Adverse Health Outcomes among Rural and Urban Breast Cancer Survivors: A Population-Based Cohort Study.

BACKGROUND: Limited population-based studies have focused on breast cancer survivors in rural populations. We sought to evaluate the risk of adverse health outcomes among rural and urban breast cancer survivors and to evaluate potential predictors for the highest risk outcomes. METHODS: A population-based cohort of rural and urban breast cancer survivors diagnosed between 1997 and 2017 was identified in the Utah Cancer Registry (UCR). Rural breast cancer survivors were matched on year (±1 year) and age at cancer diagnosis (±1 year) with up to 5 urban breast cancer survivors (2,359 rural breast cancer survivors; 11,748 urban breast cancer survivors). Cox proportional hazards models were used to calculate HRs with 99% confidence intervals (CI) for adverse health outcomes overall, within 5 years, and >5 years after cancer diagnosis. RESULTS: Compared with urban breast cancer survivors, rural breast cancer survivors had a 39% (HR, 1.39; 95% CI, 1.02-1.65) higher risk of heart failure (HF) within the 5 years of follow-up. Overall, there was no increase in the risk of other evaluated adverse health outcomes. A higher baseline body mass index and Charlson Comorbidity Index, family history of cardiovascular diseases, family history of breast cancer, and advanced cancer stage were risk factors for HF for rural and urban breast cancer survivors, with similar levels of HF risk. CONCLUSIONS: Rural residence was associated with an increased risk of HF among breast cancer survivors. IMPACT: Our study highlights the need for primary preventive strategies for rural cancer survivors at risk of heart failure.

Medication for Opioid Use Disorder During Pregnancy - Maternal and Infant Network to Understand Outcomes Associated with Use of Medication for Opioid Use Disorder During Pregnancy (MAT-LINK), 2014-2021.

Problem: Medication for opioid use disorder (MOUD) is recommended for persons with opioid use disorder (OUD) during pregnancy. However, knowledge gaps exist about best practices for management of OUD during pregnancy and these data are needed to guide clinical care. Period Covered: 2014-2021. Description of the System: Established in 2019, the Maternal and Infant Network to Understand Outcomes Associated with Medication for Opioid Use Disorder During Pregnancy (MAT-LINK) is a surveillance network of seven clinical sites in the United States. Boston Medical Center, Kaiser Permanente Northwest, The Ohio State University, and the University of Utah were the initial clinical sites in 2019. In 2021, three clinical sites were added to the network (the University of New Mexico, the University of Rochester, and the University of South Florida). Persons receiving care at the seven clinical sites are diverse in terms of geography, urbanicity, race and ethnicity, insurance coverage, and type of MOUD received. The goal of MAT-LINK is to capture demographic and clinical information about persons with OUD during pregnancy to better understand the effect of MOUD on outcomes and, ultimately, provide information for clinical care and public health interventions for this population. MAT-LINK maintains strict confidentiality through robust information technology architecture. MAT-LINK surveillance methods, population characteristics, and evaluation findings are described in this inaugural surveillance report. This report is the first to describe the system, presenting detailed information on funding, structure, data elements, and methods as well as findings from a surveillance evaluation. The findings presented in this report are limited to selected demographic characteristics of pregnant persons overall and by MOUD treatment status. Clinical and outcome data are not included because data collection and cleaning have not been completed; initial analyses of clinical and outcome data will begin in 2023. Results: The MAT-LINK surveillance network gathered data on 5,541 reported pregnancies with a known pregnancy outcome during 2014-2021 among persons with OUD from seven clinical sites. The mean maternal age was 29.7 (SD = ±5.1) years. By race and ethnicity, 86.3% of pregnant persons were identified as White, 25.4% as Hispanic or Latino, and 5.8% as Black or African American. Among pregnant persons, 81.6% had public insurance, and 84.4% lived in urban areas. Compared with persons not receiving MOUD during pregnancy, those receiving MOUD during pregnancy were more likely to be older and White and to have public insurance. The evaluation of the surveillance system found that the initial four clinical sites were not representative of demographics of the South or Southwest regions of the United States and had low representation from certain racial and ethnic groups compared with the overall U.S. population; however, the addition of three clinical sites in 2021 made the surveillance network more representative. Automated extraction and processing improved the speed of data collection and analysis. The ability to add new clinical sites and variables demonstrated the flexibility of MAT-LINK. Interpretation: MAT-LINK is the first surveillance system to collect comprehensive, longitudinal data on pregnant person-infant dyads with perinatal outcomes associated with MOUD during pregnancy from multiple clinical sites. Analyses of clinical site data demonstrated different sociodemographic characteristics between the MOUD and non-MOUD treatment groups. Public Health Actions: MAT-LINK is a timely and flexible surveillance system with data on approximately 5,500 pregnancies. Ongoing data collection and analyses of these data will provide information to support clinical and public health guidance to improve health outcomes among pregnant persons with OUD and their children.

Smoking Status, Nicotine Medication, Vaccination, and COVID-19 Hospital Outcomes: Findings from the COVID EHR Cohort at the University of Wisconsin (CEC-UW) Study.

INTRODUCTION: Available evidence is mixed concerning associations between smoking status and COVID-19 clinical outcomes. Effects of nicotine replacement therapy (NRT) and vaccination status on COVID-19 outcomes in smokers are unknown. METHODS: Electronic health record data from 104 590 COVID-19 patients hospitalized February 1, 2020 to September 30, 2021 in 21 U.S. health systems were analyzed to assess associations of smoking status, in-hospital NRT prescription, and vaccination status with in-hospital death and ICU admission. RESULTS: Current (n = 7764) and never smokers (n = 57 454) did not differ on outcomes after adjustment for age, sex, race, ethnicity, insurance, body mass index, and comorbidities. Former (vs never) smokers (n = 33 101) had higher adjusted odds of death (aOR, 1.11; 95% CI, 1.06-1.17) and ICU admission (aOR, 1.07; 95% CI, 1.04-1.11). Among current smokers, NRT prescription was associated with reduced mortality (aOR, 0.64; 95% CI, 0.50-0.82). Vaccination effects were significantly moderated by smoking status; vaccination was more strongly associated with reduced mortality among current (aOR, 0.29; 95% CI, 0.16-0.66) and former smokers (aOR, 0.47; 95% CI, 0.39-0.57) than for never smokers (aOR, 0.67; 95% CI, 0.57, 0.79). Vaccination was associated with reduced ICU admission more strongly among former (aOR, 0.74; 95% CI, 0.66-0.83) than never smokers (aOR, 0.87; 95% CI, 0.79-0.97). CONCLUSIONS: Former but not current smokers hospitalized with COVID-19 are at higher risk for severe outcomes. SARS-CoV-2 vaccination is associated with better hospital outcomes in COVID-19 patients, especially current and former smokers. NRT during COVID-19 hospitalization may reduce mortality for current smokers. IMPLICATIONS: Prior findings regarding associations between smoking and severe COVID-19 disease outcomes have been inconsistent. This large cohort study suggests potential beneficial effects of nicotine replacement therapy on COVID-19 outcomes in current smokers and outsized benefits of SARS-CoV-2 vaccination in current and former smokers. Such findings may influence clinical practice and prevention efforts and motivate additional research that explores mechanisms for these effects.

Relations of Current and Past Cancer with Severe Outcomes among 104,590 Hospitalized COVID-19 Patients: The COVID EHR Cohort at the University of Wisconsin.

BACKGROUND: There is mixed evidence about the relations of current versus past cancer with severe COVID-19 outcomes and how they vary by patient and cancer characteristics. METHODS: Electronic health record data of 104,590 adult hospitalized patients with COVID-19 were obtained from 21 United States health systems from February 2020 through September 2021. In-hospital mortality and ICU admission were predicted from current and past cancer diagnoses. Moderation by patient characteristics, vaccination status, cancer type, and year of the pandemic was examined. RESULTS: 6.8% of the patients had current (n = 7,141) and 6.5% had past (n = 6,749) cancer diagnoses. Current cancer predicted both severe outcomes but past cancer did not; adjusted odds ratios (aOR) for mortality were 1.58 [95% confidence interval (CI), 1.46-1.70] and 1.04 (95% CI, 0.96-1.13), respectively. Mortality rates decreased over the pandemic but the incremental risk of current cancer persisted, with the increment being larger among younger vs. older patients. Prior COVID-19 vaccination reduced mortality generally and among those with current cancer (aOR, 0.69; 95% CI, 0.53-0.90). CONCLUSIONS: Current cancer, especially among younger patients, posed a substantially increased risk for death and ICU admission among patients with COVID-19; prior COVID-19 vaccination mitigated the risk associated with current cancer. Past history of cancer was not associated with higher risks for severe COVID-19 outcomes for most cancer types. IMPACT: This study clarifies the characteristics that modify the risk associated with cancer on severe COVID-19 outcomes across the first 20 months of the COVID-19 pandemic. See related commentary by Egan et al., p. 3.

Cardiovascular disease risk in long-term breast cancer survivors: A population-based cohort study.

BACKGROUND: Breast cancer survival is increasing, making late effects such as cardiovascular disease (CVD) more relevant. The purpose of this study was to evaluate incident CVD following breast cancer diagnosis among long-term survivors and to investigate possible risk factors for CVD. METHODS: A population-based cohort of 6641 breast cancer survivors diagnosed between 1997 and 2009 who survived at least 10 years was identified within the Utah Cancer Registry. In addition, 36,612 cancer-free women from the general population, matched by birth year and state, were identified within the Utah Population Database. Cox proportional hazards models were used to calculate CVD hazard ratios (HRs) for >10 to 15 and >15 years. RESULTS: Long-term breast cancer survivors had an increased risk of newly diagnosed diseases of the circulatory system (HR, 1.32; 99% confidence interval [CI], 1.00-1.75) from 10 to 15 years following cancer diagnosis compared with the general population. No increased CVD risks were observed after 15 years. Breast cancer survivors with Charlson Comorbidity Index score ≥2 had a significantly higher risk of diseases of the circulatory system (HR, 2.64; 95% CI, 1.08-6.45) beyond 10 years following breast cancer diagnosis. Similarly, older age, obesity, lower education, and family history of CVD and breast cancer were risk factors for heart and circulatory system diseases among long-term breast cancer survivors. CONCLUSION: Risk of CVD compared to the general population was moderate among this cohort of long-term breast cancer survivors between 10 to 15 years since cancer diagnosis. Awareness of CVD risks is important for breast cancer survivors.

An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records.

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.

Age-Related Disease Risks in Younger versus Older B-Cell Non-Hodgkin's Lymphoma Survivors.

BACKGROUND: Younger cancer survivors may develop age-related diseases due to the cancer treatment that they undergo. The aim of this population-based study is to estimate incidence of age-related diseases besides cardiovascular disease among younger versus older B-cell non-Hodgkin's lymphoma (B-NHL) survivors compared with their respective general population cohorts. METHODS: Survivors of B-NHL were diagnosed between 1997 and 2015 from the Utah Cancer Registry. Using the Utah Population Database, up to 5 cancer-free individuals from the general population were matched with a B-NHL survivor on sex, birth year, and state of birth. Hazard ratios (HR) for age-related disease outcomes, which were identified from medical records and statewide health care facility data, were estimated using Cox Proportional Hazards models for B-NHL survivors diagnosed at <65 years versus ≥65 years at least 5 years since B-NHL diagnosis. RESULTS: Comparing 2,129 B-NHL survivors with 8,969 individuals from the general population, younger B-NHL survivors had higher relative risks of acute renal failure [HR, 2.24; 99% confidence interval (CI), 1.48-3.39; P heterogeneity = 0.017), pneumonia (HR, 2.42; 99% CI, 1.68-3.49; P heterogeneity = 0.055), and nutritional deficiencies (HR, 2.08; 99% CI, 1.48-2.92; P heterogeneity = 0.051) ≥5 years after cancer diagnosis. CONCLUSION: Younger B-NHL survivors had higher relative risks of acute renal failure, pneumonia, and nutritional deficiencies than older B-NHL survivors compared with their respective general population cohorts, ≥5 years after cancer diagnosis.

Understanding the Prevalence of Prediabetes and Diabetes in Patients With Cancer in Clinical Practice: A Real-World Cohort Study.

BACKGROUND: This study aimed to understand the prevalence of prediabetes (preDM) and diabetes mellitus (DM) in patients with cancer overall and by tumor site, cancer treatment, and time point in the cancer continuum. METHODS: This cohort study was conducted at Huntsman Cancer Institute at the University of Utah. Patients with a first primary invasive cancer enrolled in the Total Cancer Care protocol between July 2016 and July 2018 were eligible. Prevalence of preDM and DM was based on ICD code, laboratory tests for hemoglobin A1c, fasting plasma glucose, nonfasting blood glucose, or insulin prescription. RESULTS: The final cohort comprised 3,512 patients with cancer, with a mean age of 57.8 years at cancer diagnosis. Of all patients, 49.1% (n=1,724) were female. At cancer diagnosis, the prevalence of preDM and DM was 6.0% (95% CI, 5.3%-6.8%) and 12.2% (95% CI, 11.2%-13.3%), respectively. One year after diagnosis the prevalence was 16.6% (95% CI, 15.4%-17.9%) and 25.0% (95% CI, 23.6%-26.4%), respectively. At the end of the observation period, the prevalence of preDM and DM was 21.2% (95% CI, 19.9%-22.6%) and 32.6% (95% CI, 31.1%-34.2%), respectively. Patients with myeloma (39.2%; 95% CI, 32.6%-46.2%) had the highest prevalence of preDM, and those with pancreatic cancer had the highest prevalence of DM (65.1%; 95% CI, 57.0%-72.3%). Patients who underwent chemotherapy, radiotherapy, or immunotherapy had a higher prevalence of preDM and DM compared with those who did not undergo these therapies. CONCLUSIONS: Every second patient with cancer experiences preDM or DM. It is essential to foster interprofessional collaboration and to develop evidence-based practice guidelines. A better understanding of the impact of cancer treatment on the development of preDM and DM remains critical.

Endocrine and Metabolic Diseases Among Colorectal Cancer Survivors in a Population-Based Cohort.

BACKGROUND: There are an estimated 1.4 million colorectal cancer (CRC) survivors in the United States. Research on endocrine and metabolic diseases over the long term in CRC survivors is limited. Obesity is a risk factor for CRC; thus it is of interest to investigate diseases that may share this risk factor, such as diabetes, for long-term health outcomes among CRC survivors. METHODS: A total of 7114 CRC patients were identified from the Utah Population Database and matched to a general population cohort of 25 979 individuals on birth year, sex, and birth state. Disease diagnoses (assessed over three time periods of 1-5 years, 5-10 years, and >10 years) were identified using electronic medical records and statewide ambulatory and inpatient discharge data. Cox proportional hazard models were used to estimate the risk of endocrine and metabolic disease. RESULTS: Across all three time periods, risks for endocrine and metabolic diseases were statistically significantly greater for CRC survivors compared with the general population cohort. At 1-5 years postdiagnosis, CRC survivors' risk for diabetes mellitus with complications was statistically significantly elevated (hazard ratio [HR] = 1.36, 99% confidence interval [CI] = 1.09 to 1.70). CRC survivors also experienced a 40% increased risk of obesity at 1-5 years postcancer diagnosis (HR= 1.40, 99% CI= 1.66 to 2.18) and a 50% increased risk at 5-10 years postdiagnosis (HR = 1.50, 99% CI= 1.16 to 1.95). CONCLUSIONS: Endocrine and metabolic diseases were statistically significantly higher in CRC survivors throughout the follow-up periods of 1-5 years, 5-10 years, and more than 10 years postdiagnosis. As the number of CRC survivors increases, understanding the long-term trajectory is critical for improved survivorship care.

Long-term diabetes risk among endometrial cancer survivors in a population-based cohort study.

OBJECTIVE: The majority of endometrial cancer patients are overweight or obese at cancer diagnosis. Obesity is a shared risk factor for both endometrial cancer and diabetes, but it is unknown whether endometrial cancer patients have increased diabetes risks. The aim of our study was to investigate diabetes risk among endometrial cancer patients. METHODS: Endometrial cancer patients diagnosed between 1997 and 2012 in Utah (n = 2,314) were identified. Women from the general population (n = 8,583) were matched to the cancer patients on birth year and birth state. Diabetes diagnoses were identified from electronic medical records and statewide healthcare facility databases. Cox proportional hazards models were used to estimate hazard ratios for diabetes after cancer diagnosis. RESULTS: Endometrial cancer survivors had a significantly higher risk of type II diabetes when compared to women from the general population in the first year after cancer diagnosis (HR = 5.22, 95% CI = 4.05, 6.71), >1-5 years after cancer diagnosis (HR = 1.67, 95% CI = 1.31, 2.12), and >5 years after cancer diagnosis (HR = 1.65, 95% CI = 1.29, 2.11). Endometrial cancer patients who were obese at cancer diagnosis had a three-fold increase in type II diabetes risk (HR = 2.99, 95%CI = 2.59, 3.45). Although endometrial cancer patients diagnosed at distant stage had a higher risk of diabetes, cancer treatment did not appear to contribute to any diabetes risks. CONCLUSIONS: In conclusion, endometrial cancer survivors had a higher risk of diabetes than women in the general population. These results suggest that long term monitoring for diabetes is indicated for endometrial cancer survivors.

Impact of computerized provider order entry (CPOE) on length of stay and mortality.

Objective: To examine changes in patient outcome variables, length of stay (LOS), and mortality after implementation of computerized provider order entry (CPOE). Materials and Methods: A 5-year retrospective pre-post study evaluated 66 186 patients and 104 153 admissions (49 683 pre-CPOE, 54 470 post-CPOE) at an academic medical center. Generalized linear mixed statistical tests controlled for 17 potential confounders with 2 models per outcome. Results: After controlling for covariates, CPOE remained a significant statistical predictor of decreased LOS and mortality. LOS decreased by 0.90 days, P < .0001. Mortality decrease varied by model: 1 death per 1000 admissions (pre = 0.006, post = 0.0005, P < .001) or 3 deaths (pre = 0.008, post = 0.005, P < .01). Mortality and LOS decreased in medical and surgical units but increased in intensive care units. Discussion: This study examined CPOE at multiple levels. Given the inability to randomize CPOE assignment, these results may only be applicable to the local setting. Temporal trends found in this study suggest that hospital-wide implementations may have impacted nursing staff and new residents. Differences in the results were noted at the patient care unit and room levels. These differences may partly explain the mixed results from previous studies. Conclusion: Controlling for confounders, CPOE implementation remained a statistically significant predictor of LOS and mortality at this site. Mortality appears to be a sensitive outcome indicator with regard to hospital-wide implementations and should be further studied.

Value Driven Outcomes (VDO): a pragmatic, modular, and extensible software framework for understanding and improving health care costs and outcomes.

OBJECTIVE: To develop expeditiously a pragmatic, modular, and extensible software framework for understanding and improving healthcare value (costs relative to outcomes). MATERIALS AND METHODS: In 2012, a multidisciplinary team was assembled by the leadership of the University of Utah Health Sciences Center and charged with rapidly developing a pragmatic and actionable analytics framework for understanding and enhancing healthcare value. Based on an analysis of relevant prior work, a value analytics framework known as Value Driven Outcomes (VDO) was developed using an agile methodology. Evaluation consisted of measurement against project objectives, including implementation timeliness, system performance, completeness, accuracy, extensibility, adoption, satisfaction, and the ability to support value improvement. RESULTS: A modular, extensible framework was developed to allocate clinical care costs to individual patient encounters. For example, labor costs in a hospital unit are allocated to patients based on the hours they spent in the unit; actual medication acquisition costs are allocated to patients based on utilization; and radiology costs are allocated based on the minutes required for study performance. Relevant process and outcome measures are also available. A visualization layer facilitates the identification of value improvement opportunities, such as high-volume, high-cost case types with high variability in costs across providers. Initial implementation was completed within 6 months, and all project objectives were fulfilled. The framework has been improved iteratively and is now a foundational tool for delivering high-value care. CONCLUSIONS: The framework described can be expeditiously implemented to provide a pragmatic, modular, and extensible approach to understanding and improving healthcare value.

Integrating historical clinical and financial data for pharmacological research.

BACKGROUND: Retrospective research requires longitudinal data, and repositories derived from electronic health records (EHR) can be sources of such data. With Health Information Technology for Economic and Clinical Health (HITECH) Act meaningful use provisions, many institutions are expected to adopt EHRs, but may be left with large amounts of financial and historical clinical data, which can differ significantly from data obtained from newer systems, due to lack or inconsistent use of controlled medical terminologies (CMT) in older systems. We examined different approaches for semantic enrichment of financial data with CMT, and integration of clinical data from disparate historical and current sources for research. METHODS: Snapshots of financial data from 1999, 2004 and 2009 were mapped automatically to the current inpatient pharmacy catalog, and enriched with RxNorm. Administrative metadata from financial and dispensing systems, RxNorm and two commercial pharmacy vocabularies were used to integrate data from current and historical inpatient pharmacy modules, and the outpatient EHR. Data integration approaches were compared using percentages of automated matches, and effects on cohort size of a retrospective study. RESULTS: During 1999-2009, 71.52%-90.08% of items in use from the financial catalog were enriched using RxNorm; 64.95%-70.37% of items in use from the historical inpatient system were integrated using RxNorm, 85.96%-91.67% using a commercial vocabulary, 87.19%-94.23% using financial metadata, and 77.20%-94.68% using dispensing metadata. During 1999-2009, 48.01%-30.72% of items in use from the outpatient catalog were integrated using RxNorm, and 79.27%-48.60% using a commercial vocabulary. In a cohort of 16304 inpatients obtained from clinical systems, 4172 (25.58%) were found exclusively through integration of historical clinical data, while 15978 (98%) could be identified using semantically enriched financial data. CONCLUSIONS: Data integration using metadata from financial/dispensing systems and pharmacy vocabularies were comparable. Given the current state of EHR adoption, semantic enrichment of financial data and integration of historical clinical data would allow the repurposing of these data for research. With the push for HITECH meaningful use, institutions that are transitioning to newer EHRs will be able to use their older financial and clinical data for research using these methods.

Evaluating the informatics for integrating biology and the bedside system for clinical research.

BACKGROUND: Selecting patient cohorts is a critical, iterative, and often time-consuming aspect of studies involving human subjects; informatics tools for helping streamline the process have been identified as important infrastructure components for enabling clinical and translational research. We describe the evaluation of a free and open source cohort selection tool from the Informatics for Integrating Biology and the Bedside (i2b2) group: the i2b2 hive. METHODS: Our evaluation included the usability and functionality of the i2b2 hive using several real world examples of research data requests received electronically at the University of Utah Health Sciences Center between 2006 - 2008. The hive server component and the visual query tool application were evaluated for their suitability as a cohort selection tool on the basis of the types of data elements requested, as well as the effort required to fulfill each research data request using the i2b2 hive alone. RESULTS: We found the i2b2 hive to be suitable for obtaining estimates of cohort sizes and generating research cohorts based on simple inclusion/exclusion criteria, which consisted of about 44% of the clinical research data requests sampled at our institution. Data requests that relied on post-coordinated clinical concepts, aggregate values of clinical findings, or temporal conditions in their inclusion/exclusion criteria could not be fulfilled using the i2b2 hive alone, and required one or more intermediate data steps in the form of pre- or post-processing, modifications to the hive metadata, etc. CONCLUSION: The i2b2 hive was found to be a useful cohort-selection tool for fulfilling common types of requests for research data, and especially in the estimation of initial cohort sizes. For another institution that might want to use the i2b2 hive for clinical research, we recommend that the institution would need to have structured, coded clinical data and metadata available that can be transformed to fit the logical data models of the i2b2 hive, strategies for extracting relevant clinical data from source systems, and the ability to perform substantial pre- and post-processing of these data.

Efficiency of CYP2C9 genetic test representation for automated pharmacogenetic decision support.

OBJECTIVES: We investigated the suitability of representing discrete genetic test results in the electronic health record (EHR) as individual single nucleotide polymorphisms (SNPs) and as alleles, using the CYP2C9 gene and its polymorphic states, as part of a pilot study. The purpose of our investigation was to determine the appropriate level of data abstraction when reporting genetic test results in the EHR that would allow meaningful interpretation and clinical decision support based on current knowledge, while retaining sufficient information in order to enable reinterpretation of the results in the context of future discoveries. METHODS: Based on the SNP & allele models, we designed two separate lab panels within the laboratory information system, one containing SNPs and the other containing alleles, built separate rules in the clinical decision support system based on each model, and evaluated the performance of these rules in an EHR simulation environment using real-world scenarios. RESULTS: Although decision-support rules based on allele model required significantly less computational time than rules based on SNP model, no difference was observed on the total time taken to chart medication orders between rules based on these two models. CONCLUSIONS: Both, SNP- and allele-based models, can be used effectively for representing genetic test results in the EHR without impacting clinical decision support systems. While storing and reporting genetic test results as alleles allow for the construction of simpler decision-support rules, and make it easier to present these results to clinicians, SNP-based model can retain a greater amount of information that could be useful for future reinterpretation.

A clinical use case to evaluate the i2b2 Hive: predicting asthma exacerbations.

To evaluate the i2b2 Hive as a tool to query, visualize, and extract clinical data, we selected a use case from the i2b2 airways diseases driving biology project: asthma exacerbations prediction. We analyzed the cohort selection and the extraction of the clinical data used by this asthma exacerbations prediction study. The structured data included the asthma diagnosis, birthdate, age, race, sex, height, weight, and BMI. The smoking status is typically only mentioned in clinical notes, and we evaluated the Natural Language Processing (NLP) application embedded in the i2b2 NLP cell to extract the smoking status from history and physical exam reports.Querying structured data was possible with the i2b2 workbench for about half the clinical data elements. The remaining had to be queried using a commercial database management system client. The automated extraction of the smoking status reached a mean precision of 0.79 and a mean specificity of 0.90.