Polygenic risk scores for autoimmune related diseases are significantly different in cancer exceptional responders.

A small number of cancer patients respond exceptionally well to therapies and survive significantly longer than patients with similar diagnoses. Profiling the germline genetic backgrounds of exceptional responder (ER) patients, with extreme survival times, can yield insights into the germline polymorphisms that influence response to therapy. As ERs showed a high incidence in autoimmune diseases, we hypothesized the differences in autoimmune disease risk could reflect the immune background of ERs and contribute to better cancer treatment responses.

ATLAS: an automated association test using probabilistically linked health records with application to genetic studies.

Objective: Large amounts of health data are becoming available for biomedical research. Synthesizing information across databases may capture more comprehensive pictures of patient health and enable novel research studies. When no gold standard mappings between patient records are available, researchers may probabilistically link records from separate databases and analyze the linked data.

High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP).

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels.

Use of Narrative Concepts in Electronic Health Records to Validate Associations Between Genetic Factors and Response to Treatment of Inflammatory Bowel Diseases.

Crohn's disease (CD) and ulcerative colitis (UC) are heterogeneous. With availability of therapeutic classes with distinct immunologic mechanisms of action, it has become imperative to identify markers that predict likelihood of response to each drug class. However, robust development of such tools has been challenging because of need for large prospective cohorts with systematic and careful assessment of treatment response using validated indices.

Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes.

We develop an algorithm for probabilistic linkage of de-identified research datasets at the patient level, when only diagnosis codes with discrepancies and no personal health identifiers such as name or date of birth are available. It relies on Bayesian modelling of binarized diagnosis codes, and provides a posterior probability of matching for each patient pair, while considering all the data at once. Both in our simulation study (using an administrative claims dataset for data generation) and in two real use-cases linking patient electronic health records from a large tertiary care network, our method exhibits good performance and compares favourably to the standard baseline Fellegi-Sunter algorithm.

Enabling phenotypic big data with PheNorm.

Objective: Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping.

Surrogate-assisted feature extraction for high-throughput phenotyping.

Objective: Phenotyping algorithms are capable of accurately identifying patients with specific phenotypes from within electronic medical records systems. However, developing phenotyping algorithms in a scalable way remains a challenge due to the extensive human resources required. This paper introduces a high-throughput unsupervised feature selection method, which improves the robustness and scalability of electronic medical record phenotyping without compromising its accuracy.

Comparative Effectiveness of Infliximab and Adalimumab in Crohn's Disease and Ulcerative Colitis.

Background: The availability of monoclonal antibodies to tumor necrosis factor α has revolutionized management of Crohn's disease (CD) and ulcerative colitis. However, limited data exist regarding comparative effectiveness of these agents to inform clinical practice.

Methods: This study consisted of patients with CD or ulcerative colitis initiation either infliximab (IFX) or adalimumab (ADA) between 1998 and 2010.

Statin Use Is Associated With Reduced Risk of Colorectal Cancer in Patients With Inflammatory Bowel Diseases.

Background & Aims: Inflammatory bowel diseases (IBDs) such as Crohn's disease and ulcerative colitis are associated with an increased risk of colorectal cancer (CRC). Chemopreventive strategies have produced weak or inconsistent results. Statins have been associated inversely with sporadic CRC.

Identification of Nonresponse to Treatment Using Narrative Data in an Electronic Health Record Inflammatory Bowel Disease Cohort.

Background: Electronic health records, increasingly a part of healthcare, provide a wealth of untapped narrative free text data that have the potential to accurately inform clinical outcomes.

Methods: From a validated cohort of patients with Crohn's disease or ulcerative colitis, we identified patients with ≥1 coded or narrative mention of monoclonal antibodies to tumor necrosis factor α. Chart review by ascertained true use of therapy, time of initiation, and cessation of treatment, and also clinical response stratified as nonresponse, partial, or complete response at 1 year.

Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts.

Background: Typically, algorithms to classify phenotypes using electronic medical record (EMR) data were developed to perform well in a specific patient population. There is increasing interest in analyses which can allow study of a specific outcome across different diseases. Such a study in the EMR would require an algorithm that can be applied across different patient populations.

Common Genetic Variants Influence Circulating Vitamin D Levels in Inflammatory Bowel Diseases.

Background: The accuracy and utility of electronic health record (EHR)-derived phenotypes in replicating genotype-phenotype relationships have been infrequently examined. Low circulating vitamin D levels are associated with severe outcomes in inflammatory bowel disease (IBD); however, the genetic basis for vitamin D insufficiency in this population has not been examined previously.

Methods: We compared the accuracy of physician-assigned phenotypes in a large prospective IBD registry to that identified by an EHR algorithm incorporating codified and structured data.

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.

Objective: Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy.

Development of phenotype algorithms using electronic medical records and incorporating natural language processing.

Electronic medical records are emerging as a major source of data for clinical and translational research studies, although phenotypes of interest need to be accurately defined first. This article provides an overview of how to develop a phenotype algorithm from electronic medical records, incorporating modern informatics and biostatistics methods.

Validation of electronic health record phenotyping of bipolar disorder cases and controls.

Objective: The study was designed to validate use of electronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects.

Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years.

Mode of childbirth and long-term outcomes in women with inflammatory bowel diseases.

Introduction: Inflammatory bowel diseases [IBD; Crohn's disease (CD), ulcerative colitis] often affect women in their reproductive years. Few studies have analyzed the impact of mode of childbirth on long-term IBD outcomes.

Methods: We used a multi-institutional IBD cohort to identify all women in the reproductive age-group with a diagnosis of IBD prior to pregnancy.

Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records.

To reduce costs and improve clinical relevance of genetic studies, there has been increasing interest in performing such studies in hospital-based cohorts by linking phenotypes extracted from electronic medical records (EMRs) to genotypes assessed in routinely collected medical samples. A fundamental difficulty in implementing such studies is extracting accurate information about disease outcomes and important clinical covariates from large numbers of EMRs. Recently, numerous algorithms have been developed to infer phenotypes by combining information from multiple structured and unstructured variables extracted from EMRs.

Colonoscopy is associated with a reduced risk for colon cancer and mortality in patients with inflammatory bowel diseases.

Background & Aims: Crohn's disease and ulcerative colitis are associated with an increased risk of colorectal cancer (CRC). Surveillance colonoscopy is recommended at 2- to 3-year intervals beginning 8 years after diagnosis of inflammatory bowel disease (IBD). However, there have been no reports of whether colonoscopy examination reduces the risk for CRC in patients with IBD.

An electronic health records study of long-term weight gain following antidepressant use.

Importance: Short-term studies suggest antidepressants are associated with modest weight gain but little is known about longer-term effects and differences between individual medications in general clinical populations.

Objective: To estimate weight gain associated with specific antidepressants over the 12 months following initial prescription in a large and diverse clinical population.

Design, Setting, And Participants: We identified 22,610 adult patients who began receiving a medication of interest with available weight data in a large New England health care system, including 2 academic medical centers and affiliated outpatient primary and specialty care clinics.

Thromboprophylaxis is associated with reduced post-hospitalization venous thromboembolic events in patients with inflammatory bowel diseases.

Background & Aims: Patients with inflammatory bowel diseases (IBDs) have increased risk for venous thromboembolism (VTE); those who require hospitalization have particularly high risk. Few hospitalized patients with IBD receive thromboprophylaxis. We analyzed the frequency of VTE after IBD-related hospitalization, risk factors for post-hospitalization VTE, and the efficacy of prophylaxis in preventing post-hospitalization VTE.

Mortality and extraintestinal cancers in patients with primary sclerosing cholangitis and inflammatory bowel disease.

Introduction: Primary sclerosing cholangitis (PSC) and inflammatory bowel disease (IBD) frequently co-occur. PSC is associated with increased risk for colorectal cancer (CRC). However, whether PSC is associated with increased risk of extraintestinal cancers or affects mortality in an IBD cohort has not been examined previously.

Rare copy number variation in treatment-resistant major depressive disorder.

Background: While antidepressant treatment response appears to be partially heritable, no consistent genetic associations have been identified. Large, rare copy number variants (CNVs) play a role in other neuropsychiatric diseases, so we assessed their association with treatment-resistant depression (TRD).

Methods: We analyzed data from two genome-wide association studies comprising 1263 Caucasian patients with major depressive disorder.

Serum inflammatory markers and risk of colorectal cancer in patients with inflammatory bowel diseases.

Background & Aims: Patients with inflammatory bowel diseases (IBDs) (Crohn's disease, ulcerative colitis) are at increased risk of colorectal cancer (CRC). Persistent inflammation is hypothesized to increase risk of CRC in patients with IBD; however, the few studies in this area have been restricted to cross-sectional assessments of histologic severity. No prior studies have examined association between C-reactive protein (CRP) or erythrocyte sedimentation rate (ESR) elevation and risk of CRC in an IBD cohort.