Citations

AbstractPrecision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health records (EHRs) are commonly utilized to gather additional clinical data elements needed for translational research. However, manual annotation is resource‐intense and not readily scalable. Informatics‐based phenotyping presents an ideal solution, but perspectives obtained can be impacted by both data source and algorithm selection. We derived breast cancer (BC) receptor status phenotypes from structured and unstructured EHR data using rule‐based algorithms, including natural language processing (NLP). Overall, the use of NLP increased BC receptor status coverage by 39.2% from 69.1% with structured medication information alone. Using all available EHR data, estrogen receptor‐positive BC cases were ascertained with high precision (P = 0.976) and recall (R = 0.987) compared with gold standard chart‐reviewed patients. However, status negation (R = 0.591) decreased 40.2% when relying on structured medications alone. Using multiple EHR data types (and thorough understanding of the perspectives offered) are necessary to derive robust EHR‐based precision medicine phenotypes.

The MRI community is using quantitative mapping techniques to complement qualitative imaging. For quantitative imaging to reach its full potential, it is necessary to analyze measurements across systems and longitudinally. Clinical use of quantitative imaging can be facilitated through adoption and use of a standard system phantom, a calibration/standard reference object, to assess the performance of an MRI machine. The International Society of Magnetic Resonance in Medicine AdHoc Committee on Standards for Quantitative Magnetic Resonance was established in February 2007 to facilitate the expansion of MRI as a mainstream modality for multi‐institutional measurements, including, among other things, multicenter trials. The goal of the Standards for Quantitative Magnetic Resonance committee was to provide a framework to ensure that quantitative measures derived from MR data are comparable over time, between subjects, between sites, and between vendors. This paper, written by members of the Standards for Quantitative Magnetic Resonance committee, reviews standardization attempts and then details the need, requirements, and implementation plan for a standard system phantom for quantitative MRI. In addition, application‐specific phantoms and implementation of quantitative MRI are reviewed. Magn Reson Med 79:48–61, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Abstract Objective To assess clinical documentation variations across health care institutions using different electronic medical record systems and investigate how they affect natural language processing (NLP) system portability. Materials and Methods Birth cohorts from Mayo Clinic and Sanford Children’s Hospital (SCH) were used in this study (n = 298 for each). Documentation variations regarding asthma between the 2 cohorts were examined in various aspects: (1) overall corpus at the word level (ie, lexical variation), (2) topics and asthma-related concepts (ie, semantic variation), and (3) clinical note types (ie, process variation). We compared those statistics and explored NLP system portability for asthma ascertainment in 2 stages: prototype and refinement. Results There exist notable lexical variations (word-level similarity = 0.669) and process variations (differences in major note types containing asthma-related concepts). However, semantic-level corpora were relatively homogeneous (topic similarity = 0.944, asthma-related concept similarity = 0.971). The NLP system for asthma ascertainment had anF-score of 0.937 at Mayo, and produced 0.813 (prototype) and 0.908 (refinement) when applied at SCH. Discussion The criteria for asthma ascertainment are largely dependent on asthma-related concepts. Therefore, we believe that semantic similarity is important to estimate NLP system portability. As the Mayo Clinic and SCH corpora were relatively homogeneous at a semantic level, the NLP system, developed at Mayo Clinic, was imported to SCH successfully with proper adjustments to deal with the intrinsic corpus heterogeneity.

The burden and predictors of hospitalization over time in community-based patients with peripheral artery disease (PAD) have not been established. This study evaluates the frequency, reasons and predictors of hospitalization over time in community-based patients with PAD. We assembled an inception cohort of 1798 PAD cases from Olmsted County, MN, USA (mean age 71.2 years, 44% female) from 1 January 1998 through 31 December 2011 who were followed until 2014. Two age- and sex-matched controls ( n = 3596) were identified for each case. ICD-9 codes were used to ascertain the primary reasons for hospitalization. Patients were censored at death or last follow-up. The most frequent reasons for hospitalization were non-cardiovascular: 68% of 8706 hospitalizations in cases and 78% of 8005 hospitalizations in controls. A total of 1533 (85%) cases and 2286 (64%) controls ( p < 0.001) were hospitalized at least once; 1262 (70%) cases and 1588 (44%) controls ( p < 0.001) ≥ two times. In adjusted models, age, prior hospitalization and comorbid conditions were independently associated with increased risk of recurrent hospitalizations in both groups. In cases, severe PAD (ankle–brachial index < 0.5) (HR: 1.25; 95% CI: 1.15, 1.36) and poorly compressible arteries (HR: 1.26; 95% CI: 1.16, 1.38) were each associated with increased risk for recurrent hospitalization. We demonstrate an increased rate of hospitalization in community-based patients with PAD and identify predictors of recurrent hospitalizations. These observations may inform strategies to reduce the burden of hospitalization of PAD patients.

Abstract Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to customize existing systems for their individual tasks, which can require substantial NLP skills. Here we present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components, but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications. Our evaluation shows that the CLAMP default pipeline achieved good performance on named entity recognition and concept encoding. We also demonstrate the efficiency of the CLAMP graphic user interface in building customized, high-performance NLP pipelines with 2 use cases, extracting smoking status and lab test values. CLAMP is publicly available for research use, and we believe it is a unique asset for the clinical NLP community.

Abstract We propose a new flowchart for the treatment of acute cholecystitis ( AC ) in the Tokyo Guidelines 2018 (TG18). Grade III AC was not indicated for straightforward laparoscopic cholecystectomy (Lap‐C). Following analysis of subsequent clinical investigations and drawing on Big Data in particular, TG 18 proposes that some Grade III AC can be treated by Lap‐C when performed at advanced centers with specialized surgeons experienced in this procedure and for patients that satisfy certain strict criteria. For Grade I, TG 18 recommends early Lap‐C if the patients meet the criteria of Charlson comorbidity index ( CCI ) ≤5 and American Society of Anesthesiologists physical status classification ( ASA ‐ PS ) ≤2. For Grade II AC , if patients meet the criteria of CCI ≤5 and ASA ‐ PS ≤2, TG 18 recommends early Lap‐C performed by experienced surgeons; and if not, after medical treatment and/or gallbladder drainage, Lap‐C would be indicated. TG 18 proposes that Lap‐C is indicated in Grade III patients with strict criteria. These are that the patients have favorable organ system failure, and negative predictive factors, who meet the criteria of CCI ≤3 and ASA ‐ PS ≤2 and who are being treated at an advanced center (where experienced surgeons practice). If the patient is not considered suitable for early surgery, TG 18 recommends early/urgent biliary drainage followed by delayed Lap‐C once the patient's overall condition has improved. Free full articles and mobile app of TG18 are available at: http://www.jshbps.jp/modules/en/index.php?content_id=47 . Related clinical questions and references are also included.

The testis‐specific Y‐encoded‐like protein (TSPYL) gene family includes TSPYL1 to TSPYL6. We previously reported that TSPYL5 regulates cytochrome P450 (CYP) 19A1 expression. Here we show that TSPYLs, especially TSPYL 1, 2, and 4, can regulate the expression of many CYP genes, including CYP17A1, a key enzyme in androgen biosynthesis, and CYP3A4, an enzyme that catalyzes the metabolism of abiraterone, a CYP17 inhibitor. Furthermore, a common TSPYL1 single nucleotide polymorphism (SNP), rs3828743 (G/A) (Pro62Ser), abolishes TSPYL1's ability to suppress CYP3A4 expression, resulting in reduced abiraterone concentrations and increased cell proliferation. Data from a prospective clinical trial of 87 metastatic castration‐resistant prostate cancer patients treated with abiraterone acetate/prednisone showed that the variant SNP genotype (A) was significantly associated with worse response and progression‐free survival. In summary, TSPYL genes are novel CYP gene transcription regulators, and genetic alteration within these genes significantly influences response to drug therapy through transcriptional regulation of CYP450 genes.

Data are limited on whether valvular calcification is associated with aortic wall stiffness. We tested whether aortic valve calcification (AVC) and/or mitral valve calcification (MVC) is inversely associated with aortic distensibility (AD). Cross-sectional study conducted in a subset of the Multi-Ethnic Study of Atherosclerosis (MESA) included 3676 MESA participants aged 44 to 84 years with AD measured with magnetic resonance imaging and with AVC and MVC measured with noncontrast cardiac computed tomography scans. Both AVC and MVC were divided into 3 categories: zero, < median values (low), and ≥ median values (high) for patients with nonzero values. Overall, 88% (n = 3256) and 92% (n = 3365) of participants had zero AVC and MVC, while 6% (n = 211) and 4% (n = 156) had low, and 6% (n = 209) and 4% (n = 155) had high values of AVC and MVC, respectively. The AVC was independently associated with AD after adjusting for age, gender, and ethnicity ( P = .035). No association was noted between AVC groups and AD after adjustment for all covariates or MVC groups and AD in any model.