文章基本信息

标题：Antibiotic Treatment Response in Chronic Lyme Disease: Why Do Some Patients Improve While Others Do Not?
本地全文：下载
作者：Lorraine Johnson ; Mira Shapiro ; Raphael B. Stricker 等
期刊名称：Healthcare
电子版ISSN：2227-9032
出版年度：2020
卷号：8
期号：4
页码：383-403
DOI：10.3390/healthcare8040383
出版社：MDPI Publishing
摘要：There is considerable uncertainty regarding treatment of Lyme disease patients who do not respond fully to initial short-term antibiotic therapy. Choosing the best treatment approach and duration remains challenging because treatment response among these patients varies: some patients improve with treatment while others do not. A previous study examined treatment response variation in a sample of over 3500 patients enrolled in the MyLymeData patient registry developed by LymeDisease.org (San Ramon, CA, USA). That study used a validated Global Rating of Change (GROC) scale to identify three treatment response subgroups among Lyme disease patients who remained ill: nonresponders, low responders, and high responders. The present study first characterizes the health status, symptom severity, and percentage of treatment response across these three patient subgroups together with a fourth subgroup, patients who identify as well. We then employed machine learning techniques across these subgroups to determine features most closely associated with improved patient outcomes, and we used traditional statistical techniques to examine how these features relate to treatment response of the four groups. High treatment response was most closely associated with (1) the use of antibiotics or a combination of antibiotics and alternative treatments, (2) longer duration of treatment, and (3) oversight by a clinician whose practice focused on the treatment of tick-borne diseases.
关键词：Lyme disease; MyLymeData; Borrelia burgdorferi; Tickborne disease; machine learning; Global Rating of Change Scale; Likert scale; precision medicine; big data Lyme disease ; MyLymeData ; Borrelia burgdorferi ; Tickborne disease ; machine learning ; Global Rating of Change Scale ; Likert scale ; precision medicine ; big data