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Your search keyword '"Leeuwenberg, Artuur M."' showing total 17 results
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17 results on '"Leeuwenberg, Artuur M."'

1. Cross-institution text mining to uncover clinical associations: a case study relating social factors and code status in intensive care medicine

Author

Sushil, Madhumita, Butte, Atul J., Schuit, Ewoud, van Smeden, Maarten, and Leeuwenberg, Artuur M.

Subjects
Computer Science - Computation and Language, Statistics - Methodology, 68T50, 68U35, 62-xx, 62P10, 92C60, 92D30, I.2.7, G.3
Abstract

Objective: Text mining of clinical notes embedded in electronic medical records is increasingly used to extract patient characteristics otherwise not or only partly available, to assess their association with relevant health outcomes. As manual data labeling needed to develop text mining models is resource intensive, we investigated whether off-the-shelf text mining models developed at external institutions, together with limited within-institution labeled data, could be used to reliably extract study variables to conduct association studies. Materials and Methods: We developed multiple text mining models on different combinations of within-institution and external-institution data to extract social factors from discharge reports of intensive care patients. Subsequently, we assessed the associations between social factors and having a do-not-resuscitate/intubate code. Results: Important differences were found between associations based on manually labeled data compared to text-mined social factors in three out of five cases. Adopting external-institution text mining models using manually labeled within-institution data resulted in models with higher F1-scores, but not in meaningfully different associations. Discussion: While text mining facilitated scaling analyses to larger samples leading to discovering a larger number of associations, the estimates may be unreliable. Confirmation is needed with better text mining models, ideally on a larger manually labeled dataset. Conclusion: The currently used text mining models were not sufficiently accurate to be used reliably in an association study. Model adaptation using within-institution data did not improve the estimates. Further research is needed to set conditions for reliable use of text mining in medical research.

Published
2023

4. Comparing methods addressing multi-collinearity when developing prediction models

Author

Leeuwenberg, Artuur M., van Smeden, Maarten, Langendijk, Johannes A., van der Schaaf, Arjen, Mauer, Murielle E., Moons, Karel G. M., Reitsma, Johannes B., and Schuit, Ewoud

Subjects
Statistics - Methodology, 60, G.3
Abstract

Clinical prediction models are developed widely across medical disciplines. When predictors in such models are highly collinear, unexpected or spurious predictor-outcome associations may occur, thereby potentially reducing face-validity and explainability of the prediction model. Collinearity can be dealt with by exclusion of collinear predictors, but when there is no a priori motivation (besides collinearity) to include or exclude specific predictors, such an approach is arbitrary and possibly inappropriate. We compare different methods to address collinearity, including shrinkage, dimensionality reduction, and constrained optimization. The effectiveness of these methods is illustrated via simulations. In the conducted simulations, no effect of collinearity was observed on predictive outcomes. However, a negative effect of collinearity on the stability of predictor selection was found, affecting all compared methods, but in particular methods that perform strong predictor selection (e.g., Lasso).}

Published
2021

8. Predicting sepsis-related mortality and ICU admissions from telephone triage information of patients presenting to out-of-hours GP cooperatives with acute infections: A cohort study of linked routine care databases

Author

Loots, Feike J., primary, Smits, Marleen, additional, Jenniskens, Kevin, additional, Leeuwenberg, Artuur M., additional, Giesen, Paul H. J., additional, Ramerman, Lotte, additional, Verheij, Robert, additional, van Zanten, Arthur R. H., additional, and Venekamp, Roderick P., additional

Published
2023
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9. Dynamics in cardiac surgery: trends in population characteristics and the performance of the EuroSCORE II over time.

Author

Dijk, Wouter B van, Leeuwenberg, Artuur M, Grobbee, Diederick E, Siregar, Sabrina, Houterman, Saskia, Daeter, Edgar J, Vries, Martine C de, Groenwold, Rolf H H, Schuit, Ewoud, and Registration, Cardiothoracic Surgery Registration Committee of the Netherlands Heart

Subjects
*CARDIAC surgery, *ELECTIVE surgery, *OPERATIVE surgery, *HEART failure, *CONFIDENCE intervals
Abstract

Open in new tab Download slide OBJECTIVES The aim of this study was to investigate the performance of the EuroSCORE II over time and dynamics in values of predictors included in the model. METHODS A cohort study was performed using data from the Netherlands Heart Registration. All cardiothoracic surgical procedures performed between 1 January 2013 and 31 December 2019 were included for analysis. Performance of the EuroSCORE II was assessed across 3-month intervals in terms of calibration and discrimination. For subgroups of major surgical procedures, performance of the EuroSCORE II was assessed across 12-month time intervals. Changes in values of individual EuroSCORE II predictors over time were assessed graphically. RESULTS A total of 103 404 cardiothoracic surgical procedures were included. Observed mortality risk ranged between 1.9% [95% confidence interval (CI) 1.6–2.4] and 3.6% (95% CI 2.6–4.4) across 3-month intervals, while the mean predicted mortality risk ranged between 3.4% (95% CI 3.3–3.6) and 4.2% (95% CI 3.9–4.6). The corresponding observed:expected ratios ranged from 0.50 (95% CI 0.46–0.61) to 0.95 (95% CI 0.74–1.16). Discriminative performance in terms of the c -statistic ranged between 0.82 (95% CI 0.78–0.89) and 0.89 (95% CI 0.87–0.93). The EuroSCORE II consistently overestimated mortality compared to observed mortality. This finding was consistent across all major cardiothoracic surgical procedures. Distributions of values of individual predictors varied broadly across predictors over time. Most notable trends were a decrease in elective surgery from 75% to 54% and a rise in patients with no or New York Heart Association I class heart failure from 27% to 33%. CONCLUSIONS The EuroSCORE II shows good discriminative performance, but consistently overestimates mortality risks of all types of major cardiothoracic surgical procedures in the Netherlands. [ABSTRACT FROM AUTHOR]

Published
2023
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13. Additional file 6 of Performance of binary prediction models in high-correlation low-dimensional settings: a comparison of methods

Author

Leeuwenberg, Artuur M., van Smeden, Maarten, Langendijk, Johannes A., van der Schaaf, Arjen, Mauer, Murielle E., Moons, Karel G. M., Reitsma, Johannes B., and Schuit, Ewoud

Subjects
MathematicsofComputing_NUMERICALANALYSIS, Computer Science::Operating Systems
Abstract

Additional file 6. Coefficient estimation results using mean squared error.

Published
2022
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17. Dynamics in cardiac surgery: trends in population characteristics and the performance of the EuroSCORE II over time.

Author

van Dijk WB, Leeuwenberg AM, Grobbee DE, Siregar S, Houterman S, Daeter EJ, de Vries MC, Groenwold RHH, and Schuit E

Subjects
Humans, Cohort Studies, Heart, Elective Surgical Procedures, Calibration, Cardiac Surgical Procedures
Abstract

Objectives: The aim of this study was to investigate the performance of the EuroSCORE II over time and dynamics in values of predictors included in the model., Methods: A cohort study was performed using data from the Netherlands Heart Registration. All cardiothoracic surgical procedures performed between 1 January 2013 and 31 December 2019 were included for analysis. Performance of the EuroSCORE II was assessed across 3-month intervals in terms of calibration and discrimination. For subgroups of major surgical procedures, performance of the EuroSCORE II was assessed across 12-month time intervals. Changes in values of individual EuroSCORE II predictors over time were assessed graphically., Results: A total of 103 404 cardiothoracic surgical procedures were included. Observed mortality risk ranged between 1.9% [95% confidence interval (CI) 1.6-2.4] and 3.6% (95% CI 2.6-4.4) across 3-month intervals, while the mean predicted mortality risk ranged between 3.4% (95% CI 3.3-3.6) and 4.2% (95% CI 3.9-4.6). The corresponding observed:expected ratios ranged from 0.50 (95% CI 0.46-0.61) to 0.95 (95% CI 0.74-1.16). Discriminative performance in terms of the c-statistic ranged between 0.82 (95% CI 0.78-0.89) and 0.89 (95% CI 0.87-0.93). The EuroSCORE II consistently overestimated mortality compared to observed mortality. This finding was consistent across all major cardiothoracic surgical procedures. Distributions of values of individual predictors varied broadly across predictors over time. Most notable trends were a decrease in elective surgery from 75% to 54% and a rise in patients with no or New York Heart Association I class heart failure from 27% to 33%., Conclusions: The EuroSCORE II shows good discriminative performance, but consistently overestimates mortality risks of all types of major cardiothoracic surgical procedures in the Netherlands., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery.)

Published
2023
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