The use of routinely collected clinical data for large-scale pharmacogenetic studies

Having an interpretable, dynamic length-of-stay model can help hospital administrators and clinicians make better decisions and improve the quality of care. The widespread implementation of electronic medical record (EMR) systems has enabled hospitals to collect massive amounts of health data. However, how to integrate this deluge of data into healthcare operations remains unclear. We propose a framework grounded in established clinical knowledge to model patients’ lengths of stay. In particular, we impose expert knowledge when grouping raw clinical data into medically meaningful variables that summarize patients’ health trajectories. We use dynamic, predictive models to output patients’ remaining lengths of stay, future discharges, and census probability distributions based on their health trajectories up to the current stay. Evaluated with large-scale EMR data, the dynamic model significantly improves predictive power over the performance of any model in previous literature and remains medically interpretable. Summary of Contribution: The widespread implementation of electronic health systems has created opportunities and challenges to best utilize mounting clinical data for healthcare operations. In this study, we propose a new approach that integrates clinical analysis in generating variables and implementations of computational methods. This approach allows our model to remain interpretable to the medical professionals while being accurate. We believe our study has broader relevance to researchers and practitioners of healthcare operations.

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Cancer metabolic subtypes and their association with molecular and clinical features

10.1101/2021.12.16.472974 ◽

2021 ◽

Author(s):

Enrico Moiso ◽

Paolo Provero

Keyword(s):

Clinical Data ◽

Large Scale ◽

Tumor Grade ◽

Point Of View ◽

General Point ◽

Protein Abundance ◽

Phenotypic Data ◽

Cancer Subtypes ◽

Molecular Features

Alteration of metabolic pathways in cancer has been investigated for many years, beginning way before the discovery of the role of oncogenes and tumor suppressors, and the last few years have witnessed a renewed interest in this topic. Large-scale molecular and clinical data on tens of thousands of samples allow us today to tackle the problem from a general point of view. Here we show that trancriptomic profiles of tumors can be exploited to define metabolic cancer subtypes, that can be systematically investigated for association with other molecular and clinical data. We find thousands of significant associations between metabolic subtypes and molecular features such as somatic mutations, structural variants, epigenetic modifications, protein abundance and activation; and with clinical/phenotypic data including survival probability, tumor grade, and histological types. Our work provides a methodological framework and a rich database of statistical associations, accessible from https://metaminer.unito.it, that will contribute to the understanding of the role of metabolic alterations in cancer and to the development of precision therapeutic strategies.

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Clinical disease characteristics of patients with Niemann-Pick Disease Type C – findings from the International Niemann-Pick Disease Registry (INPDR)

10.21203/rs.3.rs-934191/v1 ◽

2021 ◽

Author(s):

Shaun Christopher Bolton ◽

Vina Soran ◽

Mercedes Pineda Marfa ◽

Jackie Imrie ◽

Paul Gissen ◽

...

Keyword(s):

Clinical Data ◽

Large Scale ◽

Disease Type ◽

Adult Onset ◽

Neurological Manifestations ◽

Disease Registry ◽

Niemann Pick Disease ◽

Type C ◽

Niemann Pick ◽

Pick Disease

Abstract Background Niemann-Pick Disease Type C (NPC) is an autosomal recessive rare disease characterised by progressive neurovisceral manifestations. The collection of on-going large-scale NPC clinical data may generate better understandings of the natural history of the disease. Here we report NPC patient data from the International Niemann-Pick Disease Registry (INPDR). Method The INPDR is a web-based, patient-led independent registry for the collection of prospective and retrospective clinical data from Niemann-Pick Disease patients. Baseline data from NPC patients enrolled into the INPDR from September 2014 to December 2019 was extracted to analyse the demographic, genetic and clinical features. Results A total of 203 NPC patients from six European countries were included in this study. The mean age (SD) at diagnosis was 11.2 years (14.2). Among enrolled patients, 168 had known neurological manifestations; 43 (24.2%) had early-infantile onset, 47 (26.4%) had late-infantile onset, 41 (23.0%) had juvenile onset, and 37 (20.8%) had adult onset. 10 (5.6%) patients had the neonatal rapidly fatal systemic form. Among the 97 patients with identified NPC1 variants, the most common variant was the c. 3182T > C variant responsible for the p.lle1061Thr protein change, reported in 35.1% (N = 34) of patients. The frequencies of hepatomegaly and neonatal jaundice were greatest in patients with early-infantile and late-infantile onset. Splenomegaly was the most commonly reported observation, including 80% of adult-onset patients. The most commonly reported neurological manifestations were cognitive impairment (78.5%), dysarthria (75.9%), ataxia (75.9%), vertical supranuclear gaze palsy (VSGP) (70.9%), dysphagia (69.6%). A 6-domain composite disability scale was used to calculate the overall disability score according to neurological-onset. Across all with neurological onset, the majority of patients showed moderate to severe impairments in all domains, excluding ‘swallowing’ and ‘seizure’. The age at diagnosis and death increased with increased age of neurological symptom onset. Miglustat use was recorded in 62.43% of patients and the most common symptomatic therapies used by patients were antiepileptics (32.94%), antidepressants (11.76%) and antacids (9.41%). Conclusion The proportion of participants at each age of neurological onset was relatively consistent across the cohort. Neurological manifestations, such as ataxia, dysphagia, and dysarthria, were frequently observed across all age categories.

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The Yale Department of Medicine COVID-19 Data Explorer and Repository (DOM-CovX): An Innovative Approach to Promoting Collaborative Scholarship During a Pandemic

10.1101/2021.08.23.21262241 ◽

2021 ◽

Author(s):

Tanima Arora ◽

Michael Simonov ◽

Jameel Alausa ◽

Labeebah Subair ◽

Brett Gerber ◽

...

Keyword(s):

Clinical Data ◽

Large Scale ◽

Innovative Approach ◽

Academic Productivity ◽

List Type ◽

Digital Revolution ◽

Institutional Climate ◽

Novel Approach ◽

Dissemination Of Research ◽

Academic Faculty

ABSTRACTBackgroundThe COVID-19 pandemic has led to an explosion of research publications spanning epidemiology, basic and clinical science. While a digital revolution has allowed for open access to large datasets enabling real-time tracking of the epidemic, detailed, locally-specific clinical data has been less readily accessible to a broad range of academic faculty and their trainees. This perpetuates the separation of the primary missions of clinically-focused and primary research faculty resulting in lost opportunities for improved understanding of the local epidemic; expansion of the scope of scholarship; limitation of the diversity of the research pool; lack of creation of initiatives for growth and dissemination of research skills needed for the training of the next generation of clinicians and faculty.ObjectivesCreate a common, easily accessible and up-to-date database that would promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise. By providing a robust dataset, a broad range of researchers (faculty, trainees) and clinicians are encouraged to explore and collaborate on novel clinically relevant research questions.MethodsWe constructed a research platform called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), to house cleaned, highly granular, de-identified, continually-updated data from over 7,000 patients hospitalized with COVID-19 (1/2020-present) across the Yale New Haven Health System. This included a front-end user interface for simple data visualization of aggregate data and more detailed clinical datasets for researchers after a review board process. The goal is to promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise.Expected OutcomesAccelerate generation of new knowledge and increase scholarly productivity with particular local relevanceImprove the institutional academic climate by:Broadening research scopeExpanding research capability to more diverse group of stakeholders including clinical and research-based faculty and traineesEnhancing interdepartmental collaborationsConclusionsThe DOM-CovX Data Explorer and Repository have great potential to increase academic productivity. By providing an accessible tool for simple data analysis and access to a consistently updated, standardized and large-scale dataset, it overcomes barriers for a wide variety of researchers. Beyond academic productivity, this innovative approach represents an opportunity to improve the institutional climate by fostering collaboration, diversity of scholarly pursuits and expanding medical education. It provides a novel approach that can be expanded to other diseases beyond COVID 19.

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Abstract 169: Discovery of clinically distinct CLL subgroups by integrative mapping of large-scale genetic, epigenetic, expression and clinical data

10.1158/1538-7445.am2020-169 ◽

2020 ◽

Author(s):

Binyamin A. Knisbacher ◽

Ziao Lin ◽

Chip Stewart ◽

Cynthia K. Hahn ◽

Kristen E. Stevenson ◽

...

Keyword(s):

Clinical Data ◽

Large Scale

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OPA1: 516 unique variants and 831 patients registered in an updated centralized Variome database

Orphanet Journal of Rare Diseases ◽

10.1186/s13023-019-1187-1 ◽

2019 ◽

Vol 14 (1) ◽

Cited By ~ 5

Author(s):

Bastien Le Roux ◽

Guy Lenaers ◽

Xavier Zanlonghi ◽

Patrizia Amati-Bonneau ◽

Floris Chabrun ◽

...

Keyword(s):

Clinical Data ◽

Optic Atrophy ◽

Neurological Disorders ◽

Large Scale ◽

Phenotype Ontology ◽

Human Phenotype ◽

Variation Database ◽

Source Variation ◽

Gene Panel Sequencing ◽

Panel Sequencing

Abstract Background The dysfunction of OPA1, a dynamin GTPase involved in mitochondrial fusion, is responsible for a large spectrum of neurological disorders, each of which includes optic neuropathy. The database dedicated to OPA1 ( https://www.lovd.nl/OPA1 ), created in 2005, has now evolved towards a centralized and more reliable database using the Global Variome shared Leiden Open-source Variation Database (LOVD) installation. Results The updated OPA1 database, which registers all the patients from our center as well as those reported in the literature, now covers a total of 831 patients: 697 with isolated dominant optic atrophy (DOA), 47 with DOA “plus”, and 83 with asymptomatic or unclassified DOA. It comprises 516 unique OPA1 variants, of which more than 80% (414) are considered pathogenic. Full clinical data for 118 patients are documented using the Human Phenotype Ontology, a standard vocabulary for referencing phenotypic abnormalities. Contributors may now make online submissions of phenotypes related to OPA1 mutations, giving clinical and molecular descriptions together with detailed ophthalmological and neurological data, according to an international thesaurus. Conclusions The evolution of the OPA1 database towards the LOVD, using unified nomenclature, should ensure its interoperability with other databases and prove useful for molecular diagnoses based on gene-panel sequencing, large-scale mutation statistics, and genotype-phenotype correlations.

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PP136 How To Apply Health Technology Assessment To Large Scale e-Health Processes

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462319002526 ◽

2019 ◽

Vol 35 (S1) ◽

pp. 63-64

Author(s):

Gro-Hilde Severinsen ◽

Line Silsand ◽

Anne Ekeland

Keyword(s):

Health Technology Assessment ◽

Information And Communication Technologies ◽

Clinical Data ◽

Technology Assessment ◽

Large Scale ◽

New Technologies ◽

Health Technology ◽

Added Value ◽

Health Region ◽

Wide Range

IntroductionThere are enormous expectations for e-health solutions to support high quality healthcare services, with accessibility, and effectiveness as key goals. E-health encompasses a wide range of information and communication technologies applied to health care, and focuses on combining clinical activity, technical development, and political requirements. Hence, e-health solutions must be evaluated in relation to the desired goals, to justify the high costs of such solutions.MethodsHealth technology assessment (HTA) aims to produce rational decisions for purchasing new technologies and evaluating healthcare investments, like drugs and medical equipment, by measuring added value in relation to clinical effectiveness, safety, and cost effectiveness. It is desired to also apply HTA assessment on large scale e-health solutions, but traditional quantitative HTA methodology may not be applicable to complex e-health systems developed and implemented as ongoing processes over years. Systematic reviews and meta-analyses of these processes risk being outdated when published, therefore action research designed to work with complex, large scale programs may be a more suitable approach.ResultsIn the project, we followed the development of a new process-oriented electronic patient record system (EPR) in northern Norway. Part of the process was structuring clinical data to be used in electronic forms within the system. This was the first time a health region structured the clinical data and designed the forms; receiving feedback alongside the process was very important. The goal was to use structured forms as a basis for reusing EPR data within and between systems, and to enable clinical decision support.DiscussionAfter designing a prototype of a structured form, we wrote an assessment report focusing on designing a methodology for such development, which stakeholders to include, and how to divide the work between the health region and the system vendor. The answers to such questions will have both practical and economic consequences for designing the next phase of the process.

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Large-Scale Analysis of Genetic and Clinical Patient Data

Annual Review of Biomedical Data Science ◽

10.1146/annurev-biodatasci-080917-013508 ◽

2018 ◽

Vol 1 (1) ◽

pp. 263-274 ◽

Cited By ~ 6

Author(s):

Marylyn D. Ritchie

Keyword(s):

Clinical Data ◽

Large Scale ◽

Data Science ◽

Genomic Analysis ◽

Genomic Data ◽

Data Sets ◽

Biomedical Data ◽

Data Types ◽

Phenotypic Data ◽

Clinical Patient

Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.

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