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 para
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.
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Metadaten
Author:Jörn Lötsch, Reetta M. Sipilä, Violeta Dimova, Eija Kalso
URN:urn:nbn:de:hebis:30:3-488658
DOI:http://dx.doi.org/10.1016/j.bja.2018.06.007
ISSN:1471-6771
ISSN:0007-0912
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=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
Pagenumber: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
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell - Keine Bearbeitung 4.0

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