The power of partnership: Exploring collaboration dynamics in U.S. transplant research

INTRODUCTION

Collaboration is one of the hallmarks of academic research. This study analyzes collaboration patterns in U.S. transplant research, examining publication trends, productive institutions, co-authorship networks, and citation patterns in high-impact transplant journals.

METHODS

4,265 articles published between 2012 and 2021 were analyzed using scientometric tools, logistic regression, VantagePoint software, and Gephi software for network visualization.

RESULTS

16,003 authors from 1,011 institutions and 59 countries were identified, with Harvard, Johns Hopkins, and University of Pennsylvania contributing the most papers. Odds of international collaboration significantly increased over time (OR 1.03; p ​= ​0.040), while odds of citation in single-institution collaborations decreased (OR 0.99; p ​= ​0.016). Five major scientific communities and central institutions (Harvard University and University of Pittsburgh) connecting them were identified, revealing interconnected research clusters.

CONCLUSIONS

Collaboration enhances knowledge exchange and research productivity, with an increasing trend of institutional and international collaboration in U.S. transplant research. Understanding this community is essential for promoting research impact and forming strategic partnerships.

A broadly applicable approach to enrich electronic-health-record cohorts by identifying patients with complete data: a multisite evaluation

OBJECTIVE

Patients who receive most care within a single healthcare system (colloquially called a "loyalty cohort" since they typically return to the same providers) have mostly complete data within that organization's electronic health record (EHR). Loyalty cohorts have low data missingness, which can unintentionally bias research results. Using proxies of routine care and healthcare utilization metrics, we compute a per-patient score that identifies a loyalty cohort.

MATERIALS AND METHODS

We implemented a computable program for the widely adopted i2b2 platform that identifies loyalty cohorts in EHRs based on a machine-learning model, which was previously validated using linked claims data. We developed a novel validation approach, which tests, using only EHR data, whether patients returned to the same healthcare system after the training period. We evaluated these tools at 3 institutions using data from 2017 to 2019.

RESULTS

Loyalty cohort calculations to identify patients who returned during a 1-year follow-up yielded a mean area under the receiver operating characteristic curve of 0.77 using the original model and 0.80 after calibrating the model at individual sites. Factors such as multiple medications or visits contributed significantly at all sites. Screening tests' contributions (eg, colonoscopy) varied across sites, likely due to coding and population differences.

DISCUSSION

This open-source implementation of a "loyalty score" algorithm had good predictive power. Enriching research cohorts by utilizing these low-missingness patients is a way to obtain the data completeness necessary for accurate causal analysis.

CONCLUSION

i2b2 sites can use this approach to select cohorts with mostly complete EHR data.

Anatomical structures, cell types, and biomarkers of the healthy human blood vasculature

More than 150 scientists from 17 consortia are collaborating on an international project to build a Human Reference Atlas, which maps all 37 trillion cells in the healthy adult human body. The initial release of this atlas provided hierarchical lists of the anatomical structures, cell types, and biomarkers in 11 organs. Here, we describe the methods we used as part of this initiative to build the first open, computer-readable, and comprehensive database of the adult human blood vasculature, called the Human Reference Atlas-Vasculature Common Coordinate Framework (HRA-VCCF). It includes 993 vessels and their branching connections, 10 cell types, and 10 biomarkers. With this paper we are releasing additional details on vessel types and subtypes, branching sequence, anastomoses, portal systems, microvasculature, functional tissue units, mappings to regions vessels supply or drain, geometric properties of vessels, and links to 3D reference objects. Future versions will add variants and connections to the lymph vasculature; and, it will iteratively expand and improve the database as additional experimental data become available through the participating consortia.

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND

In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients.

METHODS

We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern.

RESULTS

With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors.

CONCLUSION

In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

Mapping the lymphatic system across body scales and expertise domains: A report from the 2021 National Heart, Lung, and Blood Institute workshop at the Boston Lymphatic Symposium

Enhancing our understanding of lymphatic anatomy from the microscopic to the anatomical scale is essential to discern how the structure and function of the lymphatic system interacts with different tissues and organs within the body and contributes to health and disease. The knowledge of molecular aspects of the lymphatic network is fundamental to understand the mechanisms of disease progression and prevention. Recent advances in mapping components of the lymphatic system using state of the art single cell technologies, the identification of novel biomarkers, new clinical imaging efforts, and computational tools which attempt to identify connections between these diverse technologies hold the potential to catalyze new strategies to address lymphatic diseases such as lymphedema and lipedema. This manuscript summarizes current knowledge of the lymphatic system and identifies prevailing challenges and opportunities to advance the field of lymphatic research as discussed by the experts in the workshop.

Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium

PURPOSE

In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population.

METHODS

A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS.

RESULTS

Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%).

CONCLUSION

Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study

Background

While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking.

Methods

A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021.

Findings

Advanced age (HR 2.77, 95%CI 2.53-3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI.

Interpretation

COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery.

Funding

Authors are supported by various funders, with full details stated in the acknowledgement section.

SurvMaximin: Robust federated approach to transporting survival risk prediction models

OBJECTIVE

For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information.

MATERIALS AND METHODS

For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning.

RESULTS

Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations.

CONCLUSIONS

The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09–1.55), heart failure (RR 1.22, 95% CI 1.10–1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07–1.31), and fatigue (RR 1.18, 95% CI 1.07–1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58–2.76), venous embolism (RR 1.34, 95% CI 1.17–1.54), atrial fibrillation (RR 1.30, 95% CI 1.13–1.50), type 2 diabetes (RR 1.26, 95% CI 1.16–1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09–1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90–3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21–2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04–1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study

BACKGROUND

Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification.

OBJECTIVE

The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification.

METHODS

From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as "admitted with COVID-19" (incidental) versus specifically admitted for COVID-19 ("for COVID-19"). EHR-based phenotyping was used to find feature sets to filter out incidental admissions.

RESULTS

EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity.

CONCLUSIONS

A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

Distinguishing Admissions Specifically for COVID-19 from Incidental SARS-CoV-2 Admissions: A National EHR Research Consortium Study

Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. EHR-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. From a retrospective EHR-based cohort in four US healthcare systems, a random sample of 1,123 SARS-CoV-2 PCR-positive patients hospitalized between 3/2020â€"8/2021 was manually chart-reviewed and classified as admitted-with-COVID-19 (incidental) vs. specifically admitted for COVID-19 (for-COVID-19). EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in 26%. The top site-specific feature sets had 79-99% specificity with 62-75% sensitivity, while the best performing across-site feature set had 71-94% specificity with 69-81% sensitivity. A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

HyperMinHash: MinHash in LogLog space

In this extended abstract, we describe and analyze a lossy compression of MinHash from buckets of size O ( l o g n ) to buckets of size O ( l o g l o g n ) by encoding using floating-point notation. This new compressed sketch, which we call HyperMinHash, as we build off a HyperLogLog scaffold, can be used as a drop-in replacement of MinHash. Unlike comparable Jaccard index fingerprinting algorithms in sub-logarithmic space (such as b-bit MinHash), HyperMinHash retains MinHash's features of streaming updates, unions, and cardinality estimation. For an additive approximation error ϵ on a Jaccard index t , given a random oracle, HyperMinHash needs O ( ϵ - 2 ( l o g l o g n + l o g 1 ϵ ) ) space. HyperMinHash allows estimating Jaccard indices of 0.01 for set cardinalities on the order of 10 19 with relative error of around 10% using 2MiB of memory; MinHash can only estimate Jaccard indices for cardinalities of 10 10 with the same memory consumption.

ATLAS: an automated association test using probabilistically linked health records with application to genetic studies

OBJECTIVE

Large amounts of health data are becoming available for biomedical research. Synthesizing information across databases may capture more comprehensive pictures of patient health and enable novel research studies. When no gold standard mappings between patient records are available, researchers may probabilistically link records from separate databases and analyze the linked data. However, previous linked data inference methods are constrained to certain linkage settings and exhibit low power. Here, we present ATLAS, an automated, flexible, and robust association testing algorithm for probabilistically linked data.

MATERIALS AND METHODS

Missing variables are imputed at various thresholds using a weighted average method that propagates uncertainty from probabilistic linkage. Next, estimated effect sizes are obtained using a generalized linear model. ATLAS then conducts the threshold combination test by optimally combining P values obtained from data imputed at varying thresholds using Fisher's method and perturbation resampling.

RESULTS

In simulations, ATLAS controls for type I error and exhibits high power compared to previous methods. In a real-world genetic association study, meta-analysis of ATLAS-enabled analyses on a linked cohort with analyses using an existing cohort yielded additional significant associations between rheumatoid arthritis genetic risk score and laboratory biomarkers.

DISCUSSION

Weighted average imputation weathers false matches and increases contribution of true matches to mitigate linkage error-induced bias. The threshold combination test avoids arbitrarily choosing a threshold to rule a match, thus automating linked data-enabled analyses and preserving power.

CONCLUSION

ATLAS promises to enable novel and powerful research studies using linked data to capitalize on all available data sources.

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study

Background

Many countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic.

Objective

In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic.

Methods

Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19.

Results

Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain.

Conclusions

Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data

OBJECTIVE

The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.

MATERIALS AND METHODS

Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site.

RESULTS

The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review.

DISCUSSION

We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions.

CONCLUSIONS

We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

Expected 10-anonymity of HyperLogLog sketches for federated queries of clinical data repositories

Motivation

The rapid growth in of electronic medical records provide immense potential to researchers, but are often silo-ed at separate hospitals. As a result, federated networks have arisen, which allow simultaneously querying medical databases at a group of connected institutions. The most basic such query is the aggregate count—e.g. How many patients have diabetes? However, depending on the protocol used to estimate that total, there is always a tradeoff in the accuracy of the estimate against the risk of leaking confidential data. Prior work has shown that it is possible to empirically control that tradeoff by using the HyperLogLog (HLL) probabilistic sketch.

Results

In this article, we prove complementary theoretical bounds on the k-anonymity privacy risk of using HLL sketches, as well as exhibit code to efficiently compute those bounds.

Availability and implementation

https://github.com/tzyRachel/K-anonymity-Expectation.

International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries

IMPORTANCE

Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients.

OBJECTIVE

To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study.

MAIN OUTCOMES AND MEASURES

Patient characteristics, clinical features, and medication use.

RESULTS

There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study's cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19-directed medications.

CONCLUSIONS AND RELEVANCE

This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.

Development of a COVID-19 Application Ontology for the ACT Network

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Development of a Coronavirus Disease 2019 (COVID-19) Application Ontology for the Accrual to Clinical Trials (ACT) network

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment

Objective

Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.

Materials and Methods

The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.

Results

Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.

Conclusions

The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study

Objectives

To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions.

Design

Retrospective cohort study.

Setting

The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe.

Participants

Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2.

Primary and secondary outcome measures

Patients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction.

Results

Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites.

Conclusions

Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation

Background

Over the past decade, the emergence of several large federated clinical data networks has enabled researchers to access data on millions of patients at dozens of health care organizations. Typically, queries are broadcast to each of the sites in the network, which then return aggregate counts of the number of matching patients. However, because patients can receive care from multiple sites in the network, simply adding the numbers frequently double counts patients. Various methods such as the use of trusted third parties or secure multiparty computation have been proposed to link patient records across sites. However, they either have large trade-offs in accuracy and privacy or are not scalable to large networks.

Objective

This study aims to enable accurate estimates of the number of patients matching a federated query while providing strong guarantees on the amount of protected medical information revealed.

Methods

We introduce a novel probabilistic approach to running federated network queries. It combines an algorithm called HyperLogLog with obfuscation in the form of hashing, masking, and homomorphic encryption. It is tunable, in that it allows networks to balance accuracy versus privacy, and it is computationally efficient even for large networks. We built a user-friendly free open-source benchmarking platform to simulate federated queries in large hospital networks. Using this platform, we compare the accuracy, k-anonymity privacy risk (with k=10), and computational runtime of our algorithm with several existing techniques.

Results

In simulated queries matching 1 to 100 million patients in a 100-hospital network, our method was significantly more accurate than adding aggregate counts while maintaining k-anonymity. On average, it required a total of 12 kilobytes of data to be sent to the network hub and added only 5 milliseconds to the overall federated query runtime. This was orders of magnitude better than other approaches, which guaranteed the exact answer.

Conclusions

Using our method, it is possible to run highly accurate federated queries of clinical data repositories that both protect patient privacy and scale to large networks.

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Anticoagulation after intracranial hemorrhage in brain tumors: Risk of recurrent hemorrhage and venous thromboembolism

BACKGROUND

Intracranial hemorrhage (ICH) is a common and often devastating outcome in patients with brain tumors. Despite this, there is little evidence to guide anticoagulation management following an initial ICH event.

OBJECTIVES

To analyze the risk of recurrent hemorrhagic and thrombotic outcomes after an initial ICH event in patients with brain tumors and prior venous thromboembolism (VTE).

PATIENTS AND METHODS

A retrospective cohort study was performed. Radiographic images obtained after initial ICH were reviewed for the primary outcomes of recurrent ICH and VTE.

RESULTS AND CONCLUSIONS

A total of 79 patients with brain tumors who developed ICH on anticoagulation for VTE were analyzed. Fifty-four patients (68.4%) restarted anticoagulation following ICH. The cumulative incidence of recurrent ICH at 1 year was 6.1% (95% confidence interval [CI], 1.5-15.3) following reinitiation of anticoagulation. Following a major ICH (defined as an ICH >10 mL in size, causing symptoms, or requiring intervention), the rate of recurrent ICH upon reexposure to anticoagulation was 14.5% (95% CI, 2.1-38.35), whereas the rate of recurrent ICH following smaller ICH was 2.6% (95% CI, 0.2%-12.0%). Mortality following a recurrent ICH on anticoagulation was 67% at 30 days. The cumulative incidence of recurrent VTE was significantly lower in the restart cohort compared to patients who did not restart anticoagulation (8.1% vs 35.3%; P = .003). We conclude that resumption of anticoagulation is lowest among patients with metastatic brain tumors with small initial ICH. Following an initial major ICH, resumption of anticoagulation was associated with a high rate of recurrent ICH.

Considerations for Using the Vasculature as a Coordinate System to Map All the Cells in the Human Body

Several ongoing international efforts are developing methods of localizing single cells within organs or mapping the entire human body at the single cell level, including the Chan Zuckerberg Initiative's Human Cell Atlas (HCA), and the Knut and Allice Wallenberg Foundation's Human Protein Atlas (HPA), and the National Institutes of Health's Human BioMolecular Atlas Program (HuBMAP). Their goals are to understand cell specialization, interactions, spatial organization in their natural context, and ultimately the function of every cell within the body. In the same way that the Human Genome Project had to assemble sequence data from different people to construct a complete sequence, multiple centers around the world are collecting tissue specimens from diverse populations that vary in age, race, sex, and body size. A challenge will be combining these heterogeneous tissue samples into a 3D reference map that will enable multiscale, multidimensional Google Maps-like exploration of the human body. Key to making alignment of tissue samples work is identifying and using a coordinate system called a Common Coordinate Framework (CCF), which defines the positions, or "addresses," in a reference body, from whole organs down to functional tissue units and individual cells. In this perspective, we examine the concept of a CCF based on the vasculature and describe why it would be an attractive choice for mapping the human body.

Validation of an Electronic Health Record-Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems

Importance

Suicide is a leading cause of mortality, with suicide-related deaths increasing in recent years. Automated methods for individualized risk prediction have great potential to address this growing public health threat. To facilitate their adoption, they must first be validated across diverse health care settings.

Objective

To evaluate the generalizability and cross-site performance of a risk prediction method using readily available structured data from electronic health records in predicting incident suicide attempts across multiple, independent, US health care systems.

Design, Setting, and Participants

For this prognostic study, data were extracted from longitudinal electronic health record data comprising International Classification of Diseases, Ninth Revision diagnoses, laboratory test results, procedures codes, and medications for more than 3.7 million patients from 5 independent health care systems participating in the Accessible Research Commons for Health network. Across sites, 6 to 17 years' worth of data were available, up to 2018. Outcomes were defined by International Classification of Diseases, Ninth Revision codes reflecting incident suicide attempts (with positive predictive value >0.70 according to expert clinician medical record review). Models were trained using naive Bayes classifiers in each of the 5 systems. Models were cross-validated in independent data sets at each site, and performance metrics were calculated. Data analysis was performed from November 2017 to August 2019.

Main Outcomes and Measures

The primary outcome was suicide attempt as defined by a previously validated case definition using International Classification of Diseases, Ninth Revision codes. The accuracy and timeliness of the prediction were measured at each site.

Results

Across the 5 health care systems, of the 3 714 105 patients (2 130 454 female [57.2%]) included in the analysis, 39 162 cases (1.1%) were identified. Predictive features varied by site but, as expected, the most common predictors reflected mental health conditions (eg, borderline personality disorder, with odds ratios of 8.1-12.9, and bipolar disorder, with odds ratios of 0.9-9.1) and substance use disorders (eg, drug withdrawal syndrome, with odds ratios of 7.0-12.9). Despite variation in geographical location, demographic characteristics, and population health characteristics, model performance was similar across sites, with areas under the curve ranging from 0.71 (95% CI, 0.70-0.72) to 0.76 (95% CI, 0.75-0.77). Across sites, at a specificity of 90%, the models detected a mean of 38% of cases a mean of 2.1 years in advance.

Conclusions and Relevance

Across 5 diverse health care systems, a computationally efficient approach leveraging the full spectrum of structured electronic health record data was able to detect the risk of suicidal behavior in unselected patients. This approach could facilitate the development of clinical decision support tools that inform risk reduction interventions.

The human body at cellular resolution: the NIH Human Biomolecular Atlas Program

Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions.

Intracranial hemorrhage with direct oral anticoagulants in patients with brain tumors

Essentials Intracranial hemorrhage (ICH) is common in patients with brain tumors. We compared rates of ICH with DOACs and low molecular weight heparin. DOACs were associated with a lower incidence of ICH in primary brain tumors. DOACs appear safe to administer to patients with brain tumors. SUMMARY: Background Direct oral anticoagulants (DOACs) are efficacious in the treatment of cancer-associated thrombosis but are associated with an increased risk of hemorrhage compared with low-molecular-weight heparin in certain malignancies. Whether the DOACs increase the incidence of intracranial hemorrhage (ICH) in patients with brain tumors is not established. Objectives To determine the cumulative incidence of ICH in DOACs compared with Low-molecular-weight heparin (LMWH) in patients with brain tumors and venous thromboembolism. Patients and methods A retrospective comparative cohort study was performed. Radiographic images for all ICH events were reviewed and the primary endpoint was cumulative incidence of ICH at 12 months following initiation of anticoagulation. Results and conclusions A total of 172 patients with brain tumors were evaluated (42 DOAC and 131 LMWH). In the primary brain tumor cohort (n = 67), the cumulative incidence of any ICH was 0% in patients receiving DOACs vs. 36.8% (95% confidence interval [CI], 22.3-51.3%) in those treated with LMWH, with a major ICH incidence of 18.2% (95% CI, 8.4-31.0). In the brain metastases cohort (n = 105), DOACs did not increase the risk of any ICH relative to enoxaparin, with an incidence of 27.8% (95% CI, 5.5-56.7%) compared with 52.9% (95% CI, 37.4-66.2%). Similarly, DOAC did not increase the incidence of major ICH in brain metastases, with a cumulative incidence 11.1% (95% CI, 0.5-40.6%) vs. 17.8% (95% CI, 10.2-27.2%). We conclude that DOACs are not associated with an increased incidence of ICH relative to LMWH in patients with brain metastases or primary brain tumors.

Effect of Targeting Inflammation With Salsalate: The TINSAL-CVD Randomized Clinical Trial on Progression of Coronary Plaque in Overweight and Obese Patients Using Statins

IMPORTANCE

Inflammation may contribute to pathological associations among obesity, diabetes mellitus, and cardiovascular disease.

OBJECTIVE

To determine whether targeting inflammation using salsalate compared with placebo reduces progression of noncalcified coronary artery plaque.

DESIGN, SETTING, AND PARTICIPANTS

In the Targeting Inflammation Using Salsalate in Cardiovascular Disease (TINSAL-CVD) trial participants were randomly assigned between September 23, 2008, and July 5, 2012, to 30 months of salsalate or placebo in addition to standard, guideline-based therapies. Randomization was computerized and centrally allocated, with patients, health care professionals, and researchers masked to treatment assignment. Participants were overweight and obese statin-using patients with established, stable coronary heart disease.

INTERVENTIONS

Salsalate (3.5 g/d) or placebo orally over 30 months.

MAIN OUTCOMES AND MEASURES

The primary outcome was progression of noncalcified coronary artery plaque assessed by multidetector computed tomographic angiography. Secondary outcomes were other measures of safety and efficacy.

RESULTS

Two hundred fifty-seven participants were randomized to salsalate (n = 129) or placebo (n = 128). Their mean (SD) age was 60.8 (7.0) years, and 94.0% (236 of 251) were male. One hundred ninety participants (89 in the salsalate group and 101 in the placebo group) completed the study. Compared with baseline, there was no increase in noncalcified plaque volume in the placebo-treated patients and no difference in change between the salsalate and placebo groups (mean difference, -1 mm3; 95% CI, -11 to 9 mm3; P = .87). Salsalate treatment decreased total white blood cell, lymphocyte, monocyte, and neutrophil counts and increased adiponectin levels without change in C-reactive protein levels. Fasting glucose, triglycerides, uric acid, and bilirubin levels were decreased in the salsalate group compared with the placebo group, while hemoglobin levels were increased. Urinary albumin levels increased, with tinnitus and atrial arrhythmias more common, in the salsalate group compared with the placebo group.

CONCLUSIONS AND RELEVANCE

Salsalate when added to current therapies that include a statin does not reduce progression of noncalcified coronary plaque volume assessed by multidetector computed tomographic angiography in statin-using patients with established, stable coronary heart disease. The absence of progression of noncalcified plaque volume in the placebo group may limit interpretation of the trial results.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT00624923.

Biases in electronic health record data due to processes within the healthcare system: retrospective observational study

OBJECTIVE

To evaluate on a large scale, across 272 common types of laboratory tests, the impact of healthcare processes on the predictive value of electronic health record (EHR) data.

DESIGN

Retrospective observational study.

SETTING

Two large hospitals in Boston, Massachusetts, with inpatient, emergency, and ambulatory care.

PARTICIPANTS

All 669 452 patients treated at the two hospitals over one year between 2005 and 2006.

MAIN OUTCOME MEASURES

The relative predictive accuracy of each laboratory test for three year survival, using the time of the day, day of the week, and ordering frequency of the test, compared to the value of the test result.

RESULTS

The presence of a laboratory test order, regardless of any other information about the test result, has a significant association (P<0.001) with the odds of survival in 233 of 272 (86%) tests. Data about the timing of when laboratory tests were ordered were more accurate than the test results in predicting survival in 118 of 174 tests (68%).

CONCLUSIONS

Healthcare processes must be addressed and accounted for in analysis of observational health data. Without careful consideration to context, EHR data are unsuitable for many research questions. However, if explicitly modeled, the same processes that make EHR data complex can be leveraged to gain insight into patients' state of health.

Biases introduced by filtering electronic health records for patients with "complete data"

Objective

One promise of nationwide adoption of electronic health records (EHRs) is the availability of data for large-scale clinical research studies. However, because the same patient could be treated at multiple health care institutions, data from only a single site might not contain the complete medical history for that patient, meaning that critical events could be missing. In this study, we evaluate how simple heuristic checks for data "completeness" affect the number of patients in the resulting cohort and introduce potential biases.

Materials and Methods

We began with a set of 16 filters that check for the presence of demographics, laboratory tests, and other types of data, and then systematically applied all 216 possible combinations of these filters to the EHR data for 12 million patients at 7 health care systems and a separate payor claims database of 7 million members.

Results

EHR data showed considerable variability in data completeness across sites and high correlation between data types. For example, the fraction of patients with diagnoses increased from 35.0% in all patients to 90.9% in those with at least 1 medication. An unrelated claims dataset independently showed that most filters select members who are older and more likely female and can eliminate large portions of the population whose data are actually complete.

Discussion and Conclusion

As investigators design studies, they need to balance their confidence in the completeness of the data with the effects of placing requirements on the data on the resulting patient cohort.

Gender Differences in Receipt of National Institutes of Health R01 Grants Among Junior Faculty at an Academic Medical Center: The Role of Connectivity, Rank, and Research Productivity

OBJECTIVE

To determine whether there were gender differences in likelihood of receiving a first National Institutes of Health (NIH) R01 award among 5445 instructors and assistant professors at Harvard Medical School (HMS).

MATERIALS AND METHODS

Data on R01 award principal investigators were obtained from NIH ExPORTER and linked with faculty data. Using Cox proportional hazard regression, we examined the association of gender with receipt of first R01 award between 2008 and 2015 accounting for demographics, research productivity metrics, and professional characteristics.

RESULTS

Compared to males, females had fewer publications, lower h-index, smaller coauthor networks and were less likely to be assistant professors (p < 0.0001). Four hundred and thirteen of 5445 faculty (7.6%) received their first R01 award during the study period. There was no gender difference in receipt of R01 awards in age-adjusted (hazard ratio [HR]: 0.87, 95% confidence interval [CI]: 0.70-1.08) or multivariable-adjusted models (HR: 1.07, 95% CI: 0.86-1.34). Compared to white males, there was a nonsignificant 10%, 18%, and 30% lower rate of R01 receipt among white, Asian or Pacific Islander, and underrepresented minority females, respectively. These differences were eliminated in the multivariable-adjusted model. Network reach, age, HMS start year, h-index, academic rank, previous K award, terminal degree, and HMS training were all significant predictors of receiving an R01 award.

CONCLUSIONS

A relatively small proportion of HMS junior faculty obtained their first NIH R01 award during the study period. There was no significant gender difference in likelihood of award. However, we are unable to distinguish faculty that never applied from those who applied and were not successful.

Simultaneous inclusion of sorghum and cottonseed meal or millet in broiler diets: effects on performance and nutrient digestibility

Two experiments were conducted to investigate the use of sorghum, cottonseed meal and millet in broiler diets and their interaction when they are used simultaneously. In Experiment 1, a corn-soybean meal control diet was compared with eight experimental treatments based on low tannin sorghum (S30, S45 and S60), cottonseed meal (CM15, CM40) or both ingredients included in the same diet (S30/CM40, S45/CM25 and S60CM15). Results showed that BW gain was not affected by the inclusion of sorghum or cottonseed meal. However, feed intake tended to be affected by the cereal type with the highest values with sorghum-based diets. Feed conversion ratio increased (P<0.001) with sorghum-based diets compared with the control diet, whereas a combination of cottonseed meal and sorghum in the same diet did not affect the feed conversion ratio. Significant differences (P<0.001) were observed in apparent ileal digestibility (%) of protein and energy with the cottonseed meal and sorghum/cottonseed meal-based diets having lower protein and energy digestibility compared with corn-based diets. In Experiment 2, a control diet was compared with six diets in which corn was substituted at 60%, 80% or 100% by either sorghum or millet and other three diets with simultaneous inclusion of these two ingredients (S30/M30, S40/M40, S50/M50). Single or combined inclusion of sorghum and millet resulted in similar feed intake and growth performance as the control diet. Apparent ileal digestibility of protein and energy was higher with millet-based diets (P<0.001). Total tract digestibility of protein in sorghum and millet-based diets tended to decrease linearly with the increasing level of substitution. Sorghum-based diets resulted in lower total tract digestibility of fat compared with millet and sorghum/millet-based diets (P<0.001). Higher total tract digestibility of starch were obtained with the control diet and millet-based diets compared with the sorghum-based treatments. Results of the two experiments suggest that broiler growth performance was not affected by the dietary level of sorghum, millet or cottonseed meal. Nutrient digestion can, however, be affected by these feed ingredients.

Effects of steroid treatment on growth, nutrient partitioning, and expression of genes related to growth and nutrient metabolism in adult triploid rainbow trout (Oncorhynchus mykiss)

The contribution of sex steroids to nutrient partitioning and energy balance during gonad development was studied in rainbow trout. Specifically, 19-mo old triploid (3N) female rainbow trout were fed treatment diets supplemented with estradiol-17β (E2), testosterone (T), or dihydrotestosterone at 30-mg steroid/kg diet for a 1-mo period. Growth performance, nutrient partitioning, and expression of genes central to growth and nutrient metabolism were compared with 3N and age-matched diploid (2N) female fish consuming a control diet not supplemented with steroids. Only 2 N fish exhibited active gonad development, with gonad weights increasing from 3.7% to 5.5% of body weight throughout the study, whereas gonad weights in 3N fish remained at 0.03%. Triploid fish consuming dihydrotestosterone exhibited faster specific growth rates than 3N-controls (P < 0.05). Consumption of E2 in 3N fish reduced fillet growth and caused lower fillet yield compared with all other treatment groups (P < 0.05). In contrast, viscera fat gain was not affected by steroid consumption (P > 0.05). Gene transcripts associated with physiological pathways were identified in maturing 2N and E2-treated 3N fish that differed in abundance from 3N-control fish (P < 0.05). In liver these mechanisms included the growth hormone/insulin-like growth factor (IGF) axis (igf1, igf2), IGF binding proteins (igfbp1b1, igfbp2b1, igfbp5b1, igfbp6b1), and genes associated with lipid binding and transport (fabp3, fabp4, lpl, cd36), fatty acid oxidation (cpt1a), and the pparg transcription factor. In muscle, these mechanisms included reductions in myogenic gene expression (fst, myog) and the proteolysis-related gene, cathepsin-L, suggesting an E2-induced reduction in the capacity for muscle growth. These findings suggest that increased E2 signaling in the sexually maturing female rainbow trout alters physiological pathways in liver, particularly those related to IGF signaling and lipid metabolism, to partition nutrients away from muscle growth toward support of maturation-related processes. In contrast, the mobilization of viscera lipid stores appear to be mediated less by E2 and more by energy demands associated with gonad development. These findings improve the understanding of how steroids regulate nutrient metabolism to meet the high energy demands associated with gonad development during sexual maturation.

Faculty Promotion and Attrition: The Importance of Coauthor Network Reach at an Academic Medical Center

BACKGROUND

Business literature has demonstrated the importance of networking and connections in career advancement. This is a little-studied area in academic medicine.

OBJECTIVE

To examine predictors of intra-organizational connections, as measured by network reach (the number of first- and second-degree coauthors), and their association with probability of promotion and attrition.

DESIGN

Prospective cohort study between 2008 and 2012.

SETTING

Academic medical center.

PARTICIPANTS

A total of 5787 Harvard Medical School (HMS) faculty with a rank of assistant professor or full-time instructor as of January 1, 2008.

MAIN MEASURES

Using negative binomial models, multivariable-adjusted predictors of continuous network reach were assessed according to rank. Poisson regression was used to compute relative risk (RR) and 95 % confidence intervals (CI) for the association between network reach (in four categories) and two outcomes: promotion or attrition. Models were adjusted for demographic, professional and productivity metrics.

KEY RESULTS

Network reach was positively associated with number of first-, last- and middle-author publications and h-index. Among assistant professors, men and whites had greater network reach than women and underrepresented minorities (p < 0.001). Compared to those in the lowest category of network reach in 2008, instructors in the highest category were three times as likely to have been promoted to assistant professor by 2012 (RR: 3.16, 95 % CI: 2.60, 3.86; p-trend <0.001) after adjustment for covariates. Network reach was positively associated with promotion from assistant to associate professor (RR: 1.82, 95 % CI: 1.32, 2.50; p-trend <0.001). Those in the highest category of network reach in 2008 were 17 % less likely to have left HMS by 2012 (RR: 0.83, 95 % CI 0.70, 0.98) compared to those in the lowest category.

CONCLUSIONS

These results demonstrate that coauthor network metrics can provide useful information for understanding faculty advancement and retention in academic medicine. They can and should be investigated at other institutions.

Considering context in academic medicine: differences in demographic and professional characteristics and in research productivity and advancement metrics across seven clinical departments

PURPOSE

To understand the disciplinary contexts in which faculty work, the authors examined demographics, professional characteristics, research productivity, and advancement across seven clinical departments at Harvard Medical School (HMS) and nationally.

METHOD

HMS analyses included faculty from seven clinical departments-anesthesiology, medicine, neurology, pediatrics, psychiatry, radiology, and surgery-in May 2011 (N = 7,304). National analyses included faculty at 141 U.S. medical schools in the same seven departments as of December 31, 2011 (N = 91,414). The authors used chi-square and Wilcoxon Mann-Whitney tests to compare departmental characteristics.

RESULTS

Heterogeneity in demographics, professional characteristics, and advancement across departments was observed in HMS and national data. At HMS, psychiatry had the highest percentage of underrepresented minority faculty at 6.6% (75/1,139). In anesthesiology, 24.2% (128/530) of faculty were Asian, whereas in psychiatry only 7.9% (90/1,139) were (P < .0001). Female faculty were the majority in pediatrics and psychiatry, whereas in surgery 26.3% (172/654) of the faculty were female (P < .0001). At HMS, surgery, radiology, and neurology had the shortest median times to promotion and the highest median number of publications, H-index, and second-degree centrality. Neurology also had the highest percentage of faculty who had been principal investigators on a National Institutes of Health-funded grant.

CONCLUSIONS

There were differences in demographics, professional characteristics, and advancement across clinical departments at HMS and nationally. The context in which faculty work, of which department is a proxy, should be accounted for in research on faculty career outcomes and diversity inclusion in academic medicine.

Federated queries of clinical data repositories: Scaling to a national network

Federated networks of clinical research data repositories are rapidly growing in size from a handful of sites to true national networks with more than 100 hospitals. This study creates a conceptual framework for predicting how various properties of these systems will scale as they continue to expand. Starting with actual data from Harvard's four-site Shared Health Research Information Network (SHRINE), the framework is used to imagine a future 4000 site network, representing the majority of hospitals in the United States. From this it becomes clear that several common assumptions of small networks fail to scale to a national level, such as all sites being online at all times or containing data from the same date range. On the other hand, a large network enables researchers to select subsets of sites that are most appropriate for particular research questions. Developers of federated clinical data networks should be aware of how the properties of these networks change at different scales and design their software accordingly.

Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative 'apps' to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.

Query Health: standards-based, cross-platform population health surveillance

OBJECTIVE

Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects.

MATERIALS AND METHODS

Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language.

RESULTS

We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed.

DISCUSSIONS

This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative.

CONCLUSIONS

Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites.

Urban vs. extra-urban environments: scales of variation of intertidal benthic assemblages in north Portugal

Littoral areas are subject to severe and increasing pressures resulting from human activities occurring along or next to the coast. In this study, patterns of variability in the structure of rocky intertidal benthic assemblages and in the abundance of individual taxa were compared between locations close to the coastal cities of Porto and Vila Nova de Gaia (north Portugal) and reference locations far from it in much less urbanized conditions over a temporal scale of fourteen months and multiple spatial scales. Present findings indicated that assemblages were more heterogeneously distributed in the urban than in the extra-urban condition. The total number of taxa and several individual taxa displayed, in general, this same pattern of variability. This could be interpreted as the beginning of a habitat deterioration process with largely unpredictable consequences. The adopted sampling design supports the need for simultaneously including a range of temporal and spatial scales when evaluating responses of coastal marine biodiversity to anthropogenic disturbances.

The use and significance of a research networking system

Background

Universities have begun deploying public Internet systems that allow for easy search of their experts, expertise, and intellectual networks. Deployed first in biomedical schools but now being implemented more broadly, the initial motivator of these research networking systems was to enable easier identification of collaborators and enable the development of teams for research.

Objective

The intent of the study was to provide the first description of the usage of an institutional research “social networking” system or research networking system (RNS).

Methods

Number of visits, visitor location and type, referral source, depth of visit, search terms, and click paths were derived from 2.5 years of Web analytics data. Feedback from a pop-up survey presented to users over 15 months was summarized.

Results

RNSs automatically generate and display profiles and networks of researchers. Within 2.5 years, the RNS at the University of California, San Francisco (UCSF) achieved one-seventh of the monthly visit rate of the main longstanding university website, with an increasing trend. Visitors came from diverse locations beyond the institution. Close to 75% (74.78%, 208,304/278,570) came via a public search engine and 84.0% (210 out of a sample of 250) of these queried an individual’s name that took them directly to the relevant profile page. In addition, 20.90% (214 of 1024) visits went beyond the page related to a person of interest to explore related researchers and topics through the novel and networked information provided by the tool. At the end of the period analyzed, more than 2000 visits per month traversed 5 or more links into related people and topics. One-third of visits came from returning visitors who were significantly more likely to continue to explore networked people and topics (P<.001). Responses to an online survey suggest a broad range of benefits of using the RNS in supporting the research and clinical mission.

Conclusions

Returning visitors in an ever-increasing pool of visitors to an RNS are among those that display behavior consistent with using the tool to identify new collaborators or research topics. Through direct user feedback we know that some visits do result in research-enhancing outcomes, although we cannot address the scale of impact. With the rapid pace of acquiring visitors searching for individual names, the RNS is evolving into a new kind of gateway for the university.

Triennial Growth Symposium--Effects of dietary 25-hydroxycholecalciferol and cholecalciferol on blood vitamin D and mineral status, bone turnover, milk composition, and reproductive performance of sows

To evaluate the role of vitamin D3 during gestation and lactation of sows, 2 independent experiments were performed with the aim of investigating sow reproductive performance, milk composition (study 1 only), and changes in blood status of 25-hydroxycholecalciferol (25-OH-D3), 1,25-dihydroxycholecalciferol (1,25-(OH)2-D3; study 2 only), minerals, and bone markers of sows during gestation and lactation. Study 1 comprised 39 primi- and multiparous crossbred sows fed 1 of 3 barley meal-based diets fortified with 200 IU/kg vitamin D3 (NRC, 1998; treatment DL), 2,000 IU/kg vitamin D3 (cholecalciferol; treatment DN), or 50 μg 25-OH-D3 (calcidiol; treatment HD)/kg feed. This study was conducted over a 4-parity period under controlled conditions. Study 2, running over 1 parity only, was performed in a commercial farm with 227 primi- and multiparous sows allocated to 2 dietary treatments: control (CON), receiving 2,000 IU vitamin D3/kg (equivalent to 50 μg/kg) feed (114 sows), and test (HYD), supplemented with 50 μg 25-OH-D3/kg feed (113 sows). Blood samples of sows were collected at 84 and 110d postcoitum and 1, 5, and 33 d postpartum (study 1) and at insemination and 28 and 80 d postinsemination as well as d 5 and 28 postpartum (study 2). Colostrum and milk samples in study 1 were obtained at 1, 9, and 33 d of lactation after oxytocin administration. Plasma 25-OH-D3 concentrations were increased (P < 0.05) in sows receiving 25-OH-D3 (HD and HYD) at any time of sampling whereas circulating plasma concentrations of 1,25-(OH)2-D3, Ca, and P were not affected by treatment. Milk concentrations of Ca and P were similar, but 25-OH-D3 content (except in colostrum) was clearly increased (P< 0.05) when 25-OH-D3 was fed. Most characteristics of sow reproductive performance responded similarly to the 2 sources and levels of vitamin D3, but weight gain of piglets between birth and weaning was decreased (P< 0.05) in offspring of DL and HD sows compared with animals of treatment DN (study 1). In study 2 total litter weight and birth weight per piglet were increased (P< 0.05) with 25-OH-D3 supplementation in comparison with the control (CON). Overall, feeding sows with 25-OH-D3 was considered to improve maternal supply with vitamin D3 and thereby maintain Ca homeostasis during gestation and lactation.