University of Oulu

Iivanainen, S., Ekstrom, J., Virtanen, H. et al. Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies. BMC Med Inform Decis Mak 21, 205 (2021). https://doi.org/10.1186/s12911-021-01564-0

Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies

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Author: Iivanainen, Sanna1; Ekstrom, Jussi2; Virtanen, Henri2;
Organizations: 1Department of Oncology and Radiotherapy, Oulu University Hospital and MRC Oulu, OYS, P.B. 22, 90029, Oulu, Finland
2Kaiku Health Oy, Helsinki, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021102852761
Language: English
Published: Springer Nature, 2021
Publish Date: 2021-10-28
Description:

Abstract

Background: Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs.

Methods: The utilized dataset consisted of two data sources. The first dataset consisted of 820 completed symptom questionnaires from 34 ICI treated advanced cancer patients, including 18 monitored symptoms collected using the Kaiku Health digital platform. The second dataset included prospectively collected irAE data, Common Terminology Criteria for Adverse Events (CTCAE) class, and the severity of 26 irAEs. The ML models were built using extreme gradient boosting algorithms. The first model was trained to detect the presence and the second the onset of irAEs.

Results: The model trained to predict the presence of irAEs had an excellent performance based on four metrics: accuracy score 0.97, Area Under the Curve (AUC) value 0.99, F1-score 0.94 and Matthew’s correlation coefficient (MCC) 0.92. The prediction of the irAE onset was more difficult with accuracy score 0.96, AUC value 0.93, F1-score 0.66 and MCC 0.64 but the model performance was still at a good level.

Conclusion: The current study suggests that ML based prediction models, using ePRO data as an input, can predict the presence and onset of irAEs with a high accuracy, indicating that ePRO follow-up with ML algorithms could facilitate the detection of irAEs in ICI-treated cancer patients. The results should be validated with a larger dataset.

Trial registration: Clinical Trials Register (NCT3928938), registration date the 26th of April, 2019.

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Series: BMC medical informatics and decision making
ISSN: 1472-6947
ISSN-E: 1472-6947
ISSN-L: 1472-6947
Volume: 21
Issue: 1
Article number: 205
DOI: 10.1186/s12911-021-01564-0
OADOI: https://oadoi.org/10.1186/s12911-021-01564-0
Type of Publication: A1 Journal article – refereed
Field of Science: 3122 Cancers
Subjects:
Funding: Study was funded by Oulu University and Finnish Cancer Society. Kaiku Health employees were involved in the data acquiring and analysis.
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