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Background Many patients with rheumatoid arthritis (RA) require a trial of multiple biologic disease-modifying anti-rheumatic drugs (bDMARDs) to control their disease. With the availability of several bDMARD options, the history of bDMARDs may provide an alternative approach to understanding subphenotypes of RA. The objective of this study was to determine whether there exist distinct clusters of RA patients based on bDMARD prescription history to subphenotype RA. Methods We studied patients from a validated electronic health record-based RA cohort with data from January 1, 2008, through July 31, 2019; all subjects prescribed ≥ 1 bDMARD or targeted synthetic (ts) DMARD were included. To determine whether subjects had similar b/tsDMARD sequences, the sequences were considered as a Markov chain over the state-space of 5 classes of b/tsDMARDs. The maximum likelihood estimator (MLE)-based approach was used to estimate the Markov chain parameters to determine the clusters. The EHR data of study subjects were further linked with a registry containing prospectively collected data for RA disease activity, i.e., clinical disease activity index (CDAI). As a proof of concept, we tested whether the clusters derived from b/tsDMARD sequences correlated with clinical measures, specifically differing trajectories of CDAI. Results We studied 2172 RA subjects, mean age 52 years, RA duration 3.4 years, and 62% seropositive. We observed 550 unique b/tsDMARD sequences and identified 4 main clusters: (1) TNFi persisters (65.7%), (2) TNFi and abatacept therapy (8.0%), (3) on rituximab or multiple b/tsDMARDs (12.7%), (4) prescribed multiple therapies with tocilizumab predominant (13.6%). Compared to the other groups, TNFi persisters had the most favorable trajectory of CDAI over time. Conclusion We observed that RA subjects can be clustered based on the sequence of b/tsDMARD prescriptions over time and that the clusters were correlated with differing trajectories of disease activity over time. This study highlights an alternative approach to consider subphenotyping of patients with RA for studies aimed at understanding treatment response.

Objective Rheumatoid arthritis (RA) is a heterogeneous disease, with patients experiencing varied disease courses and responses to treatment. The objective of this study was to apply topic modeling to RA patient electronic health record (EHR) data and determine (1) the clinical topics/subgroups in those with RA before initiation of a biologic/targeted synthetic disease‐modifying antirheumatic drug (b/tsDMARD) and (2) whether the clinical topics were associated with subsequent RA treatment course. Methods We studied patients from a validated EHR‐based RA cohort who initiated a b/tsDMARD between 2011 and 2019. Diagnoses codes, laboratory data, and medication prescriptions in the year before their first b/tsDMARD initiation were extracted. Latent Dirichlet allocation, a topic modeling method, was applied to define the underlying “topics” representing clinical subgroups. We used multinomial regression to test association between the clinical topic with four previously published treatment trajectories: tumor necrosis factor inhibitor (TNFi) persisters, TNFi to abatacept, and those prescribed multiple b/tsDMARDs enriched with tocilizumab or rituximab. Results From the data of 1,102 patients with RA, diagnoses codes, laboratory data, and prescriptions from the year before b/tsDMARD initiation resulted in four main clinical topics/subgroups: (A) RA codes/methotrexate (MTX), (B) arthralgia/osteoarthritis, (C) hypertension (HTN)/cardiovascular (CV) comorbidities, and (D) mood disorders. Those with RA codes/MTX topic were more likely to persist on TNFi. Conversely, those associated with the HTN/CV topic were more likely to cycle through multiple b/tsDMARDs. Conclusion Clinical topics derived from the EHR data of patients with RA before b/tsDMARD differentiated future RA treatment course. HTN/CV comorbidities were associated with a future need for multiple b/tsDMARD therapies.

For millions of students at American colleges, freshman year starts off with an unpleasant surprise: despite graduating high school, students find themselves assigned to remedial classes in math or English, which they must pay for and pass before being allowed into college-level courses. Policymakers looking to increase postsecondary enrollment and completion have put their focus on lessening the delays created by remedial course requirements. The problem is especially acute in Tennessee, where in 2013, only one in three adults had more than a high-school diploma and two in three incoming college freshmen at local community colleges were placed in remedial classes. Studying Tennessee's experience is uniquely valuable because it provides a chance to compare two different alternatives to traditional remediation policies. First, the state began allowing students to complete their remedial math requirements while they were still in high school. Under the Seamless Alignment and Integrated Learning Support (SAILS) program, students designated as needing remediation based on their junior-year ACT math scores can enroll in an online remedial course during their senior year. In order to learn about both alternatives to prerequisite remediation, the authors look at changes in outcomes for three different waves of high schools that introduced the SAILS program between 2013 through 2016, and they compare them with outcomes at high schools that never had the program. In the first year of the program's implementation, when completing SAILS allowed students to forgo prerequisite remediation, the authors measured the impact of eliminating the delay of prerequisite college remediation. In the second and third years, after the co-requisite policy was in effect, the authors again measured the effect of SAILS participation, this time measuring the effect of eliminating co-requisite requirements. Findings suggest that both high school-based remediation like SAILS and co-requisite remediation have advantages over prerequisite college remediation. Both allow students to get a faster start and complete more credits within the first two years. In addition, co-requisite remediation also may be more successful than high-school remediation in helping students to pass their college-level math classes, by eliminating the time lag between remediation and the demands of college courses. However, the findings also suggest that the role of remedial course requirements as a cause of low completion rates has been overstated. Prerequisite remediation is neither the major cause of low completion (as many of its critics have argued) nor a major solution for students with weak math skills--the authors find no effect of SAILS participation on the math achievement of remediation-eligible students in high school, relative to the typical high-school math course.

For millions of students at American colleges, freshman year starts off with an unpleasant surprise: despite graduating high school, students find themselves assigned to remedial classes in math or English, which they must pay for and pass before being allowed into college-level courses. Given that many of these students never complete a certificate or degree, advocates have begun to refer to remediation as a “bridge to nowhere.” Thus, policymakers looking to increase postsecondary enrollment and completion have put their focus on lessening the delays created by remedial course requirements. The problem is especially acute in Tennessee, where in 2013, only one in three adults had more than a high-school diploma and two in three incoming college freshmen at local community colleges were placed in remedial classes. That year, the state launched the “Drive to 55” initiative, with the goal of increasing the number of adults with postsecondary credentials to 55 percent by 2025. It is a priority widely shared by policymakers across the country, with 41 other states working toward similarly ambitious graduation goals.

Electronic health record (EHR) data are increasingly used to support real-world evidence (RWE) studies. Yet its ability to generate reliable RWE is limited by the lack of readily available precise information on the timing of clinical events such as the onset time of heart failure. We propose a LAbel-efficienT incidenT phEnotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data. By leveraging the pre-trained semantic embedding vectors from large-scale EHR data as prior knowledge, LATTE selects predictive EHR features in a concept re-weighting module by mining their relationship to the target event and compresses their information into longitudinal visit embeddings through a visit attention learning network. LATTE employs a recurrent neural network to capture the sequential dependency between the target event and visit embeddings before/after it. To improve label efficiency, LATTE constructs highly informative longitudinal silver-standard labels from large-scale unlabeled patients to perform unsupervised pre-training and semi-supervised joint training. Finally, LATTE enhances cross-site portability via contrastive representation learning. LATTE is evaluated on three analyses: the onset of type-2 diabetes, heart failure, and the onset and relapses of multiple sclerosis. We use various evaluation metrics present in the literature including the \\(ABC_gain\\), the proportion of reduction in the area between the observed event indicator and the predicted cumulative incidences in reference to the prediction per incident prevalence. LATTE consistently achieves substantial improvement over benchmark methods such as SAMGEP and RETAIN in all settings.

Working Paper No. 26133 Many U.S. students arrive on college campus lacking the skills expected for college-level work. As state leaders seek to increase postsecondary enrollment and completion, public colleges have sought to lessen the delays created by remedial course requirements. Tennessee has taken a novel approach by allowing students to complete their remediation requirements in high school. Using both a difference-in-differences and a regression discontinuity design, we evaluate the program’s impact on college enrollment and credit accumulation, finding that the program boosted enrollment in college-level math during the first year of college and allowed students to earn a modest 4.5 additional college credits by their second year. We also report the first causal evidence on remediation's impact on students' math skills, finding that the program did not improve students’ math achievement, nor boost students’ chances of passing college math. Our findings cast doubt on the effectiveness of the current model of remediation—whether in high school or college—in improving students’ math skills. They also suggest that the time cost of remediation—whether pre-requisite or co-requisite remediation—is not the primary barrier causing low degree completion for students with weak math preparation.

Many U.S. students arrive on college campus lacking the skills expected for college-level work. As state leaders seek to increase postsecondary enrollment and completion, public colleges have sought to lessen the delays created by remedial course requirements. Tennessee has taken a novel approach by allowing students to complete their remediation requirements in high school. Using both a difference-in-differences and a regression discontinuity design, we evaluate the program’s impact on college enrollment and credit accumulation, finding that the program boosted enrollment in college-level math during the first year of college and allowed students to earn a modest 4.5 additional college credits by their second year. We also report the first causal evidence on remediation's impact on students' math skills, finding that the program did not improve students’ math achievement, nor boost students’ chances of passing college math. Our findings cast doubt on the effectiveness of the current model of remediation—whether in high school or college—in improving students’ math skills. They also suggest that the time cost of remediation—whether pre-requisite or co-requisite remediation—is not the primary barrier causing low degree completion for students with weak math preparation.