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

Author

Tariq A. Dam, Luca F. Roggeveen, Fuda van Diggelen, Lucas M. Fleuren, Ameet R. Jagesar, Martijn Otten, Heder J. de Vries, Diederik Gommers, Olaf L. Cremer, Rob J. Bosman, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Dave A. Dongelmans, Remko de Jong, Marco A. A. Peters, Marlijn J. A. Kamps, Dharmanand Ramnarain, Ralph Nowitzky, Fleur G. C. A. Nooteboom, Wouter de Ruijter, Louise C. Urlings-Strop, Ellen G. M. Smit, D. Jannet Mehagnoul-Schipper, Tom Dormans, Cornelis P. C. de Jager, Stefaan H. A. Hendriks, Sefanja Achterberg, Evelien Oostdijk, Auke C. Reidinga, Barbara Festen-Spanjer, Gert B. Brunnekreef, Alexander D. Cornet, Walter van den Tempel, Age D. Boelens, Peter Koetsier, Judith Lens, Harald J. Faber, A. Karakus, Robert Entjes, Paul de Jong, Thijs C. D. Rettig, Sesmu Arbous, Sebastiaan J. J. Vonk, Tomas Machado, Willem E. Herter, Harm-Jan de Grooth, Patrick J. Thoral, Armand R. J. Girbes, Mark Hoogendoorn, Paul W. G. Elbers, and The Dutch ICU Data Sharing Against COVID-19 Collaborators

Subjects
COVID-19, Mechanical ventilation, Acute respiratory distress syndrome, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

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.

Published
2022
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2. Predictors for extubation failure in COVID-19 patients using a machine learning approach

Author

Lucas M. Fleuren, Tariq A. Dam, Michele Tonutti, Daan P. de Bruin, Robbert C. A. Lalisang, Diederik Gommers, Olaf L. Cremer, Rob J. Bosman, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Dave A. Dongelmans, Remko de Jong, Marco Peters, Marlijn J. A. Kamps, Dharmanand Ramnarain, Ralph Nowitzky, Fleur G. C. A. Nooteboom, Wouter de Ruijter, Louise C. Urlings-Strop, Ellen G. M. Smit, D. Jannet Mehagnoul-Schipper, Tom Dormans, Cornelis P. C. de Jager, Stefaan H. A. Hendriks, Sefanja Achterberg, Evelien Oostdijk, Auke C. Reidinga, Barbara Festen-Spanjer, Gert B. Brunnekreef, Alexander D. Cornet, Walter van den Tempel, Age D. Boelens, Peter Koetsier, Judith Lens, Harald J. Faber, A. Karakus, Robert Entjes, Paul de Jong, Thijs C. D. Rettig, Sesmu Arbous, Sebastiaan J. J. Vonk, Mattia Fornasa, Tomas Machado, Taco Houwert, Hidde Hovenkamp, Roberto Noorduijn Londono, Davide Quintarelli, Martijn G. Scholtemeijer, Aletta A. de Beer, Giovanni Cinà, Adam Kantorik, Tom de Ruijter, Willem E. Herter, Martijn Beudel, Armand R. J. Girbes, Mark Hoogendoorn, Patrick J. Thoral, Paul W. G. Elbers, and the Dutch ICU Data Sharing Against Covid-19 Collaborators

Subjects
Extubation, Prediction, Risk factors, Extubation failure, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

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 (

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

Author

Lucas M. Fleuren, Tariq A. Dam, Michele Tonutti, Daan P. de Bruin, Robbert C. A. Lalisang, Diederik Gommers, Olaf L. Cremer, Rob J. Bosman, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Dave A. Dongelmans, Remko de Jong, Marco Peters, Marlijn J. A. Kamps, Dharmanand Ramnarain, Ralph Nowitzky, Fleur G. C. A. Nooteboom, Wouter de Ruijter, Louise C. Urlings-Strop, Ellen G. M. Smit, D. Jannet Mehagnoul-Schipper, Tom Dormans, Cornelis P. C. de Jager, Stefaan H. A. Hendriks, Sefanja Achterberg, Evelien Oostdijk, Auke C. Reidinga, Barbara Festen-Spanjer, Gert B. Brunnekreef, Alexander D. Cornet, Walter van den Tempel, Age D. Boelens, Peter Koetsier, Judith Lens, Harald J. Faber, A. Karakus, Robert Entjes, Paul de Jong, Thijs C. D. Rettig, Sesmu Arbous, Sebastiaan J. J. Vonk, Mattia Fornasa, Tomas Machado, Taco Houwert, Hidde Hovenkamp, Roberto Noorduijn-Londono, Davide Quintarelli, Martijn G. Scholtemeijer, Aletta A. de Beer, Giovanni Cina, Martijn Beudel, Willem E. Herter, Armand R. J. Girbes, Mark Hoogendoorn, Patrick J. Thoral, and Paul W. G. Elbers

Subjects
Database, Big data, COVID-19, Data sharing, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

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.

Published
2021
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4. 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

Lucas M. Fleuren, Michele Tonutti, Daan P. de Bruin, Robbert C. A. Lalisang, Tariq A. Dam, Diederik Gommers, Olaf L. Cremer, Rob J. Bosman, Sebastiaan J. J. Vonk, Mattia Fornasa, Tomas Machado, Nardo J. M. van der Meer, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Dave A. Dongelmans, Remko de Jong, Marco Peters, Marlijn J. A. Kamps, Dharmanand Ramnarain, Ralph Nowitzky, Fleur G. C. A. Nooteboom, Wouter de Ruijter, Louise C. Urlings-Strop, Ellen G. M. Smit, D. Jannet Mehagnoul-Schipper, Tom Dormans, Cornelis P. C. de Jager, Stefaan H. A. Hendriks, Evelien Oostdijk, Auke C. Reidinga, Barbara Festen-Spanjer, Gert Brunnekreef, Alexander D. Cornet, Walter van den Tempel, Age D. Boelens, Peter Koetsier, Judith Lens, Sefanja Achterberg, Harald J. Faber, A. Karakus, Menno Beukema, Robert Entjes, Paul de Jong, Taco Houwert, Hidde Hovenkamp, Roberto Noorduijn Londono, Davide Quintarelli, Martijn G. Scholtemeijer, Aletta A. de Beer, Giovanni Cinà, Martijn Beudel, Nicolet F. de Keizer, Mark Hoogendoorn, Armand R. J. Girbes, Willem E. Herter, Paul W. G. Elbers, Patrick J. Thoral, and Dutch ICU Data Sharing Against COVID-19 Collaborators

Subjects
COVID-19, Mortality prediction, Risk factors, Machine learning, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

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

Author

Tariq A. Dam, MD, Harm-Jan de Grooth, MD, PhD, Thomas Klausch, PhD, Lucas M. Fleuren, MD, Daan P. de Bruin, MSc, Robert Entjes, MD, Thijs C. D. Rettig, MD, PhD, Dave A. Dongelmans, MD, PhD, Age D. Boelens, MD, Sander Rigter, MD, Stefaan H. A. Hendriks, MD, Remko de Jong, MD, Marlijn J. A Kamps, MD, Marco Peters, MD, A. Karakus, MD, Diederik Gommers, MD, PhD, Dharmanand Ramnarain, MD, Evert-Jan Wils, MD, PhD, Sefanja Achterberg, MD, PhD, Ralph Nowitzky, MD, Walter van den Tempel, MD, Cornelis P. C. de Jager, MD, PhD, Fleur G. C. A. Nooteboom, MD, Evelien Oostdijk, MD, PhD, Peter Koetsier, MD, Alexander D. Cornet, MD, PhD, FRCP, Auke C. Reidinga, MD, Wouter de Ruijter, MD, PhD, Rob J. Bosman, MD, Tim Frenzel, MD, PhD, Louise C. Urlings-Strop, MD, PhD, Paul de Jong, MD, Ellen G. M. Smit, MD, Olaf L. Cremer, MD, PhD, D. Jannet Mehagnoul-Schipper, MD, PhD, Harald J. Faber, MD, Judith Lens, MD, Gert B. Brunnekreef, MD, Barbara Festen-Spanjer, MD, Tom Dormans, MD, PhD, Annemieke Dijkstra, MD, Bram Simons, MD, A. A. Rijkeboer, MD, Sesmu Arbous, MD, PhD, Marcel Aries, MD, PhD, Menno Beukema, MD, Daniël Pretorius, MD, Rutger van Raalte, MD, Martijn van Tellingen, MD, EDIC, Niels C. Gritters van den Oever, MD, Robbert C. A. Lalisang, MD, Michele Tonutti, MRes, Armand R. J. Girbes, MD, PhD, EDIC, Mark Hoogendoorn, PhD, Patrick J. Thoral, MD, EDIC, Paul W. G. Elbers, MD, PhD, EDIC, on behalf of the Dutch ICU Data Sharing Against COVID-19 Collaborators, Remko van den Akker, Tom A. Rijpstra, M. C. Reuland, Klaas Sierk Arnold, Arend Jan Meinders, Nicolas Schroten, Laura van Manen, Leon Montenij, Julia Koeter, J. W. Fijen, Jasper van Bommel, Roy van den Berg, Martha de Bruin, Roger van Rietschote, Ellen van Geest, Koen S. Simons, Anisa Hana, Joost Labout, Michael Kuiper, Albertus Beishuizen, Bart van de Gaauw, Roos Renckens, B. van den Bogaard, Peter Pickkers, Pim van der Heiden, Dennis Geutjes, Claudia (C. W.) van Gemeren, Emma Rademaker, Frits H. M. van Osch, Johan Lutisan, Jacomar J. M. van Koesveld, Bart P. Grady, Martijn de Kruif, Martin E. Haan, Luca Roggeveen, Dagmar M. Ouweneel, Ronald Driessen, Jan Peppink, G. J. Zijlstra, A. J. van Tienhoven, Evelien van der Heiden, Jan Jaap Spijkstra, Hans van der Spoel, Angelique de Man, Heder J. de Vries, Fuda van Diggelen, Ali el Hassouni, David Romero Guzman, Sandjai Bhulai, Sebastiaan J. J. Vonk, Mattia Fornasa, Tomas Machado, Adam Izdebski, Taco Houwert, Hidde Hovenkamp, Roberto Noorduijn Londono, Davide Quintarelli, Martijn G. Scholtemeijer, Aletta A. de Beer, Giovanni Cinà, Willem E. Herter, Michael de Neree tot Babberich, Olivier Thijssens, Lot Wagemakers, Hilde G. A. van der Pol, Tom Hendriks, Julie Berend, Virginia Ceni Silva, Robert F. J. Kullberg, Leo Heunks, Nicole Juffermans, Arjen J. C. Slooter, Martijn Beudel, and Nicolet F. de Keizer

Subjects
Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
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 direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials.

Published
2021
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6. A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients.

Author

Adam Izdebski, Patrick J. Thoral, Robbert C. A. Lalisang, Dean M. McHugh, Robert Entjes, Nardo J. M. van der Meer, Dave A. Dongelmans, Age D. Boelens, Sander Rigter, Stefaan H. A. Hendriks, Remko de Jong, Marlijn J. A. Kamps, Marco A. A. Peters, A. Karakus, Diederik Gommers, Dharmanand Ramnarain, Evert-Jan Wils, Sefanja Achterberg, Ralph Nowitzky, Walter van den Tempel, Cornelis P. C. de Jager, Fleur G. C. A. Nooteboom, Evelien Oostdijk, Peter Koetsier, Alexander D. Cornet, Auke C. Reidinga, Wouter de Ruijter, Rob J. Bosman, Tim Frenzel, Louise C. Urlings-Strop, Paul de Jong, Ellen G. M. Smit, Olaf L. Cremer, Frits H. M. van Osch, Harald J. Faber, Judith Lens, Gert B. Brunnekreef, Barbara Festen-Spanjer, Tom Dormans, Bram Simons, A. A. Rijkeboer, Annemieke Dijkstra, M. Sesmu Arbous, Marcel Aries, Menno Beukema, Rutger van Raalte, Martijn van Tellingen, Niels C. Gritters van den Oever, Paul W. G. Elbers, and Giovanni Cinà

Published
2021

7. 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

Iacopo Vagliano, Martijn C. Schut, Ameen Abu-Hanna, Dave A. Dongelmans, Dylan W. De Lange, Diederik Gommers, Olaf L. Cremer, Rob J. Bosman, Sander Rigter, Evert-Jan Wils, Tim Frenzel, Remko de Jong, Marco A. A. Peters, Marlijn J. A. Kamps, Dharmanand Ramnarain, Ralph Nowitzky, Fleur G. C. A. Nooteboom, Wouter de Ruijter, Louise C. Urlings-Strop, Ellen G. M. Smit, D. Jannet Mehagnoul-Schipper, Tom Dormans, Cornelis P. C. de Jager, Stefaan H. A. Hendriks, Sefanja Achterberg, Evelien Oostdijk, Auke C. Reidinga, Barbara Festen-Spanjer, Gert B. Brunnekreef, Alexander D. Cornet, Walter van den Tempel, Age D. Boelens, Peter Koetsier, Judith Lens, Harald J. Faber, Attila Karakus, Robert Entjes, Paul de Jong, Thijs C. D. Rettig, Merijn C. Reuland, M. Sesmu Arbous, Lucas M. Fleuren, Tariq A. Dam, Patrick J. Thoral, Robbert C. A. Lalisang, Michele Tonutti, Daan P. de Bruin, Paul W. G. Elbers, and Nicolette F. de Keizer

Published
2022
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8. Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care

Author

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

Subjects
Adult, Male, medicine.medical_specialty, Critical Care, medicine.medical_treatment, mechanical ventilation, COVID‐19, Intensive care, Internal medicine, medicine, Illness severity, Humans, Multicenter Studies as Topic, Hospital Mortality, corona virus, Blood urea nitrogen, Research Articles, Aged, Retrospective Studies, intensive care, Mechanical ventilation, business.industry, SARS-CoV-2, Other Research Radboud Institute for Health Sciences [Radboudumc 0], Glasgow Coma Scale, Patient Acuity, respiratory failure, COVID-19, General Medicine, Prognosis, Intensive Care Units, Observational Studies as Topic, Anesthesiology and Pain Medicine, Respiratory failure, Cohort, Observational study, business, Research Article
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., Acta Anaesthesiologica Scandinavica, 66 (1), ISSN:0001-5172, ISSN:1399-6576

Published
2022
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9. Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis

Author

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

Abstract

Background: Identification of distinct clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment facilitating more personalized treatment. However, previous attempts did not take into account temporal dynamics of the disease. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19.Methods: We used highly granular data from 3202 adult critically ill COVID patients in the multicenter Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected based on relevance and availability. Twenty-one consecutive datasets were created that each covered 24 hours of ICU data for each day of ICU treatment up until day 21. After aggregation and multiple imputation of the temporal data, clinical phenotypes in each dataset were identified by performing multiple cluster analyses. Clinical phenotypes were identified by aggregating values from all patients per cluster. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked.Results: The final patient cohort consisted of 2438 critically ill COVID-19 patients with a registered ICU mortality outcome. Forty-one parameters were chosen for the cluster analysis. On admission, both a mild and a more severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be strongly driven by inflammation and dead space ventilation. During the 21-day period only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype.Conclusions: Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.

Published
2022
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10. The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Author

Roberto Noorduijn-Londono, Sander Rigter, Willem E. Herter, Tomas Machado, Gert B. Brunnekreef, Thijs C. D. Rettig, Evert-Jan Wils, Remko de Jong, S Arbous, Harald J. Faber, Cornelis P.C. de Jager, Paul de Jong, Auke C Reidinga, Sebastiaan J. J. Vonk, Dave A. Dongelmans, Wouter de Ruijter, Taco Houwert, Ralph Nowitzky, Daan P de Bruin, Robert Entjes, Diederik Gommers, Dharmanand Ramnarain, Marlijn J A Kamps, Mark Hoogendoorn, Evelien A. N. Oostdijk, Martijn G. Scholtemeijer, Age D Boelens, Armand R. J. Girbes, Fleur G C A Nooteboom, Barbara Festen-Spanjer, Peter Koetsier, Giovanni Cinà, Hidde Hovenkamp, Sefanja Achterberg, Ellen G M Smit, Robbert C. A. Lalisang, D Jannet Mehagnoul-Schipper, Rob J. Bosman, Patrick Thoral, Judith Lens, Tariq A Dam, Olaf L. Cremer, Lucas M. Fleuren, Martijn Beudel, Paul W. G. Elbers, Tom Dormans, Aletta A. de Beer, Marco Peters, Alexander D. Cornet, Tim Frenzel, Louise C Urlings-Strop, Michele Tonutti, Mattia Fornasa, Walter van den Tempel, Davide Quintarelli, A Karakus, Stefaan H A Hendriks, Artificial intelligence, Network Institute, Computational Intelligence, Intensive Care, Intensive care medicine, ACS - Diabetes & metabolism, AII - Infectious diseases, ACS - Microcirculation, APH - Quality of Care, Intensive Care Medicine, AII - Inflammatory diseases, Neurology, and ANS - Neurodegeneration

Subjects
Critical Care, Critical Illness, Big data, Data validation, Critical Care and Intensive Care Medicine, computer.software_genre, Database, SDG 3 - Good Health and Well-being, Data Warehousing, Intensive care, Electronic Health Records, Humans, Medicine, Netherlands, RC86-88.9, business.industry, Research, Other Research Radboud Institute for Health Sciences [Radboudumc 0], COVID-19, Medical emergencies. Critical care. Intensive care. First aid, Missing data, Data warehouse, Hospitalization, Data sharing, Intensive Care Units, Life support, Pseudonymized, business, computer
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.

Published
2021
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11. Notice of Retraction and Replacement: Oostdijk et al. Effects of Decontamination of the Oropharynx and Intestinal Tract on Antibiotic Resistance in ICUs: A Randomized Clinical Trial. JAMA. 2014;312(14):1429-1437

Author

Steven F. T. Thijsen, Fred A L van Steveninck, Evert de Jonge, Jan Willem Fijen, Einar H R van Essen, Arjanne J. van Griethuysen, Frank H. Bosch, Adrienne Raben, Philip Rothbarth, Remko de Jong, Alexander J. G. H. Bindels, Frank H. van Tiel, Anne Marie G. A. de Smet, Jozef Kesecioglu, Roland Brimicombe, Evelien A. N. Oostdijk, Afke Brandenburg, Marc J. M. Bonten, Eric R. van der Vorm, Paul Peerbooms, Annemieke Rijkeboer, Lex J Winsser, Caroline E. Visser, Marcus J. Schultz, Paul Gruteke, Ilse M. Purmer, Alexandra T. Bernards, Joke Oudbier, Anne-Marie W van Elsacker-Niele, Kees Demmendaal, Wil C van der Zwet, Helga Hart, Jansz A, Mirelle Koeman, Dennis C J J Bergmans, Ellen M. Mascini, Intensive Care Medicine, AII - Infectious diseases, Medical Microbiology and Infection Prevention, Other departments, ACS - Diabetes & metabolism, ACS - Pulmonary hypertension & thrombosis, ACS - Microcirculation, Microbes in Health and Disease (MHD), and Critical care, Anesthesiology, Peri-operative and Emergency medicine (CAPE)

Subjects
medicine.medical_specialty, Notice, business.industry, General Medicine, Human decontamination, law.invention, 03 medical and health sciences, 0302 clinical medicine, Antibiotic resistance, 030228 respiratory system, Randomized controlled trial, law, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Journal Article, medicine, 030212 general & internal medicine, Intensive care medicine, business, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries)
Published
2017

12. The influence of patient demographics, disease characteristics and treatment on brain volume loss in Trial Assessing Injectable Interferon vs FTY720 Oral in Relapsing-Remitting Multiple Sclerosis (TRANSFORMS), a phase 3 study of fingolimod in multiple sclerosis

Author

Nikolaos Sfikas, Remko de Jong, Ana de Vera, Gordon Francis, Jeffrey A. Cohen, Frederik Barkhof, Radiology and nuclear medicine, NCA - Neuroinflamation, and NCA - Brain imaging technology

Subjects
Adult, Male, medicine.medical_specialty, law.invention, Multiple Sclerosis, Relapsing-Remitting, Double-Blind Method, Randomized controlled trial, Sphingosine, law, Internal medicine, Fingolimod Hydrochloride, medicine, Humans, Cerebral atrophy, medicine.diagnostic_test, business.industry, Multiple sclerosis, Interferon beta-1a, Brain, Magnetic resonance imaging, Interferon-beta, Middle Aged, Stepwise regression, medicine.disease, Fingolimod, Surgery, Drug Combinations, Neurology, Propylene Glycols, Female, Neurology (clinical), business, Immunosuppressive Agents, medicine.drug
Abstract

Background: Patients with multiple sclerosis (MS) lose brain volume (BV) faster than healthy individuals. Objective: Our purpose, within the 12-month phase 3 TRANSFORMS study, was to examine the effect of treatment on BV loss in patient subgroups, establish correlations between baseline normalized BV (NBV) and baseline disease parameters, to identify variables predictive of baseline NBV and on-study percentage BV change (PBVC), and to establish correlations between on-study PBVC and on-study efficacy outcomes. Methods: Patients received fingolimod 0.5 mg or 1.25 mg, or intramuscular (IM) interferon β-1a (IFNβ-1a) for 12 months. The effect of treatment on PBVC was examined in patient demographic, disease and magnetic resonance imaging (MRI) characteristic subgroups. Pearson’s correlation analyses and a stepwise linear regression model were used to identify variables predictive of NBV and PBVC. Results: Fingolimod reduced BV loss over 12 months versus IFNβ-1a IM in all patient subgroups assessed, including individuals with or without gadolinium (Gd)-enhancing lesions at baseline. Baseline T1 hypointense lesion volume had the strongest correlation with baseline NBV. Baseline Gd-enhancing T1 lesion count was most predictive of change in PBVC over 12 months. Conclusions: Our results improve understanding of the contributions of different baseline demographic, clinical and MRI characteristics to NBV, including factors that may be predictive of future BV loss

Published
2014
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13. Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST

Author

Dirk L. Knol, Frederik Barkhof, Ronald A. van Schijndel, Remko de Jong, E. Mulder, Keith S. Cover, Hugo Vrenken, Pieter Jelle Visser, Psychiatrie & Neuropsychologie, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, Neuroscience Campus Amsterdam - Neurodegeneration, Neuroscience Campus Amsterdam - Brain Imaging Technology, Radiology and nuclear medicine, Epidemiology and Data Science, Physics and medical technology, Neurology, NCA - neurodegeneration, and NCA - Brain imaging technology

Subjects
Male, Computer science, Software Validation, Hippocampus, computer.software_genre, Imaging, 030218 nuclear medicine & medical imaging, Pattern Recognition, Automated, Computer-Assisted, 0302 clinical medicine, Quantitative assessment, Hippocampus (mythology), Cognitive impairment, Observer Variation, medicine.diagnostic_test, SDG 10 - Reduced Inequalities, Organ Size, Magnetic Resonance Imaging, Hippocampal atrophy, Neurology, Hippocampal volume, Female, Data mining, Alzheimer's disease, Algorithms, Automated, Mild Cognitive Impairment, Cognitive Neuroscience, Pattern Recognition, Sensitivity and Specificity, 03 medical and health sciences, Atrophy, Imaging, Three-Dimensional, Neuroimaging, Alzheimer Disease, Artificial Intelligence, Image Interpretation, Computer-Assisted, medicine, Humans, Cognitive Dysfunction, Image Interpretation, Aged, Reproducibility, business.industry, Reproducibility of Results, Magnetic resonance imaging, medicine.disease, Image Enhancement, Three-Dimensional, Nuclear medicine, business, computer, 030217 neurology & neurosurgery, Software
Abstract

Background: To measure hippocampal volume change in Alzheimer's disease (AD) or mild cognitive impairment (MCI), expert manual delineation is often used because of its supposed accuracy. It has been suggested that expert outlining yields poorer reproducibility as compared to automated methods, but this has not been investigated. Aim: To determine the reproducibilities of expert manual outlining and two common automated methods for measuring hippocampal atrophy rates in healthy aging, MCI and AD. Methods: From the Alzheimer's Disease Neuroimaging Initiative (ADNI), 80 subjects were selected: 20 patients with AD, 40 patients with mild cognitive impairment (MCI) and 20 healthy controls (HCs). Left and right hippocampal volume change between baseline and month-12 visit was assessed by using expert manual delineation, and by the automated software packages FreeSurfer (longitudinal processing stream) and FIRST. To assess reproducibility of the measured hippocampal volume change, both back-to-back (BTB) MPRAGE scans available for each visit were analyzed. Hippocampal volume change was expressed in μL, and as a percentage of baseline volume. Reproducibility of the 1-year hippocampal volume change was estimated from the BTB measurements by using linear mixed model to calculate the limits of agreement (LoA) of each method, reflecting its measurement uncertainty. Using the delta method, approximate p-values were calculated for the pairwise comparisons between methods. Statistical analyses were performed both with inclusion and exclusion of visibly incorrect segmentations. Results: Visibly incorrect automated segmentation in either one or both scans of a longitudinal scan pair occurred in 7.5% of the hippocampi for FreeSurfer and in 6.9% of the hippocampi for FIRST. After excluding these failed cases, reproducibility analysis for 1-year percentage volume change yielded LoA of ±. 7.2% for FreeSurfer, ±. 9.7% for expert manual delineation, and ±. 10.0% for FIRST. Methods ranked the same for reproducibility of 1-year. μL volume change, with LoA of ±. 218. μL for FreeSurfer, ±. 319. μL for expert manual delineation, and ±. 333. μL for FIRST. Approximate p-values indicated that reproducibility was better for FreeSurfer than for manual or FIRST, and that manual and FIRST did not differ. Inclusion of failed automated segmentations led to worsening of reproducibility of both automated methods for 1-year raw and percentage volume change. Conclusion: Quantitative reproducibility values of 1-year microliter and percentage hippocampal volume change were roughly similar between expert manual outlining, FIRST and FreeSurfer, but FreeSurfer reproducibility was statistically significantly superior to both manual outlining and FIRST after exclusion of failed segmentations. © 2014 Elsevier Inc.

Published
2014
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14. Randomized phase I trials of the safety/tolerability of anti-LINGO-1 monoclonal antibody BIIB033

Author

Frederik Barkhof, Edward Parr, Jonathan Q. Tran, Mike P. Wattjes, Lei Xu, Jim Zhao, Kristin Brosofsky, Hakop Gevorkyan, Jitesh Rana, Soma Ray, Remko de Jong, Diego Cadavid, Isaac Melamed, Radiology and nuclear medicine, and NCA - Neuroinflamation

Subjects
medicine.medical_specialty, business.industry, Multiple sclerosis, Pharmacology, medicine.disease, Placebo, Gastroenterology, Confidence interval, Article, Subcutaneous injection, medicine.anatomical_structure, Neurology, Pharmacokinetics, Tolerability, Internal medicine, medicine, Neurology (clinical), Remyelination, business, Adverse effect
Abstract

Objective: To evaluate the safety, tolerability, and pharmacokinetics (PK) of BIIB033 (anti-LINGO-1 monoclonal antibody) in healthy volunteers and participants with multiple sclerosis (MS). Methods: In 2 separate randomized, placebo-controlled studies, single ascending doses (SAD; 0.1–100 mg/kg) of BIIB033 or placebo were administered via IV infusion or subcutaneous injection to 72 healthy volunteers, and multiple ascending doses (MAD; 0.3–100 mg/kg; 2 doses separated by 14 days) of BIIB033 or placebo were administered via IV infusion to 47 participants with relapsing-remitting or secondary progressive MS. Safety assessments included adverse event (AE) monitoring, neurologic examinations, conventional and nonconventional MRI, EEG, optical coherence tomography, retinal examinations, and evoked potentials. Serum and CSF PK as well as the immunogenicity of BIIB033 were also evaluated. Results: All 72 healthy volunteers and 47 participants with MS were included in the safety analyses. BIIB033 infusions were well tolerated. The frequency of AEs was similar between BIIB033 and placebo. There were no serious AEs or deaths. No clinically significant changes in any of the safety measures were observed. BIIB033 PK was similar between healthy volunteers and participants with MS. Doses of ≥10 mg/kg resulted in BIIB033 concentrations similar to or higher than the concentration associated with 90% of the maximum remyelination effect in rat remyelination studies. The incidence of anti-drug antibody production was low. Conclusions: The emerging safety, tolerability, and PK of BIIB033 support advancing BIIB033 into phase II clinical development as a potential treatment for CNS demyelination disorders. Classification of evidence: This study provides Class I evidence that BIIB033 is well tolerated and safe (serious adverse event rate 0%, 95% confidence interval 0–7.6%).

Published
2014
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15. Measurements of medial temporal lobe atrophy for prediction of Alzheimer's disease in subjects with mild cognitive impairment

Author

Leah Burns, Remko de Jong, Magdalini Tsolaki, Frans R.J. Verhey, Lutz Frölich, Ronald A. van Schijndel, Daniel Rueckert, Philip Scheltens, Frederik Barkhof, Dirk L. Knol, Flavio Nobili, Robin Wolz, Pieter Jelle Visser, Harald Hampel, Pablo Lapuerta, Hilkka Soininen, Lars Olaf Wahlund, Ineke van Rossum, Lies Clerx, Martina Bocchetta, Laura A. van de Pol, Lennart Minthon, Pauline Aalten, Neuroscience Campus Amsterdam - Neurodegeneration, Neurology, Epidemiology and Data Science, Radiology and nuclear medicine, NCA - neurodegeneration, Promovendi MHN, Psychiatrie & Neuropsychologie, MUMC+: MA Med Staf Spec Psychiatrie (9), and RS: MHeNs School for Mental Health and Neuroscience

Subjects
Male, Aging, Pathology, Neurology, Hippocampal formation, Corpus callosum, Hippocampus, 030218 nuclear medicine & medical imaging, Aβ1-42, Alzheimer's disease, Cerebrospinal fluid (CSF), Dementia, Magnetic resonance imaging, Medial temporal lobe atrophy, Mild cognitive impairment, Tau, Aged, Alzheimer Disease, Amyloid beta-Peptides, Atrophy, Biomarkers, Cognition, Disease Progression, Female, Forecasting, Humans, Magnetic Resonance Imaging, Mild Cognitive Impairment, Peptide Fragments, tau Proteins, Neurology (clinical), Neuroscience (all), Developmental Biology, Geriatrics and Gerontology, 0302 clinical medicine, Cognitive decline, General Neuroscience, Cardiology, Aβ1–42, Psychology, medicine.medical_specialty, Neuroscience(all), Clinical Neurology, Temporal lobe, 03 medical and health sciences, Internal medicine, medicine, Cognitive Dysfunction, Receiver operating characteristic, medicine.disease, Ageing, A beta 1-42, 030217 neurology & neurosurgery
Abstract

Our aim was to compare the predictive accuracy of 4 different medial temporal lobe measurements for Alzheimer's disease (AD) in subjects with mild cognitive impairment (MCI). Manual hippocampal measurement, automated atlas-based hippocampal measurement, a visual rating scale (MTA-score), and lateral ventricle measurement were compared. Predictive accuracy for AD 2 years after baseline was assessed by receiver operating characteristics analyses with area under the curve as outcome. Annual cognitive decline was assessed by slope analyses up to 5 years after baseline. Correlations with biomarkers in cerebrospinal fluid (CSF) were investigated. Subjects with MCI were selected from the Development of Screening Guidelines and Clinical Criteria for Predementia AD (DESCRIPA) multicenter study (n = 156) and the single-center VU medical center (n = 172). At follow-up, area under the curve was highest for automated atlas-based hippocampal measurement (0.71) and manual hippocampal measurement (0.71), and lower for MTA-score (0.65) and lateral ventricle (0.60). Slope analysis yielded similar results. Hippocampal measurements correlated with CSF total tau and phosphorylated tau, not with beta-amyloid 1-42. MTA-score and lateral ventricle volume correlated with CSF beta-amyloid 1-42. We can conclude that volumetric hippocampal measurements are the best predictors of AD conversion in subjects with MCI. (c) 2013 Elsevier Inc. All rights reserved. (Less)

Published
2013
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16. Bluetooth broadcasting: How far can we go? An experimental study

Author

Remko de Jong, Joel de Nes, Marco Aiello, and Distributed Systems

Subjects
business.industry, Computer science, Bluetooth, mobile communication, 05 social sciences, Mobile computing, 020206 networking & telecommunications, broadcasting, 02 engineering and technology, Broadcasting, law.invention, law, Broadcast communication network, 0202 electrical engineering, electronic engineering, information engineering, Wireless, 0501 psychology and cognitive sciences, Mobile telephony, Bluesnarfing, business, Telecommunications, Mobile device, 050107 human factors, Computer network
Abstract

Bluetooth is a wireless communication technology to build small networks of devices. It was designed as a cable replacement technology. Given its widespread adoption, especially in mobile devices, new uses are possible today. For instance, one can broadcast messages to nomadic users based on their location. In this paper, we perform an experimental evaluation of whether Bluetooth is indeed a suitable technology for spontaneous networking and broadcasting. The evaluation does not only consist of a review of current hardware and software, but also of a concrete implementation tested in a controlled indoor and in a not-controlled outdoor environment. The results of the experimentation show that, despite the intrinsic limitation of the original design, Bluetooth is indeed suitable for mobile location-based broadcasting.

Published
2009
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