Lyme disease is caused by the bacteria borrelia burgdorferi and is spread primarily through the bite of a tick. There is considerable uncertainty in the medical community regarding the best approach to treating patients with Lyme disease who do not respond fully to short-term antibiotic therapy. These patients have persistent Lyme disease symptoms resulting from lack of treatment, under-treatment, or lack of response to their antibiotic treatment protocol. In the past, treatment trials have used small restrictive samples and relied on average treatment effects as their measure of success and produced conflicting results. To provide individualized care, clinicians need information that reflects their patient population. Today, we have the ability to analyze large data bases, including patient registries, that reflect the broader range of patients more typically seen in clinical practice. This allows us to examine treatment variation within the sample and identify groups of patients that are most responsive to treatment. Using patient-reported outcome data from the MyLymeData online patient registry, we show that sub-group analysis techniques can unmask valuable information that is hidden if averages alone are used. In our analysis, this approach revealed treatment effectiveness for up to a third of patients with Lyme disease. This study is important because it can help open the door to more individualized patient care using patient-centered outcomes and real-world evidence.

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.

Background Biological sex should be included as an important variable in clinical research studies to identify outcome differences between men and women. Very few Lyme disease studies were designed to consider sex-based differences or gender bias as an important component of the research design. Methods To assess sex-based differences in Lyme disease patients who were clinically diagnosed and reported remaining ill for six or more months after receiving antibiotic treatment, we analyzed self-reported clinical data from 2170 patients in the MyLymeData patient registry. We also reviewed previous Lyme disease studies for distribution of patients by biological sex according to stage of illness, data source, and definition of disease used as enrollment criteria. Results In MyLymeData, women reported more tick-borne coinfections, worse symptoms, longer diagnostic delays, more misdiagnoses, and worse functional impairment than men. No differences were reported in antibiotic treatment response or side effects. In our review, of clinical research trials and data sources, we identified a smaller percentage of women in studies of acute Lyme disease and a larger percentage of women in studies of persistent illness. Samples and data sources that were more reflective of patients seen in clinical practice had a higher percentage of women than randomized controlled trials and post-treatment Lyme disease studies. Conclusion Our results indicate that biological sex should be integrated into Lyme disease research as a distinct variable. Future Lyme disease studies should include sex-based disaggregated data to illuminate differences that may exist between men and women with persistent illness.