Nationwide Electronic Health Record Research Articles

Response- and Progression-Based End Points in Trial and Observational Cohorts of Patients With NSCLC

Response Evaluation Criteria in Solid Tumors (RECIST) are commonly used to assess therapeutic response in clinical trials but not in routine care; thus, RECIST-based end points are difficult to include in observational studies. Clinician-anchored approaches for measuring clinical response have been validated but not widely compared with clinical trial data, limiting their use as evidence for clinical decision-making. To compare response- and progression-based end points in clinical trial and observational cohorts of patients with non-small cell lung cancer (NSCLC). This retrospective cohort study used patient-level data from the IMpower132 trial (conducted April 7, 2016, to May 31, 2017) and a nationwide electronic health record (EHR)-derived deidentified database (data collected January 1, 2011, to March 31, 2022). Patients in the observational cohort were selected according to the inclusion and exclusion criteria of the IMpower132 trial. All patients in the observational cohort had stage IV NSCLC. All patients were randomized to or received first-line carboplatin or cisplatin plus pemetrexed. End points included response rates, duration of response, and progression-free survival, compared between the trial and observational cohorts before and after weighting. Response rates for the observational cohort were derived from the EHR. A total of 769 patients met inclusion criteria, 494 in the observational cohort (median [IQR] age, 67 [60-74] years; 228 [46.2%] female; 45 [9.1%] Black or African American; 352 [71.3%] White; 53 [10.7%] American Indian or Alaska Native, Asian, Hawaiian or Pacific Islander, or multiracial) and 275 in the trial cohort (median [IQR] age, 63 [56-68] years; 90 [32.7%] female; 4 [1.5%] Black or African American; 194 [70.5%] White; 65 [23.6%] American Indian or Alaska Native, Asian, Hawaiian or Pacific Islander, or multiracial). All 3 end points were comparable between the study cohorts. Trial patients had a higher number of response assessments compared with patients in the weighted observational cohort. The EHR-derived response rate was numerically higher than the objective response rate after weighting (100.3 of 249.3 [40.2%] vs 105 of 275 [38.2%]) due to higher rates of observed partial response than RECIST-based partial response. Among patients with at least 1 response assessment, the EHR-derived response rate remained higher than the objective response rate (100.3 of 193.4 [51.9%] vs 105 of 256 [41.0%]) due to a higher proportion of patients in the observational cohort with no response assessment. In this study, response- and progression-based end points were similar between clinical trial and weighted observational cohorts, which increases confidence in the reliability of observational end points and can inform their interpretation in relation to trial end points. Additionally, the difference observed in response rates (including vs excluding patients with no response assessment) highlights the importance of future research adopting this 2-way approach when evaluating the relationship of EHR-derived and objective response rates.

User Personas for a "Better Design" of Nation-Wide EHRs Based on Thorough Expert Evaluation and Field Analysis: Modeling Users as Individuals Plus Family Members for an Enhanced Mapping of Healthcare Situations.

Electronic Health Records (EHRs) are pivotal in prevention, therapy, and care. Their design necessitates the representation of users, activities, context, and technology. Among various participative and ethnographic design methods, user personas are an effective tool for encapsulating users in the design process. Goals and methods: This research focused on the creation of user personas for the design of a nation-wide EHR, specifically the German "elektronische Patientenakte" (ePA). We employed qualitative methods, such as field analysis and expert workshops, to generate, assess, and refine a set of user personas that can cover the complexity of real-life familial care environments. We used an innovative bottom-up approach applying a whole new process for persona generation especially in the context of family management. Results: The research yielded an initial set of five personas that accurately represent fictional user types. Importantly, at least two of these personas encapsulate the unique challenges inherent in family care work. These results provide a foundation for future work, which can utilize these personas for EHR design, as well as for further evaluation and refinement.

Racial Differences in Cutaneous Events Among Patients Receiving Enfortumab Vedotin

IntroductionEnfortumab vedotin (EV) is an antibody–drug conjugate approved alone and in combination with pembrolizumab for advanced urothelial cancer (UC). EV-related-cutaneous-events (EVCEs) are common and rarely life-threatening. Black patients are frequently under-represented in oncology trials, and dermatologic conditions may vary with race. MethodsTherefore, this retrospective analysis investigated differences in EVCE frequency between Black and White patients in an urban cohort (Johns Hopkins [JH]) and a US-based, nationwide electronic health record (EHR)-derived deidentified database (Flatiron Health [FH]) with sub-group analysis of those who had received prior pembrolizumab. ResultsThe study included 12 Black patients in the JH Cohort (17.1%) and 24 Black patients in the FH Cohort (7.6%). In both cohorts, the frequency of EVCEs among Black patients was higher compared to White patients (JH: 66.7% vs. 33.3%; FH: 25.0% vs. 15.8%), though not statistically significant. In the larger FH Cohort EVCEs were significantly more common among Black compared to White patients treated with prior pembrolizumab (Odds Ratio [OR]: 4.76 [95%CI: 1.42, 15.95]) and recent pembrolizumab (within 90 days of EV initiation) (OR 9.00 [95%CI: 1.94, 41.66]). ConclusionThis hypothesis-generating retrospective study, comprising the largest population of EV-treated Black patients reported to date, emphasizes the importance of attentiveness to EVCEs among Black patients, particularly with receipt of pembrolizumab.

Broadening Eligibility Criteria and Diversity among Patients for Cancer Clinical Trials

BackgroundCertain populations have been historically underrepresented in clinical trials. Broadening eligibility criteria is one approach to inclusive clinical research and achieving enrollment goals. How broadened trial eligibility criteria affect the diversity of eligible participants is unknown.MethodsUsing a nationwide electronic health record–derived deidentified database, we identified a retrospective cohort of patients diagnosed with 22 cancer types between April 1, 2013 and December 31, 2022 who received systemic therapy (N=235,234) for cancer. We evaluated strict versus broadened eligibility criteria using performance status and liver, kidney, and hematologic function around first line of therapy. We performed logistic regression to estimate odds ratios for exclusion by strict criteria and their association with measures of patient diversity, including sex, age, race or ethnicity, and area-level socioeconomic status (SES); estimated the impact of broadening criteria on the number and distribution of eligible patients; and performed Cox regression to estimate hazard ratios for real-world overall survival (rwOS) comparing patients meeting strict versus broadened criteria.ResultsWhen applying common strict cutoffs for eligibility criteria to patients with complete data and weighting each cancer type equally, 48% of patients were eligible for clinical trials. Female (odds ratio, 1.30; 95% confidence interval [CI], 1.25 to 1.35), older (age 75+ vs. 18 to 49 years old: odds ratio, 3.04; 95% CI, 2.85 to 3.24), Latinx (odds ratio, 1.46; 95% CI, 1.39 to 1.54), non-Latinx Black (odds ratio, 1.11; 95% CI, 1.06 to 1.16), and lower-SES patients were more likely to be excluded using strict eligibility criteria. Broadening criteria increased the number of eligible patients by 78%, with the strongest impact for older, female, non-Latinx Black, and lower-SES patients. Patients who met only broadened criteria had worse rwOS versus those with strict criteria (hazard ratio, 1.31; 95% CI, 1.27 to 1.34).ConclusionsData-driven evaluation of clinical trial eligibility criteria may optimize the eligibility of certain historically underrepresented groups and promote access to more inclusive trials. (Sponsored by Flatiron Health.)

A matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes.

Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history. It is of great interest for clinicians and statisticians to identify an optimal dynamic treatment regime that can produce the best expected clinical outcome for each individual and thus maximize the treatment benefit over the population. Observational data impose various challenges for using statistical tools to estimate optimal dynamic treatment regimes. Notably, the task becomes more sophisticated when the clinical outcome of primary interest is time-to-event. Here, we propose a matching-based machine learning method to identify the optimal dynamic treatment regime with time-to-event outcomes subject to right-censoring using electronic health record data. In contrast to the established inverse probability weighting-based dynamic treatment regime methods, our proposed approach provides better protection against model misspecification and extreme weights in the context of treatment sequences, effectively addressing a prevalent challenge in the longitudinal analysis of electronic health record data. In simulations, the proposed method demonstrates robust performance across a range of scenarios. In addition, we illustrate the method with an application to estimate optimal dynamic treatment regimes for patients with advanced non-small cell lung cancer using a real-world, nationwide electronic health record database from Flatiron Health.

Socioeconomic Disparities in Glaucoma Severity at Initial Diagnosis: A Nationwide Electronic Health Record Cohort Analysis

PurposeTo assess disparities in initial disease severity among open-angle glaucoma (OAG) patients. DesignCross-sectional study. MethodsIn this analysis of Epic Cosmos, an aggregated electronic health record dataset encompassing >213 million patients, OAG patients examined in ophthalmology or optometry clinics between January 1, 2013 and June 1, 2023 were evaluated. OAG severity at presentation was classified as mild, moderate, or severe using International Classification of Disease-10 codes. Demographics, Social Vulnerability Index (SVI) scores, and Rural-Urban Commuting Area codes were evaluated as predictors of disease stage using ordinal logistic regression. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. ResultsOf 245,669 patients, 38.1% had mild, 32.5% moderate, and 29.3% severe disease at presentation. In multivariable analyses, significant determinants of worse severity included older age (OR: 1.23 per decade, 95% CI: 1.22-1.23), male sex (OR: 1.37, 95% CI: 1.35-1.39), Black race (OR: 1.61, 95% CI: 1.58-1.65), Hispanic ethnicity (OR: 1.15, 95% CI: 1.11-1.18), non-commercial insurance or uninsured status (OR: 2.53, 95% CI: 2.33-2.74), secondary OAGs (e.g., pseudoexfoliative glaucoma – OR: 1.65, 95% CI: 1.58-1.72), and higher socioeconomic SVI scores (OR: 1.25 for highest versus lowest quartile, 95% CI: 1.22-1.28). Black and Hispanic patients were diagnosed at younger ages compared to White patients (mean ages: 67.8±12.3 and 68.1±12.8 vs. 73.3±11.8 years respectively, p<0.001). ConclusionsWorse OAG at presentation was associated with older age, male sex, Black race, Hispanic ethnicity, non-commercial insurance or uninsured status, secondary OAGs, and greater socioeconomic vulnerability in this nationwide cohort. These findings can help tailor screening programs towards vulnerable populations.

Correction: A Call to Reconsider a Nationwide Electronic Health Record System: Correcting the Failures of the National Program for IT.

Correction: A Call to Reconsider a Nationwide Electronic Health Record System: Correcting the Failures of the National Program for IT.

A Call to Reconsider a Nationwide Electronic Health Record System: Correcting the Failures of the National Program for IT.

Abstract The National Programme for IT (NPfIT) was launched in 2005 to implement 7 nationwide IT services across the National Health Service (NHS). Despite the success of many of these designated “deliverables,” the establishment of a single nationwide electronic health record (EHR) system never fully materialized. As a result, NHS medical records are now stored using a diverse array of alternate EHR systems, which frequently restricts health care practitioners from accessing extensive portions of their patients’ notes. This not only limits their ability to make well-informed clinical decisions but also impacts the quality of care they are able to provide. This article assesses the medical, economic, and bureaucratic implications of an NHS-wide EHR system. Additionally, it explores how the shortcomings of the NPfIT should be addressed when attempting to introduce such a system in the future.

Occurrence of sleep disorders, anxiety, and depression among patients with mild cognitive impairment and Alzheimer’s disease in diverse populations: A large nationwide electronic health record cohort study

AbstractBackgroundPatients affected with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) often experience anxiety, depression, and sleep disturbances. Some of the published studies reported a higher incidence of these disorders in minority populations. The objectives of this study were to characterize the occurrence of sleep disorders, anxiety, and depression among patients with MCI and/or AD, stratified by gender, age and race/ethnicity in the United States clinical practice setting.MethodA retrospective cohort study was conducted using electronic health records and insurance claims from the Optum® Market Clarity database containing data from more than 63 million people in the US insured through Medicare, Medicaid, and other insurance providers. The study included patients diagnosed, utilizing ICD diagnosis codes, with MCI and/or AD between January 2017 and September 2021. Prevalence of depression, anxiety, and sleep disorders, as well as associated medication prescriptions, were identified in the 12 months before and after MCI and/or AD diagnoses. The occurrence of these diagnoses and prescriptions was evaluated stratified by gender, age, and race/ethnicity.ResultA total of 197,346 patients were diagnosed with MCI (56.9% female, mean age of 70.0 [SD 14.8]) and 144,321 with AD (63.9% female, mean age of 79.3 [SD 8.2]) at the baseline. Overall, a large proportion of MCI and AD cases had depression (pre‐MCI diagnosis: 40.3%; post‐MCI diagnosis: 44.8%; pre‐AD diagnosis: 32.2%; post‐AD diagnosis: 38.3%), anxiety (pre‐MCI diagnosis: 36.2%; post‐MCI diagnosis: 40.2%; pre‐AD diagnosis: 26.4%; post‐AD diagnosis: 31.5%), and sleep disorders (pre‐MCI diagnosis: 36.2%; post‐MCI diagnosis: 39.3%; pre‐AD diagnosis: 23.2%; post‐AD diagnosis: 25.4%). Occurrences of depression and anxiety were highest among females. Among individuals with MCI and/or AD, the gap between the proportion experiencing depression and the proportion prescribed an antidepressant was greatest for African American males and females. This was not observed for anxiety and anti‐anxiety medications.ConclusionA large proportion of individuals with MCI and/or AD also had depression, anxiety, and sleep issues. Further understanding of the populations with these comorbidities and their associated prescriptions by different ages, sex, and races/ethnicities is important when supporting patients with MCI and AD.

Diagnosis patterns among diverse populations with mild cognitive impairment and Alzheimer’s disease: A large nationwide electronic health record cohort study

AbstractBackgroundWith recent advances and emerging research on diagnostic biomarkers and evolving diagnostic criteria, practitioners managing patients with cognitive complaints operate within a complex, changing environment. Although some evidence is available on the importance of prompt diagnosis, little information exists on the diagnostic process used in real‐world settings among diverse populations. This study’s objective was to characterize diagnosis patterns among adults with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and/or dementia in the United States, and stratify by gender, age and race/ethnicity.MethodThis retrospective observational cohort study utilized the Optum® Market Clarity database from January 1, 2017–September 30, 2021. Eligible individuals had ≥1 diagnosis of MCI, AD, and/or dementia in their electronic health records or ≥2 diagnosis codes separated by ≥30 days in claims data, had ≥12 months of continuous enrollment before the cohort entry date, and had ≥1 day of continuous healthcare plan enrollment after the index date. Demographic information and the occurrence of diagnostic tests were also obtained from electronic medical records and insurance claims. Descriptive statistics were calculated and stratified by demographic factors.ResultA total of 179,419 newly diagnosed MCI cases, 81,267 AD cases, and 338,739 dementia cases were identified. The mean age at MCI, AD, or dementia diagnosis was similar overall for males and females. Among Caucasians, the mean age of MCI diagnosis was 70.5±14.8 for females and 69.8±14.5 for males. Among African Americans, the mean age at MCI diagnosis was 66.3±15.4 for females and 63.6±16.1 for males. PET scans and CSF biomarker screening occurred less often than MRI and CT (MCI: PET 0.6%, CSF 1.6%, MRI 16.6%, CT 17.5%; AD: PET 0.5%; CSF 0.9%; MRI 9.0%; CT 18.4%; dementia: PET 0.2%; CSF 1.6%; MRI 9.5%; CT 25.9%).ConclusionThe mean age at MCI, AD, and dementia diagnosis varied by demographic groups. The diagnosis of MCI, AD, and dementia remains largely clinical, with low use of brain imaging or CSF biomarkers. This knowledge of the patient populations and diagnostic experiences in a real‐world setting allows for greater understanding and growth to improve diagnosis across diverse groups and lead to faster support.

Drug utilization among patients with mild cognitive impairment and Alzheimer’s disease in diverse populations: A large nationwide electronic health record cohort study

AbstractBackgroundCholinesterase inhibitors (ChE‐Is), such as donepezil, rivastigmine, and galantamine, and N‐methyl‐d‐aspartate receptor antagonists, such as memantine, are prescribed to decrease symptoms associated with cognitive impairment. Few studies have examined the initiation and utilization of these medications among diverse populations. This study’s objective was to characterize drug utilization among patients with mild cognitive impairment (MCI) and/or Alzheimer’s disease (AD) in the United States and stratify by gender, age and race/ethnicity.MethodThis retrospective cohort study was conducted using the Optum® Market Clarity database, which contains electronic health records and insurance claims. Cases of MCI and AD were identified utilizing the International Classification of Diseases, Ninth and Tenth Revisions diagnosis codes. Drug prescriptions were determined to occur before, on, or after the date of first diagnosis. All cases were required to have ≥12 months of enrollment in the database prior to first diagnosis. The study period was from January 2017 through September 2021. Descriptive statistics were calculated stratified by demographic groups.ResultA large number of cases were identified (197,346 MCI and 144,321 AD cases). Approximately 6% of individuals with MCI had prescriptions for ChE‐Is prior to diagnosis and 14% after diagnosis. For memantine, the proportions with prescriptions before and after MCI diagnosis were 2% and 5%, respectively. Among individuals with AD, prescriptions for ChE‐Is occurred in 22% prior to diagnosis and 32% after diagnosis. Memantine prescriptions increased from 10% prior to AD diagnosis to 18% after. The results did not differ by gender. The highest proportions with ChE‐Is and memantine prescriptions were older age groups &lt;85 years. When examining race/ethnic groups, the proportion of MCI and AD cases with these prescriptions was generally lowest among African Americans.ConclusionAs expected, prescriptions for ChE‐Is and memantine increased after diagnosis. The proportion of MCI and/or AD cases with prescriptions were similar across genders. The proportion with prescriptions was generally lowest among African Americans. Future research into the discrepancy in the occurrence of prescriptions by race/ethnicity as well as the timing of certain prescriptions will be valuable in further understanding the patient population and their access to appropriate medications.

Clinical Characteristics, Treatment Trends, and Outcomes of Patients with HHV-8-Negative/Idiopathic Multicentric Castleman Disease Treated with Siltuximab in a Machine Learning-Selected Real-World Cohort

Background HHV-8-negative/Idiopathic Multicentric Castleman Disease (iMCD) is a rare lymphoproliferative disorder with limited treatment options and poor outcomes. Siltuximab remains the only FDA approved treatment for the disease (April 2014), but its uptake and associated outcomes outside of clinical trials remain unclear. Further, constructing a large enough cohort of patients with iMCD to better understand siltuximab use in real-world practice is a challenge due to its low incidence, lack of a Castleman Disease (CD)-specific ICD code until 2017, and effort required for manual chart abstraction. We built a natural language processing-based machine learning (ML) model to select patients with iMCD from a nationwide electronic health record (EHR)-derived database and described their clinical characteristics, treatment trends, and real-world overall survival (rwOS). Methods Across a cohort of over 3.5 million patients in the EHR-derived, Flatiron Health database (which comprises de-identified patient-level structured and unstructured data from ~280 US cancer clinics and ~800 sites of care), patients with ML-extracted diagnoses and dates of diagnoses for iMCD and a visit on or after 2011 were selected. Precision (positive predictive value) of the ML-selected cohort was measured on 50 human abstracted charts. Descriptive statistics summarized patient characteristics and treatment trends. Treatment patterns were assessed among patients diagnosed on or before January 31, 2023 to allow a minimum of 3 months for treatment initiation. Changes over time in first-line (1L) treatment status were measured by diagnosis year category (&amp;lt;2014, 2014-2018, 2019-2023), and were chosen based on siltuximab approval date (2014) and publication of treatment guidelines for iMCD (2018). 1L regimens were grouped into one of the following mutually exclusive categories in hierarchical order: siltuximab-containing regimen, rituximab-containing regimen, and primary chemotherapy-containing regimen. 5-year rwOS rates for the entire iMCD cohort (indexed to diagnosis date), as well as patients with 1L-siltuximab or non-siltuximab regimens (indexed to treatment start date) were estimated using the Kaplan-Meier estimator. Patients with a diagnosis date before 2011 were excluded from survival analyses to avoid immortal time bias. Results The ML model selected 267 patients as having iMCD across the Flatiron network. The precision of the model was 90% for a CD diagnosis (including unicentric, HHV-8-associated multicentric, or unclassified CD) and 76% for the iMCD cohort. Patients with iMCD were predominantly male (55%), white (53%), and had a median age at diagnosis of 52. Compared to patients treated with non-siltuximab 1L therapy (N=122), patients treated with siltuximab-based regimens in 1L (N=67) were more likely to have been diagnosed more recently, seen in the community setting, and have a documented ECOG performance status (p&amp;lt;0.05) but were otherwise similar (Table 1). Among patients with evidence of treatment, use of 1L siltuximab-based regimens increased from 35% of patients diagnosed 2014-2018 to 46% diagnosed 2019-2023 while 1L rituximab-based regimen use dropped from 46% to 35%. 1L primary chemotherapy-based regimen use dropped from 41% prior to 2014 to 19% post 2014 (Figure 1). Among patients diagnosed after 2014 and who had evidence of treatment, 51% (77/151) received siltuximab at any point. Unadjusted 5-year rwOS rate for the entire iMCD cohort, including patients without evidence of treatment, was 84% [95% CI: 78-90%]. For patients treated with a 1L siltuximab-containing regimen, unadjusted 5-year rwOS was 86% [95% CI: 78%-96%], whereas for patients treated with non-siltuximab based 1L therapy, unadjusted 5-year rwOS was 81% [95% CI: 72%-90%]. Conclusion ML extraction allowed for the largest analysis of real-world patients with iMCD to date. While use of siltuximab in the 1L setting is increasing over time, only half of patients with evidence of treatment receive it at any point. Unadjusted 5-year rwOS rates for those treated with both siltuximab and non-siltuximab based 1L therapy was higher than previously reported. This may be due to the inclusion of other CD subtypes in the cohort or sicker patients not being seen in an outpatient oncology setting. Future work should explore the drivers of poor outcomes for patients as well as the factors associated with lack of receipt of siltuximab.

Real-World Incidence, Characteristics and Management of Cytokine Release Syndrome Induced By Chimeric Antigen Receptor T-Cell Therapy across Hematologic Malignancies

Background: Cytokine release syndrome (CRS) is a potentially life-threatening, supraphysiologic response following immunotherapy resulting in the activation or engagement of endogenous or infused T cells and/or other immune effector cells. CRS occurs with varying frequency and severity, depending on the underlying disease and immunotherapy used, such as chimeric antigen receptor T-cell (CAR-T) therapy or off-the-shelf T-cell engaging bispecific antibodies. Limited research exists on the diagnosis and management of CRS in the real world. This study describes the real-world incidence, severity and clinical management of CRS induced by commercially available CAR-T therapies for diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL) and multiple myeloma (MM). Methods: Real-world data (RWD) were obtained from the Flatiron Health, US, nationwide electronic health record (EHR)-derived, de-identified database of cancer patients within a network comprised primarily of community cancer practices. The Flatiron Health network also includes practices designated as academic sites by the National Cancer Institute; however, relative proportions of community/academic practices may vary depending on the study cohort. Data as of August 31, 2022, on CAR-T therapy, clinical characteristics and disease outcomes specific to CAR-T therapy, as well as CRS and its clinical management, were abstracted from unstructured documents in EHRs (in addition to routinely available structured data), in a cohort of patients receiving commercial CAR-T therapy. Specifically, data for CRS including treatments and resolution were abstracted based on explicit mention of and attribution to CRS in charts. Results: Table 1 summarizes data by indication. Incidence of CRS ranged from 56% (104/185) in DLBCL to 72% (66/92) in MCL. Excluding categories with small numbers of cases (&amp;lt;15), CRS incidence also varied by individual CAR-T therapy and underlying indication and ranged from 35% for idecabtagene vicleucel in MM to 73% for brexucabtagene autoleucel in MCL. Highest incidence of Grade ≥2 CRS was observed among MCL patients, whereas Grade ≥3 CRS was most commonly observed among FL patients. Time to onset of CRS ranged from 0 to 16 days, with median onset between 3-4 days among lymphoma patients and earlier median onset of 1 day for MM patients. Median time to CRS resolution ranged from 2 to 6 days across indications. While 81-95% of patients received CAR-T therapy at an academic center, 5-14% received CAR-T therapy at a community center. Tocilizumab and corticosteroids were the most commonly used treatments for CRS, including 37-48% use of tocilizumab for Grade 1 CRS (Table 2). Discussion: This study uniquely summarizes RWD on CRS following the use of commercially available CAR-T therapies across multiple hematologic malignancies, based on information from clinical notes abstracted retrospectively from EHRs. Consistent with the timing of approval of commercially available CAR-T therapies, the sample size for MM was limited compared with other malignancies. Safety events are typically under-reported in RWD sources, specifically for CRS when relying on information documented in patients' EHRs, compared with other published literature. In accordance with other RWD on CRS, grading criteria for CRS as well as vasopressor and oxygen use were not consistently reported in patients' EHRs. CAR-T treatments were predominantly administered in academic centers. Limited use in community practices was also observed; however, further information to accurately characterize the size and capabilities of community centers was not available in this database. Data also showed considerable use of tocilizumab to treat low-grade CRS, suggesting the need for additional evidence on its role to treat CRS beyond its current regulatory approval. Increased evidence generation, specifically from usual clinical practice, can contribute to increased awareness and knowledge about CRS diagnosis and management, and in turn realize the maximum potential of T-cell therapeutics.

Real-world assessment of antiplatelet treatment therapies for acute coronary syndrome (ACS) patients in the United States in a nationwide Electronic Health Record in 2018-2020

Abstract Background Acute coronary syndrome (ACS) is one of the leading causes of cardiovascular related deaths for adults in US. The clinical presentation of ACS is broad, which includes ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation MI (NSTEMI), and unstable angina (UA). There are limited data around contemporary real-world treatment landscape, therefore it is critical to understand the treatment pattern of the US ACS patients in the real-world setting. Purpose This study evaluated antiplatelet therapy (APT) treatment patterns among US patients without atrial fibrillation or oral anticoagulants (OACs) who experienced their first hospitalized ACS event during observation period. Methods The Optum® de-identified Electronic Health Record dataset from January 2018 through December 2020 was used for this study. Adult patients with an inpatient primary diagnosis of ACS during the index period were identified by ICD10 codes. The date of admission for the first ACS hospitalization was the index date. Patients with atrial fibrillation diagnosis or a record for OAC use before index date and during hospitalization were excluded. ACS subgroups and percutaneous coronary intervention (PCI) status were assessed during index hospitalization; patient characteristics and in-hospital antiplatelet treatment type were also evaluated. Results Among 63,767 patients included in this study, the mean age was 64.1 and 40.5% were female; 89.2% were treated with APT. For those patients not treated with APT, majority were treated with parental anticoagulants. Among 89.2% APT treated patients, 52.7% were initiated on dual antiplatelet therapy (DAPT), 27.4% were initiated on single antiplatelet therapy (SAPT), and 9.1% switched between different DAPT regimens. The top three used DAPT were aspirin+clopidogrel (30.5%), aspirin+ticagrelor (18.9%) and aspirin+prasugrel (2.6%); the most used SAPT were aspirin (22.6%), clopidogrel (2.6%), and ticagrelor (2.0%). For ACS subtypes, 26.3% were diagnosed as STEMI, 62.9% were NSTEMI, and 10.8% were UA; of them, 45.0% had PCI procedure within 30-days before or after the index date. ACS patients with PCI procedure had the highest APT treatment rate (95.2%) and highest DAPT treatment rate (70.6%). Across ACS subtypes, patients hospitalized for STEMI had the highest usage of aspirin+ticagrelor (34.5%), whereas patients hospitalized for NSTEMI had the highest usage of aspirin+clopidogrel (34.7%). Conclusion We observed wide variability in APT treatment across STEMI vs. NSTEMI and +/-PCI. Only 38.1% of non-PCI treated ACS patients received DAPT despite current guidelines. Further analyses are planned to explore the varying APT (and/or anticoagulant) regimens.Figure 1Figure 2

Disease trajectories following myocardial infarction: insights from process mining of 145 million hospitalisation episodes

Abstract The number of survivors of MI has increased dramatically in recent decades, yet knowledge of post-myocardial infarction (MI) disease risk to date is limited. We investigated temporally ordered sequences of all conditions following MI in nationwide electronic health record data through the novel application of process mining. Here, we present, for the first time, the observed sequences of diseases that individuals accrue post MI. We conducted a national retrospective cohort study of all hospitalisations (145,670,448 episodes, 34,083,204 individuals) admitted to NHS hospitals in England (1st January 2008-31st January 2017, final follow up 27th March 2017). Through process mining we identified trajectories of all major disease diagnoses following MI and compared their adjusted relative risk (RR) and all-cause mortality hazard ratios (HR) to a risk-set matched non-MI control cohort using flexible parametric survival models. Among a total of 375,669 (1·1%) MI patients (872,450 episodes) and 1,878,345 (5·5%) matched non-MI patients (4,712,654 episodes), we identified 28,740 unique disease trajectories. The accrual of multiple circulatory diagnoses was more common amongst MI (RR 4·37, 95% CI 4·00-4·77) and conferred an increased risk of death (HR 1·32, 1·14-1·54) compared with matched controls. Further, trajectories featuring neuro-psychiatric diagnoses (including anxiety and depression) in conjunction with circulatory disorders, were markedly more common and had increased mortality post MI (HR ranging from 1·12 to 1·74) compared with non-MI individuals. Pathways involving both circulatory and digestive diseases appeared more frequently in the MI cohort versus matched controls (RR 4·03 [3·87-4·20] to 4·77 [4·28-5·32]). A number of trajectories including disorders of the eye/adnexa were also associated with an increased risk of death compared with matched controls (HR: 1·32, 1·11-1·57), with those eye/adnexa disorders consisting mainly of cataracts (77·6%). This study quantifies the relative risk and mortality burden associated with disease trajectories following MI from a nationwide hospitalisation cohort for the first time. These data show the significant ongoing cardiovascular disease burden following MI, and crucially highlight the excess accrual of neuro-psychiatric diagnoses conferring increased mortality compared with non-MI individuals. These data provide a clear opportunity for early intervention targets, such as increased focus on the psychological and behavioural pathways for survivors of MI, to mitigate ongoing adverse disease trajectories, multimorbidity and premature mortality. Further understanding of the factors contributing to these trajectories is required to facilitate identification of patients in the early post-MI setting who would benefit most from closer follow-up and prompt intervention.Trajectories: primary diseasesTrajectories: primary+secondary diseases

Gender differences in receipt of telehealth versus in person behavioral therapy, medication for opioid use disorder (MOUD), and 90-day MOUD retention during the pandemic: A retrospective veteran cohort study

BackgroundCOVID-19 significantly negatively impacted access to care among patients with opioid use disorder (OUD). The Veterans Health Administration (VHA) enacted policies to expand telehealth and medication for OUD (MOUD) during the public health emergency, which offset risk of treatment disruption. In this study, we evaluated gender differences in utilization of behavioral therapy in person and via telehealth, MOUD utilization, and achieving 90-day MOUD retention pre-post pandemic onset, given known gender differences in treatment utilization between men and women. Secondarily, we examined MOUD receipt and retention as a function of in-person vs. telehealth behavioral therapy received over time. MethodsUsing VHA's nationwide electronic health record data, we compared outcomes between men and women veterans, pre- to post-pandemic onset (January 2019–February 2020 vs. March 2020–April 2021). Primary outcomes included receipt of behavioral therapy (in person or telehealth), number of appointments attended, any MOUD, and whether patients achieved 90-day MOUD retention post-induction. ResultsVeterans with OUD were less likely to receive behavioral therapy post-pandemic onset, which was driven by marked decreases in in-person care; these effects were strongest among women. The odds of receiving MOUD also decreased pre- to post-pandemic onset, particularly among men. Receipt of or achieving 90-day MOUD retention was differentially related to receipt of behavioral therapy via in person vs. telehealth; telehealth was more strongly associated with these utilization indicators post-pandemic onset—an effect that was more pronounced for men. ConclusionThe likelihood of receiving behavioral therapy and MOUD were lower during COVID-19 and varied by gender, with men being less likely to receive MOUD over time and women being less likely to receive in-person behavioral therapy. Behavioral therapy received via telehealth was generally associated with improved MOUD utilization compared to in-person behavioral therapy, but this was less true for women than for men regarding utilization of or achieving 90-day MOUD retention. In addition to the need for further telehealth expansion for veterans with OUD, more research should explore how to better engage men in MOUD treatment and improve adherence to MOUD among women engaged in behavioral therapy.

Evaluation of US oncology electronic health record real-world data to reduce uncertainty in health technology appraisals: a retrospective cohort study

ObjectivesExamine whether data from early access to medicines in the USA can be used to inform National Institute for Health and Care Excellence (NICE) health technology assessments (HTA) in oncology.DesignRetrospective...

Chronic Cough: Characterizing and Quantifying Burden in Adults Using a Nationwide Electronic Health Records Database.

Chronic cough is a common condition; until recently, no International Classification of Diseases (ICD) code for chronic cough existed; therefore, the true scope and burden of chronic cough is unclear. Using established algorithms, we examined chronic cough patients and their risk profiles, recurrent cough episodes, and subsequent 1-year health care utilization in the nationwide Cerner EHR data resource, compared with those with acute cough. An ICD-based algorithm was applied to the Cerner Health Facts EHR database to derive a phenotype of chronic cough defined as three ICD-based "cough" encounters 14-days apart over a 56-to-120-day period from 2015 to 2017. Demographics, comorbidities, and outcomes (1-year outpatient, emergency, and inpatient encounters) were collected for the chronic cough cohort and acute cough cohort. The chronic cough cohort was 61.5% female, 70.4% white, and 15.2% African American, with 13.7% being of Asian, Native American, or unknown race. Compared with the acute cough cohort, chronic cough patients were more likely to be older, female, and have chronic pulmonary disease, obesity, and depression. Predictors of recurrent chronic cough were older age and race. Those with chronic cough had more outpatient (2.48 ± 2.10 vs. 1.48 ± 0.99; SMD = 0.94), emergency (1.90 ± 2.26 vs. 1.23 ± 0.68; SMD = 0.82), and inpatient (1.11 ± 0.36 vs. 1.05 ± 0.24, SMD = 0.24) encounters compared with acute cough. While EHR-based data may provide a useful resource to identify chronic cough phenotypes, supplementary data approaches and screening methods for chronic cough can further identify the scope of the problem.

Disease trajectories following myocardial infarction: insights from process mining of 145 million hospitalisation episodes

Knowledge of post-myocardial infarction (MI) disease risk to date is limited-yet the number of survivors of MI has increased dramatically in recent decades. We investigated temporally ordered sequences of all conditions following MI in nationwide electronic health record data through the application of process mining. We conducted a national retrospective cohort study of all hospitalisations (145,670,448 episodes; 34,083,204 individuals) admitted to NHS hospitals in England (1st January 2008-31st January 2017, final follow-up 27th March 2017). Through process mining, we identified trajectories of all major disease diagnoses following MI and compared their relative risk (RR) and all-cause mortality hazard ratios (HR) to a risk-set matched non-MI control cohort using Cox proportional hazards and flexible parametric survival models. Among a total of 375,669 MI patients (130,758 females; 34.8%) and 1,878,345 matched non-MI patients (653,790 females; 34.8%), we identified 28,799 unique disease trajectories. The accrual of multiple circulatory diagnoses was more common amongst MI patients (RR 4.32, 95% CI 3.96-4.72) and conferred an increased risk of death (HR 1.32, 1.13-1.53) compared with matched controls. Trajectories featuring neuro-psychiatric diagnoses (including anxiety and depression) following circulatory disorders were markedly more common and had increased mortality post MI (HR ranging from 1.11 to 1.73) compared with non-MI individuals. These results provide an opportunity for early intervention targets for survivors of MI-such as increased focus on the psychological and behavioural pathways-to mitigate ongoing adverse disease trajectories, multimorbidity, and premature mortality. British Heart Foundation; Alan Turing Institute.

Approach to machine learning for extraction of real-world data variables from electronic health records.

Background: As artificial intelligence (AI) continues to advance with breakthroughs in natural language processing (NLP) and machine learning (ML), such as the development of models like OpenAI's ChatGPT, new opportunities are emerging for efficient curation of electronic health records (EHR) into real-world data (RWD) for evidence generation in oncology. Our objective is to describe the research and development of industry methods to promote transparency and explainability. Methods: We applied NLP with ML techniques to train, validate, and test the extraction of information from unstructured documents (e.g., clinician notes, radiology reports, lab reports, etc.) to output a set of structured variables required for RWD analysis. This research used a nationwide electronic health record (EHR)-derived database. Models were selected based on performance. Variables curated with an approach using ML extraction are those where the value is determined solely based on an ML model (i.e. not confirmed by abstraction), which identifies key information from visit notes and documents. These models do not predict future events or infer missing information. Results: We developed an approach using NLP and ML for extraction of clinically meaningful information from unstructured EHR documents and found high performance of output variables compared with variables curated by manually abstracted data. These extraction methods resulted in research-ready variables including initial cancer diagnosis with date, advanced/metastatic diagnosis with date, disease stage, histology, smoking status, surgery status with date, biomarker test results with dates, and oral treatments with dates. Conclusion: NLP and ML enable the extraction of retrospective clinical data in EHR with speed and scalability to help researchers learn from the experience of every person with cancer.