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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Healthcare
2018
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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. |
format | Online Article Text |
id | pubmed-6182642 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer Healthcare |
record_format | MEDLINE/PubMed |
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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