Abstract
In the intensive care unit (ICU), a common task for clinicians is to choose patients who are ready-for-transfer to a lower ward in order to make limited capacity available for new arrivals. To support this process, we build three predictive models based on historical data from more than 25,000 ICU cases to evaluate patients according to their actual medical state. The decision is modeled as a classification problem to predict the chance of adverse patient outcome defined by ICU-readmission within 72 h or readmission with subsequent exitus. In addition to a screening method based on critical criteria, we propose logistic regression models relying on critical parameter counts and metrical features from measurements, scores, and patient characteristics, respectively. Performance testing using ICU data demonstrates the ability of our approach to assist the process of patient selection for transfer.
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Notes
- 1.
This refers to the scenario with exitus cases included as NRFT.
- 2.
Time windows for aggregation were defined by offset-days, i.e. multiples of 24-h prior to the date of discharge.
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Acknowledgement
This study was funded by Else-Kröner-Fresenius Zentrum (EKFZ) for Digital Health. The authors are grateful for the valuable comments by the reviewer.
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Ehm, F., Franz, V., Regner, M., Buscher, U., Held, HC., Spieth, P. (2022). Classifying Ready-for-Transfer Patients in the Intensive Care Unit Based on Clinical Data. In: Trautmann, N., Gnägi, M. (eds) Operations Research Proceedings 2021. OR 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-08623-6_32
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DOI: https://doi.org/10.1007/978-3-031-08623-6_32
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