Publications by authors named "Ning Shang"

41 Publications

Joint Landmark and Structure Learning for Automatic Evaluation of Developmental Dysplasia of the Hip.

IEEE J Biomed Health Inform 2021 Jun 8;PP. Epub 2021 Jun 8.

The ultrasound (US) screening of the infant hip is vital for early diagnosis of developmental dysplasia of the hip (DDH). The US diagnosis of DDH refers to measuring alpha and beta angles that quantify hip joint development. These two angles are calculated from key anatomical landmarks and structures of the hip. However, this measurement process is not trivial for sonographers and usually requires a thorough understanding of complex anatomical structures. In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH. Our multi-task networks are equipped with three novel modules. Firstly, we adopt Mask R-CNN as the basic framework to detect and segment key anatomical structures and add one landmark detection branch to form a new multi-task framework. Secondly, we propose a novel shape similarity loss to refine the incomplete anatomical structure prediction robustly and accurately. Thirdly, we further incorporate the landmark-structure consistent prior to ensure the consistency of the bony rim estimated from the segmented structure and the detected landmark. In our experiments, 1,231 US images of the infant hip from 632 patients are collected, of which 247 images from 126 patients are tested. The average errors in alpha and beta angles are 2.221 and 2.899. About 93% and 85% estimates of alpha and beta angles have errors less than 5 degrees, respectively. Experimental results demonstrate that the proposed method can accurately and robustly realize the automatic evaluation of DDH, showing great potential for clinical application.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/JBHI.2021.3087494DOI Listing
June 2021

Rare loss-of-function variants in type I IFN immunity genes are not associated with severe COVID-19.

J Clin Invest 2021 May 27. Epub 2021 May 27.

College of Applied Medical Sciences, Taibah University, Madina, Saudi Arabia.

A recent report found that rare predicted loss-of-function (pLOF) variants across 13 candidate genes in TLR3- and IRF7-dependent type I IFN pathways explain up to 3.5% of severe COVID-19 cases. We performed whole-exome or whole-genome sequencing of 1,934 COVID-19 cases (713 with severe and 1,221 with mild disease) and 15,251 ancestry-matched population controls across four independent COVID-19 biobanks. We then tested if rare pLOF variants in these 13 genes were associated with severe COVID-19. We identified only one rare pLOF mutation across these genes amongst 713 cases with severe COVID-19 and observed no enrichment of pLOFs in severe cases compared to population controls or mild COVID-19 cases. We find no evidence of association of rare loss-of-function variants in the proposed 13 candidate genes with severe COVID-19 outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1172/JCI147834DOI Listing
May 2021

Medical Records-Based Genetic Studies of the Complement System.

J Am Soc Nephrol 2021 May 3. Epub 2021 May 3.

Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York

Background: Genetic variants in complement genes have been associated with a wide range of human disease states, but well-powered genetic association studies of complement activation have not been performed in large multiethnic cohorts.

Methods: We performed medical records-based genome-wide and phenome-wide association studies for plasma C3 and C4 levels among participants of the Electronic Medical Records and Genomics (eMERGE) network.

Results: In a GWAS for C3 levels in 3949 individuals, we detected two genome-wide significant loci: chr.1q31.3 (CFH locus; rs3753396-A; =0.20; 95% CI, 0.14 to 0.25; =1.52x10) and chr.19p13.3 (C3 locus; rs11569470-G; =0.19; 95% CI, 0.13 to 0.24; =1.29x10). These two loci explained approximately 2% of variance in C3 levels. GWAS for C4 levels involved 3998 individuals and revealed a genome-wide significant locus at chr.6p21.32 (C4 locus; rs3135353-C; =0.40; 95% CI, 0.34 to 0.45; =4.58x10). This locus explained approximately 13% of variance in C4 levels. The multiallelic copy number variant analysis defined two structural genomic C4 variants with large effect on blood C4 levels: C4-BS (=-0.36; 95% CI, -0.42 to -0.30; =2.98x10) and C4-AL-BS (=0.25; 95% CI, 0.21 to 0.29; =8.11x10). Overall, C4 levels were strongly correlated with copy numbers of C4A and C4B genes. In comprehensive phenome-wide association studies involving 102,138 eMERGE participants, we cataloged a full spectrum of autoimmune, cardiometabolic, and kidney diseases genetically related to systemic complement activation.

Conclusions: We discovered genetic determinants of plasma C3 and C4 levels using eMERGE genomic data linked to electronic medical records. Genetic variants regulating C3 and C4 levels have large effects and multiple clinical correlations across the spectrum of complement-related diseases in humans.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1681/ASN.2020091371DOI Listing
May 2021

Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies.

NPJ Digit Med 2021 Apr 13;4(1):70. Epub 2021 Apr 13.

Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.

Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate ("A-by-G" grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41746-021-00428-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044136PMC
April 2021

Similarity-based health risk prediction using Domain Fusion and electronic health records data.

J Biomed Inform 2021 Apr 19;116:103711. Epub 2021 Feb 19.

Department of Biostatistics, Mailman School of Public Health, Columbia University, United States. Electronic address:

Electronic Health Record (EHR) data represents a valuable resource for individualized prospective prediction of health conditions. Statistical methods have been developed to measure patient similarity using EHR data, mostly using clinical attributes. Only a handful of recent methods have combined clinical analytics with other forms of similarity analytics, and no unified framework exists yet to measure comprehensive patient similarity. Here, we developed a generic framework named Patient similarity based on Domain Fusion (PsDF). PsDF performs patient similarity assessment on each available domain data separately, and then integrate the affinity information over various domains into a comprehensive similarity metric. We used the integrated patient similarity to support outcome prediction by assigning a risk score to each patient. With extensive simulations, we demonstrated that PsDF outperformed existing risk prediction methods including a random forest classifier, a regression-based model, and a naïve similarity method, especially when heterogeneous signals exist across different domains. Using PsDF and EHR data extracted from the data warehouse of Columbia University Irving Medical Center, we developed two different clinical prediction tools for two different clinical outcomes: incident cases of end stage kidney disease (ESKD) and severe aortic stenosis (AS) requiring valve replacement. We demonstrated that our new prediction method is scalable to large datasets, robust to random missingness, and generalizable to diverse clinical outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2021.103711DOI Listing
April 2021

Synthetic Studies on Selective, Proapoptotic Isomalabaricane Triterpenoids Aided by Computational Techniques.

J Am Chem Soc 2021 02 19;143(4):2138-2155. Epub 2021 Jan 19.

Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States.

The isomalabaricanes comprise a large family of marine triterpenoids with fascinating structures that have been shown to be selective and potent apoptosis inducers in certain cancer cell lines. In this article, we describe the successful total syntheses of the isomalabaricanes stelletin A, stelletin E, and rhabdastrellic acid A, as well as the development of a general strategy to access other natural products within this unique family. High-throughput experimentation and computational chemistry methods were used in this endeavor. A preliminary structure-activity relationship study of stelletin A revealed the core motif of the isomalabaricanes to be critical for their cytotoxic activity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/jacs.0c12569DOI Listing
February 2021

Failure to replicate the association of rare loss-of-function variants in type I IFN immunity genes with severe COVID-19.

medRxiv 2020 Dec 21. Epub 2020 Dec 21.

Laboratory Department, Security Forces Hospital, General Directorate of Medical Services, Ministry of Interior, Clinical Laboratory Sciences, Taibah University, Madina, Saudi Arabia.

A recent report found that rare predicted loss-of-function (pLOF) variants across 13 candidate genes in TLR3- and IRF7-dependent type I IFN pathways explain up to 3.5% of severe COVID-19 cases. We performed whole-exome or whole-genome sequencing of 1,934 COVID-19 cases (713 with severe and 1,221 with mild disease) and 15,251 ancestry-matched population controls across four independent COVID-19 biobanks. We then tested if rare pLOF variants in these 13 genes were associated with severe COVID-19. We identified only one rare pLOF mutation across these genes amongst 713 cases with severe COVID-19 and observed no enrichment of pLOFs in severe cases compared to population controls or mild COVID-19 cases. We find no evidence of association of rare loss-of-function variants in the proposed 13 candidate genes with severe COVID-19 outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1101/2020.12.18.20248226DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781338PMC
December 2020

Paper-based upconversion fluorescence aptasensor for the quantitative detection of immunoglobulin E in human serum.

Anal Chim Acta 2021 Jan 28;1143:93-100. Epub 2020 Nov 28.

College of Chemistry and Chemical Engineering, Xinyang Normal University, Xinyang, 464000, China.

Immunoglobulin E (IgE), a biomarker of allergic diseases, plays a critical role in allergic mechanism. Because of its low abundance in serum, the demand of developing sensitive, selective and simple methods for IgE detection is still very urgent. Paper-based analytical devices using upconversion nanoparticles (UCNPs) as the label can be promising point-of-care test (POCT) methods in rapid diagnosis, owing to their NIR-excitation and visible light emission nature, which can avoid the interference of autofluorescence and scattering light from biological samples and paper substrates. In this work, we proposed a paper-based analytical device for the sensitive, selective and accurate detection of total immunoglobulin E (IgE) in human serum. The assay was based on resonance energy transfer between UCNPs and organic dye tetramethylrhodamine (TAMRA), and IgE aptamer with stem-loop structure was used as the recognizing probe. The existence of IgE change the conformation of IgE aptamer, enlarge the distance between donor and acceptor, and block the energy transfer process. Thus, the luminescence of UCNPs recovered with an IgE concentration independent manner. A linear calibration was obtained in the range of 0.5-50 IU/mL, with a detection limit of 0.13 IU/mL. The results of our method were well correlated with that of commercial ELISA kit (20 human serum samples). This work suggests promising prospect of the paper-based UC-LRET analytical devices in real samples and may promote the application of paper-based analytical devices in clinical diagnosis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.aca.2020.11.036DOI Listing
January 2021

Evaluating the Treatment Efficacy of Nano-Drug in a Lung Cancer Model Using Advanced Functional Magnetic Resonance Imaging.

Front Oncol 2020 29;10:563932. Epub 2020 Sep 29.

Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.

Objectives: Nano-drug delivery system is an interesting field in precise cancer treatment, but few study has reported the microenvironmental changes after such treatment. This study aimed to detect the hemodynamic and microenvironmental changes in a lung cancer xenograft model after treated with doxorubicin (DOX) encapsulated by a cyclic arginine-glycine-aspartic acid polypeptide modified poly-(lactic-co-glycolic acid) nanosystem ([email protected]) using functional magnetic resonance imaging.

Materials And Methods: Thirty-two tumor-bearing mice were randomly divided into four groups. Group A was treated with 0.9% saline, Group B with 4 mg/kg of doxorubicin, Group C with 2 mg/kg of [email protected], and Group D with 4 mg/kg of [email protected] Intravoxel incoherent motion diffusion-weighed imaging (IVIM-DWI) and R2 mapping were performed, and D, f, D, and R2 values were obtained before and1, 2, and 3 weeks after treatment. They were sacrificed for pathological examination after examinations.

Results: The reconstructed [email protected] was homogeneous, well-dispersed, and spherical in shape, with an average size of 180 nm. Group D demonstrated the smallest tumor volume and highest tumor inhibition rate in 3 weeks. D value of Group B, C, and D manifested an upward trend in 3 weeks with the highest increase in Group D. D values shared a similar increased trends with f values in Group A, B, and C in 3 weeks, except Group D. R2 value of Group A gradually increased in 3 weeks, but the trends were reversed in the treatment groups. D value was significantly negative with Ki-67 expression ( = -0.757, < 0.001) but positive with TUNEL ( = 0.621, < 0.001), and phosphate and tension homology deleted on chromosome ten (PTEN) staining ( = 0.57, = 0.004). R2 value was closely correlated with HIF-1a ( = 0.721, < 0.001).

Conclusion: The nano-drug demonstrated an enhanced anti-tumor effect without the need of increased chemotherapeutic dosage. The tumor microenvironment such as cellular and perfusion changes during treatment can be non-invasively detected by two functional MRI including IVIM-DWI and R2 mapping.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fonc.2020.563932DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550655PMC
September 2020

Evaluation of the MC4R gene across eMERGE network identifies many unreported obesity-associated variants.

Int J Obes (Lond) 2021 Jan 20;45(1):155-169. Epub 2020 Sep 20.

Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA.

Background/objectives: Melanocortin-4 receptor (MC4R) plays an essential role in food intake and energy homeostasis. More than 170 MC4R variants have been described over the past two decades, with conflicting reports regarding the prevalence and phenotypic effects of these variants in diverse cohorts. To determine the frequency of MC4R variants in large cohort of different ancestries, we evaluated the MC4R coding region for 20,537 eMERGE participants with sequencing data plus additional 77,454 independent individuals with genome-wide genotyping data at this locus.

Subjects/methods: The sequencing data were obtained from the eMERGE phase III study, in which multisample variant call format calls have been generated, curated, and annotated. In addition to penetrance estimation using body mass index (BMI) as a binary outcome, GWAS and PheWAS were performed using median BMI in linear regression analyses. All results were adjusted for principal components, age, sex, and sites of genotyping.

Results: Targeted sequencing data of MC4R revealed 125 coding variants in 1839 eMERGE participants including 30 unreported coding variants that were predicted to be functionally damaging. Highly penetrant unreported variants included (L325I, E308K, D298N, S270F, F261L, T248A, D111V, and Y80F) in which seven participants had obesity class III defined as BMI ≥ 40 kg/m. In GWAS analysis, in addition to known risk haplotype upstream of MC4R (best variant rs6567160 (P = 5.36 × 10, Beta = 0.37), a novel rare haplotype was detected which was protective against obesity and encompassed the V103I variant with known gain-of-function properties (P = 6.23 × 10, Beta = -0.62). PheWAS analyses extended this protective effect of V103I to type 2 diabetes, diabetic nephropathy, and chronic renal failure independent of BMI.

Conclusions: MC4R screening in a large eMERGE cohort confirmed many previous findings, extend the MC4R pleotropic effects, and discovered additional MC4R rare alleles that probably contribute to obesity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41366-020-00675-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7752751PMC
January 2021

Prenatal sonographic diagnosis of partial left pulmonary artery sling: A rare case report.

J Clin Ultrasound 2021 Mar 14;49(3):257-261. Epub 2020 Sep 14.

Department of Ultrasound, Guangdong Women and Children Hospital, Guangzhou Medical University, Guangzhou, China.

Pulmonary artery sling is a rare congenital vascular anomaly. Partial anomalous left pulmonary artery is even rarer and no in utero observation has yet been reported. Here, we present the ultrasonographic findings of a 38-year-old woman at 32 weeks of gestation whose fetus showed a normal bifurcation of the pulmonary trunk into the right and left pulmonary arteries, but an anomalous origin of the left lower lobe pulmonary artery from the right pulmonary artery. These findings were confirmed by postnatal echocardiography and thoracic computed tomography.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/jcu.22913DOI Listing
March 2021

A graph-based method for reconstructing entities from coordination ellipsis in medical text.

J Am Med Inform Assoc 2020 07;27(9):1364-1373

Department of Biomedical Informatics, Columbia University, New York, New York, USA.

Objective: Coordination ellipsis is a linguistic phenomenon abound in medical text and is challenging for concept normalization because of difficulty in recognizing elliptical expressions referencing 2 or more entities accurately. To resolve this bottleneck, we aim to contribute a generalizable method to reconstruct concepts from medical coordinated elliptical expressions in a variety of biomedical corpora.

Materials And Methods: We proposed a graph-based representation model and built a pipeline to reconstruct concepts from coordinated elliptical expressions in medical text (RECEEM). There are 4 modules: (1) identify all possible candidate conjunct pairs from original coordinated elliptical expressions, (2) calculate coefficients for candidate conjuncts using the embedding model, (3) select the most appropriate decompositions by global optimization, and (4) rebuild concepts based on a pathfinding algorithm. We evaluated the pipeline's performance on 2658 coordinated elliptical expressions from 3 different medical corpora (ie, biomedical literature, clinical narratives, and eligibility criteria from clinical trials). Precision, recall, and F1 score were calculated.

Results: The F1 scores for biomedical publications, clinical narratives, and research eligibility criteria were 0.862, 0.721, and 0.870, respectively. RECEEM outperformed 2 previously released methods. By incorporating RECEEM into 2 existing NLP tools, the F1 scores increased from 0.248 to 0.460 and from 0.287 to 0.630 on concept mapping of 1125 coordination ellipses.

Conclusions: RECEEM improves concept normalization for medical coordinated elliptical expressions in a variety of biomedical corpora. It outperformed existing methods and significantly enhanced the performance of 2 notable NLP systems for mapping coordination ellipses in the evaluation. The algorithm is open sourced online (https://github.com/chiyuan1126/RECEEM).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocaa109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647336PMC
July 2020

Correction: A paper-supported sandwich immunosensor based on upconversion luminescence resonance energy transfer for the visual and quantitative determination of a cancer biomarker in human serum.

Analyst 2020 08 1;145(15):5372. Epub 2020 Jul 1.

College of Chemistry and Chemical Engineering, Xinyang Normal University, Xinyang 464000, China.

Correction for 'A paper-supported sandwich immunosensor based on upconversion luminescence resonance energy transfer for the visual and quantitative determination of a cancer biomarker in human serum' by Mengyuan He et al., Analyst, 2020, 145, 4181-4187, DOI: .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1039/d0an90071kDOI Listing
August 2020

A paper-supported sandwich immunosensor based on upconversion luminescence resonance energy transfer for the visual and quantitative determination of a cancer biomarker in human serum.

Analyst 2020 Jun;145(12):4181-4187

Key Laboratory of Analytical Chemistry for Biology and Medicine (Ministry of Education), College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China.

In this paper, a paper-supported analytical device based on a sandwich immunoreaction and luminescence resonance energy transfer (LRET) was reported for the visual and quantitative determination of a cancer biomarker, in which upconversion nanoparticles (UCNPs) were located on the surface of the paper as energy donors and gold nanoparticles (AuNPs) were used as energy acceptors. Upon the recognition of the cancer biomarker by two rationally selected antibodies, the upconversion luminescence was quenched by the AuNPs in a biomarker concentration-dependent manner. As a model target, CEA was detected using this immunosensor, and a linear relationship within 0.5-30 ng mL-1 was obtained in buffer solution, with a detection limit of 0.21 ng mL-1. The immunosensor was also applicable in 20-fold diluted human serum with a linear range of 0.5-30 ng mL-1 and a detection limit of 0.36 ng mL-1. This technique also realized the qualitative judgment of the critical concentration of CEA in serum samples by the naked eye. This approach displays great application potential for point-of-care testing in clinical applications, as well as the potentiality to be extended to other kinds of disease-related biomolecules.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1039/c9an02307kDOI Listing
June 2020

Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping.

J Biomed Inform 2020 02 19;102:103363. Epub 2019 Dec 19.

Columbia University Medical Center, New York, NY, USA; Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA. Electronic address:

Algorithms for identifying patients of interest from observational data must address missing and inaccurate data and are desired to achieve comparable performance on both administrative claims and electronic health records data. However, administrative claims data do not contain the necessary information to develop accurate algorithms for disorders that require laboratory results, and this omission can result in insensitive diagnostic code-based algorithms. In this paper, we tested our assertion that the performance of a diagnosis code-based algorithm for chronic kidney disorder (CKD) can be improved by adding other codes indirectly related to CKD (e.g., codes for dialysis, kidney transplant, suspicious kidney disorders). Following the best practices from Observational Health Data Sciences and Informatics (OHDSI), we adapted an electronic health record-based gold standard algorithm for CKD and then created algorithms that can be executed on administrative claims data and account for related data quality issues. We externally validated our algorithms on four electronic health record datasets in the OHDSI network. Compared to the algorithm that uses CKD diagnostic codes only, positive predictive value of the algorithms that use additional codes was slightly increased (47.4% vs. 47.9-48.5% respectively). The algorithms adapted from the gold standard algorithm can be used to infer chronic kidney disorder based on administrative claims data. We succeeded in improving the generalizability and consistency of the CKD phenotypes by using data and vocabulary standardized across the OHDSI network, although performance variability across datasets remains. We showed that identifying and addressing coding and data heterogeneity can improve the performance of the algorithms.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103363DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390483PMC
February 2020

Ensembles of natural language processing systems for portable phenotyping solutions.

J Biomed Inform 2019 12 23;100:103318. Epub 2019 Oct 23.

Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA. Electronic address:

Background: Manually curating standardized phenotypic concepts such as Human Phenotype Ontology (HPO) terms from narrative text in electronic health records (EHRs) is time consuming and error prone. Natural language processing (NLP) techniques can facilitate automated phenotype extraction and thus improve the efficiency of curating clinical phenotypes from clinical texts. While individual NLP systems can perform well for a single cohort, an ensemble-based method might shed light on increasing the portability of NLP pipelines across different cohorts.

Methods: We compared four NLP systems, MetaMapLite, MedLEE, ClinPhen and cTAKES, and four ensemble techniques, including intersection, union, majority-voting and machine learning, for extracting generic phenotypic concepts. We addressed two important research questions regarding automated phenotype recognition. First, we evaluated the performance of different approaches in identifying generic phenotypic concepts. Second, we compared the performance of different methods to identify patient-specific phenotypic concepts. To better quantify the effects caused by concept granularity differences on performance, we developed a novel evaluation metric that considered concept hierarchies and frequencies. Each of the approaches was evaluated on a gold standard set of clinical documents annotated by clinical experts. One dataset containing 1,609 concepts derived from 50 clinical notes from two different institutions was used in both evaluations, and an additional dataset of 608 concepts derived from 50 case report abstracts obtained from PubMed was used for evaluation of identifying generic phenotypic concepts only.

Results: For generic phenotypic concept recognition, the top three performers in the NYP/CUIMC dataset are union ensemble (F, 0.634), training-based ensemble (F, 0.632), and majority vote-based ensemble (F, 0.622). In the Mayo dataset, the top three are majority vote-based ensemble (F, 0.642), cTAKES (F, 0.615), and MedLEE (F, 0.559). In the PubMed dataset, the top three are majority vote-based ensemble (F, 0.719), training-based (F, 0.696) and MetaMapLite (F, 0.694). For identifying patient specific phenotypes, the top three performers in the NYP/CUIMC dataset are majority vote-based ensemble (F, 0.610), MedLEE (F, 0.609), and training-based ensemble (F, 0.585). In the Mayo dataset, the top three are majority vote-based ensemble (F, 0.604), cTAKES (F, 0.531) and MedLEE (F, 0.527).

Conclusions: Our study demonstrates that ensembles of natural language processing can improve both generic phenotypic concept recognition and patient specific phenotypic concept identification over individual systems. Among the individual NLP systems, each individual system performed best when they were applied in the dataset that they were primary designed for. However, combining multiple NLP systems to create an ensemble can generally improve the performance. Specifically, the ensemble can increase the results reproducibility across different cohorts and tasks, and thus provide a more portable phenotyping solution compared to individual NLP systems.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103318DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6899194PMC
December 2019

Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.

J Biomed Inform 2019 11 19;99:103293. Epub 2019 Sep 19.

Department of Biomedical Informatics, Columbia University, New York, NY, United States. Electronic address:

Background: Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms.

Methods: We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category.

Results: A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ± 1.38. Specifically, the average knowledge (K) score is 0.64 ± 0.66, interpretation (I) score is 0.33 ± 0.55, and programming (P) score is 0.40 ± 0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks.

Conclusion: This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103293DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894517PMC
November 2019

Development and Validation of a Pragmatic Electronic Phenotype for CKD.

Clin J Am Soc Nephrol 2019 09 12;14(9):1306-1314. Epub 2019 Aug 12.

Division of Renal Diseases and Hypertension, Department of Medicine, University of Minnesota, Minneapolis, Minnesota.

Background And Objectives: Poor identification of individuals with CKD is a major barrier to research and appropriate clinical management of the disease. We aimed to develop and validate a pragmatic electronic (e-) phenotype to identify patients likely to have CKD.

Design, Setting, Participants, & Measurements: The e-phenotype was developed by an expert working group and implemented among adults receiving in- or outpatient care at five healthcare organizations. To determine urine albumin (UA) dipstick cutoffs for CKD to enable use in the e-phenotype when lacking urine albumin-to-creatinine ratio (UACR), we compared same day UACR and UA results at four sites. A sample of patients, spanning no CKD to ESKD, was randomly selected at four sites for validation via blinded chart review.

Results: The CKD e-phenotype was defined as most recent eGFR <60 ml/min per 1.73 m with at least one value <60 ml/min per 1.73 m >90 days prior and/or a UACR of ≥30 mg/g in the most recent test with at least one positive value >90 days prior. Dialysis and transplant were identified using diagnosis codes. In absence of UACR, a sensitive CKD definition would consider negative UA results as normal to mildly increased (KDIGO A1), trace to 1+ as moderately increased (KDIGO A2), and ≥2+ as severely increased (KDIGO A3). Sensitivity, specificity, and diagnostic accuracy of the CKD e-phenotype were 99%, 99%, and 98%, respectively. For dialysis sensitivity was 94% and specificity was 89%. For transplant, sensitivity was 97% and specificity was 91%.

Conclusions: The CKD e-phenotype provides a pragmatic and accurate method for EHR-based identification of patients likely to have CKD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2215/CJN.00360119DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6730512PMC
September 2019

Facilitating phenotype transfer using a common data model.

J Biomed Inform 2019 08 17;96:103253. Epub 2019 Jul 17.

Department of Biomedical Informatics, Columbia University, New York, NY, United States.

Background: Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process.

Methods: Electronic Medical Records and Genomics (eMERGE) sites implemented the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model across their electronic health record (EHR)-linked DNA biobanks. Two previously implemented eMERGE phenotypes were converted to OMOP and implemented across the network.

Results: It was feasible to implement the common data model across sites, with laboratory data producing the greatest challenge due to local encoding. Sites were then able to execute the OMOP phenotype in less than one day, as opposed to weeks of effort to manually implement an eMERGE phenotype in their bespoke research EHR databases. Of the sites that could compare the current OMOP phenotype implementation with the original eMERGE phenotype implementation, specific agreement ranged from 100% to 43%, with disagreements due to the original phenotype, the OMOP phenotype, changes in data, and issues in the databases. Using the OMOP query as a standard comparison revealed differences in the original implementations despite starting from the same definitions, code lists, flowcharts, and pseudocode.

Conclusion: Using a common data model can dramatically speed phenotype implementation at the cost of having to populate that data model, though this will produce a net benefit as the number of phenotype implementations increases. Inconsistencies among the implementations of the original queries point to a potential benefit of using a common data model so that actual phenotype code and logic can be shared, mitigating human error in reinterpretation of a narrative phenotype definition.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103253DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697565PMC
August 2019

Application of the LMS method of constructing fetal reference charts: comparison with the original method.

J Matern Fetal Neonatal Med 2021 Feb 30;34(3):395-402. Epub 2019 Apr 30.

Guangdong Women and Children Hospital, Guangzhou, PR China.

In view of the concern expressed about the current references, new references for fetal biparietal diameter and head circumference should be constructed for contemporary local populations. We conducted a retrospective cross-sectional study in two hospitals in Guangdong, Southern China. Fetal biparietal diameter and head circumference percentiles regression were fitted using Cole's LMS method. The BPD and HC data were then transformed into -scores that were calculated using two series of reference equations obtained from two methods: Cole's LMS method and the original "mean and SD method." Each -score distribution was presented as the mean and standard deviation. Finally, the sensitivity and specificity of each reference for identifying fetuses <2.5th or >97.5th percentile (based on the observed distribution of -scores) were calculated. The misclassified number and Youden's index were listed. A total of 17,974 biparietal diameter and 18,269 head circumference measurements were chosen to establish a reference chart. The LMS method could fit the local population better than the "mean and SD method" as it had a lower number of misclassified fetuses and a higher Youden's index. The Cole's LMS method was able to construct a satisfied reference range of fetal head sizes in Southern China.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/14767058.2019.1608942DOI Listing
February 2021

Heritability and genome-wide association study of benign prostatic hyperplasia (BPH) in the eMERGE network.

Sci Rep 2019 04 15;9(1):6077. Epub 2019 Apr 15.

Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.

Benign prostatic hyperplasia (BPH) results in a significant public health burden due to the morbidity caused by the disease and many of the available remedies. As much as 70% of men over 70 will develop BPH. Few studies have been conducted to discover the genetic determinants of BPH risk. Understanding the biological basis for this condition may provide necessary insight for development of novel pharmaceutical therapies or risk prediction. We have evaluated SNP-based heritability of BPH in two cohorts and conducted a genome-wide association study (GWAS) of BPH risk using 2,656 cases and 7,763 controls identified from the Electronic Medical Records and Genomics (eMERGE) network. SNP-based heritability estimates suggest that roughly 60% of the phenotypic variation in BPH is accounted for by genetic factors. We used logistic regression to model BPH risk as a function of principal components of ancestry, age, and imputed genotype data, with meta-analysis performed using METAL. The top result was on chromosome 22 in SYN3 at rs2710383 (p-value = 4.6 × 10; Odds Ratio = 0.69, 95% confidence interval = 0.55-0.83). Other suggestive signals were near genes GLGC, UNCA13, SORCS1 and between BTBD3 and SPTLC3. We also evaluated genetically-predicted gene expression in prostate tissue. The most significant result was with increasing predicted expression of ETV4 (chr17; p-value = 0.0015). Overexpression of this gene has been associated with poor prognosis in prostate cancer. In conclusion, although there were no genome-wide significant variants identified for BPH susceptibility, we present evidence supporting the heritability of this phenotype, have identified suggestive signals, and evaluated the association between BPH and genetically-predicted gene expression in prostate.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-019-42427-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6465359PMC
April 2019

Penetration of different molecule sizes upon ultrasound combined with microbubbles in a superficial tumour model.

J Drug Target 2019 12 20;27(10):1068-1075. Epub 2019 Mar 20.

Department of Medical Ultrasound, Guangzhou First People's Hospital , Guangzhou , China.

Ultrasound combined with microbubbles (USMB) has been extensively applied to enhance drug and gene targeting delivery. However, the accumulation and distribution of particle size in the range of 5-30 nm (nano drug) to the tumour and the effects of intratumoral vascular density on permeability have been rarely reported. This study investigated Evans blue (EB) and fluorescein isothiocyanate-labelled dextran (FITC-dextran) distribution in tumour tissue upon USMB with various molecular sizes (3.7 nm and 30.6 nm). USMB increased the penetration of molecules with sizes of 5-20 nm in the whole tumour tissue, especially on the rim. For a molecule with sizes of 30.6 nm, USMB only increased penetration around the rim of the tumour with minimal improvement in the central of tumour. USMB enhanced the permeability of tumour tissue and increased tumour cells dose exposure without affecting tumour blood perfusion or microvessel density. The current study served as the foundation of parameter preference for therapeutic USMB drug delivery.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/1061186X.2019.1588279DOI Listing
December 2019

Low Screening Rates for Diabetes Mellitus Among Family Members of Affected Relatives.

AMIA Annu Symp Proc 2018 5;2018:1471-1477. Epub 2018 Dec 5.

Value Institute, NewYork-Presbyterian Hospital, New York, NY.

Cardiovascular disease is the leading cause of death in the United States, and abnormal blood glucose is an important risk factor. Delayed diagnosis of diabetes mellitus can increase patients' morbidity. In an urban academic medical center with a large clinical data warehouse, we used a novel algorithm to identify 56,794 family members of diabetic patients that were eligible for disease screening. We found that 30.6% of patients did not receive diabetes screening as recommended by current guidelines. Further, our analysis showed that having more than one family member affected and being a female were important contributors to being screened for diabetes mellitus. This study demonstrates that informatics methods applied to electronic health record data can be used to identify patients at risk for disease development, and therefore support clinical care.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371358PMC
January 2020

Criteria2Query: a natural language interface to clinical databases for cohort definition.

J Am Med Inform Assoc 2019 04;26(4):294-305

Department of Biomedical Informatics, Columbia University, New York, New York, USA.

Objective: Cohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. Data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data models. Criteria2Query is a natural language interface that facilitates human-computer collaboration for cohort definition and execution using clinical databases.

Materials And Methods: Criteria2Query uses a hybrid information extraction pipeline combining machine learning and rule-based methods to systematically parse eligibility criteria text, transforms it first into a structured criteria representation and next into sharable and executable clinical data queries represented as SQL queries conforming to the OMOP Common Data Model. Users can interactively review, refine, and execute queries in the ATLAS web application. To test effectiveness, we evaluated 125 criteria across different disease domains from ClinicalTrials.gov and 52 user-entered criteria. We evaluated F1 score and accuracy against 2 domain experts and calculated the average computation time for fully automated query formulation. We conducted an anonymous survey evaluating usability.

Results: Criteria2Query achieved 0.795 and 0.805 F1 score for entity recognition and relation extraction, respectively. Accuracies for negation detection, logic detection, entity normalization, and attribute normalization were 0.984, 0.864, 0.514 and 0.793, respectively. Fully automatic query formulation took 1.22 seconds/criterion. More than 80% (11+ of 13) of users would use Criteria2Query in their future cohort definition tasks.

Conclusions: We contribute a novel natural language interface to clinical databases. It is open source and supports fully automated and interactive modes for autonomous data-driven cohort definition by researchers with minimal human effort. We demonstrate its promising user friendliness and usability.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocy178DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6402359PMC
April 2019

Effect of vocabulary mapping for conditions on phenotype cohorts.

J Am Med Inform Assoc 2018 12;25(12):1618-1625

Department of Biomedical Informatics, Columbia University, New York, New York, USA.

Objective: To study the effect on patient cohorts of mapping condition (diagnosis) codes from source billing vocabularies to a clinical vocabulary.

Materials And Methods: Nine International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9-CM) concept sets were extracted from eMERGE network phenotypes, translated to Systematized Nomenclature of Medicine - Clinical Terms concept sets, and applied to patient data that were mapped from source ICD9-CM and ICD10-CM codes to Systematized Nomenclature of Medicine - Clinical Terms codes using Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) vocabulary mappings. The original ICD9-CM concept set and a concept set extended to ICD10-CM were used to create patient cohorts that served as gold standards.

Results: Four phenotype concept sets were able to be translated to Systematized Nomenclature of Medicine - Clinical Terms without ambiguities and were able to perform perfectly with respect to the gold standards. The other 5 lost performance when 2 or more ICD9-CM or ICD10-CM codes mapped to the same Systematized Nomenclature of Medicine - Clinical Terms code. The patient cohorts had a total error (false positive and false negative) of up to 0.15% compared to querying ICD9-CM source data and up to 0.26% compared to querying ICD9-CM and ICD10-CM data. Knowledge engineering was required to produce that performance; simple automated methods to generate concept sets had errors up to 10% (one outlier at 250%).

Discussion: The translation of data from source vocabularies to Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) resulted in very small error rates that were an order of magnitude smaller than other error sources.

Conclusion: It appears possible to map diagnoses from disparate vocabularies to a single clinical vocabulary and carry out research using a single set of definitions, thus improving efficiency and transportability of research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocy124DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289550PMC
December 2018

A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments.

J Am Med Inform Assoc 2018 11;25(11):1540-1546

Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.

Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocy101DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6213083PMC
November 2018

LPA Variants Are Associated With Residual Cardiovascular Risk in Patients Receiving Statins.

Circulation 2018 10;138(17):1839-1849

RIKEN Center for Integrative Medical Sciences, Yokohama, Japan (M.K., S.M., M.H., Y.M.).

Background: Coronary heart disease (CHD) is a leading cause of death globally. Although therapy with statins decreases circulating levels of low-density lipoprotein cholesterol and the incidence of CHD, additional events occur despite statin therapy in some individuals. The genetic determinants of this residual cardiovascular risk remain unknown.

Methods: We performed a 2-stage genome-wide association study of CHD events during statin therapy. We first identified 3099 cases who experienced CHD events (defined as acute myocardial infarction or the need for coronary revascularization) during statin therapy and 7681 controls without CHD events during comparable intensity and duration of statin therapy from 4 sites in the Electronic Medical Records and Genomics Network. We then sought replication of candidate variants in another 160 cases and 1112 controls from a fifth Electronic Medical Records and Genomics site, which joined the network after the initial genome-wide association study. Finally, we performed a phenome-wide association study for other traits linked to the most significant locus.

Results: The meta-analysis identified 7 single nucleotide polymorphisms at a genome-wide level of significance within the LPA/PLG locus associated with CHD events on statin treatment. The most significant association was for an intronic single nucleotide polymorphism within LPA/PLG (rs10455872; minor allele frequency, 0.069; odds ratio, 1.58; 95% confidence interval, 1.35-1.86; P=2.6×10). In the replication cohort, rs10455872 was also associated with CHD events (odds ratio, 1.71; 95% confidence interval, 1.14-2.57; P=0.009). The association of this single nucleotide polymorphism with CHD events was independent of statin-induced change in low-density lipoprotein cholesterol (odds ratio, 1.62; 95% confidence interval, 1.17-2.24; P=0.004) and persisted in individuals with low-density lipoprotein cholesterol ≤70 mg/dL (odds ratio, 2.43; 95% confidence interval, 1.18-4.75; P=0.015). A phenome-wide association study supported the effect of this region on coronary heart disease and did not identify noncardiovascular phenotypes.

Conclusions: Genetic variations at the LPA locus are associated with CHD events during statin therapy independently of the extent of low-density lipoprotein cholesterol lowering. This finding provides support for exploring strategies targeting circulating concentrations of lipoprotein(a) to reduce CHD events in patients receiving statins.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1161/CIRCULATIONAHA.117.031356DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6202211PMC
October 2018

The representativeness of eligible patients in type 2 diabetes trials: a case study using GIST 2.0.

J Am Med Inform Assoc 2018 Mar;25(3):239-247

Department of Biomedical Informatics, Columbia University, New York, NY, USA.

Objective: The population representativeness of a clinical study is influenced by how real-world patients qualify for the study. We analyze the representativeness of eligible patients for multiple type 2 diabetes trials and the relationship between representativeness and other trial characteristics.

Methods: Sixty-nine study traits available in the electronic health record data for 2034 patients with type 2 diabetes were used to profile the target patients for type 2 diabetes trials. A set of 1691 type 2 diabetes trials was identified from ClinicalTrials.gov, and their population representativeness was calculated using the published Generalizability Index of Study Traits 2.0 metric. The relationships between population representativeness and number of traits and between trial duration and trial metadata were statistically analyzed. A focused analysis with only phase 2 and 3 interventional trials was also conducted.

Results: A total of 869 of 1691 trials (51.4%) and 412 of 776 phase 2 and 3 interventional trials (53.1%) had a population representativeness of <5%. The overall representativeness was significantly correlated with the representativeness of the Hba1c criterion. The greater the number of criteria or the shorter the trial, the less the representativeness. Among the trial metadata, phase, recruitment status, and start year were found to have a statistically significant effect on population representativeness. For phase 2 and 3 interventional trials, only start year was significantly associated with representativeness.

Conclusions: Our study quantified the representativeness of multiple type 2 diabetes trials. The common low representativeness of type 2 diabetes trials could be attributed to specific study design requirements of trials or safety concerns. Rather than criticizing the low representativeness, we contribute a method for increasing the transparency of the representativeness of clinical trials.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocx091DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378875PMC
March 2018

A conceptual framework for evaluating data suitability for observational studies.

J Am Med Inform Assoc 2018 Mar;25(3):248-258

Department of Biomedical Informatics, Columbia University, New York, NY, USA.

Objective: To contribute a conceptual framework for evaluating data suitability to satisfy the research needs of observational studies.

Materials And Methods: Suitability considerations were derived from a systematic literature review on researchers' common data needs in observational studies and a scoping review on frequent clinical database design considerations, and were harmonized to construct a suitability conceptual framework using a bottom-up approach. The relationships among the suitability categories are explored from the perspective of 4 facets of data: intrinsic, contextual, representational, and accessible. A web-based national survey of domain experts was conducted to validate the framework.

Results: Data suitability for observational studies hinges on the following key categories: Explicitness of Policy and Data Governance, Relevance, Availability of Descriptive Metadata and Provenance Documentation, Usability, and Quality. We describe 16 measures and 33 sub-measures. The survey uncovered the relevance of all categories, with a 5-point Likert importance score of 3.9 ± 1.0 for Explicitness of Policy and Data Governance, 4.1 ± 1.0 for Relevance, 3.9 ± 0.9 for Availability of Descriptive Metadata and Provenance Documentation, 4.2 ± 1.0 for Usability, and 4.0 ± 0.9 for Quality.

Conclusions: The suitability framework evaluates a clinical data source's fitness for research use. Its construction reflects both researchers' points of view and data custodians' design features. The feedback from domain experts rated Usability, Relevance, and Quality categories as the most important considerations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocx095DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378879PMC
March 2018

Prenatal sonographic diagnosis of nondysraphic intramedullary lipomas: A case report.

J Clin Ultrasound 2018 May 2;46(4):278-281. Epub 2017 Jul 2.

Department of Maternal-Fetal Medicine, Guangdong Women and Children Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 510010, China.

Nondysraphic intramedullary lipomas of the spinal cord are rare, and there are currently no reports of their observation in utero. Here, we present the sonographic (US) findings in such a case. Four intraspinal hyperechoic masses were observed on US on the dorsal aspect of the fetal spine in a 30-year-old woman at 30 weeks' gestation. Findings were consistent with those of prenatal MRI and were confirmed on autopsy after induced abortion. © 2017 Wiley Periodicals, Inc. J Clin Ultrasound 46:278-281, 2018.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/jcu.22516DOI Listing
May 2018