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Objective To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19). Method Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission. Results Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73–0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave. Conclusion This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.

Objectives Hypertension is a modifiable risk factor for numerous comorbidities and treating hypertension can greatly improve health outcomes. We sought to increase the efficiency of a virtual hypertension management program through workflow automation processes. Methods We developed a customer relationship management (CRM) solution at our institution for the purpose of improving processes and workflow for a virtual hypertension management program and describe here the development, implementation, and initial experience of this CRM system. Results Notable system features include task automation, patient data capture, multi-channel communication, integration with our electronic health record (EHR), and device integration (for blood pressure cuffs). In the five stages of our program (intake and eligibility screening, enrollment, device configuration/setup, medication titration, and maintenance), we describe some of the key process improvements and workflow automations that are enabled using our CRM platform, like automatic reminders to capture blood pressure data and present these data to our clinical team when ready for clinical decision making. We also describe key limitations of CRM, like balancing out-of-the-box functionality with development flexibility. Among our first group of referred patients, 76% (39/51) preferred email as their communication method, 26/51 (51%) were able to enroll electronically, and 63% of those enrolled (32/51) were able to transmit blood pressure data without phone support. Conclusion A CRM platform could improve clinical processes through multiple pathways, including workflow automation, multi-channel communication, and device integration. Future work will examine the operational improvements of this health information technology solution as well as assess clinical outcomes.

Purpose To objectively identify the 50 most influential studies in knee arthroscopy and provide an analysis of their key characteristics. Methods The Clarivate Analytics Web of Knowledge database was used to gather data and metrics of knee arthroscopy research. The search list was sorted by the number of citations, and articles were included or excluded based on relevance to knee arthroscopy. The information extracted for each article included author name, publication year, country of origin, journal name, article type, and the level of evidence. Results For these 50 studies, the total number of citations was calculated to be 12,168, with an average of 243.4 citations per paper. The most-cited article was cited 1,220 times. The 50 studies included in this analysis were published between 1972 and 2013. All 50 articles were published in English and came from 10 different orthopaedic journals. The United States was responsible for the majority of articles (n = 26); however, many other countries were represented. The most prevalent study designs were case series (n = 20). Conclusions The 50 most influential articles in knee arthroscopy were cited a total of 12,168 times. The study designs most commonly used were case series and cohort studies. This article serves as a reference to direct orthopaedic practitioners to the 50 most influential studies in knee arthroscopy. We hope that these 50 studies and the analysis we provide help health care professionals efficiently assess consensus, trends, and needs within the field. Clinical Relevance This analysis of the 50 most influential studies in knee arthroscopy will provide medical students, residents, fellows, and attending physicians with a comprehensive inventory and citation analysis of the most impactful academic contributions to one of the most important fields within orthopaedic surgery.

The antidiabetic drug pioglitazone is, to date, the most efficacious oral drug recommended off-label for the treatment of nondiabetic or diabetic patients with biopsy-proven nonalcoholic steatohepatitis (NASH). However, weight gain and edema side effects have limited its use for NASH. Pioglitazone is a mixture of two stereoisomers ((R)-pioglitazone and (S)-pioglitazone) that interconvert in vitro and in vivo. We aimed to characterize their individual pharmacology to develop a safer and potentially more potent drug for NASH. We stabilized the stereoisomers of pioglitazone with deuterium at the chiral center. Preclinical studies with deuterium-stabilized (R)-pioglitazone (PXL065) and (S)-pioglitazone demonstrated that (R)-pioglitazone retains the efficacy of pioglitazone in NASH, including reduced hepatic triglycerides, free fatty acids, cholesterol, steatosis, inflammation, hepatocyte enlargement, and fibrosis. Although both stereoisomers inhibit the mitochondrial pyruvate carrier, PXL065 shows limited to no peroxisome proliferator-activated receptor gamma (PPARγ) activity, whereas (S)-pioglitazone appears responsible for the PPARγ activity and associated weight gain. Nonetheless, in preclinical models, both stereoisomers reduce plasma glucose and hepatic fibrosis to the same extent as pioglitazone, suggesting that these benefits may also be mediated by altered mitochondrial metabolism. In a phase 1a clinical study, we demonstrated safety and tolerability of single 7.5-mg, 22.5-mg, and 30-mg doses of PXL065 as well as preferential exposure to the (R)-stereoisomer in comparison to 45-mg pioglitazone. Conclusion: PXL065 at a dose lower than 22.5 mg is predicted to exhibit efficacy for NASH equal to, or greater than, 45-mg pioglitazone without the potentially detrimental weight gain and edema. The development of PXL065 for NASH represents a unique opportunity to leverage the therapeutic benefits of pioglitazone, while reducing or eliminating PPARγ-related side effects.

Labeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification.

Purpose In response to the outbreak of COVID-19 in Ireland, the government implemented a nationwide stay-at-home order, with the closure of all non-essential businesses. During this period, there was a significant increase in supermarket expenditure. It has been shown that stress, anxiety and boredom are triggers for unhealthy eating habits. Fat consumption is a risk factor for both the development of gallstones and, additionally, the development of acute calculous cholecystitis. The aim of this study was to assess the incidence of acute calculous cholecystitis during the nationwide lockdown and compare it to the same period one year prior. Methods A retrospective review of all emergency abdominal imaging performed during the first 5 weeks of the lockdown was completed using the hospital PACS (picture archiving and communication system). All cases of acute calculous cholecystitis were identified and compared with the same period 1 year prior. Results Eighteen cases of acute calculous cholecystitis were identified from 24 March to 27 April 2020. Eleven cases were identified during the same period in 2019. This represented an increase of 63%. Non-COVID-19-related emergency presentations decreased during this period, and imaging of emergency presentations decreased by 24%. The rate of scans positive for acute cholecystitis more than doubled (p

This study aims to predict death after COVID-19 using only the past medical information routinely collected in electronic health records (EHRs) and to understand the differences in risk factors across age groups. Combining computational methods and clinical expertise, we curated clusters that represent 46 clinical conditions as potential risk factors for death after a COVID-19 infection. We trained age-stratified generalized linear models (GLMs) with component-wise gradient boosting to predict the probability of death based on what we know from the patients before they contracted the virus. Despite only relying on previously documented demographics and comorbidities, our models demonstrated similar performance to other prognostic models that require an assortment of symptoms, laboratory values, and images at the time of diagnosis or during the course of the illness. In general, we found age as the most important predictor of mortality in COVID-19 patients. A history of pneumonia, which is rarely asked in typical epidemiology studies, was one of the most important risk factors for predicting COVID-19 mortality. A history of diabetes with complications and cancer (breast and prostate) were notable risk factors for patients between the ages of 45 and 65 years. In patients aged 65–85 years, diseases that affect the pulmonary system, including interstitial lung disease, chronic obstructive pulmonary disease, lung cancer, and a smoking history, were important for predicting mortality. The ability to compute precise individual-level risk scores exclusively based on the EHR is crucial for effectively allocating and distributing resources, such as prioritizing vaccination among the general population.

BackgroundHemodynamic stress, conditioned by the morphology of the surrounding vasculature, plays an important role in aneurysm formation. Our goal was to identify image-based location-specific parameters that are associated with posterior communicating artery (PCoA) aneurysms.MethodsThree-dimensional morphological parameters obtained from CT angiography or digital subtraction angiography from 187 patients with unilateral PCoA aneurysms, diagnosed at the Brigham and Women’s Hospital and Massachusetts General Hospital between 1990 and 2016, were evaluated. In order to control for genetic and clinical risk factors, we chose the contralateral unaffected PCoA as a control group. We examined diameters and angles of the surrounding parent and daughter vessels. Univariable and multivariable statistical analyses were performed to determine statistical significance. Sensitivity analyses with small aneurysms (≤5 mm) only and an unmatched analysis of 432 PCoA aneurysms and 197 control patients without PCoA aneurysms were also performed.ResultsIn a multivariable conditional logistic regression model we showed that smaller diameter size ratio (OR 1.45×10−5, 95% CI 1.12×10−7 to 1.88×10−3) and larger daughter-daughter angle (OR 1.04, 95% CI 1.02 to 1.07) were significantly associated with PCoA aneurysm presence after correcting for other variables. In subgroup analyses of small aneurysms (≤5 mm) and in an unmatched analysis the significance and direction of these results were preserved.ConclusionsLarger daughter-daughter angles and smaller diameter size ratio are significantly associated with the presence of PCoA aneurysms. These simple parameters can be utilized to guide the risk assessment for the formation of PCoA aneurysms in high risk patients.

Background Agitation is a common feature of many neuropsychiatric disorders. Objective Understanding the prevalence, implications, and characteristics of agitation among hospitalized populations can facilitate more precise recognition of disability arising from neuropsychiatric diseases. Methods We developed two agitation phenotypes using an expansion of expert curated term lists. These phenotypes were used to characterize five years of psychiatric admissions. The relationship of agitation symptoms and length of stay was examined. Results Among 4548 psychiatric admissions, 1134 (24.9%) included documentation of agitation based on the primary agitation phenotype. These symptoms were greater among individuals with public insurance, and those with mania and psychosis compared to major depressive disorder. Greater symptoms were associated with longer hospital stay, with ~0.9 day increase in stay for every 10% increase in agitation phenotype. Conclusion Agitation was common at hospital admission and associated with diagnosis and longer length of stay. Characterizing agitation-related symptoms through natural language processing may provide new tools for understanding agitated behaviors and their relationship to delirium.

Pneumothoraces may occur rarely in coronavirus (COVID-19) patients, often resulting from a combination of fibrotic parenchymal changes and prolonged high-pressure ventilation. Very few studies have been published describing the management of pneumothorax in the novel COVID-19 pneumonia patients. Although chest drain insertion represents the first line of treatment, a persistent pneumothorax and air leak requiring intervention could be managed by a thoracoscopic procedure or, as is the case here, by endobronchial valve insertion. Endobronchial valve insertion is a minimally invasive technique that provides a treatment option in patients with severe parenchymal COVID-19 related lung disease. As far as the authors are aware this is the first report of the use of endobronchial valves in a COVID-19 patient.

Objective High-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs. Materials and Methods We develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms. Results Phenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm’s classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations. Discussion The proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease. Conclusion Sequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.

Background Treatment decisions for Coronavirus Disease 2019 (COVID-19) depend on disease severity, but the prescribing pattern by severity and drivers of therapeutic choices remain unclear. Objectives The objectives of the study were to evaluate pharmacological treatment patterns by COVID-19 severity and identify the determinants of prescribing for COVID-19. Methods Using electronic health record data from a large Massachusetts-based healthcare system, we identified all patients aged ≥ 18 years hospitalized with laboratory-confirmed COVID-19 from 1 March to 24 May, 2020. We defined five levels of COVID-19 severity at hospital admission: (1) hospitalized but not requiring supplemental oxygen; (2–4) hospitalized and requiring oxygen ≤ 2, 3–4, and ≥ 5 L per minute, respectively; and (5) intubated or admitted to an intensive care unit. We assessed the medications used to treat COVID-19 or as supportive care during hospitalization. Results Among 2821 patients hospitalized for COVID-19, we found inpatient mortality increased by severity from 5% for level 1 to 23% for level 5. As compared to patients with severity level 1, those with severity level 5 were 3.53 times (95% confidence interval 2.73–4.57) more likely to receive a medication used to treat COVID-19. Other predictors of treatment were fever, low oxygen saturation, presence of co-morbidities, and elevated inflammatory biomarkers. The use of most COVID-19 relevant medications has dropped substantially while the use of remdesivir and therapeutic anticoagulants has increased over the study period. Conclusions Careful consideration of disease severity and other determinants of COVID-19 drug use is necessary for appropriate conduct and interpretation of non-randomized studies evaluating outcomes of COVID-19 treatments. Electronic supplementary material The online version of this article (10.1007/s40265-020-01424-7) contains supplementary material, which is available to authorized users.

Objective Due to a complex set of processes involved with the recording of health information in the Electronic Health Records (EHRs), the truthfulness of EHR diagnosis records is questionable. We present a computational approach to estimate the probability that a single diagnosis record in the EHR reflects the true disease. Materials and Methods Using EHR data on 18 diseases from the Mass General Brigham (MGB) Biobank, we develop generative classifiers on a small set of disease-agnostic features from EHRs that aim to represent Patients, pRoviders, and their Interactions within the healthcare SysteM (PRISM features). Results We demonstrate that PRISM features and the generative PRISM classifiers are potent for estimating disease probabilities and exhibit generalizable and transferable distributional characteristics across diseases and patient populations. The joint probabilities we learn about diseases through the PRISM features via PRISM generative models are transferable and generalizable to multiple diseases. Discussion The Generative Transfer Learning (GTL) approach with PRISM classifiers enables the scalable validation of computable phenotypes in EHRs without the need for domain-specific knowledge about specific disease processes. Conclusion Probabilities computed from the generative PRISM classifier can enhance and accelerate applied Machine Learning research and discoveries with EHR data.

We present the case of a 29-year-old male healthcare worker with a 6 month history of progressive left foot pain resulting in presentation to the emergency department on 3 occasions. He denied systemic symptoms. Multimodal imaging demonstrated an expansile erosive inflammatory lesion centered on the neck of the second metatarsal with aggressive features. CT of the thorax, abdomen, and pelvis demonstrated calcified mediastinal lymph nodes and left inguinal adenopathy. The lesion was biopsied under ultrasound guidance demonstrating a necrotizing granulomatous osteomyelitis with acid fact bacilli. This is consistent with TB dactylitis (spina ventosa). Treatment with antimycobacterial drugs was commenced.

Despite the widespread use of the “Informatics for Integrating Biology and the Bedside” (i2b2) platform, there are substantial challenges for loading electronic health records (EHR) into i2b2 and for querying i2b2. We have previously presented a simplified framework for semantic abstraction of EHR records into i2b2. Building on our previous work, we have created a proof-of-concept implementation of cloud services on an i2b2 data store for cohort identification. Specifically, we have implemented a graphical user interface (GUI) that declares the key components for data import, transformation, and query of EHR data. The GUI integrates with Azure cloud services to create data pipelines for importing EHR data into i2b2, creation of derived facts, and querying for generating Sankey-like flow diagrams that characterize the patient cohorts. We have evaluated the implementation using the real-world MIMIC-III dataset. We discuss the key features of this implementation and direction for future work, which will advance the efforts of the research community for patient cohort identification.

Motivation Expert-labeled data are essential to train phenotyping algorithms for cohort identification. However expert labeling is time and labor intensive, and the costs remain prohibitive for scaling phenotyping to wider use-cases. Results We present an approach referred to as polar labeling (PL), to create silver standard for training machine learning (ML) for disease classification. We test the hypothesis that ML models trained on the silver standard created by applying PL on unlabeled patient records, are comparable in performance to the ML models trained on gold standard, created by clinical experts through manual review of patient records. We perform experimental validation using health records of 38 023 patients spanning six diseases. Our results demonstrate the superior performance of the proposed approach. Availability and implementation We provide a Python implementation of the algorithm and the Python code developed for this study on Github. Supplementary information Supplementary data are available at Bioinformatics online.

Kueiyu Joshua Lin,1,2 Gary E Rosenthal,3 Shawn N Murphy,4,5 Kenneth D Mandl,6 Yinzhu Jin,1 Robert J Glynn,1 Sebastian Schneeweiss1 1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 2Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 3Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA; 4Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 5Research Information Science and Computing, Partners Healthcare, Somerville, MA, USA; 6Computational Health Informatics Program, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USACorrespondence: Kueiyu Joshua LinDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont St. Suite 3030, Boston, MA 02120, USATel +1 617 278-0930Fax +1 617 232-8602Email jklin@mgh.harvard.eduObjective: Electronic health records (EHR) data-discontinuity, i.e. receiving care outside of a particular EHR system, may cause misclassification of study variables. We aimed to validate an algorithm to identify patients with high EHR data-continuity to reduce such bias.Materials and Methods: We analyzed data from two EHR systems linked with Medicare claims data from 2007 through 2014, one in Massachusetts (MA, n=80,588) and the other in North Carolina (NC, n=33,207). We quantified EHR data-continuity by Mean Proportion of Encounters Captured (MPEC) by the EHR system when compared to complete recording in claims data. The prediction model for MPEC was developed in MA and validated in NC. Stratified by predicted EHR data-continuity, we quantified misclassification of 40 key variables by Mean Standardized Differences (MSD) between the proportions of these variables based on EHR alone vs the linked claims-EHR data.Results: The mean MPEC was 27% in the MA and 26% in the NC system. The predicted and observed EHR data-continuity was highly correlated (Spearman correlation=0.78 and 0.73, respectively). The misclassification (MSD) of 40 variables in patients of the predicted EHR data-continuity cohort was significantly smaller (44%, 95% CI: 40– 48%) than that in the remaining population.Discussion: The comorbidity profiles were similar in patients with high vs low EHR data-continuity. Therefore, restricting an analysis to patients with high EHR data-continuity may reduce information bias while preserving the representativeness of the study cohort.Conclusion: We have successfully validated an algorithm that can identify a high EHR data-continuity cohort representative of the source population.Keywords: electronic medical records, data linkage, comparative effectiveness research, information bias, continuity, external validation

Background ‘Slaintecare’ aims to address complex patient care needs in an integrated fashion with an emphasis on patient-centred, patient-empowered community care.Currently there is a lack of knowledge of the impact of rare disease management in primary care and of the information tools required by general practitioners to deliver integrated care for rare disease patients. Aims To complete a pilot survey to estimate the general practice clinical workload attributable to selected rare diseases and assess the use of relevant information sources. Methods A retrospective cross-sectional survey was carried out of general practice consultations (2013–2017) for patients with 22 commonly recognised rare diseases. Results Around 31 general practitioners from 10 Irish practices completed information on 171 patients with rare diseases over 3707 consultations. General practice-specific coding systems were inadequate for rare disease patient identification. Over 139 (81.3%) patients were adult, and 32 (18.7%) were children. Management of care was hospital and not primary care based in 63%. Those eligible for state-reimbursed care had a significantly higher median number of consultations (23 consultations, IQR = 13–37, or 5.8 consultations/year) than those who paid privately (10 consultations, IQR = 4–19, or 2.5 consultations/year) (p Conclusions Both specific rare disease-specific coding and use of the relevant rare disease information sources are lacking in general practice in Ireland.

Introduction Currently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness. Methods This study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm. Results We developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites. Discussion and conclusion Our findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms.

We present a cohort of patients with anterior communicating artery (ACoA) aneurysms to investigate morphological characteristics and clinical factors associated with rupture of the aneurysms. 505 patients with ACoA aneurysms were identified at the Brigham and Women’s Hospital and Massachusetts General Hospital between 1990 and 2016, with available CT angiography (CTA). Three-dimensional (3D) reconstructions were performed to evaluate aneurysmal morphologic features, including location, projection, irregularity, the presence of daughter dome, height, height/width ratio, and relationships between surrounding vessels. Patient risk factors assessed included patient age, sex, tobacco use, alcohol use, and family history of aneurysms and aneurysmal subarachnoid hemorrhage. Logistic regression was used to build a predictive ACoA score for rupture. Morphologic features associated with ruptured ACoA aneurysms were the presence of a daughter dome (OR 21.4, 95% CI 10.6–43.1), smaller neck diameter (OR 0.55, 95% CI 0.42–0.71), larger aspect ratio (OR 3.57, 95% CI 2.05–6.24), larger flow angle (OR 1.03, 95% CI 1.02–1.05), and smaller ipsilateral A2-ACoA angle (OR 0.98, 95% CI 0.97–1.00). Tobacco use was predominantly associated with morphological factors intrinsic to the aneurysm that were associated with rupture while younger age was also associated with morphologic features extrinsic to the aneurysm that were associated with rupture. The ACoA score had good predictive capacity for rupture with AUC = 0.92 using the 0.632 bootstrap cross-validation for correction of overfitting bias. Ruptured ACoA aneurysms were associated with morphological features that are simple to assess using a simple scoring system. Tobacco use and younger age were predominantly associated with intrinsic and extrinsic morphological features characteristic of rupture, respectively.

Morphological factors of intracranial aneurysms and the surrounding vasculature could affect aneurysm rupture risk in a location specific manner. Our goal was to identify image-based morphological parameters that correlated with ruptured basilar tip aneurysms. Three-dimensional morphological parameters obtained from CT-angiography (CTA) or digital subtraction angiography (DSA) from 200 patients with basilar tip aneurysms diagnosed at the Brigham and Women’s Hospital and Massachusetts General Hospital between 1990 and 2016 were evaluated. We examined aneurysm wall irregularity, the presence of daughter domes, hypoplastic, aplastic or fetal PCoAs, vertebral dominance, maximum height, perpendicular height, width, neck diameter, aspect and size ratio, height/width ratio, and diameters and angles of surrounding parent and daughter vessels. Univariable and multivariable statistical analyses were performed to determine statistical significance. In multivariable analysis, presence of a daughter dome, aspect ratio, and larger flow angle were significantly associated with rupture status. We also introduced two new variables, diameter size ratio and parent-daughter angle ratio, which were both significantly inversely associated with ruptured basilar tip aneurysms. Notably, multivariable analyses also showed that larger diameter size ratio was associated with higher Hunt-Hess score while smaller flow angle was associated with higher Fisher grade. These easily measurable parameters, including a new parameter that is unlikely to be affected by the formation of the aneurysm, could aid in screening strategies in high-risk patients with basilar tip aneurysms. One should note, however, that the changes in parameters related to aneurysm morphology may be secondary to aneurysm rupture rather than causal.

The guidelines are the first comprehensive consensus report on veterinary healthcare recommendations for working, assistance, and therapy dogs. This category of canine patients includes a broad assortment of animals, some with well-defined functions and others that provide a more generalized support role. The guidelines discuss recommendations for dogs trained for protection, odor/scent detection, service functions for people with diagnosed disabilities or physical limitations, emotional support, and therapeutic intervention. Although the term is often used to describe dogs providing animal-assisted activities, true therapy dogs provide goal-directed therapy, often under the supervision of a healthcare professional such as an occupational therapist or psychologist. Many working dogs undergo extensive training and have rigorous physical demands placed upon them. These factors make working, assistance, and therapy dogs inherently valuable and impose a need for a high level of primary veterinary care as described in the guidelines. Because working dogs have a particularly close relationship with their handlers, a trust relationship between the practice team and the working-dog client is imperative.

Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for an equitable deployment of PRS to clinical practice that benefits global populations. Here we integrate T2D GWAS in European, African American and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and evaluate the PRS in the multi-ethnic eMERGE study, four African American cohorts, and the Taiwan Biobank. The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined, and the top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5 fold of increase in T2D risk, suggesting the potential of using the trans-ancestry PRS as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

We implemented the BioFire® FilmArray® Meningitis/Encephalitis Panel (MEP) with guidance for use based on patient age, cerebrospinal fluid (CSF) white blood cell (WBC) count and immune system status. MEPs results over 2 years (1/1/2017 to 12/31/18) were reviewed and clinical significance of positive MEP results in patients with CSF WBC ≤ 10 evaluated. Overall, 12% (51/453) of MEPs were positive with 4/184 (2%) positive in nonimmunocompromised (non-IC) with ≤ 10 CSF WBCs. Among positive results in non-IC patient with ≤10 CSF WBCs, none were judged clinically significant. Four of 6 results in immunocompromised patients with ≤10 CSF WBCs were clinically significant. Redundant testing was common and guideline adherence could have safely decreased MEPs use 41% saving$56,000. Guideline adherence was poor and MEP use can be safely avoided in non-IC adults with10 CSF WBC, but clinically significant results did occur in IC patients with low CSF WBC. Clinical decision support could reduce unneeded testing and result in significant cost savings.

Objective Integrating and harmonizing disparate patient data sources into one consolidated data portal enables researchers to conduct analysis efficiently and effectively. Materials and Methods We describe an implementation of Informatics for Integrating Biology and the Bedside (i2b2) to create the Mass General Brigham (MGB) Biobank Portal data repository. The repository integrates data from primary and curated data sources and is updated weekly. The data are made readily available to investigators in a data portal where they can easily construct and export customized datasets for analysis. Results As of July 2021, there are 125 645 consented patients enrolled in the MGB Biobank. 88 527 (70.5%) have a biospecimen, 55 121 (43.9%) have completed the health information survey, 43 552 (34.7%) have genomic data and 124 760 (99.3%) have EHR data. Twenty machine learning computed phenotypes are calculated on a weekly basis. There are currently 1220 active investigators who have run 58 793 patient queries and exported 10 257 analysis files. Discussion The Biobank Portal allows noninformatics researchers to conduct study feasibility by querying across many data sources and then extract data that are most useful to them for clinical studies. While institutions require substantial informatics resources to establish and maintain integrated data repositories, they yield significant research value to a wide range of investigators. Conclusion The Biobank Portal and other patient data portals that integrate complex and simple datasets enable diverse research use cases. i2b2 tools to implement these registries and make the data interoperable are open source and freely available.

Crohn's disease (CD) and ulcerative colitis (UC) are heterogeneous. With availability of therapeutic classes with distinct immunologic mechanisms of action, it has become imperative to identify markers that predict likelihood of response to each drug class. However, robust development of such tools has been challenging because of need for large prospective cohorts with systematic and careful assessment of treatment response using validated indices. Most hospitals in the United States use electronic health records (EHRs) that warehouse a large amount of narrative (free-text) and codified (administrative) data generated during routine clinical care. These data have been used to construct virtual disease cohorts for epidemiologic research as well as for defining genetic basis of disease states or discrete laboratory values.1-3 Whether EHR-based data can be used to validate genetic associations for more nuanced outcomes such as treatment response has not been examined previously.

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1–2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no). PheCAP takes structured data and narrative notes from electronic medical records and enables patients with a particular clinical phenotype to be identified.

Although optimal pharmacological therapy for heart failure with reduced ejection fraction (HFrEF) is carefully scripted by treatment guidelines, many eligible patients are not treated with guideline‐directed medical therapy (GDMT) in clinical practice. We designed a strategy for remote optimization of GDMT on a population scale in patients with HFrEF leveraging nonphysician providers. An electronic health record‐based algorithm was used to identify a cohort of patients with a diagnosis of heart failure (HF) and ejection fraction (EF) ≤ 40% receiving longitudinal follow‐up at our center. Those with end‐stage HF requiring inotropic support, mechanical circulatory support, or transplantation and those enrolled in hospice or palliative care were excluded. Treating providers were approached for consent to adjust medical therapy according to a sequential, stepped titration algorithm modeled on the current American College of Cardiology (ACC)/American Heart Association (AHA) HF Guidelines within a collaborative care agreement. The program was approved by the institutional review board at Brigham and Women's Hospital with a waiver of written informed consent. All patients provided verbal consent to participate. A navigator then facilitated medication adjustments by telephone and conducted longitudinal surveillance of laboratories, blood pressure, and symptoms. Each titration step was reviewed by a pharmacist with supervision as needed from a nurse practitioner and HF cardiologist. Patients were discharged from the program to their primary cardiologist after achievement of an optimal or maximally tolerated regimen. A navigator‐led remote management strategy for optimization of GDMT may represent a scalable population‐level strategy for closing the gap between guidelines and clinical practice in patients with HFrEF.

Outbreaks of Ebola virus, a causative agent of hemorrhagic fever, in West Africa and the Democratic Republic of the Congo have highlighted the need for assays that can rapidly identify individuals infected with Ebola virus. These assays need to accurately detect and rule-out Ebola virus disease (EVD) and, ideally, not require advanced laboratory techniques. Over the course of the most recent outbreaks, field studies have evaluated the performance of rapid antigen and automated molecular tests, many of which were given emergency use authorization by the Food and Drug Administration. Overall, molecular assays demonstrated high sensitivity, while antigen testing had the highest sensitivity when the viral load was high. Specificity was high across the evaluated test options. In addition to the evaluation of diagnostic test methods, newly developed treatment options and the efficacy of ring vaccination to prevent disease transmission have been investigated. Findings from these field studies have demonstrated that many of the newly developed diagnostic assays, as well as experimental treatments and vaccination, are promising tools for controlling EVD.

BACKGROUND: The extent to which obesity and genetics determine post-operative complications is incompletely understood. METHODS: We performed a retrospective study using two population cohorts with electronic health record (EHR) data. The first included 736,726 adults with body mass index (BMI) recorded between 1990-2017 at Vanderbilt University Medical Center. The second cohort consisted of 65,174 individuals from 12 institutions contributing EHR and genome-wide genotyping data to the Electronic Medical Records & Genomics (eMERGE) Network. Pairwise logistic regression analyses were used to measure the association of BMI categories with postoperative complications derived from International Classification of Disease-9 codes, including postoperative infection, incisional hernia, and intestinal obstruction. A genetic risk score (GRS) was constructed from 97 obesity-risk single nucleotide polymorphisms for a Mendelian randomization study to determine the association of genetic risk for obesity on postoperative complications. Logistic regression analyses were adjusted for sex, age, site, and race/principal components. RESULTS: Individuals with overweight or obese BMI (≥25 kg/m(2)) had increased risk for incisional hernia (Odds ratio [OR] 1.7-5.5, p

The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

Background Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver illness with a genetically heterogeneous background that can be accompanied by considerable morbidity and attendant health care costs. The pathogenesis and progression of NAFLD is complex with many unanswered questions. We conducted genome-wide association studies (GWASs) using both adult and pediatric participants from the Electronic Medical Records and Genomics (eMERGE) Network to identify novel genetic contributors to this condition. Methods First, a natural language processing (NLP) algorithm was developed, tested, and deployed at each site to identify 1106 NAFLD cases and 8571 controls and histological data from liver tissue in 235 available participants. These include 1242 pediatric participants (396 cases, 846 controls). The algorithm included billing codes, text queries, laboratory values, and medication records. Next, GWASs were performed on NAFLD cases and controls and case-only analyses using histologic scores and liver function tests adjusting for age, sex, site, ancestry, PC, and body mass index (BMI). Results Consistent with previous results, a robust association was detected for the PNPLA3 gene cluster in participants with European ancestry. At the PNPLA3-SAMM50 region, three SNPs, rs738409, rs738408, and rs3747207, showed strongest association (best SNP rs738409 p = 1.70 × 10− 20). This effect was consistent in both pediatric (p = 9.92 × 10− 6) and adult (p = 9.73 × 10− 15) cohorts. Additionally, this variant was also associated with disease severity and NAFLD Activity Score (NAS) (p = 3.94 × 10− 8, beta = 0.85). PheWAS analysis link this locus to a spectrum of liver diseases beyond NAFLD with a novel negative correlation with gout (p = 1.09 × 10− 4). We also identified novel loci for NAFLD disease severity, including one novel locus for NAS score near IL17RA (rs5748926, p = 3.80 × 10− 8), and another near ZFP90-CDH1 for fibrosis (rs698718, p = 2.74 × 10− 11). Post-GWAS and gene-based analyses identified more than 300 genes that were used for functional and pathway enrichment analyses. Conclusions In summary, this study demonstrates clear confirmation of a previously described NAFLD risk locus and several novel associations. Further collaborative studies including an ethnically diverse population with well-characterized liver histologic features of NAFLD are needed to further validate the novel findings. Electronic supplementary material The online version of this article (10.1186/s12916-019-1364-z) contains supplementary material, which is available to authorized users.

Zusammenfassung Hintergrund Acrylnagel beherbergen mehr Bakterien als Naturnagel, und die Nutzung wird fur Gesundheitspersonal (HCWs) nicht empfohlen. Uber die neuen und beliebten Gel-Nagelprodukte ist wenig bekannt. Diese Studie untersucht die bakterielle Belastung von Gel-Nageln, Standard-Nagellack und Naturnageln an den Handen von HCWs. Methode Die Studie wurde in 3 Gesundheitszentren durchgefuhrt. Nagel auf der dominierenden Hand von 88 HCWs waren lackiert mit Standard Nagellack oder Gel. Kulturen wurden an den Tagen 1, 7 und 14 vor und nach der Handehygiene mit Alkohol-Handgel abgenommen. Ergebnisse Insgesamt wurden 741 Kulturen gewonnen. Die bakterielle Belastung ist bei allen Nageltypen gestiegen (P ≤ .0001). Eine Reduktion der bakteriellen Belastung konnte nach der Handehygiene bei den Naturnageln und dem Standardnagellack, jedoch nicht bei Gel-Nageln nachgewiesen werden (P = .001, P = .0028 bzw. P = .98). Alle 3 Nageltypen waren im Laufe der Zeit mit mehr Bakterien kontaminiert. Standard-Nagellack und Naturnagel konnen fur die Handehygiene geeigneter sein als Gel-Nagel. Schlussfolgerungen Diese Studie zeigt keine erhohte Anzahl von Mikroorganismen auf Nageln mit Gel; jedoch konnen Gelnagel mit Alkohol-Handgel schwieriger zu desinfizieren sein.

Objective The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network. Materials and Methods The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQe-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action. A pilot implementation of the system involved 6 ARCH partner sites between January 2017 and May 2018. Results The ARCH CTX has enabled the network to monitor and, if needed, adjust its data management processes to maintain complete datasets for secondary use. The system allows the network and its partner sites to profile data completeness both at the network and partner site levels. Interactive visualizations presenting the current state of completeness in the context of the entire network as well as changes in completeness across time were valued among the CTX user base. Discussion Distributed clinical data networks are complex systems. Top-down approaches that solely rely on technology to report data completeness may be necessary but not sufficient for improving completeness (and quality) of data in large-scale clinical data networks. Improving and maintaining complete (high-quality) data in such complex environments entails sociotechnical systems that exploit technology and empower human actors to engage in the process of high-quality data curating. Conclusions The CTX has increased the network’s capacity to rapidly identify data completeness issues and empowered ARCH partner sites to get involved in improving the completeness of respective data in their repositories.

Background Inherited trimethylaminuria (TMAU), a rare genetic disorder of hepatic metabolism of trimethylamine (TMA) causing excessive accumulation of malodorous trimethylamine (TMA), is a socially distressing disorder. Diagnosis is made by biochemical analysis of urine, with the calculation of flavin monooxygenase trimethylamine conversion capacity. Genetic testing, sequencing the entire coding region of the FMO3 gene has been recommended for affected individuals who convert less than 90% of the total TMA load to TMAO. Methods Genetic analysis was undertaken for 13 Irish patients with TMAU of varying phenotypic severity (three severe, six moderate, and four mild). Results A genetic diagnosis was made for seven patients, including for five of the nine moderate to severely affected cases. We noted the c.913G>T;p.(Glu305*) and c.458C>T;p.(Pro153Leu) mutations in this Irish population with severe TMAU which is consistent with our earlier findings in Australian and North American families of Irish and British descent. Three individuals were noted to be homozygous for the common variant haplotype c.472G>A;923A>G;p.(Glu158Lys);(Glu308Gly). We also identified three novel variants in this population, which are likely to be pathogenic: c.682G>A;p(Gly228Ser), c.694G>T:p(Asp232Tyr), and c.989G>A;p.(Gly330Glu). Conclusion Urinary biochemical analysis probably remains the first line diagnostic approach to classify the various types of TMAU. FMO3 gene analysis is likely only to be informative for certain presentations of TMAU.

Background: The conventional approach for clinical studies is to identify a cohort of potentially eligible patients and then screen for enrollment. In an effort to reduce the cost and manual effort involved in the screening process, several studies have leveraged electronic health records (EHR) to refine cohorts to better match the eligibility criteria, which is referred to as phenotyping. We extend this approach to dynamically identify a cohort by repeating phenotyping in alternation with manual screening. Methods: Our approach consists of multiple screen cycles. At the start of each cycle, the phenotyping algorithm is used to identify eligible patients from the EHR, creating an ordered list such that patients that are most likely eligible are listed first. This list is then manually screened, and the results are analyzed to improve the phenotyping for the next cycle. We describe the preliminary results and challenges in the implementation of this approach for an intervention study on heart failure. Results: A total of 1,022 patients were screened, with 223 (23%) of patients being found eligible for enrollment into the intervention study. The iterative approach improved the phenotyping in each screening cycle. Without an iterative approach, the positive screening rate (PSR) was expected to dip below the 20% measured in the first cycle; however, the cyclical approach increased the PSR to 23%. Conclusions: Our study demonstrates that dynamic phenotyping can facilitate recruitment for prospective clinical study. Future directions include improved informatics infrastructure and governance policies to enable real-time updates to research repositories, tooling for EHR annotation, and methodologies to reduce human annotation. J Clin Med Res. 2019;11(6):458-463 doi: https://doi.org/10.14740/jocmr3830

The All of Us Research Program was designed to enable broad-based precision medicine research in a cohort of unprecedented scale and diversity. Hypertension (HTN) is a major public health concern. The validity of HTN data and definition of hypertension cases in the All of Us (AoU) Research Program for use in rule-based algorithms is unknown. In this cross-sectional, population-based study, we compare HTN prevalence in the AoU Research Program to HTN prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES). We used AoU baseline data from patient (age ≥ 18) measurements (PM), surveys, and electronic health record (EHR) blood pressure measurements. We retrospectively examined the prevalence of HTN in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED) codes and blood pressure medications recorded in the EHR. We defined HTN as the participant having at least 2 HTN diagnosis/billing codes on separate dates in the EHR data AND at least one HTN medication. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18–39, 40–59, and ≥ 60). Among the 185,770 participants enrolled in the AoU Cohort (mean age at enrollment = 51.2 years) available in a Researcher Workbench as of October 2019, EHR data was available for at least one SNOMED code from 112,805 participants, medications for 104,230 participants, and 103,490 participants had both medication and SNOMED data. The total number of persons with SNOMED codes on at least two distinct dates and at least one antihypertensive medication was 33,310 for a crude prevalence of HTN of 32.2%. AoU age-adjusted HTN prevalence was 27.9% using 3 groups compared to 29.6% in NHANES. The AoU cohort is a growing source of diverse longitudinal data to study hypertension nationwide and develop precision rule-based algorithms for use in hypertension treatment and prevention research. The prevalence of hypertension in this cohort is similar to that in prior population-based surveys.

Electronic health records (EHR) provide an unprecedented opportunity to conduct large, cost-efficient, population-based studies. However, the studies of heterogeneous diseases, such as chronic obstructive pulmonary disease (COPD), often require labor-intensive clinical review and testing, limiting widespread use of these important resources. To develop a generalizable and efficient method for accurate identification of large COPD cohorts in EHRs, a COPD datamart was developed from 3420 participants meeting inclusion criteria in the Mass General Brigham Biobank. Training and test sets were selected and labeled with gold-standard COPD classifications obtained from chart review by pulmonologists. Multiple classes of algorithms were built utilizing both structured (e.g. ICD codes) and unstructured (e.g. medical notes) data via elastic net regression. Models explicitly including and excluding spirometry features were compared. External validation of the final algorithm was conducted in an independent biobank with a different EHR system. The final COPD classification model demonstrated excellent positive predictive value (PPV; 91.7%), sensitivity (71.7%), and specificity (94.4%). This algorithm performed well not only within the MGBB, but also demonstrated similar or improved classification performance in an independent biobank (PPV 93.5%, sensitivity 61.4%, specificity 90%). Ancillary comparisons showed that the classification model built including a binary feature for FEV1/FVC produced substantially higher sensitivity than those excluding. This study fills a gap in COPD research involving population-based EHRs, providing an important resource for the rapid, automated classification of COPD cases that is both cost-efficient and requires minimal information from unstructured medical records.

Electronic health records (EHR) discontinuity, i.e., receiving care outside of the study EHR system, can lead to information bias in EHR-based real-world evidence (RWE) studies. An algorithm has been previously developed to identify patients with high EHR-continuity. We sought to assess whether applying this algorithm to patient selection for inclusion can reduce bias caused by data-discontinuity in 4 RWE examples. Among Medicare beneficiaries aged >=65 years from 2007 to 2014, we established four cohorts assessing drug effects on short-term or long-term outcomes, respectively. We linked claims data with two US EHR systems and calculated %bias of the multivariable-adjusted effect estimates based on only EHR vs. linked EHR-claims data since the linked data capture medical information recorded outside of the study EHR. Our study cohort included 77,288 patients in system 1 and 60,309 in system 2. We found the sub-cohort in the lowest quartile of EHR-continuity captured 72-81% of the short-term and only 21-31% of the long-term outcome events, leading to %bias of 6-99% for the short-term and 62-112% for the long-term outcome examples. This trend appeared to be more pronounced in the example using a non-user comparison rather than an active comparison. We did not find significant treatment effect heterogeneity by EHR-continuity for most subgroups across empirical examples. In EHR-based RWE studies, investigators may consider excluding patients with low algorithm-predicted EHR-continuity as the EHR data capture relatively few of their actual outcomes, and treatment effect estimates in these patients may be unreliable.

Brain age estimated by machine learning from T1-weighted magnetic resonance images (T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early detection of such disorders. A fundamental step is to build an accurate age estimator from healthy brain MRIs. We focus on this step, and propose a framework to improve the accuracy, generality, and interpretation of age estimation in healthy brain MRIs. For accuracy, we used one of the largest sample sizes (N = 16,705). For each subject, our proposed algorithm first explicitly splits the T1w image, which has been commonly treated as a single-channel 3D image in other studies, into two 3D image channels representing contrast and morphometry information. We further proposed a “fusion-with-attention” deep learning convolutional neural network (FiA-Net) to learn how to best fuse the contrast and morphometry image channels. FiA-Net recognizes varying contributions across image channels at different brain anatomy and different feature layers. In contrast, multi-channel fusion does not exist for brain age estimation, and is mostly attention-free in other medical image analysis tasks (e.g., image synthesis, or segmentation), where treating channels equally may not be optimal. For generality, we used lifespan data 0–97 years of age for real-world utility; and we thoroughly tested FiA-Net for multi-site and multi-scanner generality by two phases of cross-validations in discovery and replication data, compared to most other studies with only one phase of cross-validation. For interpretation, we directly measured each artificial neuron’s correlation with the chronological age, compared to other studies looking at the saliency of features where salient features may or may not predict age. Overall, FiA-Net achieved a mean absolute error (MAE) of 3.00 years and Pearson correlation r = 0.9840 with known chronological ages in healthy brain MRIs 0–97 years of age, comparing favorably with state-of-the-art algorithms and studies for accuracy and generality across sites and datasets. We also provided interpretations on how different artificial neurons and real neuroanatomy contribute to the age estimation.

The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients’ past medical records (before their COVID-19 infection). MLHO’s architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients’ pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.

ObjectivesTo 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.DesignRetrospective cohort study.SettingThe 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.ParticipantsPatients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2.Primary and secondary outcome measuresPatients 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.ResultsOf 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.ConclusionsLaboratory 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.

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.

Simple Summary Treatment decisions represent a significant dilemma for patients diagnosed with prostate cancer. The prediction of early treatment failure would inform appropriate decision making and allow the clinician and patient to consider appropriate primary treatments and adjuvant therapies. We have developed and validated a serum biomarker-based model for predicting risk of biochemical reoccurrence in prostate cancer after radical prostatectomy. This study shows that the pre-operative biomarker PEDF can improve the accuracy of the clinical factors to predict risk of biochemical reoccurrence. PEDF has anti-inflammatory effects impacting on cytokine production. This non-invasive tool can be employed prior to treatment and demonstrates significant benefit over current clinical practice, impacting on patients’ outcomes and quality of life. Abstract This study undertook to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy using serum biomarkers and clinical features. Three radical prostatectomy cohorts were used to build and validate a model of clinical variables and serum biomarkers to predict BCR. The Cox proportional hazard model with stepwise selection technique was used to develop the model. Model evaluation was quantified by the AUC, calibration, and decision curve analysis. Cross-validation techniques were used to prevent overfitting in the Irish training cohort, and the Austrian and Norwegian independent cohorts were used as validation cohorts. The integration of serum biomarkers with the clinical variables (AUC = 0.695) improved significantly the predictive ability of BCR compared to the clinical variables (AUC = 0.604) or biomarkers alone (AUC = 0.573). This model was well calibrated and demonstrated a significant improvement in the predictive ability in the Austrian and Norwegian validation cohorts (AUC of 0.724 and 0.606), compared to the clinical model (AUC of 0.665 and 0.511). This study shows that the pre-operative biomarker PEDF can improve the accuracy of the clinical factors to predict BCR. This model can be employed prior to treatment and could improve clinical decision making, impacting on patients’ outcomes and quality of life.

Background A framework for understanding rapid diffusion changes from 0 to 6 years of age is important in the detection of neurodevelopmental disorders. Purpose To quantify patterns of normal apparent diffusion coefficient (ADC) development from 0 to 6 years of age. Materials and Methods Previously constructed age-specific ADC atlases from 201 healthy full-term children (108 male; age range, 0-6 years) with MRI scans acquired from 2006 to 2013 at one large academic hospital were analyzed to quantify four patterns: ADC trajectory, rate of ADC change, age of ADC maturation, and hemispheric asymmetries of maturation ages. Patterns were quantified in whole-brain, segmented regional, and voxelwise levels by fitting a two-term exponential model. Hemispheric asymmetries in ADC maturation ages were assessed using t tests with Bonferroni correction. Results The posterior limb of the internal capsule (mean ADC: left hemisphere, 1.18 ×103μm2/sec; right hemisphere, 1.17 ×103μm2/sec), anterior limb of the internal capsule (left, 1.11 ×103μm2/sec; right, 1.09 ×103μm2/sec), vermis (1.26 ×103μm2/sec), thalami (left, 1.17 ×103μm2/sec; right, 1.15 ×103μm2/sec), and basal ganglia (left, 1.26 ×103μm2/sec; right, 1.23 ×103μm2/sec) demonstrate low initial ADC values, indicating an earlier prenatal time course of development. ADC maturation was completed between 1.3 and 2.4 years of age, depending on the region. The vermis and left thalamus matured earliest (1.3 years). The frontolateral gray matter matured latest (right, 2.3 years; left, 2.4 years). ADC maturation occurred earlier in the left hemisphere (P < .001) in several regions, including the frontal (mean ± standard deviation) (left, 2.16 years ± 0.29; right, 2.19 years ± 0.31), temporal (left, 1.93 years ± 0.22; right, 1.99 years ± 0.22), and parietal (left, 1.92 years ± 0.30; right, 2.03 years ± 0.28) white matter. Maturation occurred earlier in the right hemisphere (P < .001) in several regions, including the thalami (left, 1.63 years ± 0.32; right, 1.45 years ± 0.33), basal ganglia (left, 1.79 years ± 0.31; right, 1.70 years ± 0.37), and hippocampi (left, 1.93 years ± 0.34; right, 1.78 years ± 0.33). Conclusion Normative apparent diffusion coefficient developmental patterns on diffusion-weighted MRI scans were quantified in children aged 0 to 6 years. This work provides knowledge about early brain development and may guide the detection of abnormal patterns of maturation. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Rollins in this issue.

An estimated 4.2 million premature deaths globally are influenced by ambient air pollution, mainly from heart disease, stroke, chronic obstructive pulmonary disease and lung cancer, besides attribu...

Hemodynamic stress is thought to play an important role in the formation of intracranial aneurysms, which is conditioned by the geometry of the surrounding vasculature. Our goal was to identify image-based morphological parameters that were associated with basilar artery tip aneurysms (BTA) in a location-specific manner. Three-dimensional morphological parameters obtained from CT-angiography (CTA) or digital subtraction angiography (DSA) from 207 patients with BTAs and a control group of 106 patients with aneurysms elsewhere to control for non-morphological factors, who were diagnosed at the Brigham and Women’s Hospital and Massachusetts General Hospital between 1990 and 2016, were evaluated. We examined the presence of hypoplastic, aplastic or fetal PCoAs, vertebral dominance, and diameters and angles of surrounding parent and daughter vessels. Univariable and multivariable statistical analyses were performed to determine statistical significance. Sensitivity analyses with small (≤ 3 mm) aneurysms only and with angles excluded, were also performed. In multivariable analysis, daughter–daughter angle was directly, and parent artery diameter and diameter size ratio were inversely associated with BTAs. These results remained significant in the subgroup analysis of small aneurysms (width ≤ 3 mm) and when angles were excluded. These easily measurable and robust parameters that are unlikely to be affected by aneurysm formation could aid in risk stratification for the formation of BTAs in high-risk patients.

IntroductionThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE) includes hundreds of hospitals internationally using a federated computational approach to COVID-19 research using the EHR.ObjectiveWe sought to develop and validate a standard definition of COVID-19 severity from readily accessible EHR data across the Consortium.MethodsWe developed an EHR-based severity algorithm and validated it on patient hospitalization data from 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also used a machine learning approach to compare selected predictors of severity to the 4CE algorithm at one site.ResultsThe 4CE severity algorithm performed with pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of single code categories for acuity were unacceptably inaccurate - varying by up to 0.65 across sites. A multivariate machine learning approach identified codes resulting in mean AUC 0.956 (95% CI: 0.952, 0.959) compared to 0.903 (95% CI: 0.886, 0.921) using expert-derived codes. Billing codes were poor proxies of ICU admission, with 49% precision and recall compared against chart review at one partner institution.DiscussionWe developed a proxy measure of severity that proved resilient to coding variability internationally by using a set of 6 code classes. In contrast, machine-learning approaches may tend to overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold standard outcomes, possibly due to pandemic conditions.ConclusionWe developed an EHR-based algorithm for COVID-19 severity and validated it at 12 international sites.