Machine-learned selection of psychological questionnaire items relevant to the development of persistent pain after breast cancer surgery
- Background: Prevention of persistent pain after breast cancer surgery, via early identification of patients at high risk, is a clinical need. Psychological factors are among the most consistently proposed predictive parameters for the development of persistent pain. However, repeated use of long psychological questionnaires in this context may be exhaustive for a patient and inconvenient in everyday clinical practice. Methods: Supervised machine learning was used to create a short form of questionnaires that would provide the same predictive performance of pain persistence as the full questionnaires in a cohort of 1000 women followed up for 3 yr after breast cancer surgery. Machine-learned predictors were first trained with the full-item set of Beck's Depression Inventory (BDI), Spielberger's State–Trait Anxiety Inventory (STAI), and the State–Trait Anger Expression Inventory (STAXI-2). Subsequently, features were selected from the questionnaires to create predictors having a reduced set of items. Results: A combined seven-item set of 10% of the original psychological questions from STAI and BDI, provided the same predictive performance parameters as the full questionnaires for the development of persistent postsurgical pain. The seven-item version offers a shorter and at least as accurate identification of women in whom pain persistence is unlikely (almost 95% negative predictive value). Conclusions: Using a data-driven machine-learning approach, a short list of seven items from BDI and STAI is proposed as a basis for a predictive tool for the persistence of pain after breast cancer surgery.
Author: | Jörn LötschORCiDGND, Reetta M. SipiläORCiD, Violeta DimovaGND, Eija KalsoORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-488658 |
DOI: | https://doi.org/10.1016/j.bja.2018.06.007 |
ISSN: | 1471-6771 |
ISSN: | 0007-0912 |
Pubmed Id: | https://pubmed.ncbi.nlm.nih.gov/30336857 |
Parent Title (English): | British journal of anaesthesia |
Publisher: | Elsevier |
Place of publication: | [Amsterdam] |
Contributor(s): | L. Colvin |
Document Type: | Article |
Language: | English |
Year of Completion: | 2018 |
Date of first Publication: | 2018/07/31 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2019/02/07 |
Tag: | breast cancer; data science; machine-learning; patients; persisting pain; psychological questionnaires |
Volume: | 121 |
Issue: | 5 |
Page Number: | 10 |
First Page: | 1123 |
Last Page: | 1132 |
Note: | © 2018 The Author(s). Published by Elsevier Ltd on behalf of British Journal of Anaesthesia. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
HeBIS-PPN: | 446477346 |
Institutes: | Medizin / Medizin |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
Sammlungen: | Universitätspublikationen |
Licence (German): | Creative Commons - Namensnennung-Nicht kommerziell - Keine Bearbeitung 4.0 |