Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

Decoding the Genomics of Abdominal Aortic Aneurysm.

A key aspect of genomic medicine is to make individualized clinical decisions from personal genomes. We developed a machine-learning framework to integrate personal genomes and electronic health record (EHR) data and used this framework to study abdominal aortic aneurysm (AAA), a prevalent irreversible cardiovascular disease with unclear etiology. Performing whole-genome sequencing on AAA patients and controls, we demonstrated its predictive precision solely from personal genomes. By modeling personal genomes with EHRs, this framework quantitatively assessed the effectiveness of adjusting personal lifestyles given personal genome baselines, demonstrating its utility as a personal health management tool. We showed that this new framework agnostically identified genetic components involved in AAA, which were subsequently validated in human aortic tissues and in murine models. Our study presents a new framework for disease genome analysis, which can be used for both health management and understanding the biological architecture of complex diseases. VIDEO ABSTRACT.

Validation of an Electronic Health Record-Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding.

BACKGROUND & AIMS: Guidelines recommend use of risk stratification scores for patients presenting with gastrointestinal bleeding (GIB) to identify very-low-risk patients eligible for discharge from emergency departments. Machine learning models may outperform existing scores and can be integrated within the electronic health record (EHR) to provide real-time risk assessment without manual data entry. We present the first EHR-based machine learning model for GIB. METHODS: The training cohort comprised 2546 patients and internal validation of 850 patients presenting with overt GIB (ie, hematemesis, melena, and hematochezia) to emergency departments of 2 hospitals from 2014 to 2019. External validation was performed on 926 patients presenting to a different hospital with the same EHR from 2014 to 2019. The primary outcome was a composite of red blood cell transfusion, hemostatic intervention (ie, endoscopic, interventional radiologic, or surgical), and 30-day all-cause mortality. We used structured data fields in the EHR, available within 4 hours of presentation, and compared the performance of machine learning models with current guideline-recommended risk scores, Glasgow-Blatchford Score, and Oakland Score. Primary analysis was area under the receiver operating characteristic curve. Secondary analysis was specificity at 99% sensitivity to assess the proportion of patients correctly identified as very low risk. RESULTS: The machine learning model outperformed the Glasgow-Blatchford Score (area under the receiver operating characteristic curve, 0.92 vs 0.89; P < .001) and Oakland Score (area under the receiver operating characteristic curve, 0.92 vs 0.89; P < .001). At the very-low-risk threshold of 99% sensitivity, the machine learning model identified more very-low-risk patients: 37.9% vs 18.5% for Glasgow-Blatchford Score and 11.7% for Oakland Score (P < .001 for both comparisons). CONCLUSIONS: An EHR-based machine learning model performs better than currently recommended clinical risk scores and identifies more very-low-risk patients eligible for discharge from the emergency department.

Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.

Multi-Omics Profiling for Health.

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Nonsteroidal anti-inflammatory drug "allergy" labeling is associated with increased postpartum opioid utilization.

BACKGROUND: Current guidelines recommend a stepwise approach to postpartum pain management, beginning with acetaminophen and nonsteroidal anti-inflammatory drugs (NSAIDs), with opioids added only if needed. Report of a prior NSAID-induced adverse drug reaction (ADR) may preclude use of first-line analgesics, despite evidence that many patients with this allergy label may safely tolerate NSAIDs. OBJECTIVE: We assessed the association between reported NSAID ADRs and postpartum opioid utilization. METHODS: We performed a retrospective cohort study of birthing people who delivered within an integrated health system (January 1, 2017, to December 31, 2020). Study outcomes were postpartum inpatient opioid administrations and opioid prescriptions at discharge. Statistical analysis was performed on a propensity score-matched sample, which was generated with the goal of matching to the covariate distributions from individuals with NSAID ADRs. RESULTS: Of 38,927 eligible participants, there were 883 (2.3%) with an NSAID ADR. Among individuals with reported NSAID ADRs, 49.5% received inpatient opioids in the postpartum period, compared to 34.5% of those with no NSAID ADRs (difference = 15.0%, 95% confidence interval 11.4-18.6%). For patients who received postpartum inpatient opioids, those with NSAID ADRs received a higher total cumulative dose between delivery and hospital discharge (median 30.0 vs 22.5 morphine milligram equivalents [MME] for vaginal deliveries; median 104.4 vs 75.0 MME for cesarean deliveries). The overall proportion of patients receiving an opioid prescription at the time of hospital discharge was higher for patients with NSAID ADRs compared to patients with no NSAID ADRs (39.3% vs 27.2%; difference = 12.1%, 95% confidence interval 8.6-15.6%). CONCLUSION: Patients with reported NSAID ADRs had higher postpartum inpatient opioid utilization and more frequently received opioid prescriptions at hospital discharge compared to those without NSAID ADRs, regardless of mode of delivery.

Applications of machine learning on electronic health record data to combat antibiotic resistance.

There is growing excitement about the clinical use of artificial intelligence and machine learning technologies. Advancements in computing and the accessibility of machine learning frameworks enable researchers to easily train predictive models using electronic health record data. However, there are several practical factors that must be considered when employing machine learning on electronic health record data. We provide a primer on machine learning and approaches commonly taken to address these challenges. To illustrate how these approaches have been applied to address antimicrobial resistance, we review the use of electronic health record data to construct machine learning models for predicting pathogen carriage or infection, optimizing empiric therapy, and aiding antimicrobial stewardship tasks. Machine learning shows promise in promoting the appropriate use of antimicrobials, although clinical deployment is limited. We conclude by describing potential dangers of, and barriers to, implementation of machine learning models in the clinic.

Incidental Coronary Artery Calcium: Opportunistic Screening of Previous Nongated Chest Computed Tomography Scans to Improve Statin Rates (NOTIFY-1 Project).

BACKGROUND: Coronary artery calcium (CAC) can be identified on nongated chest computed tomography (CT) scans, but this finding is not consistently incorporated into care. A deep learning algorithm enables opportunistic CAC screening of nongated chest CT scans. Our objective was to evaluate the effect of notifying clinicians and patients of incidental CAC on statin initiation. METHODS: NOTIFY-1 (Incidental Coronary Calcification Quality Improvement Project) was a randomized quality improvement project in the Stanford Health Care System. Patients without known atherosclerotic cardiovascular disease or a previous statin prescription were screened for CAC on a previous nongated chest CT scan from 2014 to 2019 using a validated deep learning algorithm with radiologist confirmation. Patients with incidental CAC were randomly assigned to notification of the primary care clinician and patient versus usual care. Notification included a patient-specific image of CAC and guideline recommendations regarding statin use. The primary outcome was statin prescription within 6 months. RESULTS: Among 2113 patients who met initial clinical inclusion criteria, CAC was identified by the algorithm in 424 patients. After chart review and additional exclusions were made, a radiologist confirmed CAC among 173 of 194 patients (89.2%) who were randomly assigned to notification or usual care. At 6 months, the statin prescription rate was 51.2% (44/86) in the notification arm versus 6.9% (6/87) with usual care (P<0.001). There was also more coronary artery disease testing in the notification arm (15.1% [13/86] versus 2.3% [2/87]; P=0.008). CONCLUSIONS: Opportunistic CAC screening of previous nongated chest CT scans followed by clinician and patient notification led to a significant increase in statin prescriptions. Further research is needed to determine whether this approach can reduce atherosclerotic cardiovascular disease events. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04789278.

The Study of the Epidemiology of Pediatric Hypertension Registry (SUPERHERO): Rationale and Methods.

Despite increasing prevalence of hypertension in youth and high adult cardiovascular mortality rates, the long-term consequences of youth-onset hypertension remain unknown. This is due to limitations of prior research such as small sample sizes, reliance on manual record review, and limited analytic methods that did not address major biases. The Study of the Epidemiology of Pediatric Hypertension (SUPERHERO) is a multisite retrospective Registry of youth evaluated by subspecialists for hypertension disorders. Sites obtain harmonized electronic health record data using standardized biomedical informatics scripts validated with randomized manual record review. Inclusion criteria are index visit for International Classification of Diseases Diagnostic Codes, 10th Revision (ICD-10 code)-defined hypertension disorder ≥January 1, 2015 and age <19 years. We exclude patients with ICD-10 code-defined pregnancy, kidney failure on dialysis, or kidney transplantation. Data include demographics, anthropomorphics, U.S. Census Bureau tract, histories, blood pressure, ICD-10 codes, medications, laboratory and imaging results, and ambulatory blood pressure. SUPERHERO leverages expertise in epidemiology, statistics, clinical care, and biomedical informatics to create the largest and most diverse registry of youth with newly diagnosed hypertension disorders. SUPERHERO's goals are to (i) reduce CVD burden across the life course and (ii) establish gold-standard biomedical informatics methods for youth with hypertension disorders.

Transforming the oncology data paradigm by creating, capturing, and retrieving structured cancer data at the point of care: A Mayo Clinic pilot.

INTRODUCTION: Structured data capture requires defined languages such as minimal Common Oncology Data Elements (mCODE). This pilot assessed the feasibility of capturing 5 mCODE categories (stage, disease status, performance status (PS), intent of therapy and intent to change therapy). METHODS: A tool (SmartPhrase) using existing and custom structured data elements was Built to capture 4 data categories (disease status, PS, intent of therapy and intent to change therapy) typically documented as free-text within notes. Existing functionality for stage was supported by the Build. Participant survey data, presence of data (per encounter), and time in chart were collected prior to go-live and repeat timepoints. The anticipated outcome was capture of >50% sustained over time without undue burden. RESULTS: Pre-intervention (5-weeks before go-live), participants had 1390 encounters (1207 patients). The median percent capture across all participants was 32% for stage; no structured data was available for other categories pre-intervention. During a 6-month pilot with 14 participants across three sites, 4995 encounters (3071 patients) occurred. The median percent capture across all participants and all post-intervention months increased to 64% for stage and 81%-82% for the other data categories post-intervention. No increase in participant time in chart was noted. Participants reported that data were meaningful to capture. CONCLUSIONS: Structured data can be captured (1) in real-time, (2) sustained over time without (3) undue provider burden using note-based tools. Our system is expanding the pilot, with integration of these data into clinical decision support, practice dashboards and potential for clinical trial matching.

Electronic health record-enabled routine assessment of medication adherence after solid organ transplantation: the time is now.

Medication nonadherence after solid organ transplantation is recognized as an important impediment to long-term graft survival. Yet, assessment of adherence is often not part of routine care. In this Personal Viewpoint, we call for the transplant community to consider implementing a systematic process to screen and assess medication adherence. We believe acceptable tools are available to support integrating adherence assessments into the electronic health record. Creating a standard assessment can be done efficiently and cost-effectively if we come together as a community. More importantly, such monitoring can improve outcomes and strengthen provider-patient relationships. We further discuss the practical challenges and potential rebuttals to our position.

Host genetic effects in pneumonia.

Given the coronavirus disease 2019 (COVID-19) pandemic, investigations into host susceptibility to infectious diseases and downstream sequelae have never been more relevant. Pneumonia is a lung disease that can cause respiratory failure and hypoxia and is a common complication of infectious diseases, including COVID-19. Few genome-wide association studies (GWASs) of host susceptibility and severity of pneumonia have been conducted. We performed GWASs of pneumonia susceptibility and severity in the Vanderbilt University biobank (BioVU) with linked electronic health records (EHRs), including Illumina Expanded Multi-Ethnic Global Array (MEGAEX)-genotyped European ancestry (EA, n= 69,819) and African ancestry (AA, n = 15,603) individuals. Two regions of large effect were identified: the CFTR locus in EA (rs113827944; OR = 1.84, p value = 1.2 × 10-36) and HBB in AA (rs334 [p.Glu7Val]; OR = 1.63, p value = 3.5 × 10-13). Mutations in these genes cause cystic fibrosis (CF) and sickle cell disease (SCD), respectively. After removing individuals diagnosed with CF and SCD, we assessed heterozygosity effects at our lead variants. Further GWASs after removing individuals with CF uncovered an additional association in R3HCC1L (rs10786398; OR = 1.22, p value = 3.5 × 10-8), which was replicated in two independent datasets: UK Biobank (n = 459,741) and 7,985 non-overlapping BioVU subjects, who are genotyped on arrays other than MEGAEX. This variant was also validated in GWASs of COVID-19 hospitalization and lung function. Our results highlight the importance of the host genome in infectious disease susceptibility and severity and offer crucial insight into genetic effects that could potentially influence severity of COVID-19 sequelae.

Comparison of Clinical Decision Support Tools to Improve Pediatric Lipid Screening.

OBJECTIVE: To test whether different clinical decision support tools increase clinician orders and patient completions relative to standard practice and each other. STUDY DESIGN: A pragmatic, patient-randomized clinical trial in the electronic health record was conducted between October 2019 and April 2020 at Geisinger Health System in Pennsylvania, with 4 arms: care gap-a passive listing recommending screening; alert-a panel promoting and enabling lipid screen orders; both; and a standard practice-no guideline-based notification-control arm. Data were analyzed for 13 346 9- to 11-year-old patients seen within Geisinger primary care, cardiology, urgent care, or nutrition clinics, or who had an endocrinology visit. Principal outcomes were lipid screening orders by clinicians and completions by patients within 1 week of orders. RESULTS: Active (care gap and/or alert) vs control arm patients were significantly more likely (P < .05) to have lipid screening tests ordered and completed, with ORs ranging from 1.67 (95% CI 1.28-2.19) to 5.73 (95% CI 4.46-7.36) for orders and 1.54 (95% CI 1.04-2.27) to 2.90 (95% CI 2.02-4.15) for completions. Alerts, with or without care gaps listed, outperformed care gaps alone on orders, with odds ratios ranging from 2.92 (95% CI 2.32-3.66) to 3.43 (95% CI 2.73-4.29). CONCLUSIONS: Electronic alerts can increase lipid screening orders and completions, suggesting clinical decision support can improve guideline-concordant screening. The study also highlights electronic record-based patient randomization as a way to determine relative effectiveness of support tools. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04118348.

An electronic health record (EHR)-based risk calculator can predict fractures comparably to FRAX: a proof-of-concept study.

Information in the electronic health record (EHR), such as diagnoses, vital signs, utilization, medications, and laboratory values, may predict fractures well without the need to verbally ascertain risk factors. In our study, as a proof of concept, we developed and internally validated a fracture risk calculator using only information in the EHR. PURPOSE: Fracture risk calculators, such as the Fracture Risk Assessment Tool, or FRAX, typically lie outside the clinician workflow. Conversely, the electronic health record (EHR) is at the center of the clinical workflow, and many variables in the EHR could predict fractures without having to verbally ascertain FRAX risk factors. We sought to evaluate the utility of EHR variables to predict fractures and, as a proof of concept, to create an EHR-based fracture risk model. METHODS: Routine clinical data from 24,189 subjects presenting to primary care from 2010 to 2018 was utilized. Major osteoporotic fractures (MOFs) were captured by physician diagnosis codes. Data was split into training (n = 18,141) and test sets (n = 6048). We fit Cox regression models for candidate risk factors in the training set, and then created a global model using a backward stepwise approach. We then applied the model to the test set and compared the discrimination and calibration to FRAX. RESULTS: We found variables related to vital signs, utilization, diagnoses, medications, and laboratory values to be associated with incident MOF. Our final model included 19 variables, including age, BMI, Parkinson's disease, chronic kidney disease, and albumin levels. When applied to the test set, we found the discrimination (AUC 0.73 vs. 0.70, p = 0.08) and calibration were comparable to FRAX. CONCLUSION: Routinely collected data in EHR systems can generate adequate fracture predictions without the need to verbally ascertain fracture risk factors. In the future, this could allow for automated fracture prediction at the point of care to improve osteoporosis screening and treatment rates.

Measuring Documentation Burden in Healthcare.

BACKGROUND: The enactment of the Health Information Technology for Economic and Clinical Health Act and the wide adoption of electronic health record (EHR) systems have ushered in increasing documentation burden, frequently cited as a key factor affecting the work experience of healthcare professionals and a contributor to burnout. This systematic review aims to identify and characterize measures of documentation burden. METHODS: We integrated discussions with Key Informants and a comprehensive search of the literature, including MEDLINE, Embase, Scopus, and gray literature published between 2010 and 2023. Data were narratively and thematically synthesized. RESULTS: We identified 135 articles about measuring documentation burden. We classified measures into 11 categories: overall time spent in EHR, activities related to clinical documentation, inbox management, time spent in clinical review, time spent in orders, work outside work/after hours, administrative tasks (billing and insurance related), fragmentation of workflow, measures of efficiency, EHR activity rate, and usability. The most common source of data for most measures was EHR usage logs. Direct tracking such as through time-motion analysis was fairly uncommon. Measures were developed and applied across various settings and populations, with physicians and nurses in the USA being the most frequently represented healthcare professionals. Evidence of validity of these measures was limited and incomplete. Data on the appropriateness of measures in terms of scalability, feasibility, or equity across various contexts were limited. The physician perspective was the most robustly captured and prominently focused on increased stress and burnout. DISCUSSION: Numerous measures for documentation burden are available and have been tested in a variety of settings and contexts. However, most are one-dimensional, do not capture various domains of this construct, and lack robust validity evidence. This report serves as a call to action highlighting an urgent need for measure development that represents diverse clinical contexts and support future interventions.

Gender Disparities in Electronic Health Record Usage and Inbasket Burden for Internal Medicine Residents.

BACKGROUND: Studies have demonstrated patients hold different expectations for female physicians compared to male physicians, including higher expectations for patient-centered communication and addressing socioeconomic or emotional needs. Recent evidence indicates this gender disparity extends to the electronic health record (EHR). Similar studies have not been conducted with resident physicians. OBJECTIVE: This study seeks to characterize differences in EHR workload for female resident physicians compared to male resident physicians. DESIGN: This study evaluated 12 months of 156 Mayo Clinic internal medicine residents' inbasket data from July 2020 to June 2021 using Epic's Signal and Physician Efficiency Profile (PEP) data. Excel, BlueSky Statistics, and SAS analytical software were used for analysis. Paired t-tests and analysis of variance were used to compare PEP data by gender and postgraduate year (PGY). "Male" and "female" were used in substitute for "gender" as is precedent in the literature. SUBJECTS: Mayo Clinic internal medicine residents. MAIN MEASURES: Total time spent in EHR per day; time in inbasket and notes per day; time in notes per appointment; number of patient advice requests made through the portal; message turnaround time. KEY RESULTS: Female residents received more patient advice requests per year (p = 0.004) with an average of 86.7 compared to 68, resulting in 34% more patient advice requests per day worked (p < 0.001). Female residents spent more time in inbasket per day (p = 0.002), in notes per day (p < 0.001), and in notes per appointment (p = 0.001). Resident panel comparisons revealed equivocal sizes with significantly more female patients on female (n = 55) vs male (n = 34) resident panels (p < 0.001). There was no difference in message turnaround time, total messages, or number of results received. CONCLUSIONS: Female resident physicians experience significantly more patient-initiated messages and EHR workload despite equivalent number of results and panel size. Gender differences in inbasket burden may disproportionally impact the resident educational experience.

Factors Associated With an Electronic Health Record-Based Definition of Postacute Sequelae of COVID-19 in Patients With Systemic Autoimmune Rheumatic Disease.

OBJECTIVE: Many individuals with rheumatic disease are at higher risk for severe acute coronavirus disease 2019 (COVID-19). We aimed to evaluate risk factors for postacute sequelae of COVID-19 (PASC) using an electronic health record (EHR)-based definition. METHODS: We identified patients with prevalent rheumatic diseases and COVID-19 within the Mass General Brigham healthcare system. PASC was defined by the International Classification of Diseases, 10th revision (ICD-10) codes, relevant labs, vital signs, and medications at least 30 days following the first COVID-19 infection. Patients were followed until the earliest of incident PASC, repeat COVID-19 infection, 1 year of follow-up, death, or February 19, 2023. We used multivariable Cox regression to estimate the association of baseline characteristics with PASC risk. RESULTS: Among 2459 patients (76.37% female, mean age 57.4 years), the most common incident PASC manifestations were cough (14.56%), dyspnea (12.36%), constipation (11.39%), and fatigue (10.70%). Serious manifestations including acute coronary disease (4.43%), thromboembolism (3.09%), hypoxemia (3.09%), stroke (1.75%), and myocarditis (0.12%) were rare. The Delta wave (adjusted hazard ratio [aHR] 0.63, 95% CI 0.49-0.82) and Omicron era (aHR 0.50, 95% CI 0.41-0.62) were associated with lower risk of PASC than the early pandemic period (March 2020-June 2021). Age, obesity, comorbidity burden, race, and hospitalization for acute COVID-19 infection were associated with greater risk of PASC. Glucocorticoid (GC) use (aHR 1.19, 95% CI 1.05-1.34 compared to no use) was associated with greater risk of PASC. CONCLUSION: Among patients with rheumatic diseases, following their first COVID-19 infection, we found a decreased risk of PASC over calendar time using an EHR-based definition. Aside from GCs, no specific immunomodulatory medications were associated with increased risk, and risk factors were otherwise similar to those seen in the general population.

Development and validation of a computable phenotype for Turner syndrome utilizing electronic health records from a national pediatric network.

Turner syndrome (TS) is a genetic condition occurring in ~1 in 2000 females characterized by the complete or partial absence of the second sex chromosome. TS research faces similar challenges to many other pediatric rare disease conditions, with homogenous, single-center, underpowered studies. Secondary data analyses utilizing electronic health record (EHR) have the potential to address these limitations; however, an algorithm to accurately identify TS cases in EHR data is needed. We developed a computable phenotype to identify patients with TS using PEDSnet, a pediatric research network. This computable phenotype was validated through chart review; true positives and negatives and false positives and negatives were used to assess accuracy at both primary and external validation sites. The optimal algorithm consisted of the following criteria: female sex, ≥1 outpatient encounter, and ≥3 encounters with a diagnosis code that maps to TS, yielding an average sensitivity of 0.97, specificity of 0.88, and C-statistic of 0.93 across all sites. The accuracy of any estradiol prescriptions yielded an average C-statistic of 0.91 across sites and 0.80 for transdermal and oral formulations separately. PEDSnet and computable phenotyping are powerful tools in providing large, diverse samples to pragmatically study rare pediatric conditions like TS.

Comparison of late-onset and non-late-onset systemic lupus erythematosus individuals in a real-world electronic health record cohort.

Objective: Late-onset systemic lupus erythematosus (LO-SLE) is defined as SLE diagnosed at age 50 years or later. Current studies on LO-SLE are small and have conflicting results.Methods: Using a large, electronic health record (EHR)-based cohort of SLE individuals, we compared demographics, disease characteristics, SLE-specific antibodies, and medication prescribing practices in LO (n = 123) vs. NLO-SLE (n = 402) individuals.Results: The median age (interquartile range) at SLE diagnosis was 60 (56-67) years for LO-SLE and 28 (20-38) years for NLO-SLE. Both groups were predominantly female (85% vs. 91%, p = 0.10). LO-SLE individuals were more likely to be White than NLO-SLE individuals (74% vs. 60%, p = 0.005) and less likely to have positive dsDNA (39% vs. 58%, p = 0.001) and RNP (17% vs. 32%, p = 0.02) with no differences in Smith, SSA, and SSB. Autoantibody positivity declined with increasing age at SLE diagnosis. LO-SLE individuals were less likely to develop SLE nephritis (9% vs. 29%, p < 0.001) and less likely to be prescribed multiple classes of SLE medications including antimalarials (90% vs. 95%, p = 0.04), azathioprine (17% vs. 31%, p = 0.002), mycophenolate mofetil (12% vs. 38%, p < 0.001), and belimumab (2% vs. 8%, p = 0.02).Conclusion: LO-SLE individuals may be less likely to fit an expected course for SLE with less frequent positive autoantibodies at diagnosis and lower rates of nephritis, even after adjusting for race. Understanding how age impacts SLE disease presentation could help reduce diagnostic delays in SLE.

Home environment factors associated with child BMI changes during COVID-19 pandemic.

BACKGROUND: The influence of home obesogenic environments, as assessed by the validated Family Nutrition and Physical Activity (FNPA) tool, and child obesity during the COVID pandemic were evaluated using electronic health records in this retrospective cohort study. METHODS: Historical data on BMI and the FNPA screening tool were obtained from annual well-child visits within the Geisinger Health System. The study examined youth ages 2-17 that had a BMI record and an FNPA assessment prior to the pandemic (BMI 3/1/19-2/29/20), 1 BMI record 3 months into the pandemic (6/1/20-12/31/20) and 1 BMI in the second year of the pandemic (1/1/21-12/31/21). Tertiles of obesity risk by FNPA score were examined. Mixed-effects linear regression was used to examine change in BMI slope (kg/m2 per month) pre-pandemic to pandemic using FNPA summary and subscales scores as predictors and adjusting for confounding factors. RESULTS: The analyses included 6,746 children (males: 51.7%, non-Hispanic white: 86.6%, overweight:14.8%, obesity:10.3%, severe obesity: 3.9%; mean(SD) age: 5.7(2.8) years). The rate of BMI change in BMI was greatest from early pandemic compared to pre-pandemic for children in lowest versus highest tertiles of FNPA summary score (0.079 vs. 0.044 kg/m2), FNPA-Eating (0.068 vs. 0.049 kg/m2), and FNPA-Activity (0.078 vs. 0.052 kg/m2). FNPA summary score was significantly associated with change in BMI from the pre-pandemic to early pandemic period (p = 0.014), but not associated with change in BMI during the later pandemic period. CONCLUSIONS: This study provides additional insight into the changes in the rate of BMI change observed among children and adolescents in the United States during the COVID-19 pandemic. The FNPA provides ample opportunity to continue our exploration of the negative impact of the COVID-19 pandemic on the longitudinal growth patterns among children and adolescents.