Search

Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .

We combine the Planck-SZ2 galaxy cluster catalogue with near-infrared photometry of galaxies from the VISTA Hemisphere Survey to identify candidate brightest cluster galaxies (BCGs) in 306 massive clusters in the Southern skies at redshifts of $z>0.1$. We find that 91% of these clusters have at least one candidate BCG within the 95% confidence interval on the cluster centers quoted by the Planck collaboration, providing reassurance that our analyses are statistically compatible, and find 92% to be reasonable candidates following a manual inspection. We make our catalog publicly available to assist colleagues interested in multi-wavelength studies of cluster cores, and the search for gravitationally lensed explosive transients in upcoming surveys including the Legacy Survey of Space and Time by the Vera C. Rubin Observatory., Published in RNAAS in March 2023. 3 pages, 1 figure

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: The insulin ordering process is an opportunity to provide clinicians with hypoglycemia risk predictions, but few hypoglycemia models centered around the insulin ordering process exist. Methods: We used data on adult patients, admitted in 2019 to non-ICU floors of a large teaching hospital, who had orders for subcutaneous insulin. Our outcome was hypoglycemia, defined as a blood glucose (BG) Results: Of 21 052 included insulin orders, 1839 (9%) were followed by a hypoglycemic event within 24 hours. Logistic regression, random forest, and XGBoost models had AUCs of 0.81, 0.80, and 0.79, and recall of 0.44, 0.49, and 0.32, respectively. The most significant predictor was the lowest BG value in the 24 hours preceding the order. Predictors related to the insulin order being placed at the time of the prediction were useful to the model but less important than the patient’s history of BG values over time. Conclusions: Hypoglycemia within the next 24 hours can be predicted at the time an insulin order is placed, providing an opportunity to integrate decision support into the medication ordering process to make insulin therapy safer.

The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To account for new drugs and terminology updates, we rebuilt MEDI to overhaul the knowledgebase for modern EHRs. Indications for prescribable medications were extracted using natural language processing and ontology relationships from six publicly available resources: RxNorm, Side Effect Resource 4.1, Mayo Clinic, WebMD, MedlinePlus, and Wikipedia. We compared the estimated precision and recall between the previous MEDI (MEDI-1) and the updated version (MEDI-2) with manual review. MEDI-2 contains 3031 medications and 186,064 indications. The MEDI-2 high precision subset (HPS) includes indications found within RxNorm or at least three other resources. MEDI-2 and MEDI-2 HPS contain 13% more medications and over triple the indications compared to MEDI-1 and MEDI-1 HPS, respectively. Manual review showed MEDI-2 achieves the same precision (0.60) with better recall (0.89 vs. 0.79) compared to MEDI-1. Likewise, MEDI-2 HPS had the same precision (0.92) and improved recall (0.65 vs. 0.55) than MEDI-1 HPS. The combination of MEDI-1 and MEDI-2 achieved a recall of 0.95. In updating MEDI, we present a more comprehensive medication-indication knowledgebase that can continue to facilitate applications and research with EHRs.

ObjectiveIdentifying symptoms highly specific to COVID-19 would improve the clinical and public health response to infectious outbreaks. Here, we describe a high-throughput approach – Concept-Wide Association Study (ConceptWAS) that systematically scans a disease’s clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic.MethodsUsing the Vanderbilt University Medical Center (VUMC) EHR, we parsed clinical notes through a natural language processing pipeline to extract clinical concepts. We examined the difference in concepts derived from the notes of COVID-19-positive and COVID-19-negative patients on the PCR testing date. We performed ConceptWAS using the cumulative data every two weeks for early identifying specific COVID-19 symptoms.ResultsWe processed 87,753 notes 19,692 patients (1,483 COVID-19-positive) subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020. We found 68 clinical concepts significantly associated with COVID-19. We identified symptoms associated with increasing risk of COVID-19, including “absent sense of smell” (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21–7.50), “fever” (OR = 1.43, 95% CI = 1.28–1.59), “with cough fever” (OR = 2.29, 95% CI = 1.75–2.96), and “ageusia” (OR = 5.18, 95% CI = 3.02–8.58). Using ConceptWAS, we were able to detect loss sense of smell or taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC).ConclusionConceptWAS is a high-throughput approach for exploring specific symptoms of a disease like COVID-19, with a promise for enabling EHR-powered early disease manifestations identification.

Identifying acute events as they occur is challenging in large hospital systems. Here, we describe an automated method to detect 2 rare adverse drug events (ADEs), drug-induced torsades de pointes and Stevens-Johnson syndrome and toxic epidermal necrolysis, in near real time for participant recruitment into prospective clinical studies. A text processing system searched clinical notes from the electronic health record (EHR) for relevant keywords and alerted study personnel via email of potential patients for chart review or in-person evaluation. Between 2016 and 2018, the automated recruitment system resulted in capture of 138 true cases of drug-induced rare events, improving recall from 43% to 93%. Our focused electronic alert system maintained 2-year enrollment, including across an EHR migration from a bespoke system to Epic. Real-time monitoring of EHR notes may accelerate research for certain conditions less amenable to conventional study recruitment paradigms.

Certain patient demographics and histopathologic features are risk factors for papillary thyroid cancer (PTC) recurrence after initial treatment. Our objective was to determine whether very young age is associated with aggressive pathologic features in patients with PTC.A retrospective analysis was performed for PTC patients who underwent surgical treatment at the University of Michigan between 2006 and 2012. Patients with known distant metastases were excluded. Demographics, high-risk pathologic features (capsular or vascular invasion, extrathyroidal extension, lymph node metastases, and extranodal extension), and disease recurrence were analyzed.632 PTC patients were included in the analysis. Median age was 49 y (range 10-87). Tumors in patients aged25 y had higher rates of extranodal extension (P = 0.002) compared with patients aged 25-44 y. Patients aged25 y had more vascular invasion (P 0.001) and lymph node metastasis (P = 0.001) than tumors in patients aged between 45-75 y. Patients aged75 y had higher rates of vascular invasion (P 0.001) and extrathyroidal extension (P = 0.001) compared with patients aged 45-75 y and more extrathyroidal extension (P 0.001) than patients aged 25-44 y. There were no differences in tumor characteristics between the25 and75 age groups.PTC patients aged25 y of age or older than 75 y exhibit higher rates of aggressive histopathologic features compared to PTC patients aged between 25-75 y.

IMPORTANCE: Biomarkers that reflect prognosis and cellular immunity in patients with head and neck squamous cell carcinoma (HNSCC) are a prerequisite for improving individualized treatment that limits the intensity and morbidity of conventional treatment and may be useful in the introduction of new immunotherapy regimens. OBJECTIVE: To determine if specific classes of tumor-infiltrating lymphocytes (TILs) in pretreatment biopsy specimens have prognostic value for outcomes in a large training and validation cohort of patients with HNSCC. DESIGN, SETTING, AND PARTICIPANTS: In this prospective, epidemiologic study with a median follow-up of 47.5 months, in 464 previously untreated patients with available tissue for construction of tissue microarray, HNSCC disease sites included oral cavity (228), oropharynx (147), larynx (74), and hypopharynx (15). The training cohort consisted of 241 patients and the validation cohort consisted of 223 patients. Overall tumor stage was I (55), II (69), III (71), or IV (269). Patients were enrolled between November 2008 to October 2014. Data were analyzed between October 2018 to April 2019. MAIN OUTCOMES AND MEASURES: Semiquantitative levels of CD4, CD8, and FoxP3 lymphocytes were assessed by immunohistologic analysis and correlations with clinical prognostic factors, initial treatment modality, and overall survival (OS) and disease-specific (DSS) survival were determined. A principal component analysis was performed to generate a combined TIL-weighted sum score (TIL(ws)). RESULTS: Of the 464 participants, 135 (29%) were women; mean (SD) age was 61.1 (11.8) years. Higher CD8 counts were associated with improved OS in both training and validation sets (HR, 0.94; 95% CI, 0.90-0.98; and HR, 0.97; 95% CI, 0.95-0.99, respectively). Higher TIL(ws) levels were associated with improved OS and DSS in both the training set (HR, 0.91; 95% CI, 0.86-0.96; and HR, 0.93; 95% CI, 0.87-0.99, respectively) and validation set (HR, 0.96; 95% CI, 0.93-0.99; and HR, 0.94; 95% CI, 0.89-0.99, respectively). A multivariable Cox model controlling for batch, age, clinical stage, disease site, comorbidities, HPV status, and smoking, showed that higher TIL(ws) levels were associated with improved OS and DSS (HR, 0.94; 95% CI, 0.92-0.97; and HR, 0.94; 95% CI, 0.90-0.98, respectively). When grouped by treatment (surgery vs chemoradiation) and tested for interaction, treatment was found to be an effect modifier for CD4 levels and OS. Low CD4 levels were showed greater association with decreased survival in the chemoradiation cohort than the surgery cohort. CONCLUSIONS AND RELEVANCE: The findings from this large cohort study suggest that levels of TILs are an independent prognostic factor in patients with HNSCC. Subsets of TILs and combined TIL scores may be clinically useful predictive and prognostic factors.

Objectives Salivary gland tumors are comprised of a diverse group of malignancies with widely varying prognoses. These cancers can be difficult to differentiate, especially in cases with limited potential for immunohistochemistry (IHC)-based characterization. Here, we sought to define the molecular profile of a rare salivary gland cancer called hyalinizing clear cell carcinoma (HCCC), and identify a molecular gene signature capable of distinguishing between HCCC and the histopathologically similar disease, mucoepidermoid carcinoma (MEC). Materials and methods We performed the first integrated full characterization of five independent HCCC cases. Results We discovered insulin-like growth factor alterations and aberrant IGF2 and/or IGF1R expression in HCCC tumors, suggesting a potential dependence on this pathway. Further, we identified a 354 gene signature that differentiated HCCC from MEC, and was significantly enriched for genes with an ATF1 binding motif in their promoters, supporting a transcriptional pathogenic mechanism of the characteristic EWSR1-ATF1 fusion found in these tumors. Of the differentially expressed genes, IGF1R, SGK1 and SGK3 were found to be elevated in the HCCCs relative to MECs. Finally, analysis of immune checkpoints and subsequent IHC demonstrated that CXCR4 protein was elevated in several of the HCCC cases. Conclusion Collectively, our data identify an ATF1-motif enriched gene signature that may have clinical utility for molecular differentiation of HCCCs from other salivary gland tumors and discover potential actionable alterations that may benefit the clinical care of recurrent HCCC patients.

Objective Identifying symptoms and characteristics highly specific to coronavirus disease 2019 (COVID-19) would improve the clinical and public health response to this pandemic challenge. Here, we describe a high-throughput approach – Concept-Wide Association Study (ConceptWAS) – that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. Methods We created a natural language processing pipeline to extract concepts from clinical notes in a local ER corresponding to the PCR testing date for patients who had a COVID-19 test and evaluated these concepts as predictors for developing COVID-19. We identified predictors from Firth's logistic regression adjusted by age, gender, and race. We also performed ConceptWAS using cumulative data every two weeks to identify the timeline for recognition of early COVID-19-specific symptoms. Results We processed 87,753 notes from 19,692 patients subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020 (1,483 COVID-19-positive). We found 68 concepts significantly associated with a positive COVID-19 test. We identified symptoms associated with increasing risk of COVID-19, including “anosmia” (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21–7.50), “fever” (OR = 1.43, 95% CI = 1.28–1.59), “cough with fever” (OR = 2.29, 95% CI = 1.75–2.96), and “ageusia” (OR = 5.18, 95% CI = 3.02–8.58). Using ConceptWAS, we were able to detect loss of smell and loss of taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). Conclusion ConceptWAS, a high-throughput approach for exploring specific symptoms and characteristics of a disease like COVID-19, offers a promise for enabling EHR-powered early disease manifestations identification.

A fundamental mechanistic study of the s-BuLi/chiral diamine-mediated lithiation-trapping of N-thiopivaloyl azetidine and pyrrolidine is reported. We show that lithiated thiopivalamides are configurationally unstable at -78 °C. Reaction then proceeds via a dynamic resolution of diastereomeric lithiated intermediates and this accounts for the variable sense and degree of asymmetric induction observed compared to N-Boc heterocycles.

ObjectiveThe pathogenetic underpinnings of extrapulmonary small cell carcinomas (EPSCCs) of the head and neck are poorly understood. We sought to describe the clinical case and whole-exome DNA sequencing data of a patient with sinonasal Schneiderian carcinoma in situ whose tumor progressed to small cell carcinoma (SCC).Study designCase report and whole-exome sequencing of tumor DNA.SettingAcademic medical center.Subjects and methodsA 52-year-old man with sinonasal Schneiderian carcinoma in situ whose tumor progressed to small cell carcinoma. We performed whole-exome genetic sequencing and copy-number variation (CNV) analysis of tumor and normal DNA extracted from flash-frozen, paraffin-embedded (FFPE) samples.ResultsA total of 93 high-confidence, nonsynonymous somatic mutation events were identified in sinonasal SCC, including loss-of-function mutations in TP53, MAML3, a transcriptional coactivator of the Notch pathway, and GAS6, an activating ligand of the TAM family of tyrosine kinase receptors. Focal amplifications of chromosomal regions 6p25-11.1, containing SOX4 and VEGFA, and 14q32.1-32.3, containing AKT1 and the Notch inhibitory ligand DLK1, were also seen. Further CNV analysis revealed deletions in the critical cell cycle regulators CDKN2A, RB1, RBL1, and RBL2 and the chromatin modifier EP300.ConclusionsSmall cell carcinoma may rarely arise from sinonasal Schneiderian carcinoma in situ and exhibits similar genomic aberrations (eg, SOX amplification, Notch pathway inactivation) to pulmonary small cell carcinoma.

Background Often unrecognized by providers, adverse drug reactions (ADRs) diminish patients' quality of life, cause preventable admissions and emergency department visits, and increase health care costs. Objective This article evaluates whether an automated system, the Adverse Drug Effect Recognizer (ADER), could assist clinicians in detecting and addressing inpatients' ongoing preadmission ADRs. Methods ADER uses natural language processing to extract patients' medications, findings, and past diagnoses from admission notes. It compares excerpted information to a database of known medication adverse effects and promptly warns clinicians about potential ongoing ADRs and potential confounders via alerts placed in patients' electronic health records (EHRs). A 3-month intervention trial evaluated ADER's impact on antihypertensive medication ordering behaviors. At the time of patient admission, ADER warned providers on the Internal Medicine wards of Vanderbilt University Hospital about potential ongoing preadmission antihypertensive medication ADRs. A retrospective control group, comprised similar physicians from a period prior to the intervention, received no alerts. The evaluation compared ordering behaviors for each group to determine if preadmission medications changed during hospitalization or at discharge. The study also analyzed intervention group participants' survey responses and user comments. Results ADER identified potential preadmission ADRs for 30% of both groups. Compared with controls, intervention providers more often withheld or discontinued suspected ADR-causing medications during the inpatient stay (p Conclusion Results indicate that ADER helped physicians recognize ADRs and reduced ordering of suspected ADR-causing medications. In hospitals using EHRs, ADER-like systems could improve clinicians' recognition and elimination of ongoing ADRs.

Schiff bases, derived from the condensation of salicylaldehydes with 3- and 4-aminophenol, reacted with palladium(II) acetate to give the corresponding bis(N-arylsalicylaldiminato)palladium(II) complexes. These complexes have been found to be active catalysts for the Suzuki–Miyaura cross-coupling of aryl bromides and iodides with aryl boronic acids, using water as a solvent.Key words: cross-coupling, green chemistry, palladium, salicylaldimines, Schiff base, Suzuki–Miyaura.

Schiff bases (HL) derived from sulfanilamides or aminobenzothiazoles add to Pd(OAc)2 to give complexes of the type PdL2 (1–7) in moderate to excellent yields. Reactions of Schiff bases containing pyrimidine groups, however, gave several products arising from competing coordination of the pyrimidine nitrogen. Palladium complexes and Schiff bases have been investigated as antifungal agents against Aspergillus niger and Aspergillus flavus.

SummaryObjectives: This work identified challenges associated with extraction and representation of medication-related information from publicly available electronic sources.Methods: We gained direct observational experience through creating and evaluating the Drug Evidence Base (DEB), a repository of drug indications and adverse effects (ADEs), and supplemented this through literature review. We extracted DEB content from the National Drug File Reference Terminology, from aggregated MEDLINE co-occurrence data, and from the National Library of Medicine’s DailyMed. To understand better the similarities, differences and problems with the content of DEB and the SIDER Side Effect Resource, and Vanderbilt’s MEDI Indication Resource, we carried out statistical evaluations and human expert reviews.Results: While DEB, SIDER, and MEDI often agreed on medication indications and side effects, cross-system shortcomings limit their current utility. The drug information resources we evaluated frequently employed multiple, disparate vaguely related UMLS concepts to represent a single specific clinical drug indication or adverse effect. Thus, evaluations comparing drug-indication and drug-ADE coverage for such resources will encounter substantial numbers of false negative and false positive matches. Furthermore, our review found that many indication and ADE relationships are too complex – logically and temporally – to represent within existing systems.Conclusion: To enhance applicability and utility, future drug information systems deriving indications and ADEs from public resources must represent clinical concepts uniformly and as precisely as possible. Future systems must also better represent the inherent complexity of indications and ADEs.Citation: Smith JC, Denny JC, Chen Q, Nian H, Spickard III A, Rosenbloom ST, Miller RA. Lessons learned from developing a drug evidence base to support pharmacovigilance. Appl Clin Inf 2013; 4: 596–617http://dx.doi.org/10.4338/ACI-2013-08-RA-0062

Objective To try to lower patient re-identification risks for biomedical research databases containing laboratory test results while also minimizing changes in clinical data interpretation. Materials and methods In our threat model, an attacker obtains 5–7 laboratory results from one patient and uses them as a search key to discover the corresponding record in a de-identified biomedical research database. To test our models, the existing Vanderbilt TIME database of 8.5 million Safe Harbor de-identified laboratory results from 61 280 patients was used. The uniqueness of unaltered laboratory results in the dataset was examined, and then two data perturbation models were applied—simple random offsets and an expert-derived clinical meaning-preserving model. A rank-based re-identification algorithm to mimic an attack was used. The re-identification risk and the retention of clinical meaning for each model's perturbed laboratory results were assessed. Results Differences in re-identification rates between the algorithms were small despite substantial divergence in altered clinical meaning. The expert algorithm maintained the clinical meaning of laboratory results better (affecting up to 4% of test results) than simple perturbation (affecting up to 26%). Discussion and conclusion With growing impetus for sharing clinical data for research, and in view of healthcare-related federal privacy regulation, methods to mitigate risks of re-identification are important. A practical, expert-derived perturbation algorithm that demonstrated potential utility was developed. Similar approaches might enable administrators to select data protection scheme parameters that meet their preferences in the trade-off between the protection of privacy and the retention of clinical meaning of shared data.

The synthesis of the fully fluorinated bis-anilido ligand N,N'-bis-pentafluorophenyl-3,4,5,6-tetrafluorophenylene-1,2-diamine, 1 is described. Reaction of one or two equivalents of 1 with AlMe(3) gives aluminium compounds incorporating one or two ligands, the latter being the protonated form of a new weakly coordinating anion (WCA), 3-H. Alternatively, reaction of 1 with LiAlH(4) yields the lithium salt of this anion, 3-Li, which may be employed as a reagent for preparing the trityl and N,N-dimethylanilinium salts of the aluminate anion. All ion pairs are fully characterized and the use of 3-Ph(3)C to generate titanium-based alkyl cations is also described.

Mobile users of computation and communication services have been rapidly adopting battery-powered mobile handhelds, such as PocketPCs and SmartPhones, for their work. However, the limited battery-lifetime of these devices restricts their portability and applicability, and this weakness can be exacerbated by mobile malware targeting depletion of battery energy. Such malware are usually difficult to detect and prevent, and frequent outbreaks of new malware variants also reduce the effectiveness of commonly-seen signature-based detection. To alleviate these problems, we propose a power-aware malware-detection framework that monitors, detects, and analyzes previously unknown energy-depletion threats. The framework is composed of (1) a power monitor which collects power samples and builds a power consumption history from the collected samples, and (2) a data analyzer which generates a power signature from the constructed history. To generate a power signature, simple and effective noise-filtering and data-compression are applied, thus reducing the detection overhead. Similarities between power signatures are measured by the χ2-distance, reducing both false-positive and false-negative detection rates. According to our experimental results on an HP iPAQ running a Windows Mobile OS, the proposed framework achieves significant (up to 95%) storage-savings without losing the detection accuracy, and a 99% true-positive rate in classifying mobile malware.