Purpose: Chronic pain (CP) is a complex multidimensional experience severely affecting the quality of life of individuals. Multiple cognitive, affective, emotional and interpersonal factors play a major role in CP. Furthermore, the psychological, social and physical circumstances leading to CP show high inter-individual variability, thus making it difficult to identify core syndrome characteristics. In a biopsychosocial perspective, we aim at identifying a pattern of psycho-physical impairments that can reliably discriminate between CP individuals and healthy controls (HC) with high accuracy and estimated generalizability using machine learning. Methods: 118 CP and 86 HC were recruited. All individuals were administered several scales assessing quality of life, physical and mental health, personal functioning, anxiety, depression, beliefs about medical treatments, and cognitive abilities. These features were trained to separate CP from HC using support vector classification and repeated nested cross-validation. Results: Our psycho-physical classifier could discriminate CP from HC with 86.5% Balanced Accuracy and significance (p=0.0001). The most reliable features characterizing CP were anxiety and depression scores, and beliefs of harm consequent to prolonged pharmacological treatments; for HP, the most reliable features were physical and occupational functioning, and vitality levels. Conclusion: Our findings suggest that using psychological and physical assessments it is possible to classify CP from HC with high reliability and estimated generalizability via (i) a pattern of psychological symptoms and cognitive beliefs characterizing CP, and (ii) a pattern of intact physical abilities characterizing HC. We think that our algorithm carries novel insights about potential individualized targets for CP-related early intervention programs.

An Ensemble of Psychological and Physical Health Indices Discriminates Between Individuals with Chronic Pain and Healthy Controls with High Reliability: A Machine Learning Study

Linda A. Antonucci
Conceptualization
;
Alessandro Taurino
Writing – Review & Editing
;
Domenico Laera
Writing – Review & Editing
;
Paolo Taurisano
Writing – Review & Editing
;
Chiara Abbatantuono
Writing – Review & Editing
;
Mariateresa Giglio
Writing – Review & Editing
;
Maria Fara De Caro
Supervision
;
Filomena Puntillo
Supervision
2020-01-01

Abstract

Purpose: Chronic pain (CP) is a complex multidimensional experience severely affecting the quality of life of individuals. Multiple cognitive, affective, emotional and interpersonal factors play a major role in CP. Furthermore, the psychological, social and physical circumstances leading to CP show high inter-individual variability, thus making it difficult to identify core syndrome characteristics. In a biopsychosocial perspective, we aim at identifying a pattern of psycho-physical impairments that can reliably discriminate between CP individuals and healthy controls (HC) with high accuracy and estimated generalizability using machine learning. Methods: 118 CP and 86 HC were recruited. All individuals were administered several scales assessing quality of life, physical and mental health, personal functioning, anxiety, depression, beliefs about medical treatments, and cognitive abilities. These features were trained to separate CP from HC using support vector classification and repeated nested cross-validation. Results: Our psycho-physical classifier could discriminate CP from HC with 86.5% Balanced Accuracy and significance (p=0.0001). The most reliable features characterizing CP were anxiety and depression scores, and beliefs of harm consequent to prolonged pharmacological treatments; for HP, the most reliable features were physical and occupational functioning, and vitality levels. Conclusion: Our findings suggest that using psychological and physical assessments it is possible to classify CP from HC with high reliability and estimated generalizability via (i) a pattern of psychological symptoms and cognitive beliefs characterizing CP, and (ii) a pattern of intact physical abilities characterizing HC. We think that our algorithm carries novel insights about potential individualized targets for CP-related early intervention programs.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/310763
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