TreatmentPatterns:An R package to facilitate the standardized development and analysis of treatment patterns across disease domains

Background and objectives: There is an increasing interest to use real-world data to illustrate how patients with specific medical conditions are treated in real life. Insight in the current treatment practices helps to improve and tailor patient care, but is often held back by a lack of data interoperability and a high-level of required resources. We aimed to provide an easy tool that overcomes these barriers to support the standardized development and analysis of treatment patterns for a wide variety of medical conditions. Methods: We formally defined the process of constructing treatment pathways and implemented this in an open-source R package TreatmentPatterns (https://github.com/mi-erasmusmc/TreatmentPatterns) to enable a reproducible and timely analysis of treatment patterns. Results: The developed package supports the analysis of treatment patterns of a study population of interest. We demonstrate the functionality of the package by analyzing the treatment patterns of three common chronic diseases (type II diabetes mellitus, hypertension, and depression) in the Dutch Integrated Primary Care Information (IPCI) database. Conclusion: TreatmentPatterns is a tool to make the analysis of treatment patterns more accessible, more standardized, and more interpretation friendly. We hope it thereby contributes to the accumulation of knowledge on real-world treatment patterns across disease domains. We encourage researchers to further adjust and add custom analysis to the R package based on their research needs.</p

Alignment of vaccine codes using an ontology of vaccine descriptions

BACKGROUND: Vaccine information in European electronic health record (EHR) databases is represented using various clinical and database-specific coding systems and drug vocabularies. The lack of harmonization constitutes a challenge in reusing EHR data in collaborative benefit-risk studies about vaccines. METHODS: We designed an ontology of the properties that are commonly used in vaccine descriptions, called Ontology of Vaccine Descriptions (VaccO), with a dictionary for the analysis of multilingual vaccine descriptions. We implemented five algorithms for the alignment of vaccine coding systems, i.e., the identification of corresponding codes from different coding ystems, based on an analysis of the code descriptors. The algorithms were evaluated by comparing their results with manually created alignments in two reference sets including clinical and database-specific coding systems with multilingual code descriptors. RESULTS: The best-performing algorithm represented code descriptors as logical statements about entities in the VaccO ontology and used an ontology reasoner to infer common properties and identify corresponding vaccine codes. The evaluation demonstrated excellent performance of the approach (F-scores 0.91 and 0.96). CONCLUSION: The VaccO ontology allows the identification, representation, and comparison of heterogeneous descriptions of vaccines. The automatic alignment of vaccine coding systems can accelerate the readiness of EHR databases in collaborative vaccine studies

A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC

Objective To create a multilingual gold-standard corpus for biomedical concept recognition. Materials and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. Results The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. Discussion The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. Conclusion To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotate

Anni 2.0: a multipurpose text-mining tool for the life sciences

Anni 2.0 provides an ontology-based interface to MEDLINE

QTc dispersion predicts cardiac mortality in the elderly: the Rotterdam Study

BACKGROUND: Increased QTc dispersion has been associated with an increased risk for ventricular arrhythmias and cardiac death in selected patient populations. We examined the association between computerized QTc-dispersion measurements and mortality in a prospective analysis of the p

Storing, linking, and mining microarray databases using SRS

BACKGROUND: SRS (Sequence Retrieval System) has proven to be a valuable platform for storing, linking, and querying biological databases. Due to the availability of a broad range of different scientific databases in SRS, it has become a useful platform to incorporate and mine microarray data to facilitate the analyses of biological questions and non-hypothesis driven quests. Here we report various solutions and tools for integrating and mining annotated expression data in SRS. RESULTS: We devised an Auto-Upload Tool by which microarray data can be automatically imported into SRS. The dataset can be linked to other databases and user access can be set. The linkage comprehensiveness of microarray platforms to other platforms and biological databases was examined in a network of scientific databases. The stored microarray data can also be made accessible to external programs for further processing. For example, we built an interface to a program called Venn Mapper, which collects its microarray data from SRS, processes the data by creating Venn diagrams, and saves the data for interpretation. CONCLUSION: SRS is a useful database system to store, link and query various scientific datasets, including microarray data. The user-friendly Auto-Upload Tool makes SRS accessible to biologists for linking and mining user-owned databases

Rewriting and suppressing UMLS terms for improved biomedical term identification

<p>Abstract</p> <p>Background</p> <p>Identification of terms is essential for biomedical text mining.. We concentrate here on the use of vocabularies for term identification, specifically the Unified Medical Language System (UMLS). To make the UMLS more suitable for biomedical text mining we implemented and evaluated nine term rewrite and eight term suppression rules. The rules rely on UMLS properties that have been identified in previous work by others, together with an additional set of new properties discovered by our group during our work with the UMLS. Our work complements the earlier work in that we measure the impact on the number of terms identified by the different rules on a MEDLINE corpus. The number of uniquely identified terms and their frequency in MEDLINE were computed before and after applying the rules. The 50 most frequently found terms together with a sample of 100 randomly selected terms were evaluated for every rule.</p> <p>Results</p> <p>Five of the nine rewrite rules were found to generate additional synonyms and spelling variants that correctly corresponded to the meaning of the original terms and seven out of the eight suppression rules were found to suppress only undesired terms. Using the five rewrite rules that passed our evaluation, we were able to identify 1,117,772 new occurrences of 14,784 rewritten terms in MEDLINE. Without the rewriting, we recognized 651,268 terms belonging to 397,414 concepts; with rewriting, we recognized 666,053 terms belonging to 410,823 concepts, which is an increase of 2.8% in the number of terms and an increase of 3.4% in the number of concepts recognized. Using the seven suppression rules, a total of 257,118 undesired terms were suppressed in the UMLS, notably decreasing its size. 7,397 terms were suppressed in the corpus.</p> <p>Conclusions</p> <p>We recommend applying the five rewrite rules and seven suppression rules that passed our evaluation when the UMLS is to be used for biomedical term identification in MEDLINE. A software tool to apply these rules to the UMLS is freely available at <url>http://biosemantics.org/casper</url>.</p

Spatial QRS-T angle predicts cardiac death in a general population

AIMS: The aim of this study was to assess the prognostic importance of the spatial QRS-T angle for fatal and non-fatal cardiac events. METHODS AND RESULTS: Electrocardiograms (ECGs) were recorded in 6134 men and women aged 55 years and over from the prospective population-based Rotterdam Study. Spatial QRS-T angles were categorized as normal, borderline or abnormal. Using Cox's proportional hazards model, abnormal angles showed increased hazard ratios of cardiac death (age-and sex-adjusted hazard ratio 5.2 (95% CI 4.0-6.8)), non-fatal cardiac events (2.2 (1.5-3.1)), sudden death (5.6 (3.7-8.5)) and total mortality (2.3 (2.0-2.7)). None of the classical cardiovascular and ECG predictors provided larger hazard ratios. After adjustment for these predictors, the association of abnormal spatial QRS-T angles with all fata

A NOS1AP gene variant is associated with a paradoxical increase of the QT-interval shortening effect of digoxin

Digoxin is characterized by a small therapeutic window and a QT-interval shortening effect. Moreover, it has been shown that the genetic variants of the nitric oxide synthase-1 adaptor protein (NOS1AP) gene are associated with QT-interval prolongation. We investigated whether the rs10494366 variant of the NOS1AP gene decreases the QT-interval shortening effect of digoxin in patients using this drug. We included 10,057 individuals from the prospective population-based cohort of the Rotterdam Study during a median of 12.2 (interquartile range (IQR) 6.7-18.1) years of follow-up. At study entry, the mean age was 64 years and almost 59% of participants were women. A total of 23,179 ECGs were longitudinally recorded, of which 334 ECGs were from 249 individuals on digoxin therapy. The linear mixed model analysis was used to estimate the effect of the rs10494366 variant on the association between digoxin use and QT-interval duration, adjusted for age, sex, RR interval, diabetes, heart failure, and history of myocardial infarction. In non-users of digoxin, the GG genotype was associated with a significant 6.5 ms [95% confidence interval (CI) 5.5; 7.5] longer QT-interval duration than the TT variant. In current digoxin users, however, the GG variant was associated with a significantly -23.9 [95%CI -29.5; -18.5] ms shorter mean QT-interval duration than in those with the TT variant with -15.9 [95%CI -18.7; -13.1]. This reduction was strongest in the high digoxin dose category [≥0.250 mg/day] with the GG genotype group, with -40.8 [95%CI -52.5; -29.2] ms changes compared to non-users. Our study suggests that the minor homozygous GG genotype group of the NOS1AP gene rs10494366 variant is associated with a paradoxical increase of the QT-interval shortening effect of digoxin in a population of European ancestry

Literature-aided meta-analysis of microarray data: a compendium study on muscle development and disease

Background: Comparative analysis of expression microarray studies is difficult due to the large influence of technical factors on experimental outcome. Still, the identified differentially expressed genes may hint at the same biological processes. However, manually curated assignment of genes to biological processes, such as pursued by the Gene Ontology (GO) consortium, is incomplete and limited. We hypothesised that automatic association of genes with biological processes through thesaurus-controlled mining of Medline abstracts would be more effective. Therefore, we developed a novel algorithm (LAMA: Literature-Aided Meta-Analysis) to quantify the similarity between transcriptomics studies. We evaluated our algorithm on a large compendium of 102 microarray studies published in the field of muscle development and disease, and compared it to similarity measures based on gene overlap and over-representation of biological processes assigned by GO. Results: While the overlap in both genes and overrepresented GO-terms was poor, LAMA retrieved many more biologically meaningful links between studies, with substantially lower influence of technical factors. LAMA correctly grouped muscular dystrophy, regeneration and myositis studies, and linked patient and corresponding mouse model studies. LAMA also retrieves the connecting biological concepts. Among other new discoveries, we associated cullin proteins, a class of ubiquitinylation proteins, with genes down-regulated during muscle regeneration, whereas ubiquitinylation was previously reported to be activated during the inverse process: muscle atrophy. Conclusion: Our literature-based association analysis is capable of finding hidden common biological denominators in microarray studies, and circumvents the need for raw data analysis or curated gene annotation databases