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Smit JM, Exterkate L, van Tienhoven AJ, Haaksma ME, Heldeweg MLA, Fleuren L, Thoral P, Dam TA, Heunks LMA, Gommers D, Cremer OL, Bosman RJ, Rigter S, Wils EJ, Frenzel T, Vlaar AP, Dongelmans DA, de Jong R, Peters M, Kamps MJA, Ramnarain D, Nowitzky R, Nooteboom FGCA, de Ruijter W, Urlings-Strop LC, Smit EGM, Mehagnoul-Schipper DJ, Dormans T, de Jager CPC, Hendriks SHA, Achterberg S, Oostdijk E, Reidinga AC, Festen-Spanjer B, Brunnekreef GB, Cornet AD, van den Tempel W, Boelens AD, Koetsier P, Lens J, Faber HJ, Karakus A, Entjes R, de Jong P, Rettig TCD, Arbous S, Vonk B, Machado T, Girbes ARJ, Sieswerda E, Elbers PWG, Tuinman PR. INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY. Shock 2022; 58:358-365. [PMID: 36155964 DOI: 10.1097/shk.0000000000001994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
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.
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Affiliation(s)
| | - Lotte Exterkate
- Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Institute for Infection and Immunity (AII) and Amsterdam Cardiovascular Sciences (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | | | | | | | - Lucas Fleuren
- Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Institute for Infection and Immunity (AII) and Amsterdam Cardiovascular Sciences (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Patrick Thoral
- Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Institute for Infection and Immunity (AII) and Amsterdam Cardiovascular Sciences (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Tariq A Dam
- Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Institute for Infection and Immunity (AII) and Amsterdam Cardiovascular Sciences (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Leo M A Heunks
- Department of Intensive Care, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Diederik Gommers
- Department of Intensive Care, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Olaf L Cremer
- Intensive Care, UMC Utrecht, Utrecht, the Netherlands
| | | | - Sander Rigter
- Department of Anesthesiology and Intensive Care, St Antonius Hospital, Nieuwegein, the Netherlands
| | - Evert-Jan Wils
- Department of Intensive Care, Franciscus Gasthuis and Vlietland, Rotterdam, the Netherlands
| | - Tim Frenzel
- Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Alexander P Vlaar
- Department of Intensive Care Medicine, Amsterdam UMC, Amsterdam, the Netherlands
| | - Dave A Dongelmans
- Department of Intensive Care Medicine, Amsterdam UMC, Amsterdam, the Netherlands
| | - Remko de Jong
- Intensive Care, Bovenij Ziekenhuis, Amsterdam, the Netherlands
| | - Marco Peters
- Intensive Care, Canisius Wilhelmina Ziekenhuis, Nijmegen, the Netherlands
| | - Marlijn J A Kamps
- Intensive Care, Catharina Ziekenhuis Eindhoven, Eindhoven, the Netherlands
| | | | - Ralph Nowitzky
- Intensive Care, HagaZiekenhuis, Den Haag, the Netherlands
| | | | - Wouter de Ruijter
- Department of Intensive Care Medicine, Northwest Clinics, Alkmaar, the Netherlands
| | | | - Ellen G M Smit
- Intensive Care, Spaarne Gasthuis, Haarlem en Hoofddorp, the Netherlands
| | | | - Tom Dormans
- Intensive care, Zuyderland MC, Heerlen, the Netherlands
| | | | | | | | | | - Auke C Reidinga
- ICU, SEH, BWC, Martiniziekenhuis, Groningen, the Netherlands
| | | | - Gert B Brunnekreef
- Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, the Netherlands
| | - Alexander D Cornet
- Department of Intensive Care, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Walter van den Tempel
- Department of Intensive Care, Ikazia Ziekenhuis Rotterdam, Rotterdam, the Netherlands
| | - Age D Boelens
- Anesthesiology, Antonius Ziekenhuis Sneek, Sneek, the Netherlands
| | - Peter Koetsier
- Intensive Care, Medisch Centrum Leeuwarden, Leeuwarden, the Netherlands
| | - Judith Lens
- ICU, IJsselland Ziekenhuis, Capelle aan den IJssel, the Netherlands
| | | | - A Karakus
- Department of Intensive Care, Diakonessenhuis Hospital, Utrecht, the Netherlands
| | - Robert Entjes
- Department of Intensive Care, Adrz, Goes, the Netherlands
| | - Paul de Jong
- Department of Anesthesia and Intensive Care, Slingeland Ziekenhuis, Doetinchem, the Netherlands
| | - Thijs C D Rettig
- Department of Anesthesiology, Intensive Care and Pain Medicine, Amphia Ziekenhuis, Breda, the Netherlands
| | - Sesmu Arbous
- Department of Intensive Care, LUMC, Leiden, the Netherlands
| | - Bas Vonk
- Pacmed, Amsterdam, the Netherlands
| | | | - Armand R J Girbes
- Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Institute for Infection and Immunity (AII) and Amsterdam Cardiovascular Sciences (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Elske Sieswerda
- Department of Medical Microbiology, University Medical Centre Utrecht, University of Utrecht, Utrecht, the Netherlands
| | - Paul W G Elbers
- Department of Intensive Care Medicine, Research VUmc Intensive Care (REVIVE), Amsterdam Medical Data Science (AMDS), Amsterdam Institute for Infection and Immunity (AII) and Amsterdam Cardiovascular Sciences (ACS), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
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2
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Dam TA, Roggeveen LF, van Diggelen F, Fleuren LM, Jagesar AR, Otten M, de Vries HJ, Gommers D, Cremer OL, Bosman RJ, Rigter S, Wils EJ, Frenzel T, Dongelmans DA, de Jong R, Peters MAA, Kamps MJA, Ramnarain D, Nowitzky R, Nooteboom FGCA, de Ruijter W, Urlings-Strop LC, Smit EGM, Mehagnoul-Schipper DJ, Dormans T, de Jager CPC, Hendriks SHA, Achterberg S, Oostdijk E, Reidinga AC, Festen-Spanjer B, Brunnekreef GB, Cornet AD, van den Tempel W, Boelens AD, Koetsier P, Lens J, Faber HJ, Karakus A, Entjes R, de Jong P, Rettig TCD, Arbous S, Vonk SJJ, Machado T, Herter WE, de Grooth HJ, Thoral PJ, Girbes ARJ, Hoogendoorn M, Elbers PWG. Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning. Ann Intensive Care 2022; 12:99. [PMID: 36264358 PMCID: PMC9583049 DOI: 10.1186/s13613-022-01070-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 10/06/2022] [Indexed: 11/24/2022] Open
Abstract
Background For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources. Methods From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and Extreme Gradient Boosting on readily available and clinically relevant features to predict success of prone positioning after 4 h (window of 1 to 7 h) based on various possible outcomes. These outcomes were defined as improvements of at least 10% in PaO2/FiO2 ratio, ventilatory ratio, respiratory system compliance, or mechanical power. Separate models were created for each of these outcomes. Re-supination within 4 h after pronation was labeled as failure. We also developed models using a 20 mmHg improvement cut-off for PaO2/FiO2 ratio and using a combined outcome parameter. For all models, we evaluated feature importance expressed as contribution to predictive performance based on their relative ranking. Results The median duration of prone episodes was 17 h (11–20, median and IQR, N = 2632). Despite extensive modeling using a plethora of machine learning techniques and a large number of potentially clinically relevant features, discrimination between responders and non-responders remained poor with an area under the receiver operator characteristic curve of 0.62 for PaO2/FiO2 ratio using Logistic Regression, Random Forest and XGBoost. Feature importance was inconsistent between models for different outcomes. Notably, not even being a previous responder to prone positioning, or PEEP-levels before prone positioning, provided any meaningful contribution to predicting a successful next proning episode. Conclusions In mechanically ventilated COVID-19 patients, predicting the success of prone positioning using clinically relevant and readily available parameters from electronic health records is currently not feasible. Given the current evidence base, a liberal approach to proning in all patients with severe COVID-19 ARDS is therefore justified and in particular regardless of previous results of proning. Supplementary Information The online version contains supplementary material available at 10.1186/s13613-022-01070-0.
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Affiliation(s)
- Tariq A Dam
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
| | - Luca F Roggeveen
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Fuda van Diggelen
- Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, VU University, Amsterdam, The Netherlands
| | - Lucas M Fleuren
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Ameet R Jagesar
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Martijn Otten
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Heder J de Vries
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Diederik Gommers
- Department of Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Olaf L Cremer
- Intensive Care, UMC Utrecht, Utrecht, The Netherlands
| | | | - Sander Rigter
- Department of Anesthesiology and Intensive Care, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Evert-Jan Wils
- Department of Intensive Care, Franciscus Gasthuis & Vlietland, Rotterdam, The Netherlands
| | - Tim Frenzel
- Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dave A Dongelmans
- Department of Intensive Care Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Remko de Jong
- Intensive Care, Bovenij Ziekenhuis, Amsterdam, The Netherlands
| | - Marco A A Peters
- Intensive Care, Canisius Wilhelmina Ziekenhuis, Nijmegen, The Netherlands
| | - Marlijn J A Kamps
- Intensive Care, Catharina Ziekenhuis Eindhoven, Eindhoven, The Netherlands
| | | | - Ralph Nowitzky
- Intensive Care, HagaZiekenhuis, Den Haag, The Netherlands
| | | | - Wouter de Ruijter
- Department of Intensive Care Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | | | - Ellen G M Smit
- Intensive Care, Spaarne Gasthuis, Haarlem en Hoofddorp, The Netherlands
| | | | - Tom Dormans
- Intensive Care, Zuyderland MC, Heerlen, The Netherlands
| | | | | | | | | | - Auke C Reidinga
- ICU, SEH, BWC, Martiniziekenhuis, Groningen, The Netherlands
| | | | - Gert B Brunnekreef
- Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, The Netherlands
| | - Alexander D Cornet
- Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Walter van den Tempel
- Department of Intensive Care, Ikazia Ziekenhuis Rotterdam, Rotterdam, The Netherlands
| | | | - Peter Koetsier
- Intensive Care, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Judith Lens
- ICU, IJsselland Ziekenhuis, Capelle aan den IJssel, The Netherlands
| | | | - A Karakus
- Department of Intensive Care, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - Robert Entjes
- Department of Intensive Care, Adrz, Goes, The Netherlands
| | - Paul de Jong
- Department of Anesthesia and Intensive Care, Slingeland Ziekenhuis, Doetinchem, The Netherlands
| | - Thijs C D Rettig
- Department of Anesthesiology, Intensive Care and Pain Medicine, Amphia Ziekenhuis, Breda, The Netherlands
| | | | | | | | | | - Harm-Jan de Grooth
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick J Thoral
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Armand R J Girbes
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Mark Hoogendoorn
- Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, VU University, Amsterdam, The Netherlands
| | - Paul W G Elbers
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
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3
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Vagliano I, Schut MC, Abu-Hanna A, Dongelmans DA, de Lange DW, Gommers D, Cremer OL, Bosman RJ, Rigter S, Wils EJ, Frenzel T, de Jong R, Peters MAA, Kamps MJA, Ramnarain D, Nowitzky R, Nooteboom FGCA, de Ruijter W, Urlings-Strop LC, Smit EGM, Mehagnoul-Schipper DJ, Dormans T, de Jager CPC, Hendriks SHA, Achterberg S, Oostdijk E, Reidinga AC, Festen-Spanjer B, Brunnekreef GB, Cornet AD, van den Tempel W, Boelens AD, Koetsier P, Lens J, Faber HJ, Karakus A, Entjes R, de Jong P, Rettig TCD, Reuland MC, Arbous S, Fleuren LM, Dam TA, Thoral PJ, Lalisang RCA, Tonutti M, de Bruin DP, Elbers PWG, de Keizer NF. 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. Int J Med Inform 2022; 167:104863. [PMID: 36162166 PMCID: PMC9492397 DOI: 10.1016/j.ijmedinf.2022.104863] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 08/19/2022] [Accepted: 09/03/2022] [Indexed: 11/17/2022]
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.
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Affiliation(s)
- Iacopo Vagliano
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
| | - Martijn C Schut
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ameen Abu-Hanna
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Dave A Dongelmans
- National Intensive Care Evaluation (NICE) foundation, Amsterdam, The Netherlands; Department of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Dylan W de Lange
- National Intensive Care Evaluation (NICE) foundation, Amsterdam, The Netherlands; Department of Intensive Care Medicine, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands
| | - Diederik Gommers
- Department of Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Olaf L Cremer
- Intensive Care, UMC Utrecht, Utrecht, The Netherlands
| | | | - Sander Rigter
- Department of Anesthesiology and Intensive Care, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Evert-Jan Wils
- Department of Intensive Care, Franciscus Gasthuis & Vlietland, Rotterdam, The Netherlands
| | - Tim Frenzel
- Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Remko de Jong
- Intensive Care, Bovenij Ziekenhuis, Amsterdam, The Netherlands
| | - Marco A A Peters
- Intensive Care, Canisius Wilhelmina Ziekenhuis, Nijmegen, The Netherlands
| | - Marlijn J A Kamps
- Intensive Care, Catharina Ziekenhuis Eindhoven, Eindhoven, The Netherlands
| | | | - Ralph Nowitzky
- Intensive Care, Haga Ziekenhuis, Den Haag, The Netherlands
| | | | - Wouter de Ruijter
- Department of Intensive Care Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | | | - Ellen G M Smit
- Intensive Care, Spaarne Gasthuis, Haarlem en Hoofddorp, The Netherlands
| | | | - Tom Dormans
- Intensive care, Zuyderland MC, Heerlen, The Netherlands
| | | | | | | | | | - Auke C Reidinga
- ICU, SEH, BWC, Martiniziekenhuis, Groningen, The Netherlands
| | | | - Gert B Brunnekreef
- Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, The Netherlands
| | - Alexander D Cornet
- Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Walter van den Tempel
- Department of Intensive Care, Ikazia Ziekenhuis Rotterdam, Rotterdam, The Netherlands
| | - Age D Boelens
- Anesthesiology, Antonius Ziekenhuis Sneek, Sneek, The Netherlands
| | - Peter Koetsier
- Intensive Care, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Judith Lens
- ICU, IJsselland Ziekenhuis, Capelle aan den IJssel, The Netherlands
| | | | - A Karakus
- Department of Intensive Care, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - Robert Entjes
- Department of Intensive Care, Adrz, Goes, The Netherlands
| | - Paul de Jong
- Department of Anesthesia and Intensive Care, Slingeland Ziekenhuis, Doetinchem, The Netherlands
| | - Thijs C D Rettig
- Department of Anesthesiology, Intensive Care and Pain Medicine, Amphia Ziekenhuis, Breda, The Netherlands
| | - M C Reuland
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | | | - Lucas M Fleuren
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Tariq A Dam
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick J Thoral
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | | | - Paul W G Elbers
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Nicolette F de Keizer
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands; National Intensive Care Evaluation (NICE) foundation, Amsterdam, The Netherlands
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4
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Plečko D, Bennett N, Mårtensson J, Dam TA, Entjes R, Rettig TCD, Dongelmans DA, Boelens AD, Rigter S, Hendriks SHA, Jong R, Kamps MJA, Peters M, Karakus A, Gommers D, Ramnarain D, Wils E, Achterberg S, Nowitzky R, Tempel W, Jager CPC, Nooteboom FGCA, Oostdijk E, Koetsier P, Cornet AD, Reidinga AC, Ruijter W, Bosman RJ, Frenzel T, Urlings‐Strop LC, Jong P, Smit EG, Cremer OL, Mehagnoul‐Schipper DJ, Faber HJ, Lens J, Brunnekreef GB, Festen‐Spanjer B, Dormans T, Bruin DP, Lalisang RCA, Vonk SJJ, Haan ME, Fleuren LM, Thoral PJ, Elbers PWG, Bellomo R. Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality. Acta Anaesthesiol Scand 2022; 66:65-75. [PMID: 34622441 PMCID: PMC8652966 DOI: 10.1111/aas.13991] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/16/2021] [Accepted: 09/27/2021] [Indexed: 01/08/2023]
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.
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Affiliation(s)
- Drago Plečko
- Department of Intensive Care Medicine Laboratory for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam UMC Amsterdam The Netherlands
- Department of Mathematics Seminar for Statistics ETH Zürich Zurich Switzerland
| | - Nicolas Bennett
- Department of Mathematics Seminar for Statistics ETH Zürich Zurich Switzerland
| | - Johan Mårtensson
- Department of Physiology and Pharmacology Section of Anaesthesia and Intensive Care Karolinska Institutet Stockholm Sweden
- Department of Perioperative Medicine and Intensive Care Karolinska University Hospital Stockholm Sweden
| | - Tariq A. Dam
- Department of Intensive Care Medicine Laboratory for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam UMC Amsterdam The Netherlands
| | - Robert Entjes
- Department of Intensive Care Admiraal De Ruyter Ziekenhuis Goes The Netherlands
| | | | - Dave A. Dongelmans
- Department of Intensive Care Medicine Laboratory for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam UMC Amsterdam The Netherlands
| | | | - Sander Rigter
- Department of Anesthesiology and Intensive Care St. Antonius Hospital Nieuwegein The Netherlands
| | | | - Remko Jong
- Intensive Care Bovenij Ziekenhuis Amsterdam The Netherlands
| | | | - Marco Peters
- Intensive Care Canisius Wilhelmina Ziekenhuis Nijmegen The Netherlands
| | - Attila Karakus
- Department of Intensive Care Diakonessenhuis Hospital Utrecht The Netherlands
| | - Diederik Gommers
- Department of Intensive Care Erasmus Medical Center Rotterdam The Netherlands
| | | | - Evert‐Jan Wils
- Department of Intensive Care Franciscus Gasthuis & Vlietland Rotterdam The Netherlands
| | | | | | - Walter Tempel
- Department of Intensive Care Ikazia Ziekenhuis Rotterdam Rotterdam The Netherlands
| | | | | | | | - Peter Koetsier
- Intensive Care Medisch Centrum Leeuwarden Leeuwarden The Netherlands
| | - Alexander D. Cornet
- Department of Intensive Care Medisch Spectrum Twente Enschede The Netherlands
| | | | - Wouter Ruijter
- Department of Intensive Care Medicine Northwest Clinics Alkmaar The Netherlands
| | | | - Tim Frenzel
- Department of Intensive Care Medicine Radboud University Medical Center Nijmegen The Netherlands
| | | | - Paul Jong
- Department of Anesthesia and Intensive Care Slingeland Ziekenhuis Doetinchem The Netherlands
| | - Ellen G.M. Smit
- Intensive Care Spaarne GasthuisHaarlem en Hoofddorp The Netherlands
| | | | | | | | - Judith Lens
- ICU ICU, IJsselland ZiekenhuisCapelle aan den IJssel The Netherlands
| | | | | | - Tom Dormans
- Intensive care Zuyderland MC Heerlen The Netherlands
| | | | | | | | - Martin E. Haan
- Department of Intensive Care Medicine Laboratory for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam UMC Amsterdam The Netherlands
| | - Lucas M. Fleuren
- Department of Intensive Care Medicine Laboratory for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam UMC Amsterdam The Netherlands
| | - Patrick J. Thoral
- Department of Intensive Care Medicine Laboratory for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam UMC Amsterdam The Netherlands
| | - Paul W. G. Elbers
- Department of Intensive Care Medicine Laboratory for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam UMC Amsterdam The Netherlands
| | - Rinaldo Bellomo
- Australian and New Zealand Intensive Care Research CentreSchool of Public Health and Preventative MedicineMonash University Melbourne Australia
- Department of Critical Care The University of Melbourne Melbourne Australia
- Data Analytics Research and Evaluation Centre Department of Medicine and Radiology The University of Melbourne
- Austin Hospital Melbourne Australia
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5
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Fleuren LM, Dam TA, Tonutti M, de Bruin DP, Lalisang RCA, Gommers D, Cremer OL, Bosman RJ, Rigter S, Wils EJ, Frenzel T, Dongelmans DA, de Jong R, Peters M, Kamps MJA, Ramnarain D, Nowitzky R, Nooteboom FGCA, de Ruijter W, Urlings-Strop LC, Smit EGM, Mehagnoul-Schipper DJ, Dormans T, de Jager CPC, Hendriks SHA, Achterberg S, Oostdijk E, Reidinga AC, Festen-Spanjer B, Brunnekreef GB, Cornet AD, van den Tempel W, Boelens AD, Koetsier P, Lens J, Faber HJ, Karakus A, Entjes R, de Jong P, Rettig TCD, Arbous S, Vonk SJJ, Fornasa M, Machado T, Houwert T, Hovenkamp H, Noorduijn Londono R, Quintarelli D, Scholtemeijer MG, de Beer AA, Cinà G, Kantorik A, de Ruijter T, Herter WE, Beudel M, Girbes ARJ, Hoogendoorn M, Thoral PJ, Elbers PWG. Predictors for extubation failure in COVID-19 patients using a machine learning approach. Crit Care 2021; 25:448. [PMID: 34961537 PMCID: PMC8711075 DOI: 10.1186/s13054-021-03864-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 12/13/2021] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. METHODS We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. RESULTS A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. CONCLUSION The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.
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Affiliation(s)
- Lucas M. Fleuren
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Tariq A. Dam
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | | | - Diederik Gommers
- Department of Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Olaf L. Cremer
- Department of Intensive Care, UMC Utrecht, Utrecht, The Netherlands
| | | | - Sander Rigter
- Department of Anesthesiology and Intensive Care, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Evert-Jan Wils
- Department of Intensive Care, Franciscus Gasthuis and Vlietland, Rotterdam, The Netherlands
| | - Tim Frenzel
- Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dave A. Dongelmans
- Department of Intensive Care Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Remko de Jong
- Intensive Care, Bovenij Ziekenhuis, Amsterdam, The Netherlands
| | - Marco Peters
- Intensive Care, Canisius Wilhelmina Ziekenhuis, Nijmegen, The Netherlands
| | | | | | - Ralph Nowitzky
- Intensive Care, HagaZiekenhuis, Den Haag, The Netherlands
| | | | - Wouter de Ruijter
- Department of Intensive Care Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | | | - Ellen G. M. Smit
- Intensive Care, Spaarne Gasthuis, Haarlem en Hoofddorp, The Netherlands
| | | | - Tom Dormans
- Intensive Care, Zuyderland MC, Heerlen, The Netherlands
| | | | | | | | | | | | | | - Gert B. Brunnekreef
- Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, The Netherlands
| | - Alexander D. Cornet
- Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Walter van den Tempel
- Department of Intensive Care, Ikazia Ziekenhuis Rotterdam, Rotterdam, The Netherlands
| | | | - Peter Koetsier
- Intensive Care, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Judith Lens
- ICU, IJsselland Ziekenhuis, Capelle Aan Den IJssel, The Netherlands
| | | | - A. Karakus
- Department of Intensive Care, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - Robert Entjes
- Department of Intensive Care, Adrz, Goes, The Netherlands
| | - Paul de Jong
- Department of Anesthesia and Intensive Care, Slingeland Ziekenhuis, Doetinchem, The Netherlands
| | - Thijs C. D. Rettig
- Department of Anesthesiology, Intensive Care and Pain Medicine, Amphia Ziekenhuis, Breda, The Netherlands
| | - Sesmu Arbous
- Department of Intensive Care, LUMC, Leiden, The Netherlands
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Martijn Beudel
- Department of Neurology, Amsterdam UMC, Universiteit Van Amsterdam, Amsterdam, The Netherlands
| | - Armand R. J. Girbes
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Mark Hoogendoorn
- Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick J. Thoral
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Paul W. G. Elbers
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
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Fleuren LM, Dam TA, Tonutti M, de Bruin DP, Lalisang RCA, Gommers D, Cremer OL, Bosman RJ, Rigter S, Wils EJ, Frenzel T, Dongelmans DA, de Jong R, Peters M, Kamps MJA, Ramnarain D, Nowitzky R, Nooteboom FGCA, de Ruijter W, Urlings-Strop LC, Smit EGM, Mehagnoul-Schipper DJ, Dormans T, de Jager CPC, Hendriks SHA, Achterberg S, Oostdijk E, Reidinga AC, Festen-Spanjer B, Brunnekreef GB, Cornet AD, van den Tempel W, Boelens AD, Koetsier P, Lens J, Faber HJ, Karakus A, Entjes R, de Jong P, Rettig TCD, Arbous S, Vonk SJJ, Fornasa M, Machado T, Houwert T, Hovenkamp H, Noorduijn-Londono R, Quintarelli D, Scholtemeijer MG, de Beer AA, Cina G, Beudel M, Herter WE, Girbes ARJ, Hoogendoorn M, Thoral PJ, Elbers PWG. The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients. Crit Care 2021; 25:304. [PMID: 34425864 PMCID: PMC8381710 DOI: 10.1186/s13054-021-03733-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/16/2021] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. METHODS A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. RESULTS Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. CONCLUSIONS In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.
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Affiliation(s)
- Lucas M. Fleuren
- Laboratory for Critical Care Computational Intelligence, Department of Intensive Care Medicine, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Tariq A. Dam
- Laboratory for Critical Care Computational Intelligence, Department of Intensive Care Medicine, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | | | - Diederik Gommers
- Department of Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | | | - Sander Rigter
- Department of Anesthesiology and Intensive Care, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Evert-Jan Wils
- Department of Intensive Care, Franciscus Gasthuis & Vlietland, Rotterdam, The Netherlands
| | - Tim Frenzel
- Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dave A. Dongelmans
- Department of Intensive Care Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Remko de Jong
- Intensive Care, Bovenij Ziekenhuis, Amsterdam, The Netherlands
| | - Marco Peters
- Intensive Care, Canisius Wilhelmina Ziekenhuis, Nijmegen, The Netherlands
| | | | | | - Ralph Nowitzky
- Intensive Care, HagaZiekenhuis, Den Haag, The Netherlands
| | | | - Wouter de Ruijter
- Department of Intensive Care Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | | | - Ellen G. M. Smit
- Intensive Care, Spaarne Gasthuis, Haarlem en Hoofddorp, The Netherlands
| | | | - Tom Dormans
- Intensive Care, Zuyderland MC, Heerlen, The Netherlands
| | | | | | | | | | | | | | - Gert B. Brunnekreef
- Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, The Netherlands
| | - Alexander D. Cornet
- Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Walter van den Tempel
- Department of Intensive Care, Ikazia Ziekenhuis Rotterdam, Rotterdam, The Netherlands
| | - Age D. Boelens
- Anesthesiology, Antonius Ziekenhuis Sneek, Sneek, The Netherlands
| | - Peter Koetsier
- Intensive Care, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Judith Lens
- ICU, ICU, IJsselland Ziekenhuis, Capelle aan den IJssel, The Netherlands
| | | | - A. Karakus
- Department of Intensive Care, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - Robert Entjes
- Department of Intensive Care, Admiraal De Ruyter Ziekenhuis, Goes, The Netherlands
| | - Paul de Jong
- Department of Anesthesia and Intensive Care, Slingeland Ziekenhuis, Doetinchem, The Netherlands
| | | | - Sesmu Arbous
- Department of Intensive Care, LUMC, Leiden, The Netherlands
| | | | | | | | | | | | | | | | | | | | | | - Martijn Beudel
- Department of Neurology, Amsterdam UMC, Universiteit van Amsterdam, Amsterdam, The Netherlands
| | | | - Armand R. J. Girbes
- Laboratory for Critical Care Computational Intelligence, Department of Intensive Care Medicine, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Mark Hoogendoorn
- Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrjie Universiteit, Amsterdam, The Netherlands
| | - Patrick J. Thoral
- Laboratory for Critical Care Computational Intelligence, Department of Intensive Care Medicine, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Paul W. G. Elbers
- Laboratory for Critical Care Computational Intelligence, Department of Intensive Care Medicine, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
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Brunnekreef GB, Scholten E, Bras LJ, Schepens MA, Aarts LP, Van Dongen EP. Value of postoperative C-reactive protein and leukocyte count after elective thoracoabdominal aneurysm surgery. Crit Care 2008. [PMCID: PMC4088542 DOI: 10.1186/cc6392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Brunnekreef GB, Heijmen RH, Gerritsen WB, Schepens MA, ter Beek HT, van Dongen EP. Measurements of Cerebrospinal Fluid Concentrations of S100β Protein During and After Thoracic Endovascular Stent Grafting. Eur J Vasc Endovasc Surg 2007; 34:169-72. [PMID: 17408991 DOI: 10.1016/j.ejvs.2007.01.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2006] [Accepted: 01/20/2007] [Indexed: 11/30/2022]
Abstract
OBJECTIVES Thoracic endovascular aortic repair is associated with postoperative spinal cord ischemia in approximately 1 to 12.5% of all cases. S100beta is a protein that is released during acute damage of the central nervous system. This study was performed to determine the concentration of S100beta in cerebrospinal fluid during and after stenting of the thoracic aorta in patients at high risk for spinal cord ischemia. DESIGN Prospective clinical study. MATERIALS AND METHODS Eight patients who underwent elective thoracic aortic stent grafting underwent lumbar spinal fluid drainage. These patients were at high risk to develop spinal cord ischemia. METHODS CSF samples for analysis of S100beta protein were drawn after induction of anesthesia, during stenting, once every hour the following six hours and 20 hours after repair. RESULTS No significant increase in S100beta protein could be detected in CSF and no neurological deficits were detected postoperatively. CONCLUSIONS The results of this study show us that there is no significant release of S100beta protein in CSF during stenting of the thoracic aorta in this subgroup of patients at high risk for spinal cord ischemia, consistent with clinical exam that there was no significant damage to the central nervous system.
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Affiliation(s)
- G B Brunnekreef
- Department of Anesthesiology and Intensive Care, St. Antonius Hospital, The Netherlands.
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