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Predicting Responses to Pregabalin for Painful Diabetic Peripheral Neuropathy Based on Trajectory-Focused Patient Profiles Derived from the First 4 Weeks of Treatment

INTRODUCTION: Prediction of final clinical outcomes based on early weeks of treatment can enable more effective patient care for chronic pain. Our goal was to predict, with at least 90% accuracy, 12- to 13-week outcomes for pregabalin-treated painful diabetic peripheral neuropathy (pDPN) patients ba...

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Autores principales: Edwards, Roger A., Bonfanti, Gianluca, Grugni, Roberto, Manca, Luigi, Parsons, Bruce, Alexander, Joe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Healthcare 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6182642/
https://www.ncbi.nlm.nih.gov/pubmed/30206821
http://dx.doi.org/10.1007/s12325-018-0780-3
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author Edwards, Roger A.
Bonfanti, Gianluca
Grugni, Roberto
Manca, Luigi
Parsons, Bruce
Alexander, Joe
author_facet Edwards, Roger A.
Bonfanti, Gianluca
Grugni, Roberto
Manca, Luigi
Parsons, Bruce
Alexander, Joe
author_sort Edwards, Roger A.
collection PubMed
description INTRODUCTION: Prediction of final clinical outcomes based on early weeks of treatment can enable more effective patient care for chronic pain. Our goal was to predict, with at least 90% accuracy, 12- to 13-week outcomes for pregabalin-treated painful diabetic peripheral neuropathy (pDPN) patients based on 4 weeks of pain and pain-related sleep interference data. METHODS: We utilized active treatment data from six placebo-controlled randomized controlled trials (n = 939) designed to evaluate efficacy of pregabalin for reducing pain in patients with pDPN. We implemented a three-step, trajectory-focused analytics approach based upon patient responses collected during the first 4 weeks using monotonicity, path length, frequency domain (FD), and k-nearest neighbor (kNN) methods. The first two steps were based on combinations of baseline pain, pain at 4 weeks, weekly monotonicity and path length during the first 4 weeks, and assignment of patients to one of four responder groups (based on presence/absence of 50% or 30% reduction from baseline pain at 4 and at 12/13 weeks). The third step included agreement between prediction of logistic regression of daily FD amplitudes and assignment made from kNN analyses. RESULTS: Step 1 correctly assigned 520/939 patients from the six studies to a responder group using a 3-metric combination approach based on unique assignment to a 50% responder group. Step 2 (applied to the remaining 419 patients) predicted an additional 121 patients, using a blend of 50% and 30% responder thresholds. Step 3 (using a combination of FD and kNN analyses) predicted 204 of the remaining 298 patients using the 50% responder threshold. Our approach correctly predicted 90.0% of all patients. CONCLUSION: By correctly predicting 12- to 13-week responder outcomes with 90% accuracy based on responses from the first month of treatment, we demonstrated the value of trajectory measures in predicting pDPN patient response to pregabalin. TRIAL REGISTRATION: www.clinicaltrials.gov identifiers, NCT00156078/NCT00159679/NCT00143156/NCT00553475. FUNDING: Pfizer. PLAIN LANGUAGE SUMMARY: Plain language summary available for this article. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12325-018-0780-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-61826422018-10-24 Predicting Responses to Pregabalin for Painful Diabetic Peripheral Neuropathy Based on Trajectory-Focused Patient Profiles Derived from the First 4 Weeks of Treatment Edwards, Roger A. Bonfanti, Gianluca Grugni, Roberto Manca, Luigi Parsons, Bruce Alexander, Joe Adv Ther Original Research INTRODUCTION: Prediction of final clinical outcomes based on early weeks of treatment can enable more effective patient care for chronic pain. Our goal was to predict, with at least 90% accuracy, 12- to 13-week outcomes for pregabalin-treated painful diabetic peripheral neuropathy (pDPN) patients based on 4 weeks of pain and pain-related sleep interference data. METHODS: We utilized active treatment data from six placebo-controlled randomized controlled trials (n = 939) designed to evaluate efficacy of pregabalin for reducing pain in patients with pDPN. We implemented a three-step, trajectory-focused analytics approach based upon patient responses collected during the first 4 weeks using monotonicity, path length, frequency domain (FD), and k-nearest neighbor (kNN) methods. The first two steps were based on combinations of baseline pain, pain at 4 weeks, weekly monotonicity and path length during the first 4 weeks, and assignment of patients to one of four responder groups (based on presence/absence of 50% or 30% reduction from baseline pain at 4 and at 12/13 weeks). The third step included agreement between prediction of logistic regression of daily FD amplitudes and assignment made from kNN analyses. RESULTS: Step 1 correctly assigned 520/939 patients from the six studies to a responder group using a 3-metric combination approach based on unique assignment to a 50% responder group. Step 2 (applied to the remaining 419 patients) predicted an additional 121 patients, using a blend of 50% and 30% responder thresholds. Step 3 (using a combination of FD and kNN analyses) predicted 204 of the remaining 298 patients using the 50% responder threshold. Our approach correctly predicted 90.0% of all patients. CONCLUSION: By correctly predicting 12- to 13-week responder outcomes with 90% accuracy based on responses from the first month of treatment, we demonstrated the value of trajectory measures in predicting pDPN patient response to pregabalin. TRIAL REGISTRATION: www.clinicaltrials.gov identifiers, NCT00156078/NCT00159679/NCT00143156/NCT00553475. FUNDING: Pfizer. PLAIN LANGUAGE SUMMARY: Plain language summary available for this article. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12325-018-0780-3) contains supplementary material, which is available to authorized users. Springer Healthcare 2018-09-11 2018 /pmc/articles/PMC6182642/ /pubmed/30206821 http://dx.doi.org/10.1007/s12325-018-0780-3 Text en © The Author(s) 2018 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Research
Edwards, Roger A.
Bonfanti, Gianluca
Grugni, Roberto
Manca, Luigi
Parsons, Bruce
Alexander, Joe
Predicting Responses to Pregabalin for Painful Diabetic Peripheral Neuropathy Based on Trajectory-Focused Patient Profiles Derived from the First 4 Weeks of Treatment
title Predicting Responses to Pregabalin for Painful Diabetic Peripheral Neuropathy Based on Trajectory-Focused Patient Profiles Derived from the First 4 Weeks of Treatment
title_full Predicting Responses to Pregabalin for Painful Diabetic Peripheral Neuropathy Based on Trajectory-Focused Patient Profiles Derived from the First 4 Weeks of Treatment
title_fullStr Predicting Responses to Pregabalin for Painful Diabetic Peripheral Neuropathy Based on Trajectory-Focused Patient Profiles Derived from the First 4 Weeks of Treatment
title_full_unstemmed Predicting Responses to Pregabalin for Painful Diabetic Peripheral Neuropathy Based on Trajectory-Focused Patient Profiles Derived from the First 4 Weeks of Treatment
title_short Predicting Responses to Pregabalin for Painful Diabetic Peripheral Neuropathy Based on Trajectory-Focused Patient Profiles Derived from the First 4 Weeks of Treatment
title_sort predicting responses to pregabalin for painful diabetic peripheral neuropathy based on trajectory-focused patient profiles derived from the first 4 weeks of treatment
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6182642/
https://www.ncbi.nlm.nih.gov/pubmed/30206821
http://dx.doi.org/10.1007/s12325-018-0780-3
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