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Post-treatment Lyme disease symptoms score: Developing a new tool for research
Some patients have residual non-specific symptoms after therapy for Lyme disease, referred to as post-treatment Lyme disease symptoms or syndrome, depending on whether there is functional impairment. A standardized test battery was used to characterize a diverse group of Lyme disease patients with a...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6844481/ https://www.ncbi.nlm.nih.gov/pubmed/31710647 http://dx.doi.org/10.1371/journal.pone.0225012 |
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author | Turk, Siu P. Lumbard, Keith Liepshutz, Kelly Williams, Carla Hu, Linden Dardick, Kenneth Wormser, Gary P. Norville, Joshua Scavarda, Carol McKenna, Donna Follmann, Dean Marques, Adriana |
author_facet | Turk, Siu P. Lumbard, Keith Liepshutz, Kelly Williams, Carla Hu, Linden Dardick, Kenneth Wormser, Gary P. Norville, Joshua Scavarda, Carol McKenna, Donna Follmann, Dean Marques, Adriana |
author_sort | Turk, Siu P. |
collection | PubMed |
description | Some patients have residual non-specific symptoms after therapy for Lyme disease, referred to as post-treatment Lyme disease symptoms or syndrome, depending on whether there is functional impairment. A standardized test battery was used to characterize a diverse group of Lyme disease patients with and without residual symptoms. There was a strong correlation between sleep disturbance and certain other symptoms such as fatigue, pain, anxiety, and cognitive complaints. Results were subjected to a Logistic Regression model using the Neuro-QoL Fatigue t-score together with Short Form-36 Physical Functioning scale and Mental Health component scores; and to a Decision Tree model using only the QoL Fatigue t-score. The Logistic Regression model had an accuracy of 97% and Decision Tree model had an accuracy of 93%, when compared with clinical categorization. The Logistic Regression and Decision Tree models were then applied to a separate cohort. Both models performed with high sensitivity (90%), but moderate specificity (62%). The overall accuracy was 74%. Agreement between 2 time points, separated by a mean of 4 months, was 89% using the Decision Tree model and 87% with the Logistic Regression model. These models are simple and can help to quantitate the level of symptom severity in post-treatment Lyme disease symptoms. More research is needed to increase the specificity of the models, exploring additional approaches that could potentially strengthen an operational definition for post-treatment Lyme disease symptoms. Evaluation of how sleep disturbance, fatigue, pain and cognitive complains interrelate can potentially lead to new interventions that will improve the overall health of these patients. |
format | Online Article Text |
id | pubmed-6844481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-68444812019-11-15 Post-treatment Lyme disease symptoms score: Developing a new tool for research Turk, Siu P. Lumbard, Keith Liepshutz, Kelly Williams, Carla Hu, Linden Dardick, Kenneth Wormser, Gary P. Norville, Joshua Scavarda, Carol McKenna, Donna Follmann, Dean Marques, Adriana PLoS One Research Article Some patients have residual non-specific symptoms after therapy for Lyme disease, referred to as post-treatment Lyme disease symptoms or syndrome, depending on whether there is functional impairment. A standardized test battery was used to characterize a diverse group of Lyme disease patients with and without residual symptoms. There was a strong correlation between sleep disturbance and certain other symptoms such as fatigue, pain, anxiety, and cognitive complaints. Results were subjected to a Logistic Regression model using the Neuro-QoL Fatigue t-score together with Short Form-36 Physical Functioning scale and Mental Health component scores; and to a Decision Tree model using only the QoL Fatigue t-score. The Logistic Regression model had an accuracy of 97% and Decision Tree model had an accuracy of 93%, when compared with clinical categorization. The Logistic Regression and Decision Tree models were then applied to a separate cohort. Both models performed with high sensitivity (90%), but moderate specificity (62%). The overall accuracy was 74%. Agreement between 2 time points, separated by a mean of 4 months, was 89% using the Decision Tree model and 87% with the Logistic Regression model. These models are simple and can help to quantitate the level of symptom severity in post-treatment Lyme disease symptoms. More research is needed to increase the specificity of the models, exploring additional approaches that could potentially strengthen an operational definition for post-treatment Lyme disease symptoms. Evaluation of how sleep disturbance, fatigue, pain and cognitive complains interrelate can potentially lead to new interventions that will improve the overall health of these patients. Public Library of Science 2019-11-11 /pmc/articles/PMC6844481/ /pubmed/31710647 http://dx.doi.org/10.1371/journal.pone.0225012 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Turk, Siu P. Lumbard, Keith Liepshutz, Kelly Williams, Carla Hu, Linden Dardick, Kenneth Wormser, Gary P. Norville, Joshua Scavarda, Carol McKenna, Donna Follmann, Dean Marques, Adriana Post-treatment Lyme disease symptoms score: Developing a new tool for research |
title | Post-treatment Lyme disease symptoms score: Developing a new tool for research |
title_full | Post-treatment Lyme disease symptoms score: Developing a new tool for research |
title_fullStr | Post-treatment Lyme disease symptoms score: Developing a new tool for research |
title_full_unstemmed | Post-treatment Lyme disease symptoms score: Developing a new tool for research |
title_short | Post-treatment Lyme disease symptoms score: Developing a new tool for research |
title_sort | post-treatment lyme disease symptoms score: developing a new tool for research |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6844481/ https://www.ncbi.nlm.nih.gov/pubmed/31710647 http://dx.doi.org/10.1371/journal.pone.0225012 |
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