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. Most hospitals in the United States use electronic health records (EHRs) that warehouse a large amount of narrative (free-text) and codified (administrative) data generated during routine clinical care. These data have been used to construct virtual disease cohorts for epidemiologic research as well as for defining genetic basis of disease states or discrete laboratory values.1-3 Whether EHR-based data can be used to validate genetic associations for more nuanced outcomes such as treatment response has not been examined previously.

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. We examined their role as chemopreventive agents in patients with IBD. METHODS: We collected data from 11,001 patients with IBD receiving care at hospitals in the Greater Boston metropolitan area from 1998 through 2010. Diagnoses of CRC were determined using validated International Classification of Diseases, 9th revision, Clinical Modification codes. Statin use before diagnosis was assessed through analysis of electronic prescriptions. We performed multivariate logistic regression analyses, adjusting for potential confounders including primary sclerosing cholangitis, smoking, increased levels of inflammation markers, and CRC screening practices to identify an independent association between statin use and CRC. We performed sensitivity analyses using propensity score adjustment and variation in the definition of statin use. RESULTS: In our cohort, 1376 of the patients (12.5%) received 1 or more prescriptions for a statin. Patients using statins were more likely to be older, male, white, smokers, and have greater comorbidity than nonusers. Over a follow-up period of 9 years, 2% of statin users developed CRC compared with 3% of nonusers (age-adjusted odds ratio, 0.35; 95% confidence interval, 0.24-0.53). On multivariate analysis, statin use remained independently and inversely associated with CRC (odds ratio, 0.42; 95% confidence interval, 0.28-0.62). Our findings were robust on a variety of sensitivity and subgroup analyses. CONCLUSIONS: Statin use was associated inversely with the risk of CRC in a large IBD cohort. Prospective studies on the role of statins as chemopreventive agents are warranted.

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. METHODS: In a retrospective study, we analyzed data from 6823 patients with IBD (2764 with a recent colonoscopy, 4059 without a recent colonoscopy) seen and followed up for at least 3 years at 2 tertiary referral hospitals in Boston, Massachusetts. The primary outcome was diagnosis of CRC. We examined the proportion of patients undergoing a colonoscopy within 36 months before a diagnosis of CRC or at the end of the follow-up period, excluding colonoscopies performed within 6 months before a diagnosis of CRC, to avoid inclusion of prevalent cancers. Multivariate logistic regression was performed, adjusting for plausible confounders. RESULTS: A total of 154 patients developed CRC. The incidence of CRC among patients without a recent colonoscopy (2.7%) was significantly higher than among patients with a recent colonoscopy (1.6%) (odds ratio [OR], 0.56; 95% confidence interval [CI], 0.39-0.80). This difference persisted in multivariate analysis (OR, 0.65; 95% CI, 0.45-0.93) and was robust when adjusted for a range of assumptions in sensitivity analyses. Among patients with CRC, a colonoscopy within 6 to 36 months before diagnosis was associated with a reduced mortality rate (OR, 0.34; 95% CI, 0.12-0.95). CONCLUSIONS: Recent colonoscopy (within 36 months) is associated with a reduced incidence of CRC in patients with IBD, and lower mortality rates in those diagnosed with CRC.

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. We identified the occurrence of a new diagnosis code for perianal complications, IBD-related hospitalization and surgery, and initiation of medical therapy after either a vaginal delivery or caesarean section (CS). Cox proportional hazards models adjusting for potential confounders were used to estimate independent effect of mode of childbirth on IBD outcomes. RESULTS: Our cohort included 360 women with IBD (161 CS). Women in the CS group were likely to be older and more likely to have complicated disease behavior prior to pregnancy. During follow-up, there was no difference in the likelihood of IBD-related surgery (multivariate hazard ratio 1.75, 95 % confidence interval (CI) 0.40-7.75), IBD-related hospitalization (HR 1.39), initiation of immunomodulator therapy (HR 1.45), or anti-TNF therapy (HR 1.11). Among the 133 CD pregnancies with no prior perianal disease, we found no excess risk of subsequent new diagnosis perianal fistulae with vaginal delivery compared to CS (HR 0.19, 95 % CI 0.04-1.05). CONCLUSIONS: Mode of delivery did not influence natural history of IBD. In our cohort, vaginal delivery was not associated with increased risk of subsequent perianal disease in women with CD.

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. Although these algorithms are quite accurate, they typically do not provide perfect classification due to the difficulty in inferring meaning from the text. Some algorithms can produce for each patient a probability that the patient is a disease case. This probability can be thresholded to define case-control status, and this estimated case-control status has been used to replicate known genetic associations in EMR-based studies. However, using the estimated disease status in place of true disease status results in outcome misclassification, which can diminish test power and bias odds ratio estimates. We propose to instead directly model the algorithm-derived probability of being a case. We demonstrate how our approach improves test power and effect estimation in simulation studies, and we describe its performance in a study of rheumatoid arthritis. Our work provides an easily implemented solution to a major practical challenge that arises in the use of EMR data, which can facilitate the use of EMR infrastructure for more powerful, cost-effective, and diverse genetic studies.

Genetic basis of autoantibody positive and negative rheumatoid arthritis risk in a multi-ethnic cohort derived from electronic health records.

Discovering and following up on genetic associations with complex phenotypes require large patient cohorts. This is particularly true for patient cohorts of diverse ancestry and clinically relevant subsets of disease. The ability to mine the electronic health records (EHRs) of patients followed as part of routine clinical care provides a potential opportunity to efficiently identify affected cases and unaffected controls for appropriate-sized genetic studies. Here, we demonstrate proof-of-concept that it is possible to use EHR data linked with biospecimens to establish a multi-ethnic case-control cohort for genetic research of a complex disease, rheumatoid arthritis (RA). In 1,515 EHR-derived RA cases and 1,480 controls matched for both genetic ancestry and disease-specific autoantibodies (anti-citrullinated protein antibodies [ACPA]), we demonstrate that the odds ratios and aggregate genetic risk score (GRS) of known RA risk alleles measured in individuals of European ancestry within our EHR cohort are nearly identical to those derived from a genome-wide association study (GWAS) of 5,539 autoantibody-positive RA cases and 20,169 controls. We extend this approach to other ethnic groups and identify a large overlap in the GRS among individuals of European, African, East Asian, and Hispanic ancestry. We also demonstrate that the distribution of a GRS based on 28 non-HLA risk alleles in ACPA+ cases partially overlaps with ACPA- subgroup of RA cases. Our study demonstrates that the genetic basis of rheumatoid arthritis risk is similar among cases of diverse ancestry divided into subsets based on ACPA status and emphasizes the utility of linking EHR clinical data with biospecimens for genetic studies.

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. METHODS: In a retrospective study, we analyzed data from a multi-institutional cohort of patients with Crohn's disease or ulcerative colitis and at least 1 IBD-related hospitalization. Our primary outcome was a VTE event. All patients contributed person-time from the date of the index hospitalization to development of VTE, subsequent hospitalization, or end of follow-up. Our main predictor variable was pharmacologic thromboprophylaxis. Cox proportional hazard models adjusting for potential confounders were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: From a cohort of 2788 patients with at least 1 IBD-related hospitalization, 62 patients developed VTE after discharge (2%). Incidences of VTE at 30, 60, 90, and 180 days after the index hospitalization were 3.7/1000, 4.1/1000, 5.4/1000, and 9.4/1000 person-days, respectively. Pharmacologic thromboprophylaxis during the index hospital stay was associated with a significantly lower risk of post-hospitalization VTE (HR, 0.46; 95% CI, 0.22-0.97). Increased numbers of comorbidities (HR, 1.30; 95% CI, 1.16-1.47) and need for corticosteroids before hospitalization (HR, 1.71; 95% CI, 1.02-2.87) were also independently associated with risk of VTE. Length of hospitalization or surgery during index hospitalization was not associated with post-hospitalization VTE. CONCLUSIONS: Pharmacologic thromboprophylaxis during IBD-related hospitalization is associated with reduced risk of post-hospitalization VTE.

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. METHODS: From a multi-institutional validated IBD cohort, we identified all patients with at least one measured CRP or ESR value. Patients were stratified into quartiles of severity of inflammation on the basis of their median CRP or ESR value, and subsequent diagnosis of CRC was ascertained. Logistic regression adjusting for potential confounders was used to identify the independent association between CRP or ESR elevation and risk of CRC. RESULTS: Our study included 3145 patients with at least 1 CRP value (CRP cohort) and 4008 with at least 1 ESR value (ESR cohort). Thirty-three patients in the CRP cohort and 102 patients in the ESR cohort developed CRC during a median follow-up of 5 years at a median age of 55 years. On multivariate analysis, there was a significant increase in risk of CRC across quartiles of CRP elevation (P(trend) = .017; odds ratio for quartile 4 vs quartile 1, 2.72; 95% confidence interval, 0.95-7.76). Similarly higher median ESR was also independently associated with risk of CRC across the quartiles (odds ratio, 2.06; 95% confidence interval, 1.14-3.74) (P(trend) = .007). CONCLUSIONS: An elevated CRP or ESR is associated with increased risk of CRC in patients with IBD.

Association between reduced plasma 25-hydroxy vitamin D and increased risk of cancer in patients with inflammatory bowel diseases.

BACKGROUND & AIMS: Vitamin D deficiency is common among patients with inflammatory bowel diseases (IBD) (Crohn's disease or ulcerative colitis). The effects of low plasma 25-hydroxy vitamin D (25[OH]D) on outcomes other than bone health are understudied in patients with IBD. We examined the association between plasma level of 25(OH)D and risk of cancers in patients with IBD. METHODS: From a multi-institutional cohort of patients with IBD, we identified those with at least 1 measurement of plasma 25(OH)D. The primary outcome was development of any cancer. We examined the association between plasma 25(OH)D and risk of specific subtypes of cancer, adjusting for potential confounders in a multivariate regression model. RESULTS: We analyzed data from 2809 patients with IBD and a median plasma level of 25(OH)D of 26 ng/mL. Nearly one-third had deficient levels of vitamin D (<20 ng/mL). During a median follow-up period of 11 years, 196 patients (7%) developed cancer, excluding nonmelanoma skin cancer (41 cases of colorectal cancer). Patients with vitamin D deficiency had an increased risk of cancer (adjusted odds ratio, 1.82; 95% confidence interval, 1.25-2.65) compared with those with sufficient levels. Each 1-ng/mL increase in plasma 25(OH)D was associated with an 8% reduction in risk of colorectal cancer (odds ratio, 0.92; 95% confidence interval, 0.88-0.96). A weaker inverse association was also identified for lung cancer. CONCLUSIONS: In a large multi-institutional IBD cohort, a low plasma level of 25(OH)D was associated with an increased risk of cancer, especially colorectal cancer.

Association between low density lipoprotein and rheumatoid arthritis genetic factors with low density lipoprotein levels in rheumatoid arthritis and non-rheumatoid arthritis controls.

OBJECTIVES: While genetic determinants of low density lipoprotein (LDL) cholesterol levels are well characterised in the general population, they are understudied in rheumatoid arthritis (RA). Our objective was to determine the association of established LDL and RA genetic alleles with LDL levels in RA cases compared with non-RA controls. METHODS: Using data from electronic medical records, we linked validated RA cases and non-RA controls to discarded blood samples. For each individual, we extracted data on: first LDL measurement, age, gender and year of LDL measurement. We genotyped subjects for 11 LDL and 44 non-HLA RA alleles, and calculated RA and LDL genetic risk scores (GRS). We tested the association between each GRS and LDL level using multivariate linear regression models adjusted for age, gender, year of LDL measurement and RA status. RESULTS: Among 567 RA cases and 979 controls, 80% were female and mean age at the first LDL measurement was 55 years. RA cases had significantly lower mean LDL levels than controls (117.2 vs 125.6 mg/dl, respectively, p<0.0001). Each unit increase in LDL GRS was associated with 0.8 mg/dl higher LDL levels in both RA cases and controls (p=3.0×10(-7)). Each unit increase in RA GRS was associated with 4.3 mg/dl lower LDL levels in both groups (p=0.01). CONCLUSIONS: LDL alleles were associated with higher LDL levels in RA. RA alleles were associated with lower LDL levels in both RA cases and controls. As RA cases carry more RA alleles, these findings suggest a genetic basis for epidemiological observations of lower LDL levels in RA.

A mega-analysis of genome-wide association studies for major depressive disorder.

Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 × 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 × 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.

Associations of autoantibodies, autoimmune risk alleles, and clinical diagnoses from the electronic medical records in rheumatoid arthritis cases and non-rheumatoid arthritis controls.

OBJECTIVE: The significance of non-rheumatoid arthritis (RA) autoantibodies in patients with RA is unclear. The aim of this study was to assess associations of autoantibodies with autoimmune risk alleles and with clinical diagnoses from the electronic medical records (EMRs) among RA cases and non-RA controls. METHODS: Data on 1,290 RA cases and 1,236 non-RA controls of European genetic ancestry were obtained from the EMRs of 2 large academic centers. The levels of anti-citrullinated protein antibodies (ACPAs), antinuclear antibodies (ANAs), anti-tissue transglutaminase antibodies (AGTAs), and anti-thyroid peroxidase (anti-TPO) antibodies were measured. All subjects were genotyped for autoimmune risk alleles, and the association between number of autoimmune risk alleles present and number of types of autoantibodies present was studied. A phenome-wide association study (PheWAS) was conducted to study potential associations between autoantibodies and clinical diagnoses among RA cases and non-RA controls. RESULTS: The mean ages were 60.7 years in RA cases and 64.6 years in non-RA controls. The proportion of female subjects was 79% in each group. The prevalence of ACPAs and ANAs was higher in RA cases compared to controls (each P < 0.0001); there were no differences in the prevalence of anti-TPO antibodies and AGTAs. Carriage of higher numbers of autoimmune risk alleles was associated with increasing numbers of autoantibody types in RA cases (P = 2.1 × 10(-5)) and non-RA controls (P = 5.0 × 10(-3)). From the PheWAS, the presence of ANAs was significantly associated with a diagnosis of Sjögren's/sicca syndrome in RA cases. CONCLUSION: The increased frequency of autoantibodies in RA cases and non-RA controls was associated with the number of autoimmune risk alleles carried by an individual. PheWAS of EMR data, with linkage to laboratory data obtained from blood samples, provide a novel method to test for the clinical significance of biomarkers in disease.

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. We analyzed the germline variants of 51 ERs using polygenic risk score (PRS) analysis. Compared to typical cancer patients, the ERs had significantly elevated PRSs for several autoimmune-related diseases: type 1 diabetes, hypothyroidism, and psoriasis. This indicates that an increased genetic predisposition towards these autoimmune diseases is more prevalent among the ERs. In contrast, ERs had significantly lower PRSs for developing inflammatory bowel disease. The left-skew of type 1 diabetes score was significant for exceptional responders. Variants on genes involved in the T1D PRS model associated with cancer drug response are more likely to co-occur with other variants among ERs. In conclusion, ERs exhibited different risks for autoimmune diseases compared to typical cancer patients, which suggests that changes in a patient's immune set point or immune surveillance specificity could be a potential mechanistic link to their exceptional response. These findings expand upon previous research on immune checkpoint inhibitor-treated patients to include those who received chemotherapy or radiotherapy.

Similar risk of depression and anxiety following surgery or hospitalization for Crohn's disease and ulcerative colitis.

OBJECTIVES: Psychiatric comorbidity is common in Crohn's disease (CD) and ulcerative colitis (UC). Inflammatory bowel disease (IBD)-related surgery or hospitalizations represent major events in the natural history of the disease. The objective of this study is to examine whether there is a difference in the risk of psychiatric comorbidity following surgery in CD and UC. METHODS: We used a multi-institution cohort of IBD patients without a diagnosis code for anxiety or depression preceding their IBD-related surgery or hospitalization. Demographic-, disease-, and treatment-related variables were retrieved. Multivariate logistic regression analysis was performed to individually identify risk factors for depression and anxiety. RESULTS: Our study included a total of 707 CD and 530 UC patients who underwent bowel resection surgery and did not have depression before surgery. The risk of depression 5 years after surgery was 16% and 11% in CD and UC patients, respectively. We found no difference in the risk of depression following surgery in the CD and UC patients (adjusted odds ratio, 1.11; 95% confidence interval, 0.84-1.47). Female gender, comorbidity, immunosuppressant use, perianal disease, stoma surgery, and early surgery within 3 years of care predicted depression after CD surgery; only the female gender and comorbidity predicted depression in UC patients. Only 12% of the CD cohort had ≥4 risk factors for depression, but among them nearly 44% subsequently received a diagnosis code for depression. CONCLUSIONS: IBD-related surgery or hospitalization is associated with a significant risk for depression and anxiety, with a similar magnitude of risk in both diseases.

An efficient landmark model for prediction of suicide attempts in multiple clinical settings.

Growing evidence has shown that applying machine learning models to large clinical data sources may exceed clinician performance in suicide risk stratification. However, many existing prediction models either suffer from "temporal bias" (a bias that stems from using case-control sampling) or require training on all available patient visit data. Here, we adopt a "landmark model" framework that aligns with clinical practice for prediction of suicide-related behaviors (SRBs) using a large electronic health record database. Using the landmark approach, we developed models for SRB prediction (regularized Cox regression and random survival forest) that establish a time-point (e.g., clinical visit) from which predictions are made over user-specified prediction windows using historical information up to that point. We applied this approach to cohorts from three clinical settings: general outpatient, psychiatric emergency department, and psychiatric inpatients, for varying prediction windows and lengths of historical data. Models achieved high discriminative performance (area under the Receiver Operating Characteristic curve 0.74-0.93 for the Cox model) across different prediction windows and settings, even with relatively short periods of historical data. In short, we developed accurate, dynamic SRB risk prediction models with the landmark approach that reduce bias and enhance the reliability and portability of suicide risk prediction models.

Instrumenting the health care enterprise for discovery research in the genomic era.

Tens of thousands of subjects may be required to obtain reliable evidence relating disease characteristics to the weak effects typically reported from common genetic variants. The costs of assembling, phenotyping, and studying these large populations are substantial, recently estimated at three billion dollars for 500,000 individuals. They are also decade-long efforts. We hypothesized that automation and analytic tools can repurpose the informational byproducts of routine clinical care, bringing sample acquisition and phenotyping to the same high-throughput pace and commodity price-point as is currently true of genome-wide genotyping. Described here is a demonstration of the capability to acquire samples and data from densely phenotyped and genotyped individuals in the tens of thousands for common diseases (e.g., in a 1-yr period: N = 15,798 for rheumatoid arthritis; N = 42,238 for asthma; N = 34,535 for major depressive disorder) in one academic health center at an order of magnitude lower cost. Even for rare diseases caused by rare, highly penetrant mutations such as Huntington disease (N = 102) and autism (N = 756), these capabilities are also of interest.

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. However, previous linked data inference methods are constrained to certain linkage settings and exhibit low power. Here, we present ATLAS, an automated, flexible, and robust association testing algorithm for probabilistically linked data. MATERIALS AND METHODS: Missing variables are imputed at various thresholds using a weighted average method that propagates uncertainty from probabilistic linkage. Next, estimated effect sizes are obtained using a generalized linear model. ATLAS then conducts the threshold combination test by optimally combining P values obtained from data imputed at varying thresholds using Fisher's method and perturbation resampling. RESULTS: In simulations, ATLAS controls for type I error and exhibits high power compared to previous methods. In a real-world genetic association study, meta-analysis of ATLAS-enabled analyses on a linked cohort with analyses using an existing cohort yielded additional significant associations between rheumatoid arthritis genetic risk score and laboratory biomarkers. DISCUSSION: Weighted average imputation weathers false matches and increases contribution of true matches to mitigate linkage error-induced bias. The threshold combination test avoids arbitrarily choosing a threshold to rule a match, thus automating linked data-enabled analyses and preserving power. CONCLUSION: ATLAS promises to enable novel and powerful research studies using linked data to capitalize on all available data sources.

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. We propose a scalable, fast and efficient open-source implementation in the ludic R package available on CRAN, which also includes the anonymized diagnosis code data from our real use-case. This work suggests it is possible to link de-identified research databases stripped of any personal health identifiers using only diagnosis codes, provided sufficient information is shared between the data sources.

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. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1-2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no).