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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. 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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3. 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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4. 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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5. 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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6. 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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7. 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
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8. Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse

Author

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

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

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

Published
2021
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9. 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

10. 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

11. 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

12. 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

13. 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
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14. 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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15. 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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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, 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
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19. 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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20. 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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21. 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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22. 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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23. 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

24. 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

25. 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

26. 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, and van den Tempel, Walter

Subjects
HOSPITAL mortality, CRITICAL care medicine, PROGNOSTIC tests, COVID-19, BLOOD urea nitrogen
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. [ABSTRACT FROM AUTHOR]

Published
2022
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27. Slow Ca2+ Efflux by Ca2+/H+ Exchange in Cardiac Mitochondria Is Modulated by Ca2+ Re-uptake via MCU, Extra-Mitochondrial pH, and H+ Pumping by FOF1-ATPase

Author

Haumann, Johan, primary, Camara, Amadou K. S., additional, Gadicherla, Ashish K., additional, Navarro, Christopher D., additional, Boelens, Age D., additional, Blomeyer, Christoph A., additional, Dash, Ranjan K., additional, Boswell, Michael R., additional, Kwok, Wai-Meng, additional, and Stowe, David F., additional

Published
2019
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28. Slow Ca2+ Efflux by Ca2+/H+ Exchange in Cardiac Mitochondria Is Modulated by Ca2+ Re-uptake via MCU, Extra-Mitochondrial pH, and H+ Pumping by FOF1-ATPase.

Author

Haumann, Johan, Camara, Amadou K. S., Gadicherla, Ashish K., Navarro, Christopher D., Boelens, Age D., Blomeyer, Christoph A., Dash, Ranjan K., Boswell, Michael R., Kwok, Wai-Meng, and Stowe, David F.

Subjects
MITOCHONDRIA, DINITROPHENOL, ADENOSINE triphosphatase, ION exchange (Chemistry), HYDROLYSIS
Abstract

Mitochondrial (m) Ca2+ influx is largely dependent on membrane potential (ΔΨm), whereas mCa2+ efflux occurs primarily via Ca2+ ion exchangers. We probed the kinetics of Ca2+/H+ exchange (CHEm) in guinea pig cardiac muscle mitochondria. We tested if net mCa2+ flux is altered during a matrix inward H+ leak that is dependent on matrix H+ pumping by ATPm hydrolysis at complex V (FOF1-ATPase). We measured [Ca2+]m, extra-mitochondrial (e) [Ca2+]e, ΔΨm, pHm, pHe, NADH, respiration, ADP/ATP ratios, and total [ATP]m in the presence or absence of protonophore dinitrophenol (DNP), mitochondrial uniporter (MCU) blocker Ru360, and complex V blocker oligomycin (OMN). We proposed that net slow influx/efflux of Ca2+ after adding DNP and CaCl2 is dependent on whether the ΔpHm gradient is/is not maintained by reciprocal outward H+ pumping by complex V. We found that adding CaCl2 enhanced DNP-induced increases in respiration and decreases in ΔΨm while [ATP]m decreased, ΔpHm gradient was maintained, and [Ca2+]m continued to increase slowly, indicating net mCa2+ influx via MCU. In contrast, with complex V blocked by OMN, adding DNP and CaCl2 caused larger declines in ΔΨm as well as a slow fall in pHm to near pHe while [Ca2+]m continued to decrease slowly, indicating net mCa2+ efflux in exchange for H+ influx (CHEm) until the ΔpHm gradient was abolished. The kinetics of slow mCa2+ efflux with slow H+ influx via CHEm was also observed at pHe 6.9 vs. 7.6 by the slow fall in pHm until ΔpHm was abolished; if Ca2+ reuptake via the MCU was also blocked, mCa2+ efflux via CHEm became more evident. Of the two components of the proton electrochemical gradient, our results indicate that CHEm activity is driven largely by the ΔpHm chemical gradient with H+ leak, while mCa2+ entry via MCU depends largely on the charge gradient ΔΨm. A fall in ΔΨm with excess mCa2+ loading can occur during cardiac cell stress. Cardiac cell injury due to mCa2+ overload may be reduced by temporarily inhibiting FOF1-ATPase from pumping H+ due to ΔΨm depolarization. This action would prevent additional slow mCa2+ loading via MCU and permit activation of CHEm to mediate efflux of mCa2+. HIGHLIGHTS - We examined how slow mitochondrial (m) Ca2+ efflux via Ca2+/H+ exchange (CHEm) is triggered by matrix acidity after a rapid increase in [Ca2+]m by adding CaCl2 in the presence of dinitrophenol (DNP) to permit H+ influx, and oligomycin (OMN) to block H+ pumping via FOF1-ATP synthase/ase (complex V). - Declines in ΔΨm and pHm after DNP and added CaCl2 were larger when complex V was blocked. - [Ca2+]m slowly increased despite a fall in ΔΨm but maintained pHm when H+ pumping by complex V was permitted. - [Ca2+]m slowly decreased and external [Ca2+]e increased with declines in both ΔΨm and pHm when complex V was blocked. - ATPm hydrolysis supports a falling pHm and redox state and promotes a slow increase in [Ca2+]m. - After rapid Ca2+ influx due to a bolus of CaCl2, slow mCa2+ efflux by CHEm occurs directly if pHe is low. [ABSTRACT FROM AUTHOR]

Published
2019
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30. Correction

Author

Haumann, Johan, primary, Dash, Ranjan K., additional, Stowe, David F., additional, Boelens, Age D., additional, Beard, Daniel A., additional, and Camara, Amadou K.S., additional

Published
2010
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43. Slow Ca 2+ Efflux by Ca 2+ /H + Exchange in Cardiac Mitochondria Is Modulated by Ca 2+ Re-uptake via MCU, Extra-Mitochondrial pH, and H + Pumping by F O F 1 -ATPase.

Author

Haumann J, Camara AKS, Gadicherla AK, Navarro CD, Boelens AD, Blomeyer CA, Dash RK, Boswell MR, Kwok WM, and Stowe DF

Abstract

Mitochondrial (m) Ca 2+ influx is largely dependent on membrane potential (ΔΨ m ), whereas mCa 2+ efflux occurs primarily via Ca 2+ ion exchangers. We probed the kinetics of Ca 2+ /H + exchange (CHE m ) in guinea pig cardiac muscle mitochondria. We tested if net mCa 2+ flux is altered during a matrix inward H + leak that is dependent on matrix H + pumping by ATP m hydrolysis at complex V (F O F 1 -ATPase). We measured [Ca 2+ ] m , extra-mitochondrial (e) [Ca 2+ ] e , ΔΨ m , pH m , pH e , NADH, respiration, ADP/ATP ratios, and total [ATP] m in the presence or absence of protonophore dinitrophenol (DNP), mitochondrial uniporter (MCU) blocker Ru360, and complex V blocker oligomycin (OMN). We proposed that net slow influx/efflux of Ca 2+ after adding DNP and CaCl 2 is dependent on whether the ΔpH m gradient is/is not maintained by reciprocal outward H + pumping by complex V. We found that adding CaCl 2 enhanced DNP-induced increases in respiration and decreases in ΔΨ m while [ATP] m decreased, ΔpH m gradient was maintained, and [Ca 2+ ] m continued to increase slowly, indicating net mCa 2+ influx via MCU. In contrast, with complex V blocked by OMN, adding DNP and CaCl 2 caused larger declines in ΔΨ m as well as a slow fall in pH m to near pH e while [Ca 2+ ] m continued to decrease slowly, indicating net mCa 2+ efflux in exchange for H + influx (CHE m ) until the ΔpH m gradient was abolished. The kinetics of slow mCa 2+ efflux with slow H + influx via CHE m was also observed at pH e 6.9 vs. 7.6 by the slow fall in pH m until ΔpH m was abolished; if Ca 2+ reuptake via the MCU was also blocked, mCa 2+ efflux via CHE m became more evident. Of the two components of the proton electrochemical gradient, our results indicate that CHE m activity is driven largely by the ΔpH m chemical gradient with H + leak, while mCa 2+ entry via MCU depends largely on the charge gradient ΔΨ m . A fall in ΔΨ m with excess mCa 2+ loading can occur during cardiac cell stress. Cardiac cell injury due to mCa 2+ overload may be reduced by temporarily inhibiting F O F 1 -ATPase from pumping H + due to ΔΨ m depolarization. This action would prevent additional slow mCa 2+ loading via MCU and permit activation of CHE m to mediate efflux of mCa 2+ . HIGHLIGHTS -We examined how slow mitochondrial (m) Ca 2+ efflux via Ca 2+ /H + exchange (CHE m ) is triggered by matrix acidity after a rapid increase in [Ca 2+ ] m by adding CaCl 2 in the presence of dinitrophenol (DNP) to permit H + influx, and oligomycin (OMN) to block H + pumping via F O F 1 -ATP synthase/ase (complex V).-Declines in ΔΨ m and pH m after DNP and added CaCl 2 were larger when complex V was blocked.-[Ca 2+ ] m slowly increased despite a fall in ΔΨ m but maintained pH m when H + pumping by complex V was permitted.-[Ca 2+ ] m slowly decreased and external [Ca 2+ ] e increased with declines in both ΔΨ m and pH m when complex V was blocked.-ATP m hydrolysis supports a falling pH m and redox state and promotes a slow increase in [Ca 2+ ] m .-After rapid Ca 2+ influx due to a bolus of CaCl 2 , slow mCa 2+ efflux by CHE m occurs directly if pH e is low.

Published
2019
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