated the above procedure for 7 days. Thereafter, we calculated the percentage error and the normalized root mean square error (NRMSE) of estimated against "true" PK parameters, and the percentage of optimal treatment (POT), defined as the percentage of patients who met 400 ≤ AUC24 ≤ 600 mg·h/L and Cmin ≤ 20 mg/L. Results: PK parameters were unbiasedly estimated in all investigated scenarios and the 6‐day average NRMSE were 32.5%/38.5% (CL/V, where CL is clearance and V is volume of distribution) in the trough sampling scheme and 27.3%/26.5% (CL/V) in the rich sampling scheme. Regarding POT, the sampling scheme had marginal influence, while target exposure showed clear impacts that the maximum POT of 71.5% was reached when doses were computed based on AUC24 = 500 mg·h/L. Conclusions: For model‐based TDM of vancomycin in ICU patients, sampling more frequently than taking only trough samples adds no value and dosing based on AUC24 = 500 mg·h/L lead to the best POT. [ABSTRACT FROM AUTHOR]

Published
2021
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38. Racial Disparities in Pulse Oximetry, in COVID-19 and ICU Settings.

Author

Reep CAT, Fleuren LM, Heunks L, and Wils EJ

Subjects
Adult, Aged, Female, Humans, Male, Middle Aged, Racial Groups, Reproducibility of Results, Retrospective Studies, United States epidemiology, COVID-19 ethnology, COVID-19 diagnosis, COVID-19 epidemiology, Healthcare Disparities ethnology, Intensive Care Units, Oximetry
Abstract

Objectives Background: This study aimed to assess the impact of race on pulse oximetry reliability, taking into account Spo 2 ranges, COVID-19 diagnosis, and ICU admission., Design: Retrospective cohort study covering admissions from January 2020 to April 2024., Setting: National COVID Cohort Collaborative (N3C) database, consisting of electronic health records from 80 U.S. institutions., Patients/subjects: Patients were selected from the N3C database based on the availability of data on self-identified race and both pulse oximetry estimated Spo 2 and Sao 2 . Subgroups included patients in ICU and non-ICU settings, with or without a diagnosis of COVID-19 disease., Interventions: None., Measurements and Main Results: The agreement between Spo 2 and Sao 2 was assessed across racial groups (American Indian or Alaska Native, Asian, Black, Hispanic or Latino, Pacific Islander, and White). Each patient's initial Sao 2 measurement was matched with the closest Spo 2 values recorded within the preceding 10-minute time frame. The risk of hidden hypoxemia (Spo 2 ≥ 88% but Sao 2 < 88%) was determined for various Spo 2 ranges, races, and clinical scenarios. We used a generalized logistic mixed-effects model to evaluate the impact of relevant variables, such as COVID-19, ICU admission, age, sex, race, and Spo 2 , on the risk of hidden hypoxemia, while accounting for the random effects within each hospital. A total of 80,541 patients were included, consisting of 596 American Indian or Alaska Native, 2,729 Asian, 11,889 Black, 13,154 Hispanic or Latino, 221 Pacific Islander, and 51,952 White individuals. Discrepancies between Spo 2 and Sao 2 were observed across all racial groups, with the most pronounced bias in Black patients. Hidden hypoxemia rates were higher in Black patients across all Spo 2 subgroups, for all clinical scenarios. The odds of hidden hypoxemia were higher for Black and Hispanic or Latino patients and for those with COVID-19 disease., Conclusions: Race significantly impacts pulse oximetry reliability. Not only Black and Hispanic or Latino patients were at higher risk for hidden hypoxemia, but also those admitted with a COVID-19 diagnosis. Future in-depth explorations into the underlying causes and potential solutions are needed., Competing Interests: Dr. Fleuren is the cofounder of Medscio, Dr. Heunks receives consultancy fees from Liberate Medical (Crestwood, KY), speakers fees from Mindray, and research funding to the department from European Respiratory Society and ZonMw. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Published
2024
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