AUTHOR=Cuendet Michel A. , Gatta Roberto , Wicky Alexandre , Gerard Camille L. , Dalla-Vale Margaux , Tavazzi Erica , Michielin Grégoire , Delyon Julie , Ferahta Nabila , Cesbron Julien , Lofek Sébastien , Huber Alexandre , Jankovic Jeremy , Demicheli Rita , Bouchaab Hasna , Digklia Antonia , Obeid Michel , Peters Solange , Eicher Manuela , Pradervand Sylvain , Michielin Olivier TITLE=A differential process mining analysis of COVID-19 management for cancer patients JOURNAL=Frontiers in Oncology VOLUME=12 YEAR=2022 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.1043675 DOI=10.3389/fonc.2022.1043675 ISSN=2234-943X ABSTRACT=

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.