Şentürk İA, Şentürk E, Üstün I, Gökçedağ A, Yıldırım NP, İçen NK. High-impact chronic pain: evaluation of risk factors and predictors.
Korean J Pain 2023;
36:84-97. [PMID:
36581599 PMCID:
PMC9812691 DOI:
10.3344/kjp.22357]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/12/2022] [Accepted: 12/16/2022] [Indexed: 12/31/2022] Open
Abstract
Background
The concept of high-impact chronic pain (HICP) has been proposed for patients with chronic pain who have significant limitations in work, social life, and personal care. Recognition of HICP and being able to distinguish patients with HICP from other chronic pain patients who do not have life interference allows the necessary measures to be taken in order to restore the physical and emotional functioning of the affected persons. The aim was to reveal the risk factors and predictors associated with HICP.
Methods
Patients with chronic pain without life interference (grade 1 and 2) and patients with HICP were compared. Significant data were evaluated with regression analysis to reveal the associated risk factors. Receiving operating characteristic (ROC) analysis was used to evaluate predictors and present cutoff scores.
Results
One thousand and six patients completed the study. From pain related cognitive processes, fear of pain (odds ratio [OR], 0.92; 95% confidence interval [CI], 0.87-0.98; P = 0.007) and helplessness (OR, 1.06; 95% CI, 1.01-1.12; P = 0.018) were found to be risk factors associated with HICP. Predictors of HICP were evaluated by ROC analysis. The highest discrimination value was found for pain intensity (cut-off score > 6.5; 83.8% sensitive; 68.7% specific; area under the curve = 0.823; P < 0.001).
Conclusions
This is the first study in our geography to evaluate HICP with measurement tools that evaluate all dimensions of pain. Moreover, it is the first study in the literature to evaluate predictors and cut-off scores using ROC analysis for HICP.
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