BackgroundAs a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke.ObjectiveThe aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year.MethodsData from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. Retrospective (N=823,627, calendar year 2013) and prospective (N=680,810, calendar year 2014) cohorts were formed. A machine learning algorithm, XGBoost, was adopted in the process of feature selection and model building. It generated an ensemble of classification trees and assigned a final predictive risk score to each individual.ResultsThe 1-year incident hypertension risk model attained areas under the curve (AUCs) of 0.917 and 0.870 in the retrospective and prospective cohorts, respectively. Risk scores were calculated and stratified into five risk categories, with 4526 out of 381,544 patients (1.19%) in the lowest risk category (score 0-0.05) and 21,050 out of 41,329 patients (50.93%) in the highest risk category (score 0.4-1) receiving a diagnosis of incident hypertension in the following 1 year. Type 2 diabetes, lipid disorders, CVDs, mental illness, clinical utilization indicators, and socioeconomic determinants were recognized as driving or associated features of incident essential hypertension. The very high risk population mainly comprised elderly (age>50 years) individuals with multiple chronic conditions, especially those receiving medications for mental disorders. Disparities were also found in social determinants, including some community-level factors associated with higher risk and others that were protective against hypertension.ConclusionsWith statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care.

BackgroundShark new antigen receptor variable domain (VNAR) antibodies can bind restricted epitopes that may be inaccessible to conventional antibodies.MethodsHere, we developed a library construction method based on polymerase chain reaction (PCR)-Extension Assembly and Self-Ligation (named “EASeL”) to construct a large VNAR antibody library with a size of 1.2 × 1010 from six naïve adult nurse sharks (Ginglymostoma cirratum).ResultsThe next-generation sequencing analysis of 1.19 million full-length VNARs revealed that this library is highly diversified because it covers all four classical VNAR types (Types I–IV) including 11% of classical Type I and 57% of classical Type II. About 30% of the total VNARs could not be categorized as any of the classical types. The high variability of complementarity determining region (CDR) 3 length and cysteine numbers are important for the diversity of VNARs. To validate the use of the shark VNAR library for antibody discovery, we isolated a panel of VNAR phage binders to cancer therapy-related antigens, including glypican-3, human epidermal growth factor receptor 2 (HER2), and programmed cell death-1 (PD1). Additionally, we identified binders to viral antigens that included the Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS) spike proteins. The isolated shark single-domain antibodies including Type I and Type II VNARs were produced in Escherichia coli and validated for their antigen binding. A Type II VNAR (PE38-B6) has a high affinity (Kd = 10.1 nM) for its antigen.ConclusionsThe naïve nurse shark VNAR library is a useful source for isolating single-domain antibodies to a wide range of antigens. The EASeL method may be applicable to the construction of other large diversity gene expression libraries.

BackgroundDiabetes case finding based on structured medical records does not fully identify diabetic patients whose medical histories related to diabetes are available in the form of free text. Manual chart reviews have been used but involve high labor costs and long latency.ObjectiveThis study developed and tested a Web-based diabetes case finding algorithm using both structured and unstructured electronic medical records (EMRs).MethodsThis study was based on the health information exchange (HIE) EMR database that covers almost all health facilities in the state of Maine, United States. Using narrative clinical notes, a Web-based natural language processing (NLP) case finding algorithm was retrospectively (July 1, 2012, to June 30, 2013) developed with a random subset of HIE-associated facilities, which was then blind tested with the remaining facilities. The NLP-based algorithm was subsequently integrated into the HIE database and validated prospectively (July 1, 2013, to June 30, 2014).ResultsOf the 935,891 patients in the prospective cohort, 64,168 diabetes cases were identified using diagnosis codes alone. Our NLP-based case finding algorithm prospectively found an additional 5756 uncodified cases (5756/64,168, 8.97% increase) with a positive predictive value of .90. Of the 21,720 diabetic patients identified by both methods, 6616 patients (6616/21,720, 30.46%) were identified by the NLP-based algorithm before a diabetes diagnosis was noted in the structured EMR (mean time difference = 48 days).ConclusionsThe online NLP algorithm was effective in identifying uncodified diabetes cases in real time, leading to a significant improvement in diabetes case finding. The successful integration of the NLP-based case finding algorithm into the Maine HIE database indicates a strong potential for application of this novel method to achieve a more complete ascertainment of diagnoses of diabetes mellitus.

Human epididymis protein 4 used in conjunction with CA 125 yields improved specificity for ovarian cancer compared with the use of CA 125 alone, generally similar to results seen in non-Chinese populations.