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: To identify clinical phenotypes of hip osteoarthritis (OA) within a biopsychosocial framework. : A cross-sectional analysis of 143 individuals with hip OA awaiting total hip arthroplasty (THA) was performed. Phenotyping features included sociodemographic and biomedical information, pain-related cognitions and emotions, mental disorders, traumatic experiences, self-efficacy, social support, perceived stress, and somatosensory function. Outcome measures included the hip disability and osteoarthritis outcome score and the numeric pain-rating scale. Decision tree learning was used to select the most important phenotyping features. K-means clustering analyses were performed to identify clinical phenotypes and a decision tree algorithm was trained to classify individuals in the identified clinical phenotypes. : Selected phenotyping features associated with pain and disability included a combination of biomedical, psychological, and social variables. Two distinct clinical phenotypes were identified. Individuals within the maladaptive phenotype (34%) reported more comorbidities, less self-efficacy and higher levels of anxiety, depression, pain-related fear-avoidance, and feelings of injustice. No differences were found regarding social support and somatosensory function. Regarding the outcome measures, individuals within the maladaptive phenotype reported higher levels of pain and disability. Finally, based on the Fear-Avoidance Components Scale (FACS) and the anxiety subscale of the Hospital Anxiety and Depression Scale (HADS-A), individuals could be classified into the clinical phenotypes with 87.8% accuracy. : Two clinical phenotypes, an adaptive and a maladaptive phenotype, can be identified in individuals with hip OA using the FACS and HADS-A. The identification of these clinical phenotypes represents a crucial step toward precision medicine, enabling the development of targeted treatment pathways tailored to the distinct biomedical and psychological features of each phenotype.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11594843 | PMC |
http://dx.doi.org/10.3390/jcm13226824 | DOI Listing |
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