41 results on '"Rigter, S."'
Search Results
2. How to Use Lean Thinking for the Optimization of Clinical Pathways: A Systematic Review and a Proposed Framework to Analyze Pathways on a System Level.
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Vijverberg, J.R.G., Rouppe van der Voort, Marc B.V., Nat, P.B. van der, Mosselman, M.J., Rigter, S., Biesma, D.H., Merode, F. van, Vijverberg, J.R.G., Rouppe van der Voort, Marc B.V., Nat, P.B. van der, Mosselman, M.J., Rigter, S., Biesma, D.H., and Merode, F. van
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Contains fulltext : 296938.pdf (Publisher’s version ) (Open Access), Lean Thinking and clinical pathways are commonly used concepts to improve healthcare. However, little is known on how to use Lean Thinking for the optimization of pathways or the quantification of both concepts. This study aims to create a framework to analyze pathways with Lean Thinking on a system level, by quantifying the seven wastes, flow and pull. A systematic literature review was performed. Inclusion criteria were the focus of the article on a well-defined group of patients and studied a pathway optimization with Lean Thinking. Data were extracted on measured outcomes, type of intervention and type of researched pathway. Thirty-six articles were included. No articles described the implementation of the Lean Thinking philosophy or studied the development of their people and partners ("4 P" model). Most articles used process optimization tools or problem-solving tools. The majority of the studies focused on process measures. The measures found in the review were used as input for our suggested framework to identify and quantify wastes, flow, and pull in a clinical pathway. The proposed framework can be used to create an overview of the improvement potential of a pathway or to analyze the level of improvement after an enhancement is introduced to a pathway. Further research is needed to study the use of the suggested quantifications.
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- 2023
3. Optimal patient blood management in cardiac surgery using viscoelastic point-of-care testing: Response to: Routine use of viscoelastic blood tests for diagnosis and treatment of coagulopathic bleeding in cardiac surgery: updated systematic review and meta-analysis
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Vlot, E. A., Rigter, S., and Noordzij, P. G.
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- 2017
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4. Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality
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Plecko, D, Bennett, N, Martensson, J, Dam, TA, Entjes, R, Rettig, TCD, Dongelmans, DA, Boelens, AD, Rigter, S, Hendriks, SHA, de Jong, R, Kamps, MJA, Peters, M, Karakus, A, Gommers, D, Ramnarain, D, Wils, E-J, Achterberg, S, Nowitzky, R, Tempel, W, de Jager, CPC, Nooteboom, FGCA, Oostdijk, E, Koetsier, P, Cornet, AD, Reidinga, AC, de Ruijter, W, Bosman, RJ, Frenzel, T, Urlings-Strop, LC, de Jong, P, Smit, EGM, Cremer, OL, Mehagnoul-Schipper, DJ, Faber, HJ, Lens, J, Brunnekreef, GB, Festen-Spanjer, B, Dormans, T, de Bruin, DP, Lalisang, RCA, Vonk, SJJ, Haan, ME, Fleuren, LM, Thoral, PJ, Elbers, PWG, Bellomo, R, Plecko, D, Bennett, N, Martensson, J, Dam, TA, Entjes, R, Rettig, TCD, Dongelmans, DA, Boelens, AD, Rigter, S, Hendriks, SHA, de Jong, R, Kamps, MJA, Peters, M, Karakus, A, Gommers, D, Ramnarain, D, Wils, E-J, Achterberg, S, Nowitzky, R, Tempel, W, de Jager, CPC, Nooteboom, FGCA, Oostdijk, E, Koetsier, P, Cornet, AD, Reidinga, AC, de Ruijter, W, Bosman, RJ, Frenzel, T, Urlings-Strop, LC, de Jong, P, Smit, EGM, Cremer, OL, Mehagnoul-Schipper, DJ, Faber, HJ, Lens, J, Brunnekreef, GB, Festen-Spanjer, B, Dormans, T, de Bruin, DP, Lalisang, RCA, Vonk, SJJ, Haan, ME, Fleuren, LM, Thoral, PJ, Elbers, PWG, and Bellomo, R
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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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- 2022
5. Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality
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Plečko, D., Bennett, N., Mårtensson, J., Dam, T.A., Entjes, R., Rettig, T.C., Dongelmans, Dave A., Boelens, A.D., Rigter, S., Hendriks, S.H., Jong, R. de, Kamps, M.J., Peters, Marco, Karakus, A., Gommers, D., Ramnarain, D., Wils, E.J., Achterberg, S., Nowitzky, R., Tempel, W., Jager, C.P.C. de, Nooteboom, F., Oostdijk, E., Koetsier, P., Cornet, A.D., Reidinga, A.C., Ruijter, W. de, Bosman, R.J., Frenzel, T., Urlings-Strop, L.C., Jong, p de, Smit, Egbert F., Cremer, O.L., Mehagnoul-Schipper, D.J., Faber, H.J., Lens, J., Brunnekreef, G.B., Festen-Spanjer, B., Dormans, T., Bruin, D.P. de, Lalisang, R.C.A., Vonk, S.J.J., Haan, M.E., Fleuren, L.M., Thoral, P.J., Elbers, P.W.G., Bellomo, R., Plečko, D., Bennett, N., Mårtensson, J., Dam, T.A., Entjes, R., Rettig, T.C., Dongelmans, Dave A., Boelens, A.D., Rigter, S., Hendriks, S.H., Jong, R. de, Kamps, M.J., Peters, Marco, Karakus, A., Gommers, D., Ramnarain, D., Wils, E.J., Achterberg, S., Nowitzky, R., Tempel, W., Jager, C.P.C. de, Nooteboom, F., Oostdijk, E., Koetsier, P., Cornet, A.D., Reidinga, A.C., Ruijter, W. de, Bosman, R.J., Frenzel, T., Urlings-Strop, L.C., Jong, p de, Smit, Egbert F., Cremer, O.L., Mehagnoul-Schipper, D.J., Faber, H.J., Lens, J., Brunnekreef, G.B., Festen-Spanjer, B., Dormans, T., Bruin, D.P. de, Lalisang, R.C.A., Vonk, S.J.J., Haan, M.E., Fleuren, L.M., Thoral, P.J., Elbers, P.W.G., and Bellomo, R.
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Contains fulltext : 252102.pdf (Publisher’s version ) (Open Access), 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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- 2022
6. Dynamic prediction of mortality in COVID-19 patients in the intensive care unit:A retrospective multi-center cohort study
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Smit, J. M., Krijthe, J. H., Endeman, H., Tintu, A. N., de Rijke, Y. B., Gommers, D. A.M.P.J., Cremer, O. L., Bosman, R. J., Rigter, S., Wils, E. J., Frenzel, T., Dongelmans, D. A., De Jong, R., Peters, M. A.A., Kamps, M. J.A., Ramnarain, D., Nowitzky, R., Nooteboom, F. G.C.A., De Ruijter, W., Urlings-Strop, L. C., Smit, E. G.M., Mehagnoul-Schipper, D. J., Dormans, T., De Jager, C. P.C., Hendriks, S. H.A., Achterberg, S., Oostdijk, E., Reidinga, A. C., Festen-Spanjer, B., Brunnekreef, G. B., Cornet, A. D., Van den Tempel, W., Boelens, A. D., Koetsier, P., Lens, J. A., Faber, H. J., karakus, A., Entjes, R., De Jong, P., Rettig, T. C.D., Arbous, M. S., Lalisang, R. C.A., Tonutti, M., De Bruin, D. P., Elbers, P. W.G., Van Bommel, J., Reinders, M. J.T., Smit, J. M., Krijthe, J. H., Endeman, H., Tintu, A. N., de Rijke, Y. B., Gommers, D. A.M.P.J., Cremer, O. L., Bosman, R. J., Rigter, S., Wils, E. J., Frenzel, T., Dongelmans, D. A., De Jong, R., Peters, M. A.A., Kamps, M. J.A., Ramnarain, D., Nowitzky, R., Nooteboom, F. G.C.A., De Ruijter, W., Urlings-Strop, L. C., Smit, E. G.M., Mehagnoul-Schipper, D. J., Dormans, T., De Jager, C. P.C., Hendriks, S. H.A., Achterberg, S., Oostdijk, E., Reidinga, A. C., Festen-Spanjer, B., Brunnekreef, G. B., Cornet, A. D., Van den Tempel, W., Boelens, A. D., Koetsier, P., Lens, J. A., Faber, H. J., karakus, A., Entjes, R., De Jong, P., Rettig, T. C.D., Arbous, M. S., Lalisang, R. C.A., Tonutti, M., De Bruin, D. P., Elbers, P. W.G., Van Bommel, J., and Reinders, M. J.T.
- Abstract
Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27; −0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.
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- 2022
7. Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study
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Arts-assistenten DV&B, Medische Staf Intensive Care, Infection & Immunity, Smit, J M, Krijthe, J H, Endeman, H, Tintu, A N, de Rijke, Y B, Gommers, D A M P J, Cremer, O L, Bosman, R J, Rigter, S, Wils, E-J, Frenzel, T, Dongelmans, D A, De Jong, R, Peters, M A A, Kamps, M J A, Ramnarain, D, Nowitzky, R, Nooteboom, F G C A, De Ruijter, W, Urlings-Strop, L C, Smit, E G M, Mehagnoul-Schipper, D J, Dormans, T, De Jager, C P C, Hendriks, S H A, Achterberg, S, Oostdijk, E, Reidinga, A C, Festen-Spanjer, B, Brunnekreef, G B, Cornet, A D, Van den Tempel, W, Boelens, A D, Koetsier, P, Lens, J A, Faber, H J, Karakus, A, Entjes, R, De Jong, P, Rettig, T C D, Arbous, M S, Lalisang, R C A, Tonutti, M, De Bruin, D P, Elbers, P W G, Van Bommel, J, Reinders, M J T, Arts-assistenten DV&B, Medische Staf Intensive Care, Infection & Immunity, Smit, J M, Krijthe, J H, Endeman, H, Tintu, A N, de Rijke, Y B, Gommers, D A M P J, Cremer, O L, Bosman, R J, Rigter, S, Wils, E-J, Frenzel, T, Dongelmans, D A, De Jong, R, Peters, M A A, Kamps, M J A, Ramnarain, D, Nowitzky, R, Nooteboom, F G C A, De Ruijter, W, Urlings-Strop, L C, Smit, E G M, Mehagnoul-Schipper, D J, Dormans, T, De Jager, C P C, Hendriks, S H A, Achterberg, S, Oostdijk, E, Reidinga, A C, Festen-Spanjer, B, Brunnekreef, G B, Cornet, A D, Van den Tempel, W, Boelens, A D, Koetsier, P, Lens, J A, Faber, H J, Karakus, A, Entjes, R, De Jong, P, Rettig, T C D, Arbous, M S, Lalisang, R C A, Tonutti, M, De Bruin, D P, Elbers, P W G, Van Bommel, J, and Reinders, M J T
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- 2022
8. 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
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Vagliano, I., Schut, M.C., Abu-Hanna, A., Dongelmans, D.A., Lange, D.W. de, Gommers, D., Cremer, O.L., Bosman, R.J., Rigter, S., Wils, E.J., Frenzel, T., Jong, R. de, Peters, M.A.A., Kamps, M.J.A., Ramnarain, D., Nowitzky, R., Nooteboom, F.G.C.A., Ruijter, W. de, Urlings-Strop, L.C., Smit, E.G.M., Mehagnoul-Schipper, D.J., Dormans, T., Jager, C.P.C. de, Hendriks, S.H.A., Achterberg, S., Oostdijk, E., Reidinga, A.C., Festen-Spanjer, B., Brunnekreef, G.B., Cornet, A.D., Tempel, W. van den, Boelens, A.D., Koetsier, P., Lens, J., Faber, H.J., Karakus, A., Entjes, R., Jong, P. de, Rettig, T.C.D., Reuland, M.C., Arbous, S., Fleuren, L.M., Dam, T.A., Thoral, P.J., Lalisang, R.C.A., Tonutti, M., Bruin, D.P. de, Elbers, P.W.G., Keizer, N.F. de, Dutch COVID-19 Res Consortium, Dutch ICU Data Sharing Against COV, Intensive Care, Intensive care medicine, ACS - Diabetes & metabolism, ACS - Microcirculation, Amsterdam Cardiovascular Sciences, Medical Informatics, APH - Methodology, APH - Digital Health, Laboratory for General Clinical Chemistry, APH - Aging & Later Life, Intensive Care Medicine, and APH - Quality of Care
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Electronic Health Record [E05.318.308.940.968.625.500] ,Machine learning [G17.035.250.500] ,Other Research Radboud Institute for Health Sciences [Radboudumc 0] ,COVID-19 ,Health Informatics ,In-hospital mortality [E05.318.308.985.550.400] ,Covid-19 [C01.748.610.763.500] ,[E05.318.308.940.968.625.500] ,Critical care [E02.760.190] ,Prognosis [E01.789] ,In-hospital mortality ,Intensive Care Units ,All institutes and research themes of the Radboud University Medical Center ,[E05.318.308.985.550.400] ,Electronic Health Records ,Humans ,Electronic Health Record ,Hospital Mortality ,Registries ,Netherlands ,Retrospective Studies - 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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- 2022
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9. Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome
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Dam, T.A., Grooth, H.J. de, Klausch, T., Fleuren, L.M., Bruin, D.P. de, Entjes, R., Rettig, T.C., Dongelmans, Dave A., Boelens, A.D., Rigter, S., Hendriks, S.H., Jong, R. de, Kamps, M.J., Peters, M., Karakus, A., Gommers, D., Ramnarain, D., Wils, E.J., Achterberg, S., Nowitzky, R., Tempel, W., Jager, C.P.C. de, Nooteboom, F., Oostdijk, E., Koetsier, P., Cornet, A.D., Reidinga, A.C., Ruijter, W. de, Bosman, R.J., Frenzel, T., Urlings-Strop, L.C., Jong, p de, Smit, Egbert F., Cremer, O.L., Mehagnoul-Schipper, D.J., Faber, H.J., Lens, J., Brunnekreef, G.B., Festen-Spanjer, B., Dormans, T., Dijkstra, A., Simons, B., Rijkeboer, A.A., Arbous, S., Aries, M., Beukema, M., Pretorius, D., Raalte, R. van, Tellingen, M. van, Oever, N.C. Gritters van de, Lalisang, R.C.A., Tonutti, M., Girbes, Armand R.J., Hoogendoorn, M., Thoral, P.J., Elbers, P.W.G., Dam, T.A., Grooth, H.J. de, Klausch, T., Fleuren, L.M., Bruin, D.P. de, Entjes, R., Rettig, T.C., Dongelmans, Dave A., Boelens, A.D., Rigter, S., Hendriks, S.H., Jong, R. de, Kamps, M.J., Peters, M., Karakus, A., Gommers, D., Ramnarain, D., Wils, E.J., Achterberg, S., Nowitzky, R., Tempel, W., Jager, C.P.C. de, Nooteboom, F., Oostdijk, E., Koetsier, P., Cornet, A.D., Reidinga, A.C., Ruijter, W. de, Bosman, R.J., Frenzel, T., Urlings-Strop, L.C., Jong, p de, Smit, Egbert F., Cremer, O.L., Mehagnoul-Schipper, D.J., Faber, H.J., Lens, J., Brunnekreef, G.B., Festen-Spanjer, B., Dormans, T., Dijkstra, A., Simons, B., Rijkeboer, A.A., Arbous, S., Aries, M., Beukema, M., Pretorius, D., Raalte, R. van, Tellingen, M. van, Oever, N.C. Gritters van de, Lalisang, R.C.A., Tonutti, M., Girbes, Armand R.J., Hoogendoorn, M., Thoral, P.J., and Elbers, P.W.G.
- Abstract
Contains fulltext : 244701.pdf (Publisher’s version ) (Open Access), 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, Fio(2), 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 Pao(2)/Fio(2) 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.
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- 2021
10. The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients
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Fleuren, L.M., Dam, T.A., Tonutti, M., Bruin, D.P. de, Lalisang, R.C.A., Gommers, D., Cremer, O.L., Bosman, R.J., Rigter, S., Wils, E.J., Frenzel, T., Dongelmans, Dave A., Jong, R. de, Peters, M., Kamps, M.J., Ramnarain, D., Nowitzky, R., Nooteboom, F., Ruijter, W. de, Urlings-Strop, L.C., Smit, Egbert F., Mehagnoul-Schipper, D.J., Dormans, T., Jager, C.P.C. de, Hendriks, S.H., Achterberg, S., Oostdijk, E., Reidinga, A.C., Festen-Spanjer, B., Brunnekreef, G.B., Cornet, A.D., Tempel, W., Boelens, A.D., Koetsier, P., Lens, J., Faber, H.J., Karakus, A., Entjes, R., Jong, p de, Rettig, T.C., Arbous, S., Vonk, S.J.J., Fornasa, M., Machado, T., Houwert, T., Hovenkamp, H., Noorduijn-Londono, R., Quintarelli, D., Scholtemeijer, M.G., Beer, A.A. de, Cina, G., Beudel, M., Herter, W.E., Girbes, Armand R.J., Hoogendoorn, M., Thoral, P.J., Elbers, P.W.G., Fleuren, L.M., Dam, T.A., Tonutti, M., Bruin, D.P. de, Lalisang, R.C.A., Gommers, D., Cremer, O.L., Bosman, R.J., Rigter, S., Wils, E.J., Frenzel, T., Dongelmans, Dave A., Jong, R. de, Peters, M., Kamps, M.J., Ramnarain, D., Nowitzky, R., Nooteboom, F., Ruijter, W. de, Urlings-Strop, L.C., Smit, Egbert F., Mehagnoul-Schipper, D.J., Dormans, T., Jager, C.P.C. de, Hendriks, S.H., Achterberg, S., Oostdijk, E., Reidinga, A.C., Festen-Spanjer, B., Brunnekreef, G.B., Cornet, A.D., Tempel, W., Boelens, A.D., Koetsier, P., Lens, J., Faber, H.J., Karakus, A., Entjes, R., Jong, p de, Rettig, T.C., Arbous, S., Vonk, S.J.J., Fornasa, M., Machado, T., Houwert, T., Hovenkamp, H., Noorduijn-Londono, R., Quintarelli, D., Scholtemeijer, M.G., Beer, A.A. de, Cina, G., Beudel, M., Herter, W.E., Girbes, Armand R.J., Hoogendoorn, M., Thoral, P.J., and Elbers, P.W.G.
- Abstract
Contains fulltext : 238831.pdf (Publisher’s version ) (Open Access), 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 pa
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- 2021
11. Predictors for extubation failure in COVID-19 patients using a machine learning approach
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Fleuren, L.M., Dam, T.A., Tonutti, M., Bruin, D.P. de, Lalisang, R.C.A., Gommers, D., Cremer, O.L., Bosman, R.J., Rigter, S., Wils, E.J., Frenzel, T., Dongelmans, Dave A., Jong, R. de, Peters, M., Kamps, M.J., Ramnarain, D., Nowitzky, R., Nooteboom, F., Ruijter, W. de, Urlings-Strop, L.C., Smit, Egbert F., Mehagnoul-Schipper, D.J., Dormans, T., Jager, C.P.C. de, Hendriks, S.H., Achterberg, S., Oostdijk, E., Reidinga, A.C., Festen-Spanjer, B., Brunnekreef, G.B., Cornet, A.D., Tempel, W., Boelens, A.D., Koetsier, P., Lens, J., Faber, H.J., Karakus, A., Entjes, R., Jong, p de, Rettig, T.C., Arbous, S., Vonk, S.J.J., Fornasa, M., Machado, T., Houwert, T., Hovenkamp, H., Londono, R. Noorduijn, Quintarelli, D., Scholtemeijer, M.G., Beer, A.A. de, Cinà, G., Kantorik, A., Ruijter, T., Herter, W.E., Beudel, M., Girbes, Armand R.J., Hoogendoorn, M., Thoral, P.J., Elbers, P.W.G., Fleuren, L.M., Dam, T.A., Tonutti, M., Bruin, D.P. de, Lalisang, R.C.A., Gommers, D., Cremer, O.L., Bosman, R.J., Rigter, S., Wils, E.J., Frenzel, T., Dongelmans, Dave A., Jong, R. de, Peters, M., Kamps, M.J., Ramnarain, D., Nowitzky, R., Nooteboom, F., Ruijter, W. de, Urlings-Strop, L.C., Smit, Egbert F., Mehagnoul-Schipper, D.J., Dormans, T., Jager, C.P.C. de, Hendriks, S.H., Achterberg, S., Oostdijk, E., Reidinga, A.C., Festen-Spanjer, B., Brunnekreef, G.B., Cornet, A.D., Tempel, W., Boelens, A.D., Koetsier, P., Lens, J., Faber, H.J., Karakus, A., Entjes, R., Jong, p de, Rettig, T.C., Arbous, S., Vonk, S.J.J., Fornasa, M., Machado, T., Houwert, T., Hovenkamp, H., Londono, R. Noorduijn, Quintarelli, D., Scholtemeijer, M.G., Beer, A.A. de, Cinà, G., Kantorik, A., Ruijter, T., Herter, W.E., Beudel, M., Girbes, Armand R.J., Hoogendoorn, M., Thoral, P.J., and Elbers, P.W.G.
- Abstract
Contains fulltext : 244677.pdf (Publisher’s version ) (Open Access), 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 a
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- 2021
12. 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
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Fleuren, L.M., Tonutti, M., Bruin, D.P. de, Lalisang, R.C.A., Dam, T.A., Gommers, D., Cremer, O.L., Bosman, R.J., Vonk, S.J.J., Fornasa, M., Machado, T., Meer, N.J. van der, Rigter, S., Wils, E.J., Frenzel, T., Dongelmans, Dave A., Jong, R. de, Peters, M., Kamps, M.J., Ramnarain, D., Nowitzky, R., Nooteboom, F., Ruijter, W. de, Urlings-Strop, L.C., Smit, Egbert F., Mehagnoul-Schipper, D.J., Dormans, T., Jager, C.P.C. de, Hendriks, S.H., Oostdijk, E., Reidinga, A.C., Festen-Spanjer, B., Brunnekreef, G., Cornet, A.D., Tempel, W., Boelens, A.D., Koetsier, P., Lens, J., Achterberg, S., Faber, H.J., Karakus, A., Beukema, M., Entjes, R., Jong, p de, Houwert, T., Hovenkamp, H., Londono, R. Noorduijn, Quintarelli, D., Scholtemeijer, M.G., Beer, A.A. de, Cinà, G., Beudel, M., Keizer, N.F. de, Hoogendoorn, M., Girbes, Armand R.J., Herter, W.E., Elbers, P.W.G., Thoral, P.J., Fleuren, L.M., Tonutti, M., Bruin, D.P. de, Lalisang, R.C.A., Dam, T.A., Gommers, D., Cremer, O.L., Bosman, R.J., Vonk, S.J.J., Fornasa, M., Machado, T., Meer, N.J. van der, Rigter, S., Wils, E.J., Frenzel, T., Dongelmans, Dave A., Jong, R. de, Peters, M., Kamps, M.J., Ramnarain, D., Nowitzky, R., Nooteboom, F., Ruijter, W. de, Urlings-Strop, L.C., Smit, Egbert F., Mehagnoul-Schipper, D.J., Dormans, T., Jager, C.P.C. de, Hendriks, S.H., Oostdijk, E., Reidinga, A.C., Festen-Spanjer, B., Brunnekreef, G., Cornet, A.D., Tempel, W., Boelens, A.D., Koetsier, P., Lens, J., Achterberg, S., Faber, H.J., Karakus, A., Beukema, M., Entjes, R., Jong, p de, Houwert, T., Hovenkamp, H., Londono, R. Noorduijn, Quintarelli, D., Scholtemeijer, M.G., Beer, A.A. de, Cinà, G., Beudel, M., Keizer, N.F. de, Hoogendoorn, M., Girbes, Armand R.J., Herter, W.E., Elbers, P.W.G., and Thoral, P.J.
- Abstract
Contains fulltext : 238677.pdf (Publisher’s version ) (Open Access), 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 cmH(2)O. CONCLUSION: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ve
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- 2021
13. Strong increase in total delta-THC in cannabis preparations sold in Dutch coffee shops
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PIJLMAN, F. T.A., RIGTER, S. M., HOEK, J., GOLDSCHMIDT, H. M. J., and NIESINK, R. J.M.
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- 2005
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14. THC-concentraties in wiet, nederwiet en hasj in Nederlandse coffeeshops 2016-2017
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Rigter, S., Niesink, R.J.M., Department Science, and RS-Research Program Learning and Innovation in Resilient systems (LIRS)
- Abstract
Dit rapport beschrijft de resultaten van het achttiende jaarlijks onderzoek naar de sterkte van cannabisproducten zoals die in Nederlandse coffeeshops te koop worden aangeboden. Het onderzoek is uitgevoerd op verzoek van het ministerie van VWS. At random zijn 50 Nederlandse coffeeshops geselecteerd uit de meest recente lijst van gedoogde coffeeshops. Ten behoeve van het onderzoek zijn 63 monsters nederwiet (meest populaire variant), 56 monsters buitenlandse hasj, 10 monsters wiet van buitenlandse herkomst en 21 monsters hasj bereid uit nederwiet aangekocht. Ook werden 50 wietmonsters aangekocht die door de medewerkers van de coffeeshops werden aangemerkt als het “meest sterk”. Het gemiddelde THC-percentage in nederwiet was dit jaar 16,9% (meting 2016/2017) ten opzichte van 16,1% in de meting van vorig jaar (2015/2016), maar dit verschil is niet significant. Het gemiddelde percentage THC in nederwiet zoals gemeten in dit onderzoek is dus het afgelopen jaar gelijk gebleven. Tot in 2004 steeg het gemiddelde percentage THC in nederwiet (Pijlman e.a., 2005). In de daaropvolgende jaren was, tot 2013, sprake van een geleidelijke daling, sinds 2013 is weer een stijging waarneembaar (13,5% in 2013 vs 16,9% in 2017). Dat geldt voor de meest populaire variant, dus de soort die in de coffeeshop het meest wordt verkocht.
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- 2017
15. THC-concentraties in wiet, nederwiet en hasj in Nederlandse coffeeshops 2015-2016
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Rigter, S., Niesink, R.J.M., Department Science, and RS-Research Program Learning and Innovation in Resilient systems (LIRS)
- Published
- 2016
16. P5593VA-ECMO in primary PCI for ST-elevation myocardial infarction
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Van Den Brink, F.S., primary, Magan, A.D., additional, Noordzij, P.G., additional, Van Der Heyden, J.A.S., additional, Agostoni, P., additional, Eefting, F.D., additional, Ten Berg, J.M., additional, Rigter, S., additional, Suttorp, M.J., additional, Rensing, B.J., additional, Van Kuijk, J.P., additional, Daeter, E., additional, Zivelonghi, C., additional, and Scholten, E., additional
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- 2017
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17. mCPP: an undesired addition to the ecstasy market.
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Bossong, M. G., Brunt, T. M., Van Dijk, J. P., Rigter, S. M., Hoek, J., Goldschmidt, H. M. J., and Niesink, R. J. M.
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ECSTASY (Drug) ,DRUG monitoring ,PIPERAZINE ,TOXICITY testing ,PSYCHIATRIC drugs - Abstract
A new ecstasy-like substance, meta-chlorophenylpiperazine (mCPP), has been detected in street drugs in the Netherlands. Theoretically, mCPP possesses the potential to become a non-neurotoxic alternative for methylenedioxymethamphetamine (MDMA), the regular psychoactive substance of ecstasy. Since its introduction on the Dutch market of synthetic drugs, the percentage of mCPP-containing tablets has increased, including both tablets that contain only mCPP and tablets containing a combination of mCPP and MDMA. These tablets occur in many different colours, shapes and sizes and with various logos, making it impossible to distinguish mCPP-containing tablets from regular MDMA tablets. In addition, the reports of users concerning the effects of mCPP are predominantly negative. All these aspects together lead to the conclusion that mCPP is an undesired addition to the ecstasy market from the user's perspective. [ABSTRACT FROM AUTHOR]
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- 2010
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18. Content of ecstasy in the Netherlands: 1993-2008.
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Vogels N, Brunt TM, Rigter S, van Dijk P, Vervaeke H, and Niesink RJM
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Aims The present paper outlines the results of analyses carried out on the content of tablets sold as ecstasy, collected in the Netherlands by the Drugs Information Monitoring System (DIMS) from January 1993 to December 2008. Methods During a period of 16 years, the DIMS analysed the content of 33 006 tablets sold as ecstasy that were handed in by numerous individual (potential) substance users. The DIMS results were compared with the results from various seized tablets to determine whether the DIMS is a monitor of the ecstasy consumer market. Results The DIMS system appears to be a market monitor that gives an accurate reflection of what is actually available on the hidden Dutch ecstasy market. During 16 years of monitoring, the purity [tablets containing only 3,4-methylenedioxymethamphetamine (MDMA)] was lowest around 1997. During this time-period many tablets contained other substances in addition to or instead of MDMA [e.g. 3,4-methylene-dioxyamphetamine (MDA), 3,4-methylene-dioxyethylamphetamine (MDEA) and N-methyl-a-(1,3-benzodixol-5-yl)-2-butamine (MBDB), amphetamine and caffeine]. From 1998 to 2008, the number of high-dose tablets (>=106 mg MDMA per tablet) gradually increased. The same holds true for the proportion of tablets that contained only MDMA, reaching the highest levels in 2000 and 2004. After 2004, the purity of ecstasy tablets decreased again, caused mainly by a growing proportion of tablets containing meta-chlorophenylpiperazine (mCPP). Conclusions The DIMS results provide valuable qualitative information on the content of ecstasy tablets in the Netherlands, and its changes throughout the years. Moreover, the results were used for national and international risk assessments and important warning and prevention activities. [ABSTRACT FROM AUTHOR]
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- 2009
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19. Clinical course of COVID-19 in the Netherlands: An overview of 2607 patients in hospital during the first wave,Klinisch beloop van covid-19 in Nederland: Een overzicht van 2607 ziekenhuispatiënten uit de eerste golf
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Ariës, M., Den Bergh, J. P., Beudel, M., Boersma, W., Dormans, T., Douma, R., Eerens, A., Elbers, P., Fleuren, L., Den Oever, N. G., Haan, L., Horst, I., Hu, S., Hubers, D., Janssen, M., Kruif, M., Kubben, P., Kuijk, S., Noordzij, P., Ottenhoff, M., Piña-Fuentes, D., Potters, W. V., Reidinga, A., Renckens, R., Rigter, S., Rusch, D., Simsek, S., Schinkel, M., Sigaloff, K., Stassen, P., Stassen, R., Thomas, R., Wingen, G., anton vonk noordegraaf, Welling, M., Wiersinga, W. J., Wyers, C. E., and Wolvers, M.
20. VIII. HOTELS, RESTAURANTS EN ZIEKENHUIZEN
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Rigter, S., primary
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- 1946
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21. Applying machine learning to international drug monitoring: classifying cannabis resin collected in Europe using cannabinoid concentrations.
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Freeman TP, Beeching E, Craft S, Di Forti M, Frison G, Lindholst C, Oomen PE, Potter D, Rigter S, Rømer Thomsen K, Zamengo L, Cunningham A, Groshkova T, and Sedefov R
- Abstract
In Europe, concentrations of ∆
9 -tetrahydrocannabinol (THC) in cannabis resin (also known as hash) have risen markedly in the past decade, potentially increasing risks of mental health disorders. Current approaches to international drug monitoring cannot distinguish between different types of cannabis resin which may have contrasting health effects due to THC and cannabidiol (CBD) content. Here, we compared concentrations of THC and CBD in different types of cannabis resin collected in Europe (either Moroccan-type, or Dutch-type). We then tested the ability of machine learning algorithms to classify the type of cannabis resin (either Moroccan-type, or Dutch-type) using routinely collected monitoring data on THC and CBD. Finally, we applied the optimal algorithm to new samples collected in countries where the type of cannabis resin was unknown, the UK and Denmark. Results showed that overall, Dutch-type samples had higher THC (Hedges' g = 2.39) and lower CBD (Hedges' g = 0.81) than Moroccan-type samples. A Support Vector Machine algorithm achieved classification accuracy exceeding 95%, with little variation in this estimate, good interpretability, and plausibility. It made contrasting predictions about the type of cannabis resin collected in the UK (94% Moroccan-type; 6% Dutch-type) and Denmark (36% Moroccan-type; 64% Dutch-type). In conclusion, we provide proof-of-concept evidence for the potential of machine learning to inform international drug monitoring. Our findings should not be interpreted as objective confirmatory evidence but suggest that Dutch-type cannabis resin has higher THC concentrations than Moroccan-type cannabis resin, which may contribute to variation in drug markets and health outcomes for people who use cannabis in Europe., (© 2024. The Author(s).)- Published
- 2024
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22. Prolonged use of intravenous administration sets on central line associated bloodstream infection, nursing workload and material use: A before-after study.
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van de Pol I, Roescher N, Rigter S, and Noordzij PG
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- Humans, Retrospective Studies, Controlled Before-After Studies, Workload, Administration, Intravenous, Catheter-Related Infections epidemiology, Catheter-Related Infections etiology, Catheter-Related Infections prevention & control, Central Venous Catheters adverse effects, Sepsis etiology, Catheterization, Central Venous adverse effects
- Abstract
Objectives: One of the interventions to reduce risk of central line associated bloodstream infection (CLABSI) is routine replacement of the intravenous administration sets. Guidelines advises a time interval that ranges between four and seven days. However many hospitals replace intravenous administration sets every four days to prevent CLABSI., Research Methodology: In this single centre retrospective study we analysed whether the extension of the time interval from four to seven days for routine replacement of intravenous administration sets had impact on the incidence of CLABSI and colonization of the central venous catheter. Secondary outcomes were the effects on nursing workload, material use and costs., Results: In total, 1,409 patients with 1,679 central lines were included. During the pre-intervention period 2.8 CLABSI cases per 1,000 catheter days were found in comparison with 1.3 CLABSI cases per 1,000 catheter days during the post-intervention period. The rate difference between the groups was 1.52 CLABSI cases per 1,000 catheter days (95% CI: -0.50 to +4.13, p = 0.138). The intervention resulted in a saving of 345 intravenous single use plastic administration sets and 260 hours nursing time, and reduced cost with an estimate of at least 17.250 Euros., Conclusion: Extension of the time interval from four to seven days for routine replacement of intravenous administration sets did not negatively affect the incidence of CLABSI., Implications for Clinical Practice: Additional benefits of the prolonged time interval were saving of nursing time by avoiding unnecessary routine procedures, the reducing of waste because of reducing the use of disposable materials and healthcare costs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)
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- 2023
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23. How to Use Lean Thinking for the Optimization of Clinical Pathways: A Systematic Review and a Proposed Framework to Analyze Pathways on a System Level.
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Vijverberg JRG, Rouppe van der Voort MBV, van der Nat PB, Mosselman MJ, Rigter S, Biesma DH, and van Merode F
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Lean Thinking and clinical pathways are commonly used concepts to improve healthcare. However, little is known on how to use Lean Thinking for the optimization of pathways or the quantification of both concepts. This study aims to create a framework to analyze pathways with Lean Thinking on a system level, by quantifying the seven wastes, flow and pull. A systematic literature review was performed. Inclusion criteria were the focus of the article on a well-defined group of patients and studied a pathway optimization with Lean Thinking. Data were extracted on measured outcomes, type of intervention and type of researched pathway. Thirty-six articles were included. No articles described the implementation of the Lean Thinking philosophy or studied the development of their people and partners ("4 P" model). Most articles used process optimization tools or problem-solving tools. The majority of the studies focused on process measures. The measures found in the review were used as input for our suggested framework to identify and quantify wastes, flow, and pull in a clinical pathway. The proposed framework can be used to create an overview of the improvement potential of a pathway or to analyze the level of improvement after an enhancement is introduced to a pathway. Further research is needed to study the use of the suggested quantifications.
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- 2023
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24. Quality of life after extra corporeal life support therapy.
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de Vlugt R, Spek B, van de Pol I, and Rigter S
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- Humans, Retrospective Studies, Case-Control Studies, Critical Care, Quality of Life, Anxiety epidemiology, Anxiety etiology
- Abstract
Background: Extra Corporeal Life Support (ECLS) may be a life-saving treatment for patients with reversible cardiac and/or respiratory failure. ECLS is associated with a high risk of complications and mortality. Because only a small number of studies have been conducted into the long-term effects of ECLS, we investigated the difference in quality of life, anxiety and depressive complaints and PTSD 3 months after ICU discharge., Method: It is a retrospective case-control study covering the period January 2012 to December 2017. The ECLS patient group was compared to a matched similar patient group in the Intensive Care (IC) that did not have ECLS therapy. Quality of life was measured with the Short-Form-36 (SF-36) questionnaire, anxiety and depression was measured with the Hospital Anxiety and Depression Scale (HADS) questionnaire and for PTSD the Impact of Events Scale (IES) questionnaire was used, comparing sum scores and cut-off points of scores from both groups., Results: Included were 19 patients in the ECLS group and 38 in the control group. The mean sum scores on the sub scales of the SF36 questionnaire were the same for both groups. Only the mean score of 66.2 (scale 0-100) on the domain 'general health experience' was statistically significantly different in the ECLS group than in the control group (56.8, p = .02). There was no significant difference between the sum scores of both groups on anxiety and depressive complaints. In the ECLS group 32% of the patients may have a depressive disorder versus 18% from the control group ( p = .32). And 26% of the patients from the ECLS group may have an anxiety disorder versus 7% from the control group ( p = .51). The incidence of PTSD was 42% in the ECLS group and 24% in the control group ( p = .22)., Conclusion: We found no statistically significant difference in quality of life, anxiety and depressive symptoms and PTSD symptoms between ECLS patients and the matched control group - 3 months after the ICU discharge. The incidence of anxiety and depressive symptoms and PTSD in the ECLS group is higher than in the control group, however, this difference is not significant.
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- 2023
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25. INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY.
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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, and Tuinman PR
- Subjects
- Humans, Critical Illness, Incidence, Retrospective Studies, Risk Factors, Catheter-Related Infections epidemiology, Catheter-Related Infections etiology, Catheterization, Central Venous adverse effects, COVID-19 epidemiology, Central Venous Catheters adverse effects
- 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., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 by the Shock Society.)
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- 2022
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26. Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.
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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, and Elbers PWG
- 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 PaO
2 /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., (© 2022. The Author(s).)- Published
- 2022
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27. Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality.
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Plečko D, Bennett N, Mårtensson J, Dam TA, Entjes R, Rettig TCD, Dongelmans DA, Boelens AD, Rigter S, Hendriks SHA, de Jong R, Kamps MJA, Peters M, Karakus A, Gommers D, Ramnarain D, Wils EJ, Achterberg S, Nowitzky R, van den Tempel W, de Jager CPC, Nooteboom FGCA, Oostdijk E, Koetsier P, Cornet AD, Reidinga AC, de Ruijter W, Bosman RJ, Frenzel T, Urlings-Strop LC, de Jong P, Smit EGM, Cremer OL, Mehagnoul-Schipper DJ, Faber HJ, Lens J, Brunnekreef GB, Festen-Spanjer B, Dormans T, de Bruin DP, Lalisang RCA, Vonk SJJ, Haan ME, Fleuren LM, Thoral PJ, Elbers PWG, and Bellomo R
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- Adult, Aged, Critical Care, Hospital Mortality, Humans, Intensive Care Units, Male, Multicenter Studies as Topic, Observational Studies as Topic, Patient Acuity, Prognosis, Retrospective Studies, SARS-CoV-2, COVID-19
- 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., (© 2021 The Authors. Acta Anaesthesiologica Scandinavica published by John Wiley & Sons Ltd on behalf of Acta Anaesthesiologica Scandinavica Foundation.)
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- 2022
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28. Predictors for extubation failure in COVID-19 patients using a machine learning approach.
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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, and Elbers PWG
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- Adult, Critical Illness, Humans, Machine Learning, Airway Extubation, COVID-19 therapy, Treatment Failure
- 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., (© 2021. The Author(s).)
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- 2021
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29. Automated Monitoring of Plasma-free Hemoglobin on Routine Clinical Chemistry Platforms.
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Bosma M, Scholten E, Rigter S, and Hackeng CM
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- Blood Coagulation Tests, Hemoglobins analysis, Humans, Chemistry, Clinical, Hemolysis
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- 2021
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30. Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.
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Dam TA, de Grooth HJ, Klausch T, Fleuren LM, de Bruin DP, Entjes R, Rettig TCD, Dongelmans DA, Boelens AD, Rigter S, Hendriks SHA, de Jong R, Kamps MJA, Peters M, Karakus A, Gommers D, Ramnarain D, Wils EJ, Achterberg S, Nowitzky R, van den Tempel W, de Jager CPC, Nooteboom FGCA, Oostdijk E, Koetsier P, Cornet AD, Reidinga AC, de Ruijter W, Bosman RJ, Frenzel T, Urlings-Strop LC, de Jong P, Smit EGM, Cremer OL, Mehagnoul-Schipper DJ, Faber HJ, Lens J, Brunnekreef GB, Festen-Spanjer B, Dormans T, Dijkstra A, Simons B, Rijkeboer AA, Arbous S, Aries M, Beukema M, Pretorius D, van Raalte R, van Tellingen M, Gritters van den Oever NC, Lalisang RCA, Tonutti M, Girbes ARJ, Hoogendoorn M, Thoral PJ, and Elbers PWG
- 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, Fio
2 , 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., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)- Published
- 2021
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31. The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.
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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, and Elbers PWG
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- Critical Care, Humans, Netherlands, COVID-19 epidemiology, Critical Illness epidemiology, Data Warehousing statistics & numerical data, Electronic Health Records statistics & numerical data, Hospitalization statistics & numerical data, Intensive Care Units statistics & numerical data
- 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., (© 2021. The Author(s).)
- Published
- 2021
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32. Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse.
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Fleuren LM, Tonutti M, de Bruin DP, Lalisang RCA, Dam TA, Gommers D, Cremer OL, Bosman RJ, Vonk SJJ, Fornasa M, Machado T, van der Meer NJM, 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, Oostdijk E, Reidinga AC, Festen-Spanjer B, Brunnekreef G, Cornet AD, van den Tempel W, Boelens AD, Koetsier P, Lens J, Achterberg S, Faber HJ, Karakus A, Beukema M, Entjes R, de Jong P, Houwert T, Hovenkamp H, Noorduijn Londono R, Quintarelli D, Scholtemeijer MG, de Beer AA, Cinà G, Beudel M, de Keizer NF, Hoogendoorn M, Girbes ARJ, Herter WE, Elbers PWG, and Thoral PJ
- 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 cmH
2 O., 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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33. [Clinical course of COVID-19 in the Netherlands: an overview of 2607 patients in hospital during the first wave].
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Ariës MJH, van den Bergh JP, Beudel M, Boersma W, Dormans T, Douma RA, Eerens A, Elbers PWG, Fleuren LM, Gritters van den Oever NC, de Haan L, van der Horst IJCC, Hu S, Hubers D, Janssen MLF, de Kruif M, Kubben PL, van Kuijk SMJ, Noordzij PG, Ottenhoff M, Piña-Fuentes DAI, Potters WV, Reidinga AC, Renckens RSC, Rigter S, Rusch D, Schinkel M, Sigaloff KCE, Simsek S, Stassen P, Stassen R, Thomas RM, van Wingen GA, Vonk Noordegraaf A, Welling M, Wiersinga WJ, Wolvers MDJ, and Wyers CE
- Subjects
- Age Factors, Aged, Comorbidity, Critical Care methods, Critical Care statistics & numerical data, Female, Hospital Mortality, Humans, Kaplan-Meier Estimate, Male, Netherlands epidemiology, Risk Factors, Severity of Illness Index, COVID-19 epidemiology, COVID-19 prevention & control, COVID-19 therapy, Cardiovascular Diseases epidemiology, Diagnostic Tests, Routine methods, Diagnostic Tests, Routine statistics & numerical data, SARS-CoV-2 isolation & purification
- Abstract
Objective: To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands., Design: Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported., Methods: Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission., Results: Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels., Conclusion: Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission.
- Published
- 2021
34. Automated and cost-efficient early detection of hemolysis in patients with extracorporeal life support: Use of the hemolysis-index of routine clinical chemistry platforms.
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Bosma M, Waanders F, Van Schaik HP, Van Loon D, Rigter S, Scholten E, and Hackeng CM
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- Adult, Aged, Chemistry, Clinical, Cost-Benefit Analysis, Early Diagnosis, Female, Humans, Male, Middle Aged, Retrospective Studies, Extracorporeal Membrane Oxygenation, Hemolysis physiology
- Abstract
Purpose: Patients with extracorporeal life support (ECLS) are at risk for hemolysis-related complications. Therefore, monitoring of free hemoglobin (fHb) levels is indicated. Conventional methods for fHb are laborious and not always available. Here we evaluated the suitability of the hemolysis-index (H-index), an internal quality control parameter of clinical chemistry platforms, as a clinical parameter for ECLS patients., Materials and Methods: The performance of the H-index assay was evaluated using standard procedures. Furthermore, H-index data from ECLS patients (n = 56) was analyzed retrospectively., Results: The H-index significantly correlated with fHb and showed good analytical performance. During ECLS 19.6% of the patients had an H-index above 20 in at least 2 consecutive blood draws, indicating significant hemolysis. In the patients with clot formation in the pumphead the H-index peaked above 100. Visible clots at other locations did not always coincide with hemolysis. H-index peaks were more prevalent in patients that died during ECLS support., Conclusions: We conclude that the H-index is a suitable and cost-efficient alternative for the conventional fHb analysis with good analytic performance. The H-index aids in the early detection of hemolysis in patients with ECLS. A repeated H-index>20 was a predictor of mortality., (Copyright © 2019. Published by Elsevier Inc.)
- Published
- 2019
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35. Survival and neurological outcome with extracorporeal cardiopulmonary resuscitation for refractory cardiac arrest caused by massive pulmonary embolism: A two center observational study.
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Mandigers L, Scholten E, Rietdijk WJR, den Uil CA, van Thiel RJ, Rigter S, Heijnen BGADH, Gommers D, and Dos Reis Miranda D
- Subjects
- Adult, Controlled Before-After Studies, Female, Humans, Intensive Care Units statistics & numerical data, Male, Middle Aged, Out-of-Hospital Cardiac Arrest etiology, Out-of-Hospital Cardiac Arrest mortality, Pulmonary Embolism complications, Pulmonary Embolism diagnosis, Time-to-Treatment, Cardiopulmonary Resuscitation mortality, Extracorporeal Membrane Oxygenation mortality, Out-of-Hospital Cardiac Arrest therapy
- Abstract
Background: Cardiac arrest (CA) due to pulmonary embolism (PE) is associated with low survival rates and poor neurological outcomes. We examined whether Extracorporeal Cardiopulmonary Resuscitation (ECPR) improves the outcomes of patients who suffer from CA due to massive PE., Methods: We retrospectively included 39 CA patients with proven or strongly suspected PE in two hospitals in the Netherlands, in a 'before/after'-design. 20 of these patients were treated with Conventional Cardiopulmonary Resuscitation (CCPR) and 19 patients with ECPR., Results: The main outcomes of this study were ICU survival and favourable neurological outcome, defined as Cerebral Performance Category (CPC) score 1-2. The ICU survival rate in CCPR patients was 5% compared to 26% in ECPR patients (p<0.01). Survival with favourable neurological outcome was present in 0/20 (0%) CCPR patients compared to 4/19 (21%) of the ECPR patients (p<0.05)., Conclusion: ECPR seems a promising treatment for cardiac arrest patients due to (suspected) massive pulmonary embolism compared to conventional CPR, though outcomes remain poor., (Copyright © 2018 Elsevier B.V. All rights reserved.)
- Published
- 2019
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36. Changes in cannabis potency and first-time admissions to drug treatment: a 16-year study in the Netherlands.
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Freeman TP, van der Pol P, Kuijpers W, Wisselink J, Das RK, Rigter S, van Laar M, Griffiths P, Swift W, Niesink R, and Lynskey MT
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- Cannabinoid Receptor Agonists adverse effects, Cannabis adverse effects, Dronabinol adverse effects, Drug Monitoring, Humans, Marijuana Abuse therapy, Netherlands epidemiology, Cannabinoid Receptor Agonists analysis, Cannabis chemistry, Dronabinol analysis, Marijuana Abuse epidemiology
- Abstract
Background: The number of people entering specialist drug treatment for cannabis problems has increased considerably in recent years. The reasons for this are unclear, but rising cannabis potency could be a contributing factor., Methods: Cannabis potency data were obtained from an ongoing monitoring programme in the Netherlands. We analysed concentrations of δ-9-tetrahydrocannabinol (THC) from the most popular variety of domestic herbal cannabis sold in each retail outlet (2000-2015). Mixed effects linear regression models examined time-dependent associations between THC and first-time cannabis admissions to specialist drug treatment. Candidate time lags were 0-10 years, based on normative European drug treatment data., Results: THC increased from a mean (95% CI) of 8.62 (7.97-9.27) to 20.38 (19.09-21.67) from 2000 to 2004 and then decreased to 15.31 (14.24-16.38) in 2015. First-time cannabis admissions (per 100 000 inhabitants) rose from 7.08 to 26.36 from 2000 to 2010, and then decreased to 19.82 in 2015. THC was positively associated with treatment entry at lags of 0-9 years, with the strongest association at 5 years, b = 0.370 (0.317-0.424), p < 0.0001. After adjusting for age, sex and non-cannabis drug treatment admissions, these positive associations were attenuated but remained statistically significant at lags of 5-7 years and were again strongest at 5 years, b = 0.082 (0.052-0.111), p < 0.0001., Conclusions: In this 16-year observational study, we found positive time-dependent associations between changes in cannabis potency and first-time cannabis admissions to drug treatment. These associations are biologically plausible, but their strength after adjustment suggests that other factors are also important.
- Published
- 2018
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37. Systemic Inflammation after Transcatheter Aortic Valve Implantation: A Prospective Exploratory Study.
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Rettig TCD, Nijenhuis VJ, Meek B, Rigter S, Ten Berg JM, Vlaminckx B, van Klei WA, van de Garde EMW, Peelen LM, and Noordzij PG
- Subjects
- Aged, Aged, 80 and over, Female, Humans, Male, Postoperative Complications diagnosis, Prospective Studies, Systemic Inflammatory Response Syndrome diagnosis, Transcatheter Aortic Valve Replacement trends, Treatment Outcome, Postoperative Complications blood, Postoperative Complications etiology, Systemic Inflammatory Response Syndrome blood, Systemic Inflammatory Response Syndrome etiology, Transcatheter Aortic Valve Replacement adverse effects
- Published
- 2018
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38. Focus on cryptomarkets and online reviews too narrow to debate harms of drugs bought online.
- Author
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van der Gouwe D, Rigter S, and Brunt TM
- Subjects
- Harm Reduction, Drug Trafficking, Illicit Drugs
- Published
- 2018
- Full Text
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39. Potency trends of Δ9-tetrahydrocannabinol, cannabidiol and cannabinol in cannabis in the Netherlands: 2005-15.
- Author
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Niesink RJ, Rigter S, Koeter MW, and Brunt TM
- Subjects
- Chromatography, Gas, Netherlands, Time Factors, Cannabidiol analysis, Cannabinol analysis, Cannabis chemistry, Dronabinol analysis, Psychotropic Drugs analysis
- Abstract
Background and Aims: Between 2000 and 2005 the average percentage of Δ(9) -tetrahydrocannabinol (THC) in marijuana as sold in Dutch coffeeshops has increased substantially; the potency of domestic products (Nederwiet and Nederhasj) has particularly increased. In contrast with imported marijuana, Nederwiet hardly contained any cannabidiol (CBD), a cannabinoid that is thought to offset some of the adverse effects of THC. In 2005, the THC content in Nederwiet was significantly lower than in 2004. This study investigates the further decrease or increase of cannabinoids in these cannabis products., Methods: From 2005 to 2015 five different cannabis products were bought anonymously in 50 coffeeshops that were selected randomly each year from all coffeeshops in the Netherlands. A total of 2126 cannabis samples were bought, consisting of 664 Nederwiet samples (most popular), 537 Nederwiet samples (supposed strongest varieties), 183 imported herbal cannabis samples, 140 samples of cannabis resin made of Nederwiet and 602 samples of imported cannabis resin. All samples were analysed chemically for their THC, CBD and cannabinol (CBN) content., Results: Between 2005 and 2015, the mean potencies of the most popular and the strongest Nederwiet and of imported cannabis resin were 16.0±4.0%, 17.0±3,9% and 16.5±6.3%, respectively. Imported herbal cannabis (6.5±3.5%) and cannabis resin made from Nederwiet (30.2±16.4%) contained, respectively, less (β=-10.0, P<0.001) and more (β=13.7, P<0.001) THC than imported cannabis resin. Linear regression models were used to study the trends in THC of the different cannabis products over time. A marginal, but significant (P<0.001), overall decline of THC per year of 0.22% was found in all cannabis products. However, no significant difference was found between the five products in the THC linear trajectories across time. Of all the cannabis products, only imported cannabis resin contained a relatively high CBD/THC ratio (median 0.42)., Conclusion: The average tetrahydrocannabinol (THC) content of the most popular herbal cannabis products in the Netherlands has decreased slightly since 2005. The popular Nederwiet type still has a relatively high THC to cannabidiol ratio., (© 2015 Society for the Study of Addiction.)
- Published
- 2015
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40. The systemic inflammatory response syndrome predicts short-term outcome after transapical transcatheter aortic valve implantation.
- Author
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Rettig TC, Rigter S, Nijenhuis VJ, van Kuijk JP, ten Berg JM, Heijmen RH, van de Garde EM, and Noordzij PG
- Subjects
- Acute Kidney Injury epidemiology, Aged, Aged, 80 and over, Female, Heart Valve Prosthesis, Hospital Mortality, Humans, Length of Stay statistics & numerical data, Male, Retrospective Studies, Risk Assessment, Risk Factors, Severity of Illness Index, Heart Valve Prosthesis Implantation, Outcome and Process Assessment, Health Care statistics & numerical data, Postoperative Complications epidemiology, Systemic Inflammatory Response Syndrome epidemiology
- Abstract
Objective: Despite the minimally invasive nature of transcatheter aortic valve implantation (TAVI), the incidence of acute kidney injury (AKI) and mortality is of major concern. Several studies showed that outcome was influenced by the systemic inflammatory response syndrome (SIRS) in patients undergoing percutaneous TAVI. The purpose of this study was to investigate whether SIRS after transapical TAVI was associated with short-term outcome., Design: Retrospective analysis of prospectively collected data., Setting: Intensive care unit in a tertiary-care hospital., Participants: In 121 patients undergoing transapical TAVI for severe aortic stenosis between March 2010 and October 2013, the incidence of SIRS during the first 48 hours was studied. The relation between the occurrence of SIRS and any adverse event during hospital stay was investigated. Any adverse event was defined as the composite of mortality, AKI, infection, stroke, myocardial infarction, and bleeding., Intervention: none., Measurements and Main Results: Sixty-five (53.7%) patients developed SIRS during 48 hours after transapical TAVI. The occurrence of SIRS was associated independently with an increased risk of any adverse event (adjusted odds ratio: 4.0, 95% confidence interval [CI]: 1.6-9.6; p=0.002), which was mainly an increased risk of death (odds ratio: 5.5, 95% CI: 1.1-25.9; p=0.031). Patients with SIRS had a longer median duration of intensive care unit stay compared with patients without SIRS (2 v 1 day; p<0.001)., Conclusions: SIRS predicts short-term outcome in patients undergoing transapical TAVI., (Copyright © 2015 Elsevier Inc. All rights reserved.)
- Published
- 2015
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41. An analysis of cocaine powder in the Netherlands: content and health hazards due to adulterants.
- Author
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Brunt TM, Rigter S, Hoek J, Vogels N, van Dijk P, and Niesink RJ
- Subjects
- Crack Cocaine chemistry, Illicit Drugs chemistry, Netherlands, Regression Analysis, Cocaine chemistry, Drug Contamination statistics & numerical data
- Abstract
Aims: To report on trends in the content and adulterants present in street cocaine (powder) in the Netherlands and to describe the associated health hazards., Design and Participants: Drug consumers handed in samples of cocaine powder from 1999 to 2007 for analysis. Reports were compiled of users' experiences with the samples received., Measurements and Analysis: Linear regression analysis was used to assess the trend in adulterated cocaine powder across the study period, and comparison of reported adverse effects of adulterated with those of unadulterated cocaine by Fisher's exact test., Findings: There has been a statistically significant upward trend in the occurrence of adulterated cocaine powder over the years. Adulterated cocaine was associated more frequently with reported adverse effects than unadulterated cocaine. Phenacetin, hydroxyzine and diltiazem appeared to be three adulterants contributing to these adverse effects., Conclusions: An increase in adulterants was detected in the analysed cocaine powder between 1999 and 2007. This increase is associated with relatively more adverse effects with cocaine use. The cardiac and hallucinatory effects that were reported more frequently are not understood clearly. Adverse effects are likely to be due to several factors, including interactions of adulterants with cocaine and the route of administration.
- Published
- 2009
- Full Text
- View/download PDF
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