Explore the vast range of titles available.

Diverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique. We developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes. Our developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. As the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation. A systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data.

Purpose Use of genomic sequencing is increasing at a pace that requires technological solutions to effectively meet the needs of a growing patient population. We developed GUÍA, a web-based application, to enhance the delivery of genomic results and related clinical information to patients and families. Methods GUÍA development occurred in five overlapping phases: formative research, content development, stakeholder/community member input, user interface design, and web application development. Development was informed by formative qualitative research involving parents ( N  = 22) whose children underwent genomic testing. Participants enrolled in the NYCKidSeq pilot study ( N  = 18) completed structured feedback interviews post–result disclosure using GUÍA. Genetic specialists, researchers, patients, and community stakeholders provided their perspectives on GUÍA’s design to ensure technical, cultural, and literacy appropriateness. Results NYCKidSeq participants responded positively to the use of GUÍA to deliver their children’s results. All participants ( N  = 10) with previous experience with genetic testing felt GUÍA improved result disclosure, and 17 (94%) participants said the content was clear. Conclusion GUÍA communicates complex genomic information in an understandable and personalized manner. Initial piloting demonstrated GUÍA’s utility for families enrolled in the NYCKidSeq pilot study. Findings from the NYCKidSeq clinical trial will provide insight into GUÍA’s effectiveness in communicating results among diverse, multilingual populations.

The emergence of genomic data in biobanks and health systems offers new ways to derive medically important phenotypes, including acute phenotypes occurring during inpatient clinical care. Here we study the genetic underpinnings of the rapid response to phenylephrine, an α1-adrenergic receptor agonist commonly used to treat hypotension during anesthesia and surgery. We quantified this response by extracting blood pressure (BP) measurements 5 min before and after the administration of phenylephrine. Based on this derived phenotype, we show that systematic differences exist between self-reported ancestry groups: European-Americans (EA; n = 1387) have a significantly higher systolic response to phenylephrine than African-Americans (AA; n = 1217) and Hispanic/Latinos (HA; n = 1713) (31.3% increase, p value < 6e−08 and 22.9% increase, p value < 5e−05 respectively), after adjusting for genetic ancestry, demographics, and relevant clinical covariates. We performed a genome-wide association study to investigate genetic factors underlying individual differences in this derived phenotype. We discovered genome-wide significant association signals in loci and genes previously associated with BP measured in ambulatory settings, and a general enrichment of association in these genes. Finally, we discovered two low frequency variants, present at ~1% in EAs and AAs, respectively, where patients carrying one copy of these variants show no phenylephrine response. This work demonstrates our ability to derive a quantitative phenotype suited for comparative statistics and genome-wide association studies from dense clinical and physiological measures captured for managing patients during surgery. We identify genetic variants underlying non response to phenylephrine, with implications for preemptive pharmacogenomic screening to improve safety during surgery.

Risk variants in the apolipoprotein L1 (APOL1 [OMIM 603743]) gene on chromosome 22 are common in individuals of West African ancestry and confer increased risk of kidney failure for people with African ancestry and hypertension. Whether disclosing APOL1 genetic testing results to patients of African ancestry and their clinicians affects blood pressure, kidney disease screening, or patient behaviors is unknown. To determine the effects of testing and disclosing APOL1 genetic results to patients of African ancestry with hypertension and their clinicians. This pragmatic randomized clinical trial randomly assigned 2050 adults of African ancestry with hypertension and without existing chronic kidney disease in 2 US health care systems from November 1, 2014, through November 28, 2016; the final date of follow-up was January 16, 2018. Patients were randomly assigned to undergo immediate (intervention) or delayed (waiting list control group) APOL1 testing in a 7:1 ratio. Statistical analysis was performed from May 1, 2018, to July 31, 2020. Patients randomly assigned to the intervention group received APOL1 genetic testing results from trained staff; their clinicians received results through clinical decision support in electronic health records. Waiting list control patients received the results after their 12-month follow-up visit. Coprimary outcomes were the change in 3-month systolic blood pressure and 12-month urine kidney disease screening comparing intervention patients with high-risk APOL1 genotypes and those with low-risk APOL1 genotypes. Secondary outcomes compared these outcomes between intervention group patients with high-risk APOL1 genotypes and controls. Exploratory analyses included psychobehavioral factors. Among 2050 randomly assigned patients (1360 women [66%]; mean [SD] age, 53 [10] years), the baseline mean (SD) systolic blood pressure was significantly higher in patients with high-risk APOL1 genotypes vs those with low-risk APOL1 genotypes and controls (137 [21] vs 134 [19] vs 133 [19] mm Hg; P = .003 for high-risk vs low-risk APOL1 genotypes; P = .001 for high-risk APOL1 genotypes vs controls). At 3 months, the mean (SD) change in systolic blood pressure was significantly greater in patients with high-risk APOL1 genotypes vs those with low-risk APOL1 genotypes (6 [18] vs 3 [18] mm Hg; P = .004) and controls (6 [18] vs 3 [19] mm Hg; P = .01). At 12 months, there was a 12% increase in urine kidney disease testing among patients with high-risk APOL1 genotypes (from 39 of 234 [17%] to 68 of 234 [29%]) vs a 6% increase among those with low-risk APOL1 genotypes (from 278 of 1561 [18%] to 377 of 1561 [24%]; P = .10) and a 7% increase among controls (from 33 of 255 [13%] to 50 of 255 [20%]; P = .01). In response to testing, patients with high-risk APOL1 genotypes reported more changes in lifestyle (a subjective measure that included better dietary and exercise habits; 129 of 218 [59%] vs 547 of 1468 [37%]; P < .001) and increased blood pressure medication use (21 of 218 [10%] vs 68 of 1468 [5%]; P = .005) vs those with low-risk APOL1 genotypes; 1631 of 1686 (97%) declared they would get tested again. In this randomized clinical trial, disclosing APOL1 genetic testing results to patients of African ancestry with hypertension and their clinicians was associated with a greater reduction in systolic blood pressure, increased kidney disease screening, and positive self-reported behavior changes in those with high-risk genotypes. ClinicalTrials.gov Identifier: NCT02234063.

For almost 50 years, the Icahn School of Medicine at Mount Sinai has continually invested in genetics and genomics, facilitating a healthy ecosystem that provides widespread support for the ongoing programs in translational pharmacogenomics. These programs can be broadly cataloged into discovery, education, clinical implementation and testing, which are collaboratively accomplished by multiple departments, institutes, laboratories, companies and colleagues. Focus areas have included drug response association studies and allele discovery, multiethnic pharmacogenomics, personalized genotyping and survey-based education programs, pre-emptive clinical testing implementation and novel assay development. This overview summarizes the current state of translational pharmacogenomics at Mount Sinai, including a future outlook on the forthcoming expansions in overall support, research and clinical programs, genomic technology infrastructure and the participating faculty.

This study assessed physician attitudes toward adopting genome-guided prescribing through clinical decision support (CDS), prior to enlisting in the Clinical Implementation of Personalized Medicine through Electronic Health Records and Genomics pilot pharmacogenomics project (CLIPMERGE PGx). We developed a survey instrument that includes the Evidence Based Practice Attitude Scale, adapted to measure attitudes toward adopting genome-informed interventions (EBPAS-GII). The survey also includes items to measure physicians’ characteristics (awareness, experience, and perceived usefulness), attitudes about personal genome testing (PGT) services, and comfort using technology. We surveyed 101 General Internal Medicine physicians from the Icahn School of Medicine at Mount Sinai (ISMMS). The majority were residency program trainees (~88%). Prior to enlisting into CLIPMERGE PGx, most physicians were aware of and had used decision support aids. Few physicians, however, were aware of and had used genome-guided prescribing. The majority of physicians viewed decision support aids and genotype data as being useful for making prescribing decisions. Most physicians had not heard of, but were willing to use, PGT services and felt comfortable interpreting PGT results. Most physicians were comfortable with technology. Physicians who perceived genotype data to be useful in making prescribing decisions, had more positive attitudes toward adopting genome-guided prescribing through CDS. Our findings suggest that internal medicine physicians have a deficit in their familiarity and comfort interpreting and using genomic information. This has reinforced the importance of gathering feedback and guidance from our enrolled physicians when designing genome-guided CDS and the importance of prioritizing genomic medicine education at our institutions.

The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities.

Background: The emergence of genomic data in biobanks and health systems offers new ways to derive medically important phenotypes, including acute phenotypes that occur during in-patient clinical care. We hypothesized that there is a genetic underpinning to the magnitude of the response to phenylephrine, an alpha1-adrenergic receptor agonist commonly used to treat hypotension during anesthesia and surgery. Methods: We quantified the response to phenylephrine by determining the delta between the minimum blood pressure (BP) within five minutes before and the maximum BP within five minutes after bolus administration. We then performed a genome-wide association study (GWAS) adjusted for genetic ancestry, demographics, and relevant clinical covariates to investigate genetic factors underlying individual differences systolic BP response to phenylephrine (Delta SBP), as well as mean arterial pressure (Delta MAP) and diastolic BP (Delta DBP), for both the entire study cohort as well as for each of 3 ancestry sub-cohorts; European American (EA), African American (AA), and Hispanic American (HA). Results: 4,317 patients met inclusion criteria, of which 3,699 were genotyped. Average ΔBP values over the entire cohort were Delta SBP= 17 (+- 25) mmHg, Delta MAP = 14 (+- 18) mmHg, Delta DBP = 11(+- 14) mmHg. The largest difference between populations was observed for Delta SBP (Delta SBP EA = 20(+- 24) mmHg; Delta SBP HA = 16 (+- 25) mmHg; Delta SBP AA = 15 (+- 25) mmHg). The differences remained after adjusting for clinical covariates and ancestry (EA vs. HA: Delta SBP, p<0.032; Delta MAP, p<0.021; Delta DBP, p<0.008); (EA vs. AA: Delta SBP, p<5.13x10-5; Delta MAP, p<2.1x10-4; Delta DBP, p<3.3x10-4). GWAS revealed significant associations between loci and BP response in 5 different genome regions (p<5x10-8) in the entire cohort, and suggestive associations in 2 different regions in EAs (p<6x10-8,p<7x10-8). We observed non-random enrichment in association with SBP drug response in 165 loci previously reported to be associated with systolic blood pressure. Finally, we discovered rare variants, rs188427942 and rs147664194 present at ~1% in EAs and rs146535276 present at ~1% in AAs respectively, where patients carrying one copy of these variants show no response to phenylephrine. Conclusions: It is possible to derive a quantitative phenotype suited for comparative statistics and genome-wide association studies from routinely collected perioperative data. There are population differences in rapid response to phenylephrine, large effect alleles and novel genes affecting pharmaceutical response, and phenylephrine non-responders, with implications for personalized treatment during surgery.

Electronic health record (EHR) systems contain structured data and unstructured documentation. Clinical insights can be derived from analyzing both but optimal methods for this have not been studied extensively. We compared various approaches to analyzing EHR data for non-alcoholic fatty liver disease (NAFLD). We compared analysis of structured and unstructured EHR data using natural language processing (NLP), free-text search, and diagnostic codes against expert adjudication as the reference standard. Out of 38,575 patients, we identified 2,281 patients with NAFLD. From the remainder, 10,653 patients with similar data density were selected as a control group. NLP was more sensitive than ICD and text search (NLP 0.93 vs. ICD 0.28 vs. text search 0.81) with higher a F2 score (NLP 0.92 vs. ICD 0.34 vs. text search 0.81). 619 patients had suspected NAFLD documented in radiology notes not acknowledged in other forms of clinical documentation. Of these, 232 (37.5%) were found to have more advanced liver disease after a median of 1,057 days. NLP-based approaches have superior accuracy in identifying NAFLD within the EHR compared to ICD/text search-based approaches. Suspected NAFLD on imaging is often not acknowledged in subsequent clinical documentation. Many such patients are later found to have more advanced liver disease. For identification of NAFLD, NLP performed better than alternative selection modalities and facilitated follow-on analysis of information flow. If accuracy can be proven to persist across clinical domains, NLP can identify patient phenotypes for biomedical research in an accurate and high-throughput manner.

Abstract Background and aims Diverticular disease is among the most prevalent conditions encountered by gastroenterologists, affecting ∼50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with diverticular disease, utilizing the electronic health record (EHR) with Natural Language Processing (NLP). Methods We developed a NLP-enriched phenotype algorithm that incorporated colonoscopy or abdominal imaging reports to accurately identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of diverticular disease in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in the clinical phenome. For more in-depth investigation of associated clinical phenotypes, we also performed PheWAS with the previously reported 52 GWAS susceptibility variants for diverticular disease. Results Ancestry-stratified GWAS analyses confirmed the well-established associations between ARHGAP15 loci with diverticular disease in European cohorts, and found similar positive effect sizes in African cohorts but with non-significant p-values. With overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients, we found substantial genetic correlations between diverticulosis and diverticulitis, up to 0.997 in European ancestry. PheWAS analyses identified associations between the diverticular disease GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. Conclusion Our multiancestry GWAS-PheWAS study demonstrated an effective use of multidimensional EHR information in disease case/control classification with NLP for more comprehensive and scalable phenotyping, and implementation of an integrative analytical pipeline to facilitate etiological investigation of a disease from a clinical perspective. Competing Interest Statement The authors have declared no competing interest. Footnotes * Grant Support: The eMERGE Network was initiated and funded by NHGRI through the following grants: U01HG006828 (Cincinnati Children’s Hospital Medical Center/Boston Children’s Hospital); U01HG006830 (Children’s Hospital of Philadelphia); U01HG006389 (Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University); U01HG006382 (Geisinger Clinic); U01HG006375 (Group Health Cooperative/University of Washington); U01HG006379 (Mayo Clinic); U01HG006380 (Icahn School of Medicine at Mount Sinai); U01HG006388 (Northwestern University); U01HG006378 (Vanderbilt University Medical Center); U01HG006385 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as genotyping centers. * Disclosures: The authors have no conflicts to declare * Accession numbers: The genotypes and phenotypes used in this study were deposited to the NCBI Database of Genotypes and Phenotypes (dbGaP; accession number phs000888.v1.p1). * Writing Assistance: None * Abbreviations AA African ancestry; ADPKD autosomal dominant polycystic kidney disease; BMI body mass index; CI Confidence interval; EA European ancestry; EHR electronic health record; eMERGE Electronic Medical Records and Genomics network; FDR false discovery rate; GI gastrointestinal; GWAS genome-wide association study; HWE Hardy-Weinberg equilibrium; KPWA/UW Kaiser Permanente Washington/ University of Washington; LD linage disequilibrium; MAF minor allele frequency; NLP natural language processing; NU Northwestern University; PCA Principal Components Analysis; PheWAS phenomewide association study; PPV positive predictive value; EAF effect allele frequency; SNP single nucleotide variant; VU Vanderbilt University