A differential process mining analysis of COVID-19 management for cancer patients.

Autor:	Cuendet MA; Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.; Swiss Institute of Bioinformatics, Lausanne, Switzerland.; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States., Gatta R; Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.; Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy., Wicky A; Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland., Gerard CL; Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.; The Francis Crick Institute, London, United Kingdom., Dalla-Vale M; Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland., Tavazzi E; Department of Information Engineering, University of Padova, Padova, Italy., Michielin G; Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland., Delyon J; Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland., Ferahta N; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Cesbron J; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Lofek S; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Huber A; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Jankovic J; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Demicheli R; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Bouchaab H; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Digklia A; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Obeid M; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Peters S; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Eicher M; Department of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.; Institute of Higher Education and Research in Health Care, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland., Pradervand S; Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland., Michielin O; Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2022 Dec 07; Vol. 12, pp. 1043675. Date of Electronic Publication: 2022 Dec 07 (Print Publication: 2022).
DOI:	10.3389/fonc.2022.1043675
Abstrakt:	During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death. The same trees can also show probability of death or time-to-event statistics in each node. We introduce a new tool, called Differential Process Mining, which enables comparison of two patient strata in each node of the tree, in terms of hits and death rate, together with a statistical significance test. We thus compare management of COVID-19 patients with an active cancer in the first vs. second COVID-19 waves to quantify hospital adaptation to the pandemic. We also compare patients having undergone systemic therapy within 1 year to the rest of the cohort to understand the impact of an active cancer and/or its treatment on COVID-19 outcome. This study demonstrates the value of Process Mining to analyze complex event-based real-world data and generate hypotheses on hospital resource management or on clinical patient care. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Cuendet, Gatta, Wicky, Gerard, Dalla-Vale, Tavazzi, Michielin, Delyon, Ferahta, Cesbron, Lofek, Huber, Jankovic, Demicheli, Bouchaab, Digklia, Obeid, Peters, Eicher, Pradervand and Michielin.)
Databáze:	MEDLINE
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