Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics.

OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

Analytics to monitor local impact of the Protecting Access to Medicare Act's imaging clinical decision support requirements.

OBJECTIVE: This study aimed is to: (1) extend the Integrating the Biology and the Bedside (i2b2) data and application models to include medical imaging appropriate use criteria, enabling it to serve as a platform to monitor local impact of the Protecting Access to Medicare Act's (PAMA) imaging clinical decision support (CDS) requirements, and (2) validate the i2b2 extension using data from the Medicare Imaging Demonstration (MID) CDS implementation. MATERIALS AND METHODS: This study provided a reference implementation and assessed its validity and reliability using data from the MID, the federal government's predecessor to PAMA's imaging CDS program. The Star Schema was extended to describe the interactions of imaging ordering providers with the CDS. New ontologies were added to enable mapping medical imaging appropriateness data to i2b2 schema. z-Ratio for testing the significance of the difference between 2 independent proportions was utilized. RESULTS: The reference implementation used 26 327 orders for imaging examinations which were persisted to the modified i2b2 schema. As an illustration of the analytical capabilities of the Web Client, we report that 331/1192 or 28.1% of imaging orders were deemed appropriate by the CDS system at the end of the intervention period (September 2013), an increase from 162/1223 or 13.2% for the first month of the baseline period, December 2011 (P = .0212), consistent with previous studies. CONCLUSIONS: The i2b2 platform can be extended to monitor local impact of PAMA's appropriateness of imaging ordering CDS requirements.

Longitudinal Data Discontinuity in Electronic Health Records and Consequences for Medication Effectiveness Studies.

Electronic health record (EHR) discontinuity (i.e., receiving care outside of the study EHR system), can lead to information bias in EHR-based real-world evidence (RWE) studies. An algorithm has been previously developed to identify patients with high EHR-continuity. We sought to assess whether applying this algorithm to patient selection for inclusion can reduce bias caused by data-discontinuity in four RWE examples. Among Medicare beneficiaries aged >=65 years from 2007 to 2014, we established 4 cohorts assessing drug effects on short-term or long-term outcomes, respectively. We linked claims data with two US EHR systems and calculated %bias of the multivariable-adjusted effect estimates based on only EHR vs. linked EHR-claims data because the linked data capture medical information recorded outside of the study EHR. Our study cohort included 77,288 patients in system 1 and 60,309 in system 2. We found the subcohort in the lowest quartile of EHR-continuity captured 72-81% of the short-term and only 21-31% of the long-term outcome events, leading to %bias of 6-99% for the short-term and 62-112% for the long-term outcome examples. This trend appeared to be more pronounced in the example using a nonuser comparison rather than an active comparison. We did not find significant treatment effect heterogeneity by EHR-continuity for most subgroups across empirical examples. In EHR-based RWE studies, investigators may consider excluding patients with low algorithm-predicted EHR-continuity as the EHR data capture relatively few of their actual outcomes, and treatment effect estimates in these patients may be unreliable.

Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches.

Background Real-world healthcare data are an important resource for epidemiologic research. However, accurate identification of patient cohorts-a crucial first step underpinning the validity of research results-remains a challenge. We developed and evaluated claims-based case ascertainment algorithms for pulmonary hypertension (PH), comparing conventional decision rules with state-of-the-art machine-learning approaches. Methods and Results We analyzed an electronic health record-Medicare linked database from two large academic tertiary care hospitals (years 2007-2013). Electronic health record charts were reviewed to form a gold standard cohort of patients with (n=386) and without PH (n=164). Using health encounter data captured in Medicare claims (including patients' demographics, diagnoses, medications, and procedures), we developed and compared 2 approaches for identifying patients with PH: decision rules and machine-learning algorithms using penalized lasso regression, random forest, and gradient boosting machine. The most optimal rule-based algorithm-having ≥3 PH-related healthcare encounters and having undergone right heart catheterization-attained an area under the receiver operating characteristic curve of 0.64 (sensitivity, 0.75; specificity, 0.48). All 3 machine-learning algorithms outperformed the most optimal rule-based algorithm (P<0.001). A model derived from the random forest algorithm achieved an area under the receiver operating characteristic curve of 0.88 (sensitivity, 0.87; specificity, 0.70), and gradient boosting machine achieved comparable results (area under the receiver operating characteristic curve, 0.85; sensitivity, 0.87; specificity, 0.70). Penalized lasso regression achieved an area under the receiver operating characteristic curve of 0.73 (sensitivity, 0.70; specificity, 0.68). Conclusions Research-grade case identification algorithms for PH can be derived and rigorously validated using machine-learning algorithms. Simple decision rules commonly applied in published literature performed poorly; more complex rule-based algorithms may potentially address the limitation of this approach. PH research using claims data would be considerably strengthened through the use of validated algorithms for cohort ascertainment.

Informatics can help providers incorporate context into care.

Most determinants of health originate from the "contexts" in which we live, which has remained outside the confines of the U.S. healthcare system. This issue has left providers unprepared to operate with an ample understanding of the challenges patients may face beyond their purview. The recent shift to value-based care and increasing prevalence of Electronic Health Record (EHR) systems provide opportunities to incorporate upstream contextual factors into care. We discuss that incorporating context into care is hindered by a chicken-and-egg dilemma - ie, lack of evidence on the utility of contextual data at the point of care, where contextual data are missing due to the lack of an informatics infrastructure. We argue that if we build the informatics infrastructure today, EHRs can give the tomorrow's clinicians the tools and the data they need to transform the U.S. healthcare from episodic and reactive to preventive and proactive. We also discuss system design considerations to improve efficacy of the suggested informatics infrastructure, which include systematically prioritizing contextual data domains, developing interoperability standards, and ensuring that integration of contextual data does not disrupt clinicians' workflow.

Biases introduced by filtering electronic health records for patients with "complete data".

OBJECTIVE: One promise of nationwide adoption of electronic health records (EHRs) is the availability of data for large-scale clinical research studies. However, because the same patient could be treated at multiple health care institutions, data from only a single site might not contain the complete medical history for that patient, meaning that critical events could be missing. In this study, we evaluate how simple heuristic checks for data "completeness" affect the number of patients in the resulting cohort and introduce potential biases. MATERIALS AND METHODS: We began with a set of 16 filters that check for the presence of demographics, laboratory tests, and other types of data, and then systematically applied all 216 possible combinations of these filters to the EHR data for 12 million patients at 7 health care systems and a separate payor claims database of 7 million members. RESULTS: EHR data showed considerable variability in data completeness across sites and high correlation between data types. For example, the fraction of patients with diagnoses increased from 35.0% in all patients to 90.9% in those with at least 1 medication. An unrelated claims dataset independently showed that most filters select members who are older and more likely female and can eliminate large portions of the population whose data are actually complete. DISCUSSION AND CONCLUSION: As investigators design studies, they need to balance their confidence in the completeness of the data with the effects of placing requirements on the data on the resulting patient cohort.

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. METHODS: In a multi-institutional IBD cohort of IBD, we established a diagnosis of PSC using a novel algorithm incorporating narrative and codified data with high positive and negative predictive value. Our primary outcome was occurrence of extraintestinal and digestive tract cancers. Mortality was determined through monthly linkage to the social security master death index. RESULTS: In our cohort of 5506 patients with CD and 5522 patients with UC, a diagnosis of PSC was established in 224 patients (2%). Patients with IBD-PSC were younger and more likely to be male compared to IBD patients without PSC; three-quarters had UC. IBD-PSC patients had significantly increased overall risk of cancers compared to patients without PSC (OR 4.36, 95% CI 2.99-6.37). Analysis of specific cancer types revealed that a statistically significant excess risk for digestive tract cancer (OR 10.40, 95% CI 6.86-15.76), pancreatic cancer (OR 11.22, 95% CI 4.11-30.62), colorectal cancer (OR 5.00, 95% CI 2.80-8.95), and cholangiocarcinoma (OR 55.31, 95% CI 22.20-137.80) but not for other solid organ or hematologic malignancies. CONCLUSIONS: PSC is associated with increased risk of colorectal and pancreatobiliary cancer but not with excess risk of other solid organ cancers.

Strategies for maintaining patient privacy in i2b2.

BACKGROUND: The re-use of patient data from electronic healthcare record systems can provide tremendous benefits for clinical research, but measures to protect patient privacy while utilizing these records have many challenges. Some of these challenges arise from a misperception that the problem should be solved technically when actually the problem needs a holistic solution. OBJECTIVE: The authors' experience with informatics for integrating biology and the bedside (i2b2) use cases indicates that the privacy of the patient should be considered on three fronts: technical de-identification of the data, trust in the researcher and the research, and the security of the underlying technical platforms. METHODS: The security structure of i2b2 is implemented based on consideration of all three fronts. It has been supported with several use cases across the USA, resulting in five privacy categories of users that serve to protect the data while supporting the use cases. RESULTS: The i2b2 architecture is designed to provide consistency and faithfully implement these user privacy categories. These privacy categories help reflect the policy of both the Health Insurance Portability and Accountability Act and the provisions of the National Research Act of 1974, as embodied by current institutional review boards. CONCLUSION: By implementing a holistic approach to patient privacy solutions, i2b2 is able to help close the gap between principle and practice.