Brenda W Dyal1, Miriam O Ezenwa1, Saunjoo L Yoon1, Roger B Fillingim2, Yingwei Yao1, Judith M Schlaeger3, Marie L Suarez4, Zaijie J Wang5, Robert E Molokie6,7, Diana J Wilkie1. 1. Department of Biobehavioral Nursing Science, College of Nursing, University of Florida, Gainesville, Florida, U.S.A. 2. Department of Community Dentistry and Behavioral Science, University of Florida, Gainesville, Florida, U.S.A. 3. Department of Women, Children and Family Health Science, University of Illinois at Chicago, Chicago, Illinois, U.S.A. 4. Department of Biobehavioral Health Science, College of Nursing, University of Illinois at Chicago, Chicago, Illinois, U.S.A. 5. Department of Biopharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, Illinois, U.S.A. 6. Department of Medicine College of Medicine, University of Illinois at Chicago, Chicago, IL, U.S.A. 7. Jesse Brown VA Medical Center, Chicago, Illinois, U.S.A, .
Abstract
BACKGROUND: We sought to refine a screening measure for discriminating a sensitized or normal sensation pain phenotype among African American adults with sickle cell disease (SCD). OBJECTIVE: To develop scoring schemes based on sensory pain quality descriptors; evaluate their performance on classifying patients with SCD who had sensitization or normal sensation, and compare with scores on the Self-report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) and the Neuropathic Pain Symptom Inventory (NPSI). METHODS: Participants completed PAINReportIt, quantitative sensory testing (QST), S-LANSS, and NPSI. Conventional binary logistic regression and least absolute shrinkage and selection operator (lasso) regression were used to obtain 2 sets of weights resulting in 2 scores: the PR-Logistic (PAINReportIt score weighted by conventional binary logistic regression coefficients) and PR-Lasso (PAINReportIt score weighted by lasso regression coefficients). Performance of the proposed scores and the existing scores were evaluated. RESULTS: Lasso regression resulted in a parsimonious model with non-zero weights assigned to 2 neuropathic descriptors, cold and spreading. We found positive correlations between the PR-Lasso and other scores: S-LANSS (r = 0.22, P < 0.01), NPSI (r = 0.22, P < 0.01), and PR-Logistic (r = 0.35, P < 0.01). The NPSI and PR-Lasso performed similarly at different levels of required specificity and outperformed the S-LANSS and PR-Logistic at the various specificity points. CONCLUSION: The PR-Lasso offers a way to discriminate a SCD pain phenotype.
BACKGROUND: We sought to refine a screening measure for discriminating a sensitized or normal sensation pain phenotype among African American adults with sickle cell disease (SCD). OBJECTIVE: To develop scoring schemes based on sensory pain quality descriptors; evaluate their performance on classifying patients with SCD who had sensitization or normal sensation, and compare with scores on the Self-report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) and the Neuropathic Pain Symptom Inventory (NPSI). METHODS:Participants completed PAINReportIt, quantitative sensory testing (QST), S-LANSS, and NPSI. Conventional binary logistic regression and least absolute shrinkage and selection operator (lasso) regression were used to obtain 2 sets of weights resulting in 2 scores: the PR-Logistic (PAINReportIt score weighted by conventional binary logistic regression coefficients) and PR-Lasso (PAINReportIt score weighted by lasso regression coefficients). Performance of the proposed scores and the existing scores were evaluated. RESULTS: Lasso regression resulted in a parsimonious model with non-zero weights assigned to 2 neuropathic descriptors, cold and spreading. We found positive correlations between the PR-Lasso and other scores: S-LANSS (r = 0.22, P < 0.01), NPSI (r = 0.22, P < 0.01), and PR-Logistic (r = 0.35, P < 0.01). The NPSI and PR-Lasso performed similarly at different levels of required specificity and outperformed the S-LANSS and PR-Logistic at the various specificity points. CONCLUSION: The PR-Lasso offers a way to discriminate a SCD pain phenotype.
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Authors: Miriam O Ezenwa; Yingwei Yao; Molly W Mandernach; David A Fedele; Robert J Lucero; Inge Corless; Brenda W Dyal; Mary H Belkin; Abhinav Rohatgi; Diana J Wilkie Journal: JMIR Res Protoc Date: 2022-07-29