Severe aortic stenosis detection by deep learning applied to echocardiography.

BACKGROUND AND AIMS: Early diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that relies on two-dimensional (2D) parasternal long axis videos from transthoracic echocardiography without Doppler imaging to identify severe AS, suitable for point-of-care ultrasonography. METHODS AND RESULTS: In a training set of 5257 studies (17 570 videos) from 2016 to 2020 [Yale-New Haven Hospital (YNHH), Connecticut], an ensemble of three-dimensional convolutional neural networks was developed to detect severe AS, leveraging self-supervised contrastive pretraining for label-efficient model development. This deep learning model was validated in a temporally distinct set of 2040 consecutive studies from 2021 from YNHH as well as two geographically distinct cohorts of 4226 and 3072 studies, from California and other hospitals in New England, respectively. The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI: 0.966, 0.988) for detecting severe AS in the temporally distinct test set, maintaining its diagnostic performance in geographically distinct cohorts [0.952 AUROC (95% CI: 0.941, 0.963) in California and 0.942 AUROC (95% CI: 0.909, 0.966) in New England]. The model was interpretable with saliency maps identifying the aortic valve, mitral annulus, and left atrium as the predictive regions. Among non-severe AS cases, predicted probabilities were associated with worse quantitative metrics of AS suggesting an association with various stages of AS severity. CONCLUSION: This study developed and externally validated an automated approach for severe AS detection using single-view 2D echocardiography, with potential utility for point-of-care screening.

Comparative Safety and Effectiveness of Biologic Therapy for Crohn's Disease: A CA-IBD Cohort Study.

BACKGROUND & AIMS: We compared the safety and effectiveness of tumor necrosis factor α (TNF-α) antagonists vs vedolizumab vs ustekinumab in patients with Crohn's disease (CD) in a multicenter cohort (CA-IBD). METHODS: We created an electronic health record-based cohort of adult patients with CD who were initiating a new biologic agent (TNF-α antagonists, ustekinumab, vedolizumab) from 5 health systems in California between 2010 and 2017. We compared the risk of serious infections (safety) and all-cause hospitalization and inflammatory bowel disease-related surgery (effectiveness) between different biologic classes using propensity score (PS) matching. RESULTS: As compared with TNF-α antagonists (n = 1030), 2:1 PS-matched, ustekinumab-treated patients with CD (n = 515) experienced a lower risk of serious infections (hazard ratio [HR], 0.36; 95% CI, 0.20-0.64), without any difference in the risk of hospitalization (HR, 0.99; 95% CI, 0.89-1.21) or surgery (HR, 1.08; 95% CI, 0.69-1.70). Compared with vedolizumab (n = 221), 1:1 PS-matched, ustekinumab-treated patients with CD (n = 221) experienced a lower risk of serious infections (HR, 0.20; 95% CI, 0.07-0.60), without significant differences in risk of hospitalization (HR, 0.76; 95% CI, 0.54-1.07) or surgery (HR, 1.42; 95% CI, 0.54-3.72). Compared with TNF-α antagonists (n = 442), 2:1 PS-matched, vedolizumab-treated patients with CD (n = 221) had a similar risk of serious infections (HR, 1.53; 95% CI, 0.84-2.78), hospitalization (HR, 1.32; 95% CI, 0.98-1.77), and surgery (HR, 0.63; 95% CI, 0.27-1.47). High comorbidity burden, concomitant opiate use, and prior hospitalization were associated with serious infections and hospitalization in biologic-treated patients with CD. CONCLUSION: In a multicenter cohort of biologic-treated patients with CD, ustekinumab was associated with a lower risk of serious infections compared with TNF-α antagonists and vedolizumab, without any differences in risk of hospitalization or surgery. The risk of serious infections was similar for TNF-α antagonists vs vedolizumab.

Effectiveness and Safety of Biologic Therapy in Hispanic Vs Non-Hispanic Patients With Inflammatory Bowel Diseases: A CA-IBD Cohort Study.

BACKGROUND & AIMS: There are limited data on outcomes of biologic therapy in Hispanic patients with inflammatory bowel diseases (IBDs). We compared risk of hospitalization, surgery, and serious infections in Hispanic vs non-Hispanic patients with IBD in a multicenter, electronic health record-based cohort of biologic-treated patients. METHODS: We identified adult patients with IBD who were new users of biologic agents (tumor necrosis factor α [TNF-α] antagonists, ustekinumab, vedolizumab) from 5 academic institutions in California between 2010 and 2017. We compared the risk of all-cause hospitalization, IBD-related surgery, and serious infections in Hispanic vs non-Hispanic patients using 1:4 propensity score matching and survival analysis. RESULTS: We compared 240 Hispanic patients (53% male; 45% with ulcerative colitis; 73% TNF-α antagonist-treated; 20% with prior biologic exposure) with 960 non-Hispanic patients (51% male; 44% with ulcerative colitis; 67% TNF-α antagonist-treated; 27% with prior biologic exposure). After propensity score matching, Hispanic patients were younger (37 ± 15 vs 40 ± 16 y; P = .02) and had a higher burden of comorbidities (Elixhauser index, >0; 37% vs 26%; P < .01), without any differences in patterns of medication use, burden of inflammation, and hospitalizations. Within 1 year of biologic initiation, Hispanic patients had higher rates of hospitalizations (31% vs 23%; adjusted hazard ratio [aHR], 1.32; 95% CI, 1.01-1.74) and IBD-related surgery (7.1% vs 4.6%; aHR, 2.00; 95% CI, 1.07-3.72), with a trend toward higher risk of serious infections (8.8% vs 4.9%; aHR, 1.74; 95% CI, 0.99-3.05). CONCLUSIONS: In a multicenter, propensity score-matched cohort of biologic-treated patients with IBD, Hispanic patients experienced higher rates of hospitalization, surgery, and serious infections. Future studies are needed to investigate the biological, social, and environmental drivers of these differences.

How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles.

OBJECTIVE: This study aims at reviewing novel coronavirus disease (COVID-19) datasets extracted from PubMed Central articles, thus providing quantitative analysis to answer questions related to dataset contents, accessibility and citations. METHODS: We downloaded COVID-19-related full-text articles published until 31 May 2020 from PubMed Central. Dataset URL links mentioned in full-text articles were extracted, and each dataset was manually reviewed to provide information on 10 variables: (1) type of the dataset, (2) geographic region where the data were collected, (3) whether the dataset was immediately downloadable, (4) format of the dataset files, (5) where the dataset was hosted, (6) whether the dataset was updated regularly, (7) the type of license used, (8) whether the metadata were explicitly provided, (9) whether there was a PubMed Central paper describing the dataset and (10) the number of times the dataset was cited by PubMed Central articles. Descriptive statistics about these seven variables were reported for all extracted datasets. RESULTS: We found that 28.5% of 12 324 COVID-19 full-text articles in PubMed Central provided at least one dataset link. In total, 128 unique dataset links were mentioned in 12 324 COVID-19 full text articles in PubMed Central. Further analysis showed that epidemiological datasets accounted for the largest portion (53.9%) in the dataset collection, and most datasets (84.4%) were available for immediate download. GitHub was the most popular repository for hosting COVID-19 datasets. CSV, XLSX and JSON were the most popular data formats. Additionally, citation patterns of COVID-19 datasets varied depending on specific datasets. CONCLUSION: PubMed Central articles are an important source of COVID-19 datasets, but there is significant heterogeneity in the way these datasets are mentioned, shared, updated and cited.

Common and rare variants associated with cardiometabolic traits across 98,622 whole-genome sequences in the All of Us research program.

All of Us is a biorepository aiming to advance biomedical research by providing various types of data in diverse human populations. Here we present a demonstration project validating the program's genomic data in 98,622 participants. We sought to replicate known genetic associations for three diseases (atrial fibrillation [AF], coronary artery disease, type 2 diabetes [T2D]) and two quantitative traits (height and low-density lipoprotein [LDL]) by conducting common and rare variant analyses. We identified one known risk locus for AF, five loci for T2D, 143 loci for height, and nine loci for LDL. In gene-based burden tests for rare loss-of-function variants, we replicated associations between TTN and AF, GIGYF1 and T2D, ADAMTS17, ACAN, NPR2 and height, APOB, LDLR, PCSK9 and LDL. Our results are consistent with previous literature, indicating that the All of Us program is a reliable resource for advancing the understanding of complex diseases in diverse human populations.

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

BACKGROUND: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships. METHODS: We present a multi-step data generating process with customizable options and flexible modules to support a variety of simulation requirements. Synthetic patients with nonlinear and correlated features are assigned to provider and institution case series. The probability of treatment and outcome assignment are associated with patient features based on user definitions. Risk due to experiential learning by providers and/or institutions when novel treatments are introduced is injected at various speeds and magnitudes. To further reflect real-world complexity, users can request missing values and omitted variables. We illustrate an implementation of our method in a case study using MIMIC-III data for reference patient feature distributions. RESULTS: Realized data characteristics in the simulated data reflected specified values. Apparent deviations in treatment effects and feature distributions, though not statistically significant, were most common in small datasets (n < 3000) and attributable to random noise and variability in estimating realized values in small samples. When learning effects were specified, synthetic datasets exhibited changes in the probability of an adverse outcomes as cases accrued for the treatment group impacted by learning and stable probabilities as cases accrued for the treatment group not affected by learning. CONCLUSIONS: Our framework extends clinical data simulation techniques beyond generation of patient features to incorporate hierarchical learning effects. This enables the complex simulation studies required to develop and rigorously test algorithms developed to disentangle treatment safety signals from the effects of experiential learning. By supporting such efforts, this work can help identify training opportunities, avoid unwarranted restriction of access to medical advances, and hasten treatment improvements.

A hierarchical strategy to minimize privacy risk when linking "De-identified" data in biomedical research consortia.

Linking data across studies offers an opportunity to enrich data sets and provide a stronger basis for data-driven models for biomedical discovery and/or prognostication. Several techniques to link records have been proposed, and some have been implemented across data repositories holding molecular and clinical data. Not all these techniques guarantee appropriate privacy protection; there are trade-offs between (a) simple strategies that can be associated with data that will be linked and shared with any party and (b) more complex strategies that preserve the privacy of individuals across parties. We propose an intermediary, practical strategy to support linkage in studies that share de-identified data with Data Coordinating Centers. This technology can be extended to link data across multiple data hubs to support privacy preserving record linkage, considering data coordination centers and their awardees, which can be extended to a hierarchy of entities (e.g., awardees, data coordination centers, data hubs, etc.) b.

Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in All of Us Research Program Participants, 2 January to 18 March 2020.

BACKGROUND: With limited severe acute respiratory syndrome coronavirus (SARS-CoV-2) testing capacity in the United States at the start of the epidemic (January-March 2020), testing was focused on symptomatic patients with a travel history throughout February, obscuring the picture of SARS-CoV-2 seeding and community transmission. We sought to identify individuals with SARS-CoV-2 antibodies in the early weeks of the US epidemic. METHODS: All of Us study participants in all 50 US states provided blood specimens during study visits from 2 January to 18 March 2020. Participants were considered seropositive if they tested positive for SARS-CoV-2 immunoglobulin G (IgG) antibodies with the Abbott Architect SARS-CoV-2 IgG enzyme-linked immunosorbent assay (ELISA) and the EUROIMMUN SARS-CoV-2 ELISA in a sequential testing algorithm. The sensitivity and specificity of these ELISAs and the net sensitivity and specificity of the sequential testing algorithm were estimated, along with 95% confidence intervals (CIs). RESULTS: The estimated sensitivities of the Abbott and EUROIMMUN assays were 100% (107 of 107 [95% CI: 96.6%-100%]) and 90.7% (97 of 107 [83.5%-95.4%]), respectively, and the estimated specificities were 99.5% (995 of 1000 [98.8%-99.8%]) and 99.7% (997 of 1000 [99.1%-99.9%]), respectively. The net sensitivity and specificity of our sequential testing algorithm were 90.7% (97 of 107 [95% CI: 83.5%-95.4%]) and 100.0% (1000 of 1000 [99.6%-100%]), respectively. Of the 24 079 study participants with blood specimens from 2 January to 18 March 2020, 9 were seropositive, 7 before the first confirmed case in the states of Illinois, Massachusetts, Wisconsin, Pennsylvania, and Mississippi. CONCLUSIONS: Our findings identified SARS-CoV-2 infections weeks before the first recognized cases in 5 US states.

Digital Health Technologies for Remote Monitoring and Management of Inflammatory Bowel Disease: A Systematic Review.

INTRODUCTION: Digital health technologies may be useful tools in the management of chronic diseases. We performed a systematic review of digital health interventions in the management of patients with inflammatory bowel diseases (IBD) and evaluated its impact on (i) disease activity monitoring, (ii) treatment adherence, (iii) quality of life (QoL) measures, and/or (iv) health care utilization. METHODS: Through a systematic review of multiple databases through August 31, 2020, we identified randomized controlled trials in patients with IBD comparing digital health technologies vs standard of care (SoC) for clinical management and monitoring and reporting impact on IBD disease activity, treatment adherence, QoL, and/or health care utilization or cost-effectiveness. We performed critical qualitative synthesis of the evidence supporting digital health interventions in patients with IBD and rated certainty of evidence using Grading of Recommendations Assessment, Development and Evaluation. RESULTS: Overall, we included 14 randomized controlled trials (median, 98 patients; range 34-909 patients; follow-up <12 months) that compared web-based interventions, mobile applications, and different telemedicine platforms with SoC (clinic-based encounters). Although overall disease activity and risk of relapse were comparable between digital health technologies and SoC (very low certainty of evidence), digital health interventions were associated with lower rate of health care utilization and health care costs (low certainty of evidence). Digital health interventions did not significantly improve patients' QoL and treatment adherence compared with SoC (very low certainty of evidence). Trials may have intrinsic selection bias due to nature of digital interventions. DISCUSSION: Digital health technologies may be effective in decreasing health care utilization and costs, though may not offer advantage in reducing risk of relapse, QoL, and improving treatment adherence in patients with IBD. These techniques may offer value-based care for population health management.

Effect of Obesity on Risk of Hospitalization, Surgery, and Serious Infection in Biologic-Treated Patients With Inflammatory Bowel Diseases: A CA-IBD Cohort Study.

INTRODUCTION: Obesity is variably associated with treatment response in biologic-treated patients with inflammatory bowel diseases (IBD). We evaluated the association between obesity and risk of hospitalization, surgery, or serious infections in patients with IBD in new users of biologic agents in a large, multicenter, electronic health record (EHR)-based cohort (CA-IBD). METHODS: We created an EHR-based cohort of adult patients with IBD who were new users of biologic agents (tumor necrosis factor [TNF-α] antagonists, ustekinumab, and vedolizumab) between January 1, 2010, and June 30, 2017, from 5 health systems in California. Patients were classified as those with normal body mass index (BMI), overweight, or obese based on the World Health Organization classification. We compared the risk of all-cause hospitalization, IBD-related surgery, or serious infections among patients with obesity vs those overweight vs those with normal BMI, using Cox proportional hazard analyses, adjusting for baseline demographic, disease, and treatment characteristics. RESULTS: Of 3,038 biologic-treated patients with IBD (69% with Crohn's disease and 76% on TNF-α antagonists), 28.2% (n = 858) were overweight, and 13.7% (n = 416) were obese. On a follow-up after biologic initiation, obesity was not associated with an increased risk of hospitalization (adjusted hazard ratio [aHR] vs normal BMI, 0.90; [95% confidence interval, 0.72-1.13]); IBD-related surgery (aHR, 0.62 [0.31-1.22]); or serious infection (aHR, 1.11 [0.73-1.71]). Similar results were observed on stratified analysis by disease phenotype (Crohn's disease vs ulcerative colitis) and index biologic therapy (TNF-α antagonists vs non-TNF-α antagonists). DISCUSSION: In a multicenter, EHR-based cohort of biologic-treated patients with IBD, obesity was not associated with hospitalization, surgery, or serious infections. Further studies examining the effect of visceral obesity on patient-reported and endoscopic outcomes are needed.

VERTICOX: Vertically Distributed Cox Proportional Hazards Model Using the Alternating Direction Method of Multipliers.

The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the estimation of model parameters and statistics to that of its centralized realization. The proposed algorithm converges linearly under the ADMM framework. Its computational complexity and communication costs are polynomially and linearly associated with the number of subjects, respectively. Experimental results show that VERTICOX can achieve accurate model parameter estimation to support federated survival analysis over vertically distributed data by saving bandwidth and avoiding exchange of information about individual patients. The source code for VERTICOX is available at: https://github.com/daiwenrui/VERTICOX.

NLM's sponsorship of research in biomedical informatics (1985-2016).

The U.S. National Library of Medicine's (NLM) funding for biomedical informatics research in the 1980s and 1990s focused on clinical decision support systems, which were also the focus of research for Donald A.B. Lindberg M.D. prior to becoming NLM's director. The portfolio of projects expanded over the years. At NLM, Dr. Lindberg supported various large infrastructure programs that enabled biomedical informatics research, as well as investigator-initiated research projects that increasingly included biotechnology/bioinformatics and health services research. The authors review NLM's sponsorship of research during Dr. Lindberg's tenure as its Director. NLM's funding significantly increased in the 2000's and beyond. Authors report an analysis of R01 topics from 1985-2016 using data from NIH RePORTER. Dr. Lindberg's legacy for biomedical informatics research is reflected by the research NLM supported under his leadership. The number of R01s remained steady over the years, but the funds provided within awards increased over time. A significant amount of NLM funds listed in RePORTER went into various types of infrastructure projects that laid a solid foundation for biomedical informatics research over multiple decades.

Frailty Is Independently Associated with Mortality and Readmission in Hospitalized Patients with Inflammatory Bowel Diseases.

BACKGROUND & AIMS: Old age must be considered in weighing the risks of complications vs benefits of treatment for patients with inflammatory bowel diseases (IBD). We conducted a nationally representative cohort study to estimate the independent effects of frailty on burden, costs, and causes for hospitalization in patients with IBD. METHODS: We searched the Nationwide Readmissions Database to identify 47,402 patients with IBD, hospitalized from January through June 2013 and followed for readmission through December 31, 2013. Based on a validated hospital frailty risk scoring system, 15,507 patients were considered frail and 31,895 were considered non-frail at index admission. We evaluated the independent effect of frailty on longitudinal burden and costs of hospitalization, inpatient mortality, risk of readmission and surgery, and reasons for readmission. RESULTS: Over a median follow-up time of 10 months, adjusting for age, sex, income, comorbidity index, depression, obesity, severity, and indication for index hospitalization, frailty was independently associated with 57% higher risk of mortality (adjusted hazard ratio [aHR], 1.57; 95% CI, 1.34-1.83), 21% higher risk of all-cause readmission (adjusted hazard ratio [HR], 1.21; 95% CI, 1.17-1.25), and 22% higher risk of readmission for severe IBD (aHR, 1.22; 95% CI, 1.16-1.29). Frail patients with IBD spent more days in the hospital annually (median 9 days; interquartile range, 4-18 days vs median 5 days for non-frail patients; interquartile range, 3-10 days; P < .01) with higher costs of hospitalization ($17,791; interquartile range, $8368-$38,942 vs $10,924 for non-frail patients, interquartile range, $5571-$22,632; P < .01). Infections, rather than IBD, were the leading cause of hospitalization for frail patients. CONCLUSIONS: Frailty is independently associated with higher mortality and burden of hospitalization in patients with IBD; infections are the leading cause of hospitalization. Frailty should be considered in treatment approach, especially in older patients with IBD.

Prevalence and Effects of Food Insecurity and Social Support on Financial Toxicity in and Healthcare Use by Patients With Inflammatory Bowel Diseases.

BACKGROUND & AIMS: We estimated the prevalence of social determinants of health (SDH, food insecurity and social support) in adults with inflammatory bowel diseases (IBD) in the United States and evaluated associations with financial toxicity and healthcare use. METHODS: In the National Health Interview Survey 2015, we identified adults with IBD and estimated the prevalence of food insecurity and/or lack of social support. We evaluated associations with financial toxicity (financial hardship due to medical bills, personal and health-related financial distress, cost-related medication nonadherence, healthcare affordability) and emergency department use. RESULTS: Of estimated 3.1 million adults with IBD in the US, 42% or estimated 1,277,215 patients with IBD reported at least one negative SDH, with 12% reporting both food insecurity and lack of social support. On multivariable analysis adjusting for age, sex, race, family income and comorbidities, patients with food insecurity were significantly more likely to experience financial hardship due to medical bills (odds ratio [OR], 3.31; 95% CI, 1.48-7.39), financial distress (OR, 6.92; 95% CI, 2.28-21.0) and cost-related medication non-adherence (OR, 8.07; 95% CI, 3.16-20.6). Similarly, patients with inadequate social support were significantly more likely to experience financial hardship due to medical bills (OR, 2.98; 95% CI, 1.56-5.67), financial distress (OR, 3.05; 95% CI, 1.64-5.67) and cost-related medication non-adherence (OR, 2.71; 95% CI, 1.10-6.66). Food insecurity and/or lack of social support was not associated with increased risk of emergency department use. CONCLUSIONS: In an analysis of data from the National Health Interview Survey 2015, we found that 1 in 8 patients with IBD have food insecurity and lack social support, which is associated with higher financial toxicity. Patients with IBD should be assessed for SDH to tailor healthcare delivery and improve population health.

Calibrating predictive model estimates in a distributed network of patient data.

BACKGROUND: Protecting the privacy of patient data is an important issue. Patient data are typically protected in local health systems, but this makes integration of data from different healthcare systems difficult. To build high-performance predictive models, a large number of samples are needed, and performance measures such as calibration and discrimination are essential. While distributed algorithms for building models and measuring discrimination have been published, distributed algorithms to measure calibration and recalibrate models have not been proposed. OBJECTIVE: Recalibration models have been shown to improve calibration, but they have not been proposed for data that are distributed in various health systems, or "sites". Our goal is to measure calibration performance and build a global recalibration model using data from multiple health systems, without sharing patient-level data. MATERIALS AND METHODS: We developed a distributed smooth isotonic regression recalibration model and extended established calibration measures, such as Hosmer-Lemeshow Tests, Expected Calibration Error, and Maximum Calibration Error in a distributed manner. RESULTS: Experiments on both simulated and clinical data were conducted, and the recalibration results produced by a traditional (ie, centralized) versus a distributed smooth isotonic regression were compared. The results were exactly the same. DISCUSSION: Our algorithms demonstrated that calibration can be improved and measured in a distributed manner while protecting data privacy, albeit at some cost in terms of computational efficiency. It also gives researchers who may have too few instances in their own institutions a method to construct robust recalibration models. CONCLUSION: Preserving data privacy and improving model calibration are both important to advancing predictive analysis in clinical informatics. The algorithms alleviate the difficulties in model building across sites.

Geographic Variation in Obesity at the State Level in the All of Us Research Program.

INTRODUCTION: National obesity prevention strategies may benefit from precision health approaches involving diverse participants in population health studies. We used cohort data from the National Institutes of Health All of Us Research Program (All of Us) Researcher Workbench to estimate population-level obesity prevalence. METHODS: To estimate state-level obesity prevalence we used data from physical measurements made during All of Us enrollment visits and data from participant electronic health records (EHRs) where available. Prevalence estimates were calculated and mapped by state for 2 categories of body mass index (BMI) (kg/m2): obesity (BMI >30) and severe obesity (BMI >35). We calculated and mapped prevalence by state, excluding states with fewer than 100 All of Us participants. RESULTS: Data on height and weight were available for 244,504 All of Us participants from 33 states, and corresponding EHR data were available for 88,840 of these participants. The median and IQR of BMI taken from physical measurements data was 28.4 (24.4- 33.7) and 28.5 (24.5-33.6) from EHR data, where available. Overall obesity prevalence based on physical measurements data was 41.5% (95% CI, 41.3%-41.7%); prevalence of severe obesity was 20.7% (95% CI, 20.6-20.9), with large geographic variations observed across states. Prevalence estimates from states with greater numbers of All of Us participants were more similar to national population-based estimates than states with fewer participants. CONCLUSION: All of Us participants had a high prevalence of obesity, with state-level geographic variation mirroring national trends. The diversity among All of Us participants may support future investigations on obesity prevention and treatment in diverse populations.

Genomics and electronic health record systems.

Several reviews and case reports have described how information derived from the analysis of genomes are currently included in electronic health records (EHRs) for the purposes of supporting clinical decisions. Since the introduction of this new type of information in EHRs is relatively new (for instance, the widespread adoption of EHRs in the United States is just about a decade old), it is not surprising that a myriad of approaches has been attempted, with various degrees of success. EHR systems undergo much customization to fit the needs of health systems; these approaches have been varied and not always generalizable. The intent of this article is to present a high-level view of these approaches, emphasizing the functionality that they are trying to achieve, and not to advocate for specific solutions, which may become obsolete soon after this review is published. We start by broadly defining the end goal of including genomics in EHRs for healthcare and then explaining the various sources of information that need to be linked to arrive at a clinically actionable genomics analysis using a pharmacogenomics example. In addition, we include discussions on open issues and a vision for the next generation systems that integrate whole genome sequencing and EHRs in a seamless fashion.

Efficient determination of equivalence for encrypted data.

Secure computation of equivalence has fundamental application in many different areas, including health-care. We study this problem in the context of matching an individual's identity to link medical records across systems under the socialist millionaires' problem: Two millionaires wish to determine if their fortunes are equal without disclosing their net worth (Boudot, et al. 2001). In Theorem 2, we show that when a "greater than" algorithm is carried out on a totally ordered set it is easy to achieve secure matching without additional rounds of communication. We present this efficient solution to assess equivalence using a set intersection algorithm designed for "greater than" computation and demonstrate its effectiveness on equivalence of arbitrary data values, as well as demonstrate how it meets regulatory criteria for risk of disclosure.

Obesity Is Independently Associated With Higher Annual Burden and Costs of Hospitalization in Patients With Inflammatory Bowel Diseases.

BACKGROUND & AIMS: Approximately 15%-40% patients with inflammatory bowel diseases (IBD) are obese. There is an inconsistent association between obesity and IBD phenotype and course. We conducted a nationally representative cohort study to estimate and compare the burden, costs, and causes for hospitalization in obese vs non-obese patients with IBD. METHODS: Using the Nationwide Readmissions Database 2013, we identified obese (based on administrative claims code) and non-obese patients who had been hospitalized at least once, from January through June 2013, and followed them for re-hospitalization until December 2013. We compared annual burden (total days spent in hospital), costs, causes, and outcomes of hospitalization between obese and non-obese patients after 1:1 propensity score matching. RESULTS: We identified 42,285 patients with IBD, of which 12.4% were obese. After propensity score matching, we included 5128 obese and 5128 non-obese IBD patients in our analysis. Compared to non-obese patients, obese patients spent more days in hospital annually (median, 8 vs 5 days) (P < .01), with higher hospitalization-related costs (median, $17,277 vs $11,847) (P < .01); this pattern persisted in subsets of high-need and high-cost patients. Compared to non-obese patients, obese patients were more likely to be hospitalized with preventable admissions (19% vs 15%) or cardiopulmonary complications (16% vs 12%). CONCLUSIONS: In an analysis of data on patients with IBD from the Nationwide Readmissions Database 2013, we found obesity to be independently associated with higher burden and costs of hospitalizations. Strategies should be considered to target obesity as adjunctive therapy for patients with IBD.