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OBJECTIVES/GOALS: Side effects are why up to 50% of women with hormone-positive breast cancer prematurely discontinue aromatase inhibitor (AI) therapy. Pain from altered nociception without clear tissue/nerve damage (nociplastic) is a hypothesized contributing factor to this. Our objective was to evaluate the relationship between nociplastic pain and AI duration. METHODS/STUDY POPULATION: Patients with breast cancer diagnosed between 2012-19 were identified from the University of Michigan Genomics Initiative (MGI). Patients who were female, >65 years old at time of breast cancer diagnosis, and had hormone receptor-positive disease met inclusion criteria. Prior to undergoing surgery, patients completed validated surveys about overall worst pain (Brief Pain Inventory [BPI]), nociplastic pain (2011 Fibromyalgia Survey [FS]), and life satisfaction, with higher scores representing more of the factor. Breast cancer history, treatment, and patient demographics were abstracted from the medical record. Univariate analysis was conducted to evaluate the relationship between age, body-mass index (BMI), chemotherapy, BPI, FS, life satisfaction, and time to discontinuation of initial AI. RESULTS/ANTICIPATED RESULTS: 207 patients were eligible, 133 of whom initiated AI therapy. Of the 133 analyzed patients, mean age was 70.7 years and mean BMI was 30.3. 28 (21%) underwent adjuvant chemotherapy and 79 (59%) received adjuvant radiation prior to initiation of AI therapy. Average nociplastic pain score was 4.0/31 (standard deviation [SD] 4.6), worst pain was 1.5/10 (SD 1.9), and life satisfaction score was 7.3/10 (SD 2.8). The initial AI for 94% of patients (125 patients) was anastrozole. On univariate analysis, only higher nociplastic pain score was statistically associated with premature discontinuation of AI with a HR 1.07 (95%CI 1.00-1.13, p = 0.036). On multivariable analysis, no factors remained statistically significant, although there was a trend for nociplastic pain (HR 1.06, 95%CI 1.00-1.13, p = 0.068). DISCUSSION/SIGNIFICANCE: It is important to identify variables predicting tolerance to therapy so patients can be optimally counseled. Our study suggests that patients with pre-existing baseline pain disorders may be more likely to be nonpersistent with AI therapy. Future study should be conducted to determine if treatments for nocipastic pain improve AI persistence.
OBJECTIVES/SPECIFIC AIMS: Peripheral neuropathy is the dose limiting toxicity of paclitaxel treatment. Paclitaxel pharmacokinetics (PK), specifically the Cmax and amount of time the concentration remains above 0.05 µM (Tc>0.05), have been associated with occurrence of severe, clinician-documented neuropathy. The objective of this study was to confirm that paclitaxel PK predicts progression of patient-reported neuropathy. METHODS/STUDY POPULATION: This observational trial enrolled breast cancer patients receiving weekly 1-hour paclitaxel infusions (80 mg/m2×12 cycles) at the University of Michigan Comprehensive Cancer Center. Paclitaxel concentration was measured via LC/MS in plasma samples collected at the end of (Cmax) and 16–24 hours after (Tc>0.05) first infusion. Patient-reported neuropathy was collected (EORTC CIPN20) at baseline and each cycle. The rate of neuropathy severity increase per treatment cycle is being modeled for each patient. Cmax and Tc>0.05 values will be introduced into the model to confirm that PK independently contributes to neuropathy progression. RESULTS/ANTICIPATED RESULTS: PK and neuropathy data have been collected from 60 patients for ongoing analysis. Our initial model will characterize the expected severity of neuropathy after each cycle of paclitaxel treatment. The PK-neuropathy model will include either PK parameter to validate their contribution to the progression of neuropathy severity during treatment. We anticipate, based on our preliminary analysis of the first 16 patients, that both PK parameters will significantly contribute to the model but Tc>0.05 will be more strongly associated with neuropathy progression. DISCUSSION/SIGNIFICANCE OF IMPACT: This project will generate a model that can be used to predict a patient’s neuropathy severity throughout treatment using a single, conveniently collected and easily measured PK sample during their first cycle. The next steps of this project include identifying genetic and metabolomic biomarkers that predict which patients experienced more severe neuropathy than would be anticipated based on their paclitaxel PK, and a planned interventional trial of personalized paclitaxel dosing to enhance efficacy and/or prevent neuropathy.
It is increasingly essential for medical researchers to be literate in statistics, but the requisite degree of literacy is not the same for every statistical competency in translational research. Statistical competency can range from ‘fundamental’ (necessary for all) to ‘specialized’ (necessary for only some). In this study, we determine the degree to which each competency is fundamental or specialized.
We surveyed members of 4 professional organizations, targeting doctorally trained biostatisticians and epidemiologists who taught statistics to medical research learners in the past 5 years. Respondents rated 24 educational competencies on a 5-point Likert scale anchored by ‘fundamental’ and ‘specialized.’
There were 112 responses. Nineteen of 24 competencies were fundamental. The competencies considered most fundamental were assessing sources of bias and variation (95%), recognizing one’s own limits with regard to statistics (93%), identifying the strengths, and limitations of study designs (93%). The least endorsed items were meta-analysis (34%) and stopping rules (18%).
We have identified the statistical competencies needed by all medical researchers. These competencies should be considered when designing statistical curricula for medical researchers and should inform which topics are taught in graduate programs and evidence-based medicine courses where learners need to read and understand the medical research literature.
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