ORIGINAL ARTICLE   Free access

European Journal of Physical and Rehabilitation Medicine 2020 June;56(3):286-96

DOI: 10.23736/S1973-9087.20.05465-9

Copyright © 2020 EDIZIONI MINERVA MEDICA

language: English

Mapping patient reported outcome measures for low back pain to the International Classification of Functioning, Disability and Health using random forests

Kerstin TUECHLER ¹ ✉, Elisabeth FEHRMANN ^{1, 2}, Thomas KIENBACHER ¹, Patrick MAIR ³, Linda FISCHER-GROTE ¹, Gerold EBENBICHLER ⁴

¹ Karl Landsteiner Institute for Outpatient Rehabilitation Research, Vienna, Austria; ² Department of Psychology, Karl Landsteiner University of Health Sciences, Krems, Austria; ³ Department of Psychology, Harvard University, Cambridge, MA, USA; ⁴ Department of Physical Medicine and Rehabilitation, Medical University Vienna, Vienna, Austria

BACKGROUND: There is need for feasible and efficient concepts to document patients functioning impairment according to the International Classification of Functioning, Disability and Health (ICF) without imposing additional burden to clinical practice.
AIM: The aim of this study was to develop and validate an automatic linking approach that translates information derived from patient reported outcome measures (PROMs) into the ICF.
DESIGN: Proof-of-concept study.
SETTING: Participants completed both the Roland-Morris disability questionnaire and the Pain Disability Index and were interviewed using the activity and participation component of the ICF brief core set for low back pain.
POPULATION: A total of 244 patients with light to moderate chronic low back pain (cLBP); additionally, 19 patients with higher levels of pain were recruited and assessed for validation purposes.
METHODS: Based on information extracted from the PROMs and considering the factors age and gender, random forest models that predicted the presence or absence of an impairment at the specific ICF category were computed and validated.
RESULTS: Accuracy of the models was found to be acceptable for the most relevant ICF brief core set categories for low back pain if applied at the population level.
CONCLUSIONS: The presented approach can be assumed valid if applied at large on population level. The results are of relevance for the further development of automatic linking programs that would allow the ICF-based classification of functioning properties within the International Classification of Diseases (ICD-11) for any health condition.
CLINICAL REHABILITATION IMPACT: The presented approach eases the documentation of patients’ functioning impairment according to the standardized ICF.

KEY WORDS: International classification of functioning, Disability and Health; Patient reported outcome measures; Low back pain; Machine learning