(1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for predicting possible disease flares. (2) Methods: Participants were recruited from the LupusCorner online community. Adults self-reporting an SLE diagnosis were consented and given a mobile application to record patient profile (PP), PRO, and QOL metrics, and enlisted participants received smartwatches for digital biometric monitoring. The resulting data were profiled using feature selection and classification algorithms. (3) Results: 550 participants completed digital surveys, 144 (26%) agreed to wear smartwatches, and medical records (MRs) were obtained for 68. Mining of PP, PRO, QOL, and biometric data yielded a 26-feature model for classifying participants according to MR-identified disease flare risk. ROC curves significantly distinguished true from false positives (ten-fold cross-validation: p < 0.00023; five-fold: p < 0.00022). A 25-feature Bayesian model enabled time-variant prediction of participant-reported possible flares (P(true) > 0.85, p < 0.001; P(nonflare) > 0.83, p < 0.0001). (4) Conclusions: Regular profiling of patient well-being and biometric activity may support proactive screening for circumstances warranting clinical assessment.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that poses serious long-term patient burdens. (1) Background: SLE patient classification and care are often complicated by case heterogeneity (diverse variations in symptoms and severity). Large language models (LLMs) and generative artificial intelligence (genAI) may mitigate this challenge by profiling medical records to assess key medical criteria. (2) Methods: To demonstrate genAI-based profiling, ACR (American College of Rheumatology) 1997 SLE classification criteria were used to define medically relevant LLM prompts. Records from 78 previously studied patients (45 classified as having SLE; 33 indeterminate or negative) were computationally profiled, via five genAI replicate runs. (3) Results: GenAI determinations of the “Discoid Rash” and “Pleuritis or Pericarditis” classification criteria yielded perfect concurrence with clinical classification, while some factors such as “Immunologic Disorder” (56% accuracy) were statistically unreliable. Compared to clinical classification, our genAI approach achieved a 72% predictive success rate. (4) Conclusions: GenAI classifications may prove sufficiently predictive to aid medical professionals in evaluating SLE patients and structuring care strategies. For individual criteria, accuracy seems to correlate inversely with complexities in clinical determination, implying that improvements in AI patient profiling tools may emerge from continued advances in clinical classification efficacy.

We report the design of a pilot study employing clinic visits and a synergized digital, health coaching, and biomarker platform (aiSLE® MGMT [Management]) aimed at improving patient self-efficacy and patient-physician interactions to mitigate heightened disease activity, curtail clinical flares, and reduce potential long-term organ damage in systemic lupus erythematosus (SLE). This 12-month study will utilize a longitudinal cross-over design. Adult participants with confirmed SLE will be recruited and appropriately consented at five community-based US rheumatology clinics. Board certified rheumatologists will perform an exam, documenting the SLE Physician Global assessment (PGA) at Baseline, 3-, 6- and 9-month visits. Blood samples will be collected for completion of a newly developed and validated Lupus Flare Risk Index (L-FRI) that predicts the risk of developing a clinical flare in the next 12 weeks. The digital component of the study will be initiated following the 3-month visit. Participants will be given a mobile study app interfaced with a smartwatch to record activity, heart rate, and sleep quality. The app will be used to administer patient-reported outcome (PRO) surveys and health coaching. In the first three months participants and their rheumatologists will be blinded to L-FRI results; all L-FRI and digital results will be available starting in month four. This prospective pilot study will assess the impact of a comprehensive disease management platform employing a novel blood biomarker test, a mobile app interfaced with a smartwatch to collect biometric data, and app-based video health coaching on self-efficacy, clinical decisions, and outcomes in SLE disease management.

Background/Objectives: In clinical informatics, the term ‘information overload’ is increasingly used to describe the operational impediments of excessive documentation. While electronic health records (EHRs) are growing in abundance, many medical records (MRs) remain in legacy formats that impede efficient, systematic processing, contributing to the extenuating challenges of care fragmentation. Thus, there is a growing interest in using generative AI (genAI) for automated MR summarization and characterization. Methods: MRs for a set of 78 individuals were digitized. Some were known systemic lupus erythematosus (SLE) cases, while others were under evaluation for possible SLE classification. A two-pass genAI assessment strategy was implemented using the Claude 3.5 large language model (LLM) to mine MRs for information relevant to classifying SLE vs. undifferentiated connective tissue disorder (UCTD) vs. neither via the 22-criteria EULAR 2019 model. Results: Compared to clinical determination, the antinuclear antibody (ANA) criterion (whose results are crucial for classifying SLE-negative cases) exhibited favorable sensitivity 0.78 ± 0.09 (95% confidence interval) and a positive predictive value 0.85 ± 0.08 but a marginal performance for specificity 0.60 ± 0.11 and uncertain predictivity for the negative predictive value 0.48 ± 0.11. Averaged over the remaining 21 criteria, these four performance metrics were 0.69 ± 0.11, 0.87 ± 0.04, 0.54 ± 0.10, and 0.93 ± 0.03. Conclusions: ANA performance statistics imply that genAI yields confident assessments of SLE negativity (per high sensitivity) but weaker positivity. The remaining genAI criterial determinations support (per specificity) confident assertions of SLE-positivity but tend to misclassify a significant fraction of clinical positives as UCTD.

9508 Background: The proportion of breast cancer attributable to germline mutations in highly penetrant genes (e.g., BRCA1/2) is small because of the low frequency of these mutations. Common, but weakly penetrant, functional genetic polymorphisms are likely to account for most of the genetic risk for breast cancer in the general population. Methods: Potential gene-gene interactions among ten genes, with known or predicted functional consequences in development of breast carcinoma, were examined in 1050 cases and 1845 controls of Caucasian descent. Association of breast cancer risk with variation in single genes and two- and three- gene combinations was analyzed for two age groups: 53 years and under, and over 53 years. The odds ratio (OR) for a genotype was calculated and compared to a null distribution of ORs generated for this genotype by randomizing (10,000 times in these analyses) the case-control status of the individuals in the sample to give an empirical estimate for the p-value of the observed OR....