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Br J Anaesth. 2018 Nov;121(5):1123-1132. doi: 10.1016/j.bja.2018.06.007. Epub 2018 Jul 31.

Machine-learned selection of psychological questionnaire items relevant to the development of persistent pain after breast cancer surgery.

Author information

1
Institute of Clinical Pharmacology, Goethe University Frankfurt, Frankfurt am Main, Germany; Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Branch for Translational Medicine and Pharmacology TMP, Frankfurt am Main, Germany. Electronic address: j.loetsch@em.uni-frankfurt.de.
2
Pain Clinic, Department of Anaesthesiology, Intensive Care and Pain Medicine, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
3
Department of Neurology, University Medical Centre of the Johannes Gutenberg University, Mainz, Germany.

Abstract

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.

KEYWORDS:

breast cancer; data science; machine-learning; patients; persisting pain; psychological questionnaires

PMID:
30336857
DOI:
10.1016/j.bja.2018.06.007
[Indexed for MEDLINE]
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