Search

Your search keyword '"Schipper D"' showing total 536 results
Start Over Author "Schipper D"
536 results on '"Schipper D"'

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

3. 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
Full Text
View/download PDF

4. 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
Full Text
View/download PDF

5. Age Moderates the Effect of Obesity on Mortality Risk in Critically Ill Patients With COVID-19: A Nationwide Observational Cohort Study*

Author

den Uil, Corstiaan A., Termorshuizen, Fabian, Rietdijk, Wim J. R., Sablerolles, Roos S. G., van der Kuy, Hugo P. M., Haas, Lenneke E. M., van der Voort, Peter H. J., de Lange, Dylan W., Pickkers, Peter, de Keizer, Nicolette F., Arbous, M. S., Barnas, M. G. W., Boer, D. P., Bosman, R. J., Brunnekreef, G. B., de Bruin, M. Th., de Graaff, M. J., de Jong, R. M., de Meijer, A. R., de Ruijter, W., de Waal, R., Dijkhuizen, A., Dongelmans, D. A., Dormans, T. P. J., Draisma, A., Drogt, I., Eikemans, B. J. W., Elbers, P. W. G., Epker, J. L., Erkamp, M. L., Festen-Spanjer, B., Frenzel, T., Georgieva, L., Gritters, N. C., Hené, I. Z., Hoeksema, M., Holtkamp, J. W. M., Hoogendoorn, M. E., Jacobs, C. J. G. M., Janssen, I. T. A., Kieft, H., Koetsier, M. P., Koning, T. J. J., Kreeftenberg, H., Kusadasi, N., Lens, J. A., Lutisan, J. G., Mehagnoul-Schipper, D. J., Moolenaar, D., Nooteboom, F., Postma, N., Pruijsten, R. V., Ramnarain, D., Reidinga, A. C., Rengers, E., Rijkeboer, A. A., Rijpstra, T., Rozendaal, F. W., Schnabel, R. M., Silderhuis, V. M., Spijkstra, J. J., Spronk, P. E., te Velde, L. F., Urlings-Strop, L. C., van den Berg, A. E., van den Berg, R., van der Horst, I. C. C., van Driel, E. M., van Gulik, L., van Iersel, F. M., van Lieshout, M., van Oers, J. A. H., van Slobbe-Bijlsma, E. R., van Tellingen, M., Vandeputte, J., Verbiest, D. P., Versluis, D. J., Verweij, E., Vrolijk-de Mos, M., and Wesselink, R. M. J.

Published
2023
Full Text
View/download PDF

6. 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
Full Text
View/download PDF

7. 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
Full Text
View/download PDF

9. 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
Full Text
View/download PDF

10. Incidence, Risk Factors and Outcome of Suspected Central Venous Catheter-related Infections in Critically Ill COVID-19 Patients

Author

Smit, Jasper M., Exterkate, Lotte, Van Tienhoven, Arne J., Haaksma, Mark E., Heldeweg, Micah L.A., Fleuren, Lucas, Thoral, Patrick, Dam, Tariq A., Heunks, Leo M.A., Gommers, Diederik, Cremer, Olaf L., Bosman, Rob J., Rigter, Sander, Wils, Evert Jan, Frenzel, Tim, Vlaar, Alexander P., 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, Bas, Machado, Tomas, Girbes, Armand R.J., Sieswerda, Elske, Elbers, Paul W.G., Tuinman, Pieter R., Intensive care medicine, Radiology and nuclear medicine, Anesthesiology, Internal medicine, ACS - Diabetes & metabolism, ACS - Microcirculation, Amsterdam Cardiovascular Sciences, Cardio-thoracic surgery, General practice, AII - Infectious diseases, Medical Microbiology and Infection Prevention, ACS - Pulmonary hypertension & thrombosis, Intensive Care Medicine, APH - Quality of Care, Graduate School, AII - Cancer immunology, CCA - Cancer biology and immunology, and Intensive Care

Subjects
catheter-related infections, Catheterization, Central Venous, Critical Illness, Incidence, Other Research Radboud Institute for Health Sciences [Radboudumc 0], COVID-19, Critical Care and Intensive Care Medicine, Central venous catheters, All institutes and research themes of the Radboud University Medical Center, Risk Factors, Emergency Medicine, Humans, Retrospective Studies, intensive care
Abstract

Background: Aims of this study were to investigate the prevalence and incidence of catheter-related infection, identify risk factors, and determine the relation of catheter-related infection with mortality in critically ill COVID-19 patients. Methods: This was a retrospective cohort study of central venous catheters (CVCs) in critically ill COVID-19 patients. Eligible CVC insertions required an indwelling time of at least 48 hours and were identified using a full-admission electronic health record database. Risk factors were identified using logistic regression. Differences in survival rates at day 28 of follow-up were assessed using a log-rank test and proportional hazard model. Results: In 538 patients, a total of 914 CVCs were included. Prevalence and incidence of suspected catheter-related infection were 7.9% and 9.4 infections per 1,000 catheter indwelling days, respectively. Prone ventilation for more than 5 days was associated with increased risk of suspected catheter-related infection; odds ratio, 5.05 (95% confidence interval 2.12-11.0). Risk of death was significantly higher in patients with suspected catheter-related infection (hazard ratio, 1.78; 95% confidence interval, 1.25-2.53). Conclusions: This study shows that in critically ill patients with COVID-19, prevalence and incidence of suspected catheter-related infection are high, prone ventilation is a risk factor, and mortality is higher in case of catheter-related infection.

Published
2022

11. SARS-CoV-2 ORF8 accessory protein is a virulence factor

Author

Bello-Perez, M., Hurtado-Tamayo, J., Mykytyn, A. Z., Lamers, M. M., Requena-Platek, R., Schipper, D., Muñoz-Santos, D., Ripoll-Gómez, J., Esteban, A., Sánchez-Cordón, P. J., Enjuanes, L., Haagmans, B. L., Sola, I., Bello-Perez, M., Hurtado-Tamayo, J., Mykytyn, A. Z., Lamers, M. M., Requena-Platek, R., Schipper, D., Muñoz-Santos, D., Ripoll-Gómez, J., Esteban, A., Sánchez-Cordón, P. J., Enjuanes, L., Haagmans, B. L., and Sola, I.

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encodes six accessory proteins (3a, 6, 7a, 7b, 8, and 9b) for which limited information is available on their role in pathogenesis. We showed that the deletion of open reading frames (ORFs) 6, 7a, or 7b individually did not significantly impact viral pathogenicity in humanized K18-hACE2 transgenic mice. In contrast, the deletion of ORF8 partially attenuated SARS-CoV-2, resulting in reduced lung pathology and 40% less mortality, indicating that ORF8 is a critical determinant of SARS-CoV-2 pathogenesis. Attenuation of SARS-CoV-2-∆8 was not associated with a significant decrease in replication either in the lungs of mice or in organoid-derived human airway cells. An increase in the interferon signaling at early times post-infection (1 dpi) in the lungs of mice and a decrease in the pro-inflammatory and interferon response at late times post-infection, both in the lungs of mice (6 dpi) and in organoid-derived human airway cells [72 hours post-infection (hpi)], were observed. The early, but not prolonged, interferon response along with the lower inflammatory response could explain the partial attenuation of SARS-CoV-∆8. The presence of ORF8 in SARS-CoV-2 was associated with an increase in the number of macrophages in the lungs of mice. In addition, the supernatant of SARS-CoV-2-WT (wild-type)-infected organoid-derived cells enhanced the activation of macrophages as compared to SARS-CoV-2-∆8-infected cells. These results show that ORF8 is a virulence factor involved in inflammation that could be targeted in COVID-19 therapies. IMPORTANCE The relevance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ORF8 in the pathogenesis of COVID-19 is unclear. Virus natural isolates with deletions in ORF8 were associated with wild milder disease, suggesting that ORF8 might contribute to SARS-CoV-2 virulence. This manuscript shows that ORF8 is involved in inflammation and in the activation of macrophages in

Published
2023

12. SARS-CoV-2 ORF8 accessory protein is a virulence factor

Author

Ministerio de Economía y Competitividad (España), Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), European Commission, National Institutes of Health (US), Netherlands Organisation for Health Research and Development, Bello-Pérez, Melissa [0000-0002-9212-083X], https://ror.org/02gfc7t72, Bello-Pérez, Melissa, Hurtado-Tamayo, Jesús, Mykytyn, Anna Z., Lamers, Mart M., Requena-Platek, Ricardo, Schipper, D., Muñoz-Santos, Diego, Ripoll-Gómez, Jorge, Esteban, Ana, Sánchez-Cordón, P. J., Enjuanes Sánchez, Luis, Haagmans, Bart L., Solá Gurpegui, Isabel, Ministerio de Economía y Competitividad (España), Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), European Commission, National Institutes of Health (US), Netherlands Organisation for Health Research and Development, Bello-Pérez, Melissa [0000-0002-9212-083X], https://ror.org/02gfc7t72, Bello-Pérez, Melissa, Hurtado-Tamayo, Jesús, Mykytyn, Anna Z., Lamers, Mart M., Requena-Platek, Ricardo, Schipper, D., Muñoz-Santos, Diego, Ripoll-Gómez, Jorge, Esteban, Ana, Sánchez-Cordón, P. J., Enjuanes Sánchez, Luis, Haagmans, Bart L., and Solá Gurpegui, Isabel

Abstract

The relevance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ORF8 in the pathogenesis of COVID-19 is unclear. Virus natural isolates with deletions in ORF8 were associated with wild milder disease, suggesting that ORF8 might contribute to SARS-CoV-2 virulence. This manuscript shows that ORF8 is involved in inflammation and in the activation of macrophages in two experimental systems: humanized K18-hACE2 transgenic mice and organoid-derived human airway cells. These results identify ORF8 protein as a potential target for COVID-19 therapies.

Published
2023

13. Validity of Amontons’ law for run-in short-cut aramid fiber reinforced elastomers: The effect of epoxy coated fibers

Author

Khafidh, M., Schipper, D. J., Masen, M. A., Vleugels, N., Dierkes, W. K., and Noordermeer, J. W. M.

Abstract

Friction between two contacting surfaces is studied extensively. One of the known friction theories is Amontons’ law which states that the friction force is proportional to the normal force. However, Amontons’ law has been found to be invalid for elastomers. In the present study, the validity of Amontons’ law for short-cut aramid fiber reinforced elastomers is studied. Two types of fillers are used to reinforce the elastomers, namely highly dispersible silica and short-cut aramid fibers. Short-cut aramid fibers with two different surface treatments are used, namely non-reactive fibers with standard oily finish (SF-fibers) and fibers treated with an epoxy coating (EF-fibers). A pin-on-disc tribometer is used to investigate the frictional behavior of the composites in sliding contact with a granite counter surface. The results show that, after the run-in phase, Amontons’ law is valid for those composites that are reinforced by short-cut aramid fibers (without reinforcing filler, i.e., silica) if the contact pressure is below a threshold value. However, once the contact pressure exceeds this threshold value, Amontons’ law will be invalid. The threshold contact pressure of the composites containing EF-fibers is higher than of the composites containing SF-fibers. The composites that are reinforced by silica and short-cut aramid fibers do not follow Amontons’ law.

Published
2024
Full Text
View/download PDF

16. Comparison of patient characteristics and long-term mortality between transferred and non-transferred COVID-19 patients in Dutch intensive care units: A national cohort study

Author

Wortel, Safira A., Bakhshi-Raiez, Ferishta, Termorshuizen, Fabian, de Lange, Dylan W., Dongelmans, Dave A., de Keizer, Nicolette F., Arbous, M. S., Barnas, M. G. W., Bindels, A. J. G. H., Boer, D. P., Bosman, R. J., Brunnekreef, G. B., de Bruin, M. Th., de Graaff, M., de Jong, R. M., de Meijer, A. R., de Ruijter, W., de Waal, R., Dijkhuizen, A., Dormans, T. P. J., Draisma, A., Drogt, I., Eikemans, B. J. W., Elbers, P. W. G., Epker, J. L., Erkamp, M. L., Festen-Spanjer, B., Frenzel, T., Gommers, D., Gritters, N. C., Hené, I. Z., Hoeksema, M., Holtkamp, J. W. M., Hoogendoorn, M. E., Houwink, A. P. I., Jacobs, C. J. M. G., Janssen, I. T. A., Kieft, H., Koetsier, M. P., Koning, T. J. J., Kusadasi, N., Lens, J. A., Lutisan, J. G., Mehagnoul-Schipper, D. J., Moolenaar, D., Nooteboom, F., Pruijsten, R. V., Ramnarain, D., Reidinga, A. C., Rengers, E., Rijkeboer, A. A., Rozendaal, F. W., Schnabel, R. M., Silderhuis, V. M., Spijkstra, J. J., Spronk, P., te Velde, L. F., Urlings-Strop, L. C., van Bussel, B. C. T., van den Berg, A. E., van den Berg, R., van der Voort, P. H. J., van Driel, E. M., van Gulik, L., van Iersel, F. M., van Lieshout, M., van Slobbe-Bijlsma, E. R., van Tellingen, M., Vandeputte, J., Verbiest, D. P., Versluis, D. J., Verweij, E., Mos, M. Vrolijk-de, Wesselink, R. M. J., Graduate School, Medical Informatics, APH - Methodology, APH - Quality of Care, Intensive Care Medicine, APH - Digital Health, Radiology and Nuclear Medicine, ACS - Microcirculation, and ANS - Neurovascular Disorders

Subjects
COVID-19, intrahospital transfer, severity of illness, intensive care unit, mortality
Abstract

Background: COVID-19 patients were often transferred to other intensive care units (ICUs) to prevent that ICUs would reach their maximum capacity. However, transferring ICU patients is not free of risk. We aim to compare the characteristics and outcomes of transferred versus non-transferred COVID-19 ICU patients in the Netherlands. Methods: We included adult COVID-19 patients admitted to Dutch ICUs between March 1, 2020 and July 1, 2021. We compared the patient characteristics and outcomes of non-transferred and transferred patients and used a Directed Acyclic Graph to identify potential confounders in the relationship between transfer and mortality. We used these confounders in a Cox regression model with left truncation at the day of transfer to analyze the effect of transfers on mortality during the 180 days after ICU admission. Results: We included 10,209 patients: 7395 non-transferred and 2814 (27.6%) transferred patients. In both groups, the median age was 64 years. Transferred patients were mostly ventilated at ICU admission (83.7% vs. 56.2%) and included a larger proportion of low-risk patients (70.3% vs. 66.5% with mortality risk

Published
2022

25. Dynamic prediction of mortality in COVID-19 patients in the intensive care unit:A retrospective multi-center cohort study

Author

Smit, J. M., Krijthe, J. H., Endeman, H., Tintu, A. N., de Rijke, Y. B., Gommers, D. A.M.P.J., Cremer, O. L., Bosman, R. J., Rigter, S., Wils, E. J., Frenzel, T., Dongelmans, D. A., De Jong, R., Peters, M. A.A., Kamps, M. J.A., Ramnarain, D., Nowitzky, R., Nooteboom, F. G.C.A., De Ruijter, W., Urlings-Strop, L. C., Smit, E. G.M., Mehagnoul-Schipper, D. J., Dormans, T., De Jager, C. P.C., Hendriks, S. H.A., Achterberg, S., Oostdijk, E., Reidinga, A. C., Festen-Spanjer, B., Brunnekreef, G. B., Cornet, A. D., Van den Tempel, W., Boelens, A. D., Koetsier, P., Lens, J. A., Faber, H. J., karakus, A., Entjes, R., De Jong, P., Rettig, T. C.D., Arbous, M. S., Lalisang, R. C.A., Tonutti, M., De Bruin, D. P., Elbers, P. W.G., Van Bommel, J., Reinders, M. J.T., Smit, J. M., Krijthe, J. H., Endeman, H., Tintu, A. N., de Rijke, Y. B., Gommers, D. A.M.P.J., Cremer, O. L., Bosman, R. J., Rigter, S., Wils, E. J., Frenzel, T., Dongelmans, D. A., De Jong, R., Peters, M. A.A., Kamps, M. J.A., Ramnarain, D., Nowitzky, R., Nooteboom, F. G.C.A., De Ruijter, W., Urlings-Strop, L. C., Smit, E. G.M., Mehagnoul-Schipper, D. J., Dormans, T., De Jager, C. P.C., Hendriks, S. H.A., Achterberg, S., Oostdijk, E., Reidinga, A. C., Festen-Spanjer, B., Brunnekreef, G. B., Cornet, A. D., Van den Tempel, W., Boelens, A. D., Koetsier, P., Lens, J. A., Faber, H. J., karakus, A., Entjes, R., De Jong, P., Rettig, T. C.D., Arbous, M. S., Lalisang, R. C.A., Tonutti, M., De Bruin, D. P., Elbers, P. W.G., Van Bommel, J., and Reinders, M. J.T.

Abstract

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27; −0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.

Published
2022

26. 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

27. 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

28. Incidence, Risk Factors and Outcome of Suspected Central Venous Catheter-related Infections in Critically Ill COVID-19 Patients: A Multicenter Retrospective Cohort Study

Author

Medische Staf Intensive Care, Infection & Immunity, MMB Medische Staf, Exterkate, Lotte, van Tienhoven, Arne J, Haaksma, Mark E, Heldeweg, Micah L A, Fleuren, Lucas, Thoral, Patrick, Dam, Tariq A, Heunks, Leo M A, Gommers, Diederik, Cremer, Olaf L, Bosman, Rob J, Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Vlaar, Alexander P, 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, Bas, Machado, Tomas, Girbes, Armand R J, Sieswerda, Elske, Elbers, Paul W G, Tuinman, Pieter R, Medische Staf Intensive Care, Infection & Immunity, MMB Medische Staf, Exterkate, Lotte, van Tienhoven, Arne J, Haaksma, Mark E, Heldeweg, Micah L A, Fleuren, Lucas, Thoral, Patrick, Dam, Tariq A, Heunks, Leo M A, Gommers, Diederik, Cremer, Olaf L, Bosman, Rob J, Rigter, Sander, Wils, Evert-Jan, Frenzel, Tim, Vlaar, Alexander P, 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, Bas, Machado, Tomas, Girbes, Armand R J, Sieswerda, Elske, Elbers, Paul W G, and Tuinman, Pieter R

Published
2022

29. Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

Author

Arts-assistenten DV&B, Medische Staf Intensive Care, Infection & Immunity, Smit, J M, Krijthe, J H, Endeman, H, Tintu, A N, de Rijke, Y B, Gommers, D A M P J, Cremer, O L, Bosman, R J, Rigter, S, Wils, E-J, Frenzel, T, Dongelmans, D A, De Jong, R, Peters, M A A, Kamps, M J A, Ramnarain, D, Nowitzky, R, Nooteboom, F G C A, De Ruijter, W, Urlings-Strop, L C, Smit, E G M, Mehagnoul-Schipper, D J, Dormans, T, De Jager, C P C, Hendriks, S H A, Achterberg, S, Oostdijk, E, Reidinga, A C, Festen-Spanjer, B, Brunnekreef, G B, Cornet, A D, Van den Tempel, W, Boelens, A D, Koetsier, P, Lens, J A, Faber, H J, Karakus, A, Entjes, R, De Jong, P, Rettig, T C D, Arbous, M S, Lalisang, R C A, Tonutti, M, De Bruin, D P, Elbers, P W G, Van Bommel, J, Reinders, M J T, Arts-assistenten DV&B, Medische Staf Intensive Care, Infection & Immunity, Smit, J M, Krijthe, J H, Endeman, H, Tintu, A N, de Rijke, Y B, Gommers, D A M P J, Cremer, O L, Bosman, R J, Rigter, S, Wils, E-J, Frenzel, T, Dongelmans, D A, De Jong, R, Peters, M A A, Kamps, M J A, Ramnarain, D, Nowitzky, R, Nooteboom, F G C A, De Ruijter, W, Urlings-Strop, L C, Smit, E G M, Mehagnoul-Schipper, D J, Dormans, T, De Jager, C P C, Hendriks, S H A, Achterberg, S, Oostdijk, E, Reidinga, A C, Festen-Spanjer, B, Brunnekreef, G B, Cornet, A D, Van den Tempel, W, Boelens, A D, Koetsier, P, Lens, J A, Faber, H J, Karakus, A, Entjes, R, De Jong, P, Rettig, T C D, Arbous, M S, Lalisang, R C A, Tonutti, M, De Bruin, D P, Elbers, P W G, Van Bommel, J, and Reinders, M J T

Published
2022

30. 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

31. 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
Full Text
View/download PDF

32. 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

33. Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis

Author

Siepel, Sander, primary, Dam, Tariq A., additional, Fleuren, Lucas M., additional, Gommers, Diederik, additional, Cremer, Olaf L., additional, Bosman, Rob J., additional, Rigter, Sander, additional, Wils, Evert-Jan, additional, Frenzel, Tim, additional, Dongelmans, Dave A., additional, de Jong, Remko, additional, Peters, Marco, additional, Kamps, Marlijn J.A, additional, Ramnarain, Dharmanand, additional, Nowitzky, Ralph, additional, Nooteboom, Fleur G.C.A., additional, de Ruijter, Wouter, additional, Urlings-Strop, Louise C., additional, Smit, Ellen G.M., additional, Mehagnoul-Schipper, D. Jannet, additional, Dormans, Tom, additional, de Jager, Cornelis P.C., additional, Hendriks, Stefaan H.A., additional, Achterberg, Sefanja, additional, Oostdijk, Evelien, additional, Reidinga, Auke C., additional, Festen-Spanjer, Barbara, additional, Brunnekreef, Gert B., additional, Cornet, Alexander D., additional, Tempel, Walter van den, additional, Boelens, Age D., additional, Koetsier, Peter, additional, Lens, Judith, additional, Faber, Harald J., additional, Karakus, A., additional, Entjes, Robert, additional, de Jong, Paul, additional, Rettig, Thijs C.D., additional, Arbous, Sesmu, additional, Vonk, Sebastiaan J.J., additional, Machado, Tomas, additional, Herter, Willem E., additional, Girbes, Armand R.J., additional, Hoogendoorn, Mark, additional, Thoral, Patrick J., additional, Elbers, Paul W.G., additional, and Bennis, Frank C., additional

Published
2022
Full Text
View/download PDF

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

Author

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

Published
2021
Full Text
View/download PDF

41. 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
Full Text
View/download PDF

42. 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
Full Text
View/download PDF

43. 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
Full Text
View/download PDF

44. 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
Full Text
View/download PDF

45. Sars-cov-2 entry into human airway organoids is serine protease-mediated and facilitated by the multibasic cleavage site

Author

Mykytyn, A.Z. (Anna Z.), Breugem, T.I. (Tim I.), Riesebosch, S. (Samra), Schipper, D. (Debby), Doel, P. (Petra) van den, Rottier, R.J. (Robbert), Lamers, M.M. (Mart M.), Haagmans, B.L. (Bart), Mykytyn, A.Z. (Anna Z.), Breugem, T.I. (Tim I.), Riesebosch, S. (Samra), Schipper, D. (Debby), Doel, P. (Petra) van den, Rottier, R.J. (Robbert), Lamers, M.M. (Mart M.), and Haagmans, B.L. (Bart)

Abstract

Coronavirus entry is mediated by the spike protein that binds the receptor and mediates fusion after cleavage by host proteases. The proteases that mediate entry differ between cell lines, and it is currently unclear which proteases are relevant in vivo. A remarkable feature of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike is the presence of a multibasic cleavage site (MBCS), which is absent in the SARS-CoV spike. Here, we report that the SARS-CoV-2 spike MBCS increases infectivity on human airway organoids (hAOs). Compared with SARS-CoV, SARS-CoV-2 entered faster into Calu-3 cells and, more frequently, formed syncytia in hAOs. Moreover, the MBCS increased entry speed and plasma membrane serine protease usage relative to cathepsin-mediated endosomal entry. Blocking serine proteases, but not cathepsins, effectively inhibited SARS-CoV-2 entry and replication in hAOs. Our findings demonstrate that SARS-CoV-2 enters relevant airway cells using serine proteases, and suggest that the MBCS is an adaptation to this viral entry strategy.

Published
2021
Full Text
View/download PDF

46. An organoid-derived bronchioalveolar model for SARS-CoV-2 infection of human alveolar type II-like cells

Author

Lamers, M.M. (Mart M.), van der Vaart, J. (Jelte), Knoops, K. (Kèvin), Riesebosch, S. (Samra), Breugem, T.I. (Tim I), Mykytyn, A.Z. (Anna Z), Beumer, J. (Joep), Schipper, D. (Debby), Bezstarosti, K. (Karel), Koopman, C.D. (Charlotte D), Groen, N. (Nathalie), Ravelli, R.B.G. (Raimond B.), Duimel, H.Q. (Hans Q), Demmers, J.A.A. (Jeroen), Verjans, G.M.G.M. (George), Koopmans D.V.M., M.P.G. (Marion), Muraro, M.J. (Mauro J), Peters, P.J. (Peter J), Clevers, H.C. (Hans), Haagmans, B.L. (Bart), Lamers, M.M. (Mart M.), van der Vaart, J. (Jelte), Knoops, K. (Kèvin), Riesebosch, S. (Samra), Breugem, T.I. (Tim I), Mykytyn, A.Z. (Anna Z), Beumer, J. (Joep), Schipper, D. (Debby), Bezstarosti, K. (Karel), Koopman, C.D. (Charlotte D), Groen, N. (Nathalie), Ravelli, R.B.G. (Raimond B.), Duimel, H.Q. (Hans Q), Demmers, J.A.A. (Jeroen), Verjans, G.M.G.M. (George), Koopmans D.V.M., M.P.G. (Marion), Muraro, M.J. (Mauro J), Peters, P.J. (Peter J), Clevers, H.C. (Hans), and Haagmans, B.L. (Bart)

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), which may result in acute respiratory distress syndrome (ARDS), multiorga

Published
2021
Full Text
View/download PDF

47. 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

48. 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

49. 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

50. Human Respiratory Syncytial Virus Subgroup A and B Infections in Nasal, Bronchial, Small-Airway, and Organoid-Derived Respiratory Cultures

Author

Rijsbergen, L. C., Lamers, M. M., Comvalius, A. D., Koutstaal, R. W., Schipper, D., Duprex, W. P., Haagmans, B. L., de Vries, R. D., de Swart, R. L., Rijsbergen, L. C., Lamers, M. M., Comvalius, A. D., Koutstaal, R. W., Schipper, D., Duprex, W. P., Haagmans, B. L., de Vries, R. D., and de Swart, R. L.

Abstract

Human respiratory syncytial virus (HRSV) is the leading cause of bronchioli- tis in infants. Two subgroups of HRSV (A and B) routinely cocirculate. Most research has been performed with HRSV-A strains because these are easier to culture than HRSV-B strains. In this study, we aimed to compare the replicative fitness and HRSV-induced innate cytokine responses of HRSV-A and HRSV-B strains in disease-relevant cell culture models. We used two recombinant (r) clinical isolate-based HRSV strains (A11 and B05) and one recombinant laboratory-adapted HRSV strain (A2) to infect commercially avail- able nasal, bronchial, and small-airway cultures. Epithelial cells from all anatomical loca- tions were susceptible to HRSV infection despite the induction of a dominant type III interferon response. Subgroup A viruses disseminated and replicated faster than the subgroup B virus. Additionally, we studied HRSV infection and innate responses in air- way organoids (AOs) cultured at air-liquid interface (ALI). Results were similar to the commercially obtained bronchial cells. In summary, we show that HRSV replicates well in cells from both the upper and the lower airways, with a slight replicative advantage for subgroup A viruses. Lastly, we showed that AOs cultured at ALI are a valuable model for studying HRSV ex vivo and that they can be used in the future to study factors that influence HRSV disease severity. IMPORTANCE Human respiratory syncytial virus (HRSV) is the major cause of bron- chiolitis and pneumonia in young infants and causes almost 200,000 deaths per year. Currently, there is no vaccine or treatment available, only a prophylactic mono- clonal antibody (palivizumab). An important question in HRSV pathogenesis research is why only a fraction (1 to 3%) of infants develop severe disease. Model systems comprising disease-relevant HRSV isolates and accurate and reproducible cell culture models are indispensable to study infection, replication, and innate immune responses. H

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results