Limitations of Noninvasive Tests-Based Population-Level Risk Stratification Strategy for Nonalcoholic Fatty Liver Disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are highly prevalent but underdiagnosed. AIMS: We used an electronic health record data network to test a population-level risk stratification strategy using noninvasive tests (NITs) of liver fibrosis. METHODS: Data were obtained from PCORnet® sites in the East, Midwest, Southwest, and Southeast United States from patients aged [Formula: see text] 18 with or without ICD-10-CM diagnosis codes for NAFLD, NASH, and NASH-cirrhosis between 9/1/2017 and 8/31/2020. Average and standard deviations (SD) for Fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS), and Hepatic Steatosis Index (HSI) were estimated by site for each patient cohort. Sample-wide estimates were calculated as weighted averages across study sites. RESULTS: Of 11,875,959 patients, 0.8% and 0.1% were coded with NAFLD and NASH, respectively. NAFLD diagnosis rates in White, Black, and Hispanic patients were 0.93%, 0.50%, and 1.25%, respectively, and for NASH 0.19%, 0.04%, and 0.16%, respectively. Among undiagnosed patients, insufficient EHR data for estimating NITs ranged from 68% (FIB-4) to 76% (NFS). Predicted prevalence of NAFLD by HSI was 60%, with estimated prevalence of advanced fibrosis of 13% by NFS and 7% by FIB-4. Approximately, 15% and 23% of patients were classified in the intermediate range by FIB-4 and NFS, respectively. Among NAFLD-cirrhosis patients, a third had FIB-4 scores in the low or intermediate range. CONCLUSIONS: We identified several potential barriers to a population-level NIT-based screening strategy. HSI-based NAFLD screening appears unrealistic. Further research is needed to define merits of NFS- versus FIB-4-based strategies, which may identify different high-risk groups.

Associations of Community and Individual Social Determinants of Health With Medication Adherence in Patients With Atrial Fibrillation: A Retrospective Cohort Study.

Background Despite guideline-recommended use of oral anticoagulation (OAC) for stroke prevention in atrial fibrillation (AF), OAC medication adherence among patients with AF in the United States ranges from 47% to 82%. To characterize potential causes of nonadherence, we analyzed associations between community and individual social risk factors and OAC adherence for stroke prevention in AF. Methods and Results A retrospective cohort analysis of patients with AF was conducted using the IQVIA PharMetrics Plus claims data from January 2016 to June 2020, and 3-digit ZIP code-level social risk scores were calculated using American Community Survey and commercial data. Logistic regression models evaluated associations between community social determinants of health, community social risk scores for 5 domains (economic climate, food landscape, housing environment, transportation network, and health literacy), patient characteristics and comorbidities, and 2 adherence outcomes: persistence on OAC for 180 days and proportion of days covered ≥0.80 at 360 days. Of 28 779 patients with AF included in the study, 70.8% of patients were male, 94.6% were commercially insured, and the average patient age was 59.2 years. Multivariable regression found that greater health literacy risk was negatively associated with 180-day persistence (odds ratio [OR]=0.80 [95% CI, 0.76-0.83]) and 360-day proportion of days covered (OR, 0.81 [95% CI, 0.76-0.87]). Patient age and higher AF stroke risk score and AF bleeding risk scores were positively associated with both 180-day persistence and 360-day proportion of days covered. Conclusions Social risk domains, such as health literacy, may affect OAC adherence among patients with AF. Future studies should explore associations between social risk factors and nonadherence with greater geographic granularity.

Implementation and Use of Risk Evaluation and Mitigation Strategies Programs in Practice: A Scoping Review of the Literature.

BACKGROUND: Risk Evaluation and Mitigation Strategy (REMS) programs intend to improve medication safety but can add significant administrative burdens to providers and health systems. Various stakeholders have made efforts to use technology to improve REMS programs. OBJECTIVES: The objective of this scoping review is to describe studies evaluating workflows, automation, or electronic data exchange related to REMS programs. METHODS: A literature search of PubMed, Embase, and Web of Science was performed for articles published between January 2007 and July 2021. Studies were identified using the relevant Medical Subject Headings terms and related keywords. Articles must have described a workflow change and measured the impact of the workflow change. RESULTS: Of 299 citations initially identified, 7 were included in the final review after removing duplicates and articles not meeting predefined inclusion criteria. Included studies consisted of three manuscripts and four conference abstracts. Electronic health record interventions, such as customized order sets and clinical decision support alerts, were the most common strategy reported. Other strategies included developing a portal to verify REMS enrollment, requiring prescribers to contact the dispensing pharmacy, provider education, and restrictions based on the provider specialty. One study evaluated automated data exchange for REMS program processes in a mock environment. Although only three studies reported baseline metrics for comparison, all studies noted an improvement or benefit to the implemented workflow process changes. CONCLUSION: There is limited evidence describing REMS workflows, automation, and electronic data exchange. Various strategies to address REMS program requirements were reported, but no studies described the use of data exchange standards in a real-world setting despite efforts by the Food and Drug Administration and other stakeholders. Additional efforts are needed to automate REMS programs.

Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED).

BACKGROUND: Many patients with atrial fibrillation (AF) remain undiagnosed despite availability of interventions to reduce stroke risk. Predictive models to date are limited by data requirements and theoretical usage. We aimed to develop a model for predicting the 2-year probability of AF diagnosis and implement it as proof-of-concept (POC) in a production electronic health record (EHR). METHODS: We used a nested case-control design using data from the Indiana Network for Patient Care. The development cohort came from 2016 to 2017 (outcome period) and 2014 to 2015 (baseline). A separate validation cohort used outcome and baseline periods shifted 2 years before respective development cohort times. Machine learning approaches were used to build predictive model. Patients ≥ 18 years, later restricted to age ≥ 40 years, with at least two encounters and no AF during baseline, were included. In the 6-week EHR prospective pilot, the model was silently implemented in the production system at a large safety-net urban hospital. Three new and two previous logistic regression models were evaluated using receiver-operating characteristics. Number, characteristics, and CHA2DS2-VASc scores of patients identified by the model in the pilot are presented. RESULTS: After restricting age to ≥ 40 years, 31,474 AF cases (mean age, 71.5 years; female 49%) and 22,078 controls (mean age, 59.5 years; female 61%) comprised the development cohort. A 10-variable model using age, acute heart disease, albumin, body mass index, chronic obstructive pulmonary disease, gender, heart failure, insurance, kidney disease, and shock yielded the best performance (C-statistic, 0.80 [95% CI 0.79-0.80]). The model performed well in the validation cohort (C-statistic, 0.81 [95% CI 0.8-0.81]). In the EHR pilot, 7916/22,272 (35.5%; mean age, 66 years; female 50%) were identified as higher risk for AF; 5582 (70%) had CHA2DS2-VASc score ≥ 2. CONCLUSIONS: Using variables commonly available in the EHR, we created a predictive model to identify 2-year risk of developing AF in those previously without diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk.

Establishment of a Pharmacogenetics Service Focused on Optimizing Existing Pharmacogenetic Testing at a Large Academic Health Center.

Multiple groups have described strategies for clinical implementation of pharmacogenetics (PGx) that often include internal laboratory tests that are specifically developed for their implementation needs. However, many institutions are not able to follow this practice and instead must utilize external laboratories to obtain PGx testing results. As each external laboratory might have different ordering and reporting workflows, consistent reporting and storing of PGx results within the medical record can be a challenge. This might result in patient safety concerns as important PGx information might not be easily identifiable at the point of current or future prescribing. Herein, we describe initial PGx clinical implementation efforts at a large academic medical center, focusing on optimizing three different test ordering workflows and two distinct result reporting strategies. From this, we identified common issues such as variable reporting location and structure of PGx results, as well as duplicate PGx testing. We identified several opportunities to optimize our current processes, including-(1) PGx laboratory stewardship, (2) increasing visibility of PGx tests, and (3) clinician and patient education. Key to the success was the importance of engaging clinician, informatics, and pathology stakeholders, as we developed interventions to improve our PGX implementation processes.

Insulin Bolus Calculator in a Pediatric Hospital. Safety and User Perceptions.

BACKGROUND: Insulin dosing in hospitalized pediatric patients is challenging and requires dosing to be matched with the specific clinical and nutritional circumstances. We implemented a customized subcutaneous insulin bolus dose calculator tool integrated with the electronic health record to improve patient care. Here we describe this tool, its utilization and safety, and assess user satisfaction and perceptions of the tool. METHODS: Blood glucose results for all patients who received insulin with and without the calculator tool were compared to assess safety. To assess user perceptions and satisfaction, a survey was sent to all identified users who interacted with the tool during the period from May 2015 to the end of November 2015. Survey responses were summarized, mean user satisfaction calculated, and correlation of Likert scale items with overall satisfaction assessed. RESULTS: Hypoglycemia rates (2.2% and 2.9%, p = 0.17) and severe hypoglycemia rates (0.04% and 0.1%, p = 0.21) were similar for the groups that received insulin with and without the calculator tool. Overall satisfaction for all survey respondents was high (4.05, SD = 0.83). Physicians indicated a slightly higher satisfaction than nurses (4.33 versus 3.94, p = 0.04). User agreement with improvement of quality of care showed the highest correlation with overall satisfaction (r = 0.80, 95% CI 0.7 - 0.87). CONCLUSION: Implementation of an insulin calculator tool streamlined ordering and administration of insulin in a pediatric academic institution while maintaining patient safety. Users indicated high overall satisfaction with the tool.

The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials.

BACKGROUND: An increasing number of clinical trials are conducted in primary care settings. Making better use of existing data in the electronic health records to identify eligible subjects can improve efficiency of such studies. Our study aims to quantify the proportion of eligibility criteria that can be addressed with data in electronic health records and to compare the content of eligibility criteria in primary care with previous work. METHODS: Eligibility criteria were extracted from primary care studies downloaded from the UK Clinical Research Network Study Portfolio. Criteria were broken into elemental statements. Two expert independent raters classified each statement based on whether or not structured data items in the electronic health record can be used to determine if the statement was true for a specific patient. Disagreements in classification were discussed until 100 % agreement was reached. Statements were also classified based on content and the percentages of each category were compared to two similar studies reported in the literature. RESULTS: Eligibility criteria were retrieved from 228 studies and decomposed into 2619 criteria elemental statements. 74 % of the criteria elemental statements were considered likely associated with structured data in an electronic health record. 79 % of the studies had at least 60 % of their criteria statements addressable with structured data likely to be present in an electronic health record. Based on clinical content, most frequent categories were: "disease, symptom, and sign", "therapy or surgery", and "medication" (36 %, 13 %, and 10 % of total criteria statements respectively). We also identified new criteria categories related to provider and caregiver attributes (2.6 % and 1 % of total criteria statements respectively). CONCLUSIONS: Electronic health records readily contain much of the data needed to assess patients' eligibility for clinical trials enrollment. Eligibility criteria content categories identified by our study can be incorporated as data elements in electronic health records to facilitate their integration with clinical trial management systems.