BioSearch: a semantic search engine for Bio2RDF.

Database (Oxford). 2017 Jan 01;2017:

Authors: Hu W, Qiu H, Huang J, Dumontier M

Abstract
Database URL: http://ws.nju.edu.cn/biosearch/.

PMID: 29220451 [PubMed - indexed for MEDLINE]

Flexibility of thought in high creative individuals represented by percolation analysis.

Proc Natl Acad Sci U S A. 2018 01 30;115(5):867-872

Authors: Kenett YN, Levy O, Kenett DY, Stanley HE, Faust M, Havlin S

Abstract
Flexibility of thought is theorized to play a critical role in the ability of high creative individuals to generate novel and innovative ideas. However, this has been examined only through indirect behavioral measures. Here we use network percolation analysis (removal of links in a network whose strength is below an increasing threshold) to computationally examine the robustness of the semantic memory networks of low and high creative individuals. Robustness of a network indicates its flexibility and thus can be used to quantify flexibility of thought as related to creativity. This is based on the assumption that the higher the robustness of the semantic network, the higher its flexibility. Our analysis reveals that the semantic network of high creative individuals is more robust to network percolation compared with the network of low creative individuals and that this higher robustness is related to differences in the structure of the networks. Specifically, we find that this higher robustness is related to stronger links connecting between different components of similar semantic words in the network, which may also help to facilitate spread of activation over their network. Thus, we directly and quantitatively examine the relation between flexibility of thought and creative ability. Our findings support the associative theory of creativity, which posits that high creative ability is related to a flexible structure of semantic memory. Finally, this approach may have further implications, by enabling a quantitative examination of flexibility of thought, in both healthy and clinical populations.

PMID: 29339514 [PubMed - indexed for MEDLINE]

A document-centric approach for developing the tolAPC ontology.

J Biomed Semantics. 2017 Nov 28;8(1):54

Authors: Blfgeh A, Warrender J, Hilkens CMU, Lord P

Abstract
BACKGROUND: There are many challenges associated with ontology building, as the process often touches on many different subject areas; it needs knowledge of the problem domain, an understanding of the ontology formalism, software in use and, sometimes, an understanding of the philosophical background. In practice, it is very rare that an ontology can be completed by a single person, as they are unlikely to combine all of these skills. So people with these skills must collaborate. One solution to this is to use face-to-face meetings, but these can be expensive and time-consuming for teams that are not co-located. Remote collaboration is possible, of course, but one difficulty here is that domain specialists use a wide-variety of different "formalisms" to represent and share their data - by the far most common, however, is the "office file" either in the form of a word-processor document or a spreadsheet. Here we describe the development of an ontology of immunological cell types; this was initially developed by domain specialists using an Excel spreadsheet for collaboration. We have transformed this spreadsheet into an ontology using highly-programmatic and pattern-driven ontology development. Critically, the spreadsheet remains part of the source for the ontology; the domain specialists are free to update it, and changes will percolate to the end ontology.
RESULTS: We have developed a new ontology describing immunological cell lines built by instantiating ontology design patterns written programmatically, using values from a spreadsheet catalogue.
CONCLUSIONS: This method employs a spreadsheet that was developed by domain experts. The spreadsheet is unconstrained in its usage and can be freely updated resulting in a new ontology. This provides a general methodology for ontology development using data generated by domain specialists.

PMID: 29179777 [PubMed - indexed for MEDLINE]

Continuity of Primary Care and Emergency Hospital Admissions Among Older Patients in England.

Ann Fam Med. 2017 Nov;15(6):515-522

Authors: Tammes P, Purdy S, Salisbury C, MacKichan F, Lasserson D, Morris RW

Abstract
PURPOSE: Secondary health care services have been under considerable pressure in England as attendance rates increase, resulting in longer waiting times and greater demands on staff. This study's aim was to examine the association between continuity of care and risk of emergency hospital admission among older adults.
METHODS: We analyzed records from 10,000 patients aged 65 years and older in 2012 within 297 English general practices obtained from the Clinical Practice Research Datalink and linked with Hospital Episode Statistics. We used the Bice and Boxerman (BB) index and the appointed general practitioner index (last general practitioner consulted before hospitalization) to quantify patient-physician continuity. The BB index was used in a prospective cohort approach to assess impact of continuity on risk of admission. Both indices were used in a separate retrospective nested case-control approach to test the effect of changing physician on the odds of hospital admission in the following 30 days.
RESULTS: In the prospective cohort analysis, the BB index showed a graded, non-significant inverse relationship of continuity of care with risk of emergency hospital admission, although the hazard ratio for patients experiencing least continuity was 2.27 (95% CI, 1.37-3.76) compared with those having complete continuity. In the retrospective nested case-control analysis, we found a graded inverse relationship between continuity of care and emergency hospital admission for both BB and appointed general practitioner indices: for the latter, the odds ratio for those experiencing least continuity was 2.32 (95% CI, 1.48-3.63) relative to those experiencing most continuity.
CONCLUSIONS: Marked discontinuity of care might contribute to increased unplanned hospital admissions among patients aged 65 years and older. Schemes to enhance continuity of care have the potential to reduce hospital admissions.

PMID: 29133489 [PubMed - indexed for MEDLINE]

A guide to evaluating linkage quality for the analysis of linked data.

Int J Epidemiol. 2017 Oct 01;46(5):1699-1710

Authors: Harron KL, Doidge JC, Knight HE, Gilbert RE, Goldstein H, Cromwell DA, van der Meulen JH

Abstract
Linked datasets are an important resource for epidemiological and clinical studies, but linkage error can lead to biased results. For data security reasons, linkage of personal identifiers is often performed by a third party, making it difficult for researchers to assess the quality of the linked dataset in the context of specific research questions. This is compounded by a lack of guidance on how to determine the potential impact of linkage error. We describe how linkage quality can be evaluated and provide widely applicable guidance for both data providers and researchers. Using an illustrative example of a linked dataset of maternal and baby hospital records, we demonstrate three approaches for evaluating linkage quality: applying the linkage algorithm to a subset of gold standard data to quantify linkage error; comparing characteristics of linked and unlinked data to identify potential sources of bias; and evaluating the sensitivity of results to changes in the linkage procedure. These approaches can inform our understanding of the potential impact of linkage error and provide an opportunity to select the most appropriate linkage procedure for a specific analysis. Evaluating linkage quality in this way will improve the quality and transparency of epidemiological and clinical research using linked data.

PMID: 29025131 [PubMed - indexed for MEDLINE]

Any-k: Anytime Top-k Tree Pattern Retrieval in Labeled Graphs.

Proc Int World Wide Web Conf. 2018 Apr;2018:489-498

Authors: Yang X, Nicholson PK, Ajwani D, Riedewald M, Gatterbauer W, Sala A

Abstract
Many problems in areas as diverse as recommendation systems, social network analysis, semantic search, and distributed root cause analysis can be modeled as pattern search on labeled graphs (also called "heterogeneous information networks" or HINs). Given a large graph and a query pattern with node and edge label constraints, a fundamental challenge is to find the top-k matches according to a ranking function over edge and node weights. For users, it is difficult to select value k. We therefore propose the novel notion of an any-k ranking algorithm: for a given time budget, return as many of the top-ranked results as possible. Then, given additional time, produce the next lower-ranked results quickly as well. It can be stopped anytime, but may have to continue until all results are returned. This paper focuses on acyclic patterns over arbitrary labeled graphs. We are interested in practical algorithms that effectively exploit (1) properties of heterogeneous networks, in particular selective constraints on labels, and (2) that the users often explore only a fraction of the top-ranked results. Our solution, KARPET, carefully integrates aggressive pruning that leverages the acyclic nature of the query, and incremental guided search. It enables us to prove strong non-trivial time and space guarantees, which is generally considered very hard for this type of graph search problem. Through experimental studies we show that KARPET achieves running times in the order of milliseconds for tree patterns on large networks with millions of nodes and edges.

PMID: 30003197 [PubMed]

OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies.

Front Pharmacol. 2018;9:609

Authors: Natsiavas P, Boyce RD, Jaulent MC, Koutkias V

Abstract
Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present OpenPVSignal, a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. OpenPVSignal is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. OpenPVSignal is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of OpenPVSignal and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of OpenPVSignal is demonstrated by using PV signal information published in: (a) the World Health Organization's Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.

PMID: 29997499 [PubMed]

Meta-path methods for prioritizing candidate disease miRNAs.

IEEE/ACM Trans Comput Biol Bioinform. 2017 Nov 22;:

Authors: Zhang X, Zou Q, Rodruguez-Paton A, Zeng X

Abstract
MicroRNAs (miRNAs) play critical roles in regulating gene expression at post-transcriptional levels. Predicting potential miRNAdisease association is beneficial not only to explore the pathogenesis of diseases, but also to understand biological processes. In this work, we propose two methods that can effectively predict potential miRNAdisease associations using our reconstructed miRNA and disease similarity networks, which are based on the latest experimental data. We reconstruct a miRNA functional similarity network using the following biological information: the miRNA family information, miRNA cluster information, experimentally valid miRNA target association and disease miRNA information. We also reconstruct a disease similarity network using disease functional information and disease semantic information. We present Katz with specific weights and Katz with machine learning, on the comprehensive heterogeneous network. These methods, which achieve corresponding AUC values of 0.897 and 0.919, exhibit performance superior to the existing methods. Comprehensive data networks and reasonable considerations guarantee the high performance of our methods. Contrary to several methods, which cannot work in such situations, the proposed methods also predict associations for diseases without any known related miRNAs. A web service for the download and prediction of relationships between diseases and miRNAs is available at http://lab.malab.cn/soft/MDPredict/.

PMID: 29990255 [PubMed - as supplied by publisher]

Peculiarities of Precocious Puberty in Boys and Girls With McCune-Albright Syndrome.

Front Endocrinol (Lausanne). 2018;9:337

Authors: Corica D, Aversa T, Pepe G, De Luca F, Wasniewska M

Abstract
McCune-Albright Syndrome (MAS; OMIM # 174800) is a rare, sporadic disease caused by a post-zygotic, activating mutation in the guanine-nucleotide binding protein α-subunit (GNAS1) gene. MAS is characterized by the clinical triad of polyostotic fibrous dysplasia of bone, café-au-lait skin pigmentation and peripheral precocious puberty. However, clinical presentation is highly variable depending on mosaic tissue distribution of mutant-bearing cells. Precocious puberty is the most common endocrine manifestation of MAS and is often the presenting, and sometimes the only, clinical sign of MAS. Due to the very low prevalence of MAS, data on course of precocious puberty, effectiveness of treatments and gonadal function during post-pubertal period are lacking. Our knowledge on this issue derives essentially from case reports and small cohorts of patients. The aim of this review is to report all available literature data on clinical aspects, therapeutic management and outcomes of precocious puberty in children with MAS. A systematic research was carried out through MEDLINE via PubMed, EMBASE, Web of Science, Semantic Scholar, Cochrane Library.

PMID: 29988390 [PubMed]

Relationship between out-of-home care placement history characteristics and educational achievement: A population level linked data study.

Child Abuse Negl. 2017 Aug;70:146-159

Authors: Maclean MJ, Taylor CL, O'Donnell M

Abstract
Studies generally show children who have entered out-of-home care have worse educational outcomes than the general population, although recent research suggests maltreatment and other adversities are major contributing factors. Children's out-of-home care experiences vary and may affect their outcomes. This study examined the influence of placement stability, reunification, type of care, time in care and age at entry to care on children's educational outcomes. We conducted a population-based record-linkage study of children born in Western Australia between 1990 and 2010 who sat State or national Year 3 reading achievement tests (N=235,045 children, including 2160 children with a history of out-of-home care). Children's educational outcomes varied with many aspects of their care experience. Children placed in residential care were particularly likely to have low scores, with an unadjusted OR 6.81, 95% CI[4.94, 9.39] for low reading scores, which was partially attenuated after adjusting for background risk factors but remained significant (OR=1.50, 95% CIs [1.08, 2.08]). Reading scores were also lower for children who had experienced changes in care arrangements in the year of the test. A dose-response effect for multiple placements was expected but not found. Older age at entering care was also associated with worse reading scores. Different characteristics of a child's care history were interwoven with each other as well as child, family and neighbourhood characteristics, highlighting a need for caution in attributing causality. Although the level of educational difficulties varied, the findings suggest a widespread need for additional educational support for children who have entered care, including after reunification.

PMID: 28609694 [PubMed - indexed for MEDLINE]

SteeringWheel: A Locality-Preserving Magnification Interface for Low Vision Web Browsing.

Proc SIGCHI Conf Hum Factor Comput Syst. 2018 Apr;2018:

Authors: Billah SM, Ashok V, Porter DE, Ramakrishnan IV

Abstract
Low-vision users struggle to browse the web with screen magnifiers. Firstly, magnifiers occlude significant portions of the webpage, thereby making it cumbersome to get the webpage overview and quickly locate the desired content. Further, magnification causes loss of spatial locality and visual cues that commonly define semantic relationships in the page; reconstructing semantic relationships exclusively from narrow views dramatically increases the cognitive burden on the users. Secondly, low-vision users have widely varying needs requiring a range of interface customizations for different page sections; dynamic customization in extant magnifiers is disruptive to users' browsing. We present SteeringWheel, a magnification interface that leverages content semantics to preserve local context. In combination with a physical dial, supporting simple rotate and press gestures, users can quickly navigate different webpage sections, easily locate desired content, get a quick overview, and seamlessly customize the interface. A user study with 15 low-vision participants showed that their web-browsing efficiency improved by at least 20 percent with SteeringWheel compared to extant screen magnifiers.

PMID: 29978857 [PubMed]

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples.

Proc IEEE WIC ACM Int Conf Web Intell Intell Agent Technol. 2017 Aug;2017:1-9

Authors: Sheth A, Perera S, Wijeratne S, Thirunarayan K

Abstract
Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

PMID: 29962511 [PubMed]

A novel framework for biomedical entity sense induction.

J Biomed Inform. 2018 Jun 20;:

Authors: Lossio-Ventura JA, Bian J, Jonquet C, Roche M, Teisseire M

Abstract
BACKGROUND: Rapid advancements in biomedical research have accelerated the number of relevant electronic documents published online, ranging from scholarly articles to news, blogs, and user-generated social media content. Nevertheless, the vast amount of this information is poorly organized, making it difficult to navigate. Emerging technologies such as ontologies and knowledge bases (KBs) could help organize and track the information associated with biomedical research developments. A major challenge in the automatic construction of ontologies and KBs is the identification of words with its respective sense(s) from a free-text corpus. Word-sense induction (WSI) is a task to automatically induce the different senses of a target word in the different contexts. In the last two decades, there have been several efforts on WSI. However, few methods are effective in biomedicine and life sciences.
METHODS: We developed a framework for biomedical entity sense induction using a mixture of natural language processing, supervised, and unsupervised learning methods with promising results. It is composed of three main steps: 1) a polysemy detection method to determine if a biomedical entity has many possible meanings; 2) a clustering quality index-based approach to predict the number of senses for the biomedical entity; and 3) a method to induce the concept(s) (i.e., senses) of the biomedical entity in a given context.
RESULTS: To evaluate our framework, we used the well-known MSH WSD polysemic dataset that contains 203 annotated ambiguous biomedical entities, where each entity is linked to 2 to 5 concepts. Our polysemy detection method obtained an F-measure of 98%. Second, our approach for predicting the number of senses achieved an F-measure of 93%. Finally, we induced the concepts of the biomedical entities based on a clustering algorithm and then extracted the keywords of reach cluster to represent the concept.
CONCLUSIONS: We have developed a framework for biomedical entity sense induction with promising results. Our study results can benefit a number of downstream applications, for example, help to resolve concept ambiguities when building Semantic Web KBs from biomedical text.

PMID: 29935347 [PubMed - as supplied by publisher]

Predicting drug-disease associations by using similarity constrained matrix factorization.

BMC Bioinformatics. 2018 Jun 19;19(1):233

Authors: Zhang W, Yue X, Lin W, Wu W, Liu R, Huang F, Liu F

Abstract
BACKGROUND: Drug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task.
RESULTS: In this paper, we proposed a similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD), which makes use of known drug-disease associations, drug features and disease semantic information. SCMFDD projects the drug-disease association relationship into two low-rank spaces, which uncover latent features for drugs and diseases, and then introduces drug feature-based similarities and disease semantic similarity as constraints for drugs and diseases in low-rank spaces. Different from the classic matrix factorization technique, SCMFDD takes the biological context of the problem into account. In computational experiments, the proposed method can produce high-accuracy performances on benchmark datasets, and outperform existing state-of-the-art prediction methods when evaluated by five-fold cross validation and independent testing.
CONCLUSION: We developed a user-friendly web server by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD/ . The case studies show that the server can find out novel associations, which are not included in the CTD database.

PMID: 29914348 [PubMed - in process]

A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability.

JMIR Public Health Surveill. 2018 Jun 15;4(2):e10218

Authors: Al Manir MS, Brenas JH, Baker CJ, Shaban-Nejad A

Abstract
BACKGROUND: According to the World Health Organization, malaria surveillance is weakest in countries and regions with the highest malaria burden. A core obstacle is that the data required to perform malaria surveillance are fragmented in multiple data silos distributed across geographic regions. Furthermore, consistent integrated malaria data sources are few, and a low degree of interoperability exists between them. As a result, it is difficult to identify disease trends and to plan for effective interventions.
OBJECTIVE: We propose the Semantics, Interoperability, and Evolution for Malaria Analytics (SIEMA) platform for use in malaria surveillance based on semantic data federation. Using this approach, it is possible to access distributed data, extend and preserve interoperability between multiple dynamic distributed malaria sources, and facilitate detection of system changes that can interrupt mission-critical global surveillance activities.
METHODS: We used Semantic Automated Discovery and Integration (SADI) Semantic Web Services to enable data access and improve interoperability, and the graphical user interface-enabled semantic query engine HYDRA to implement the target queries typical of malaria programs. We implemented a custom algorithm to detect changes to community-developed terminologies, data sources, and services that are core to SIEMA. This algorithm reports to a dashboard. Valet SADI is used to mitigate the impact of changes by rebuilding affected services.
RESULTS: We developed a prototype surveillance and change management platform from a combination of third-party tools, community-developed terminologies, and custom algorithms. We illustrated a methodology and core infrastructure to facilitate interoperable access to distributed data sources using SADI Semantic Web services. This degree of access makes it possible to implement complex queries needed by our user community with minimal technical skill. We implemented a dashboard that reports on terminology changes that can render the services inactive, jeopardizing system interoperability. Using this information, end users can control and reactively rebuild services to preserve interoperability and minimize service downtime.
CONCLUSIONS: We introduce a framework suitable for use in malaria surveillance that supports the creation of flexible surveillance queries across distributed data resources. The platform provides interoperable access to target data sources, is domain agnostic, and with updates to core terminological resources is readily transferable to other surveillance activities. A dashboard enables users to review changes to the infrastructure and invoke system updates. The platform significantly extends the range of functionalities offered by malaria information systems, beyond the state-of-the-art.

PMID: 29907554 [PubMed]

Novel phenotype-disease matching tool for rare genetic diseases.

Genet Med. 2018 Jun 12;:

Authors: Chen J, Xu H, Jegga A, Zhang K, White PS, Zhang G

Abstract
PURPOSE: To improve the accuracy of matching rare genetic diseases based on patient's phenotypes.
METHODS: We introduce new methods to prioritize diagnosis of genetic diseases based on integrated semantic similarity (method 1) and ontological overlap (method 2) between the phenotypes expressed by a patient and phenotypes annotated to known diseases.
RESULTS: We evaluated the performance of our methods by two sets of simulated data and one set of patient's data derived from electronic health records. We demonstrated that the two methods achieved significantly improved performance compared with previous methods in correctly prioritizing candidate diseases in all of the three sets. Our methods are freely available as a web application ( https://gddp.
RESEARCH: cchmc.org/ ) to aid diagnosis of genetic diseases.
CONCLUSION: Our methods can capture the diagnostic information embedded in the phenotype ontology, consider all phenotypes exhibited by a patient, and are more robust than the existing methods when phenotypes are incorrectly or imprecisely specified. These methods can assist the diagnosis of rare genetic diseases and help the interpretation of the results of DNA tests.

PMID: 29895857 [PubMed - as supplied by publisher]

An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin.

Sensors (Basel). 2018 May 21;18(5):

Authors: Hu C, Li J, Lin X, Chen N, Yang C

Abstract
Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors.

PMID: 29883425 [PubMed - in process]

Understanding the health economic burden of patients with tuberous sclerosis complex (TSC) with epilepsy: a retrospective cohort study in the UK Clinical Practice Research Datalink (CPRD).

BMJ Open. 2017 Oct 05;7(10):e015236

Authors: Shepherd C, Koepp M, Myland M, Patel K, Miglio C, Siva V, Gray E, Neary M

Abstract
INTRODUCTION: Epilepsy is highly prevalent in tuberous sclerosis complex (TSC), a multi-system genetic disorder. The clinical and economic burden of this condition is expected to be substantial due to treatment challenges, debilitating co-morbidities and the relationship between TSC-related manifestations. This study estimated healthcare resource utilisation (HCRU) and costs for patients with TSC with epilepsy (TSC+E) in the UK.
METHODS: Patients with TSC+E in the Clinical Practice Research Datalink (CPRD) linked to Hospital Episodes Statistics were identified from April 1997 to March 2012. Clinical data were extracted over the entire history, and costs were reported over the most recent 3-year period. HCRU was compared with a matched Comparator cohort, and the key cost drivers were identified by regression modelling.
RESULTS: In total, 209 patients with TSC+E were identified, of which 40% recorded ≥2 other primary organ system manifestations and 42% had learning disability. Treatment with ≥2 concomitant antiepileptic drugs (AEDs) was prevalent (60%), potentially suggesting refractory epilepsy. Notwithstanding, many patients with TSC+E (12%) had no record of AED use in their entire history, which may indicate undertreatment for these patients.Brain surgery was recorded in 12% of patients. Routine electroencephalography and MRI were infrequently performed (30% of patients), yet general practitioner visits, hospitalisations and outpatient visits were more frequent in patients with TSC+E than the Comparator. This translated to threefold higher clinical costs (£14 335 vs £4448), which significantly increased with each additional primary manifestation (p<0.0001).
CONCLUSIONS: Patients with TSC+E have increased HCRU compared with the general CPRD population, likely related to manifestations in several organ systems, substantial cognitive impairment and severe epilepsy, which is challenging to treat and may be intractable. Disease surveillance and testing appears to be inadequate with few treatments trialled.Multidisciplinary care in TSC clinics with specialist neurologist input may alleviate some of the morbidity of patients, but more innovative treatment and management options should be sought.

PMID: 28982809 [PubMed - indexed for MEDLINE]

Trends, causes and timing of 30-day readmissions after hospitalization for heart failure: 11-year population-based analysis with linked data.

Int J Cardiol. 2017 Dec 01;248:246-251

Authors: Fernandez-Gasso L, Hernando-Arizaleta L, Palomar-Rodríguez JA, Abellán-Pérez MV, Pascual-Figal DA

Abstract
BACKGROUND: Reliable data are necessary if the burden of early readmissions following hospitalization for heart failure (HF) is to be addressed. We studied unplanned 30-day readmissions, their causes and timing over an 11-year period, using population-based linked data.
METHODS: All hospitalizations from 2003 to 2013 were analyzed by using administrative linked data based on the Minimum Basic Set discharge registry of the Department of Health (Region of Murcia, Spain). Index hospitalizations with HF as principal diagnosis (n=27,581) were identified. Transfers between centers were merged into one discharge. Readmissions were defined as unplanned admissions to any hospital within 30-days after discharge.
RESULTS: In the 2003-2013 period, 30-day readmission rates had a relative mean annual growth of +1.36%, increasing from 17.6% to 22.1%, with similar trends for cardiovascular and non-cardiovascular causes. The figure of 22.1% decreased to 19.8% when only same-hospital readmissions were considered. Most readmissions were due to cardiovascular causes (60%), HF being the most common single cause (34%). The timing of readmission shows an early peak on the fourth day post discharge (+13.29%) due to causes other than HF, followed by a gradual decline (-3.32%); readmission for HF decreased steadily from the first day (-2.22%). Readmission for HF (12.7%) or non-cardiovascular causes (13.3%) had higher in-hospital mortality rates than the index hospitalization (9.2%, p<0.001). Age and comorbidity burden were the main predictors of any readmission, but the performance of a predictive model was poor.
CONCLUSION: These findings support the need for population-based strategies to reduce the burden of early-unplanned readmissions.

PMID: 28801153 [PubMed - indexed for MEDLINE]

The National Sleep Research Resource: towards a sleep data commons.

J Am Med Inform Assoc. 2018 May 31;:

Authors: Zhang GQ, Cui L, Mueller R, Tao S, Kim M, Rueschman M, Mariani S, Mobley D, Redline S

Abstract
Objective: The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.
Approach: We used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor.
Results: The authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data.
Conclusions: The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research.

PMID: 29860441 [PubMed - as supplied by publisher]