A differential process mining analysis of COVID-19 management for cancer patients
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 Lau...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.1043675/full |
| _version_ | 1811158287778840576 |
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| author | Michel A. Cuendet Michel A. Cuendet Michel A. Cuendet Roberto Gatta Roberto Gatta Alexandre Wicky Camille L. Gerard Camille L. Gerard Margaux Dalla-Vale Erica Tavazzi Grégoire Michielin Julie Delyon Nabila Ferahta Julien Cesbron Sébastien Lofek Alexandre Huber Jeremy Jankovic Rita Demicheli Hasna Bouchaab Antonia Digklia Michel Obeid Solange Peters Manuela Eicher Manuela Eicher Sylvain Pradervand Olivier Michielin Olivier Michielin |
| author_facet | Michel A. Cuendet Michel A. Cuendet Michel A. Cuendet Roberto Gatta Roberto Gatta Alexandre Wicky Camille L. Gerard Camille L. Gerard Margaux Dalla-Vale Erica Tavazzi Grégoire Michielin Julie Delyon Nabila Ferahta Julien Cesbron Sébastien Lofek Alexandre Huber Jeremy Jankovic Rita Demicheli Hasna Bouchaab Antonia Digklia Michel Obeid Solange Peters Manuela Eicher Manuela Eicher Sylvain Pradervand Olivier Michielin Olivier Michielin |
| author_sort | Michel A. Cuendet |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T05:21:57Z |
| format | Article |
| id | doaj.art-f4628a004fd14e529fe8270985d792b9 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-10T05:21:57Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-f4628a004fd14e529fe8270985d792b92023-03-08T07:28:56ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-12-011210.3389/fonc.2022.10436751043675A differential process mining analysis of COVID-19 management for cancer patientsMichel A. Cuendet0Michel A. Cuendet1Michel A. Cuendet2Roberto Gatta3Roberto Gatta4Alexandre Wicky5Camille L. Gerard6Camille L. Gerard7Margaux Dalla-Vale8Erica Tavazzi9Grégoire Michielin10Julie Delyon11Nabila Ferahta12Julien Cesbron13Sébastien Lofek14Alexandre Huber15Jeremy Jankovic16Rita Demicheli17Hasna Bouchaab18Antonia Digklia19Michel Obeid20Solange Peters21Manuela Eicher22Manuela Eicher23Sylvain Pradervand24Olivier Michielin25Olivier Michielin26Precision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, SwitzerlandSwiss Institute of Bioinformatics, Lausanne, SwitzerlandDepartment of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United StatesPrecision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, SwitzerlandDepartment of Clinical and Experimental Sciences, University of Brescia, Brescia, ItalyPrecision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, SwitzerlandPrecision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, SwitzerlandThe Francis Crick Institute, London, United KingdomPrecision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, SwitzerlandDepartment of Information Engineering, University of Padova, Padova, ItalyPrecision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, SwitzerlandPrecision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Oncology, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandInstitute of Higher Education and Research in Health Care, Faculty of Biology and Medicine, University of Lausanne, Lausanne, SwitzerlandPrecision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, SwitzerlandPrecision Oncology Center, Department of Oncology, Lausanne University Hospital, Lausanne, SwitzerlandSwiss Institute of Bioinformatics, Lausanne, SwitzerlandDuring 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.https://www.frontiersin.org/articles/10.3389/fonc.2022.1043675/fullprocess miningCOVID-19oncologyprocess analysisclinical pathways |
| spellingShingle | Michel A. Cuendet Michel A. Cuendet Michel A. Cuendet Roberto Gatta Roberto Gatta Alexandre Wicky Camille L. Gerard Camille L. Gerard Margaux Dalla-Vale Erica Tavazzi Grégoire Michielin Julie Delyon Nabila Ferahta Julien Cesbron Sébastien Lofek Alexandre Huber Jeremy Jankovic Rita Demicheli Hasna Bouchaab Antonia Digklia Michel Obeid Solange Peters Manuela Eicher Manuela Eicher Sylvain Pradervand Olivier Michielin Olivier Michielin A differential process mining analysis of COVID-19 management for cancer patients Frontiers in Oncology process mining COVID-19 oncology process analysis clinical pathways |
| title | A differential process mining analysis of COVID-19 management for cancer patients |
| title_full | A differential process mining analysis of COVID-19 management for cancer patients |
| title_fullStr | A differential process mining analysis of COVID-19 management for cancer patients |
| title_full_unstemmed | A differential process mining analysis of COVID-19 management for cancer patients |
| title_short | A differential process mining analysis of COVID-19 management for cancer patients |
| title_sort | differential process mining analysis of covid 19 management for cancer patients |
| topic | process mining COVID-19 oncology process analysis clinical pathways |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.1043675/full |
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