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...

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Main Authors: Siu P Turk, Keith Lumbard, Kelly Liepshutz, Carla Williams, Linden Hu, Kenneth Dardick, Gary P Wormser, Joshua Norville, Carol Scavarda, Donna McKenna, Dean Follmann, Adriana Marques
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0225012
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spelling doaj-9ce3eee6fff0492b9936bccd41942d732021-03-04T11:21:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-011411e022501210.1371/journal.pone.0225012Post-treatment Lyme disease symptoms score: Developing a new tool for research.Siu P TurkKeith LumbardKelly LiepshutzCarla WilliamsLinden HuKenneth DardickGary P WormserJoshua NorvilleCarol ScavardaDonna McKennaDean FollmannAdriana MarquesSome 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.https://doi.org/10.1371/journal.pone.0225012
collection DOAJ
language English
format Article
sources DOAJ
author Siu P Turk
Keith Lumbard
Kelly Liepshutz
Carla Williams
Linden Hu
Kenneth Dardick
Gary P Wormser
Joshua Norville
Carol Scavarda
Donna McKenna
Dean Follmann
Adriana Marques
spellingShingle Siu P Turk
Keith Lumbard
Kelly Liepshutz
Carla Williams
Linden Hu
Kenneth Dardick
Gary P Wormser
Joshua Norville
Carol Scavarda
Donna McKenna
Dean Follmann
Adriana Marques
Post-treatment Lyme disease symptoms score: Developing a new tool for research.
PLoS ONE
author_facet Siu P Turk
Keith Lumbard
Kelly Liepshutz
Carla Williams
Linden Hu
Kenneth Dardick
Gary P Wormser
Joshua Norville
Carol Scavarda
Donna McKenna
Dean Follmann
Adriana Marques
author_sort Siu P Turk
title 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_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_sort post-treatment lyme disease symptoms score: developing a new tool for research.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2019-01-01
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.
url https://doi.org/10.1371/journal.pone.0225012
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