Lorraine Johnson

Background/Objectives: Although eligibility criteria for clinical trials significantly impact study outcomes, these criteria are often established without scientific justification, leading to delayed recruitment, small sample sizes, and limited study generalizability. Persistent Lyme disease (PLD) presents unique challenges due to symptom variability, inconsistent treatment responses, and the lack of reliable biomarkers, underscoring the need for scientifically justified eligibility criteria. Objective: This study examines the effects of commonly used enrollment criteria on sample yield in PLD clinical trials using real-world data (RWD) from the MyLymeData patient registry. The study also compares the effects of these criteria on enrollment for PLD versus acute Lyme disease (ALD) trials and evaluates the scientific rationale for each criterion. Methods: Data from 4183 Lyme disease patients enrolled in the MyLymeData registry were analyzed to assess the prevalence and cumulative impact of various criteria on sample yield. A comparative analysis of cohorts with PLD (n = 3589) versus ALD (n = 594) was conducted to identify differences in sample attrition. Results: In a large PLD cohort study, we found that current commonly used eligibility criteria would exclude approximately 90% of patients, significantly limiting study generalizability. Substantial differences in sample attrition between PLD and ALD cohorts highlight the need for tailored criteria. The strength of scientific justification varied widely among criteria. Conclusions: This study demonstrates the importance of using RWD to optimize eligibility criteria in PLD clinical trials. By providing insights into the balance between sample attrition and scientific justification, researchers can enhance trial feasibility, generalizability, and robustness. Our RWD sample demonstrates that researchers could substantially increase the sample yield from 10% to 64% by loosening restrictions on coinfections and misdiagnoses of chronic fatigue syndrome, fibromyalgia syndrome, and psychiatric conditions.

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.

Patients with persistent Lyme disease/chronic Lyme disease (PLD/CLD) encounter significant barriers to accessing medical care. Although this health inequity has been explored from the patient perspective, the obstacles clinicians encounter when providing care to this group of patients have not been examined. The primary goal of this study was to identify the challenges faced by clinicians who provide care for patients with PLD/CLD. Clinicians who treat PLD/CLD were surveyed regarding their professional backgrounds, general challenges to providing care, supply and demand constraints, insurance restrictions, and regulatory and legal challenges. Clinicians treating patients with PLD/CLD have developed substantial clinical expertise but encounter multiple clinical, regulatory and financial impediments to providing care. Clinician-encountered barriers may be powerful disincentives for providing care patients with PLD/CLD and make it difficult to retain and recruit clinicians who will care for the rapidly expanding PLD/CLD populations. Understanding these barriers and identifying potential solutions is essential to resolving the current supply/demand imbalance that makes it difficult for patients to receive the care they need to become well.

Governance structures allow multi-stakeholder research teams to establish goals, implement strategies, make decisions, and provide project stewardship. This chapter describes approaches to governance and decision-making in participatory health research. Topics include governance goals and processes, challenges, and principles that guide implementation. The chapter presents a series of case studies to illustrate governance solutions across a range of ...

Lyme disease is a rapidly growing illness that remains poorly understood within the medical community. Critical questions about when and why patients respond to treatment or stay ill, what kinds of treatments are effective, and even how to properly diagnose the disease remain largely unanswered. We investigate these questions by applying machine learning techniques to a large scale Lyme disease patient registry, MyLymeData, developed by the nonprofit LymeDisease.org. We apply various machine learning methods in order to measure the effect of individual features in predicting participants’ answers to the Global Rating of Change (GROC) survey questions that assess the self-reported degree to which their condition improved, worsened, or remained unchanged following antibiotic treatment. We use basic linear regression, support vector machines, neural networks, entropy-based decision tree models, and k-nearest neighbors approaches. We first analyze the general performance of the model and then identify the most important features for predicting participant answers to GROC. After we identify the “key” features, we separate them from the dataset and demonstrate the effectiveness of these features at identifying GROC. In doing so, we highlight possible directions for future study both mathematically and clinically.

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.

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.

Lyme disease caused by the spirochete Borrelia burgdorferi has become a major worldwide epidemic. Recent studies based on Big Data registries show that >300,000 people are diagnosed with Lyme disease each year in the USA, and up to two-thirds of individuals infected with B. burgdorferi will fail conventional 30-year-old antibiotic therapy for Lyme disease. In addition, animal and human evidence suggests that sexual transmission of the Lyme spirochete may occur. Improved companion diagnostic tests for Lyme disease need to be implemented, and novel treatment approaches are urgently needed to combat the epidemic. In particular, therapies based on the principles of precision medicine could be modeled on successful “designer drug” treatment for HIV/AIDS and hepatitis C virus infection featuring targeted protease inhibitors. The use of Big Data registries, companion diagnostics and precision medicine will revolutionize the diagnosis and treatment of Lyme disease.

Evidence-based guidelines for the management of patients with Lyme disease were developed by the International Lyme and Associated Diseases Society (ILADS). The guidelines address three clinical questions – the usefulness of antibiotic prophylaxis for known tick bites, the effectiveness of erythema migrans treatment and the role of antibiotic retreatment in patients with persistent manifestations of Lyme disease. Healthcare providers who evaluate and manage patients with Lyme disease are the intended users of the new ILADS guidelines, which replace those issued in 2004 (Exp Rev Anti-infect Ther 2004;2:S1–13). These clinical practice guidelines are intended to assist clinicians by presenting evidence-based treatment recommendations, which follow the Grading of Recommendations Assessment, Development and Evaluation system. ILADS guidelines are not intended to be the sole source of guidance in managing Lyme disease and they should not be viewed as a substitute for clinical judgment nor used to establish trea...