Workforce readiness for pharmacogenomics and key elements for sustainment within the Veterans Health Administration.

Aim: Understanding barriers and facilitators to pharmacogenomics (PGx) implementation and how to structure a clinical program with the Veterans Health Administration (VA). Materials & methods: Healthcare provider (HCP) survey at 20 VA facilities assessing PGx knowledge/acceptance and qualitative interviews to understand how best to design and sustain a national program. Results: 186 (12% response rate) surveyed believed PGx informs drug efficacy (74.7%) and adverse events (71.0%). Low confidence in knowledge (43.0%) and ability to implement (35.4-43.5%). 23 (60.5% response rate) interviewees supported a nationally program to oversee VA education, consultation and IT resources. Prescribing HCPs should be directing local activities. Conclusion: HCPs recognize PGx value but are not prepared to implement. Healthcare systems should build system-wide programs for implementation education and support.

Development and Validation of an Algorithm for Thrombosis with Thrombocytopenia Syndrome (TTS) at Unusual Sites.

Introduction: Thrombosis with thrombocytopenia syndrome (TTS) has been reported following receipt of adenoviral vector-based COVID-19 vaccines. However, no validation studies evaluating the accuracy of International Classification of Diseases-10-Clinical Modification (ICD-10-CM)-based algorithm for unusual site TTS are available in the published literature. Methods: The purpose of this study was to assess the performance of clinical coding to 1) leverage literature review and clinical input to develop an ICD-10-CM-based algorithm to identify unusual site TTS as a composite outcome and 2) validate the algorithm against the Brighton Collaboration's interim case definition using laboratory, pathology, and imaging reports in an academic health network electronic health record (EHR) within the US Food and Drug Administration (FDA) Biologics Effectiveness and Safety (BEST) Initiative. Validation of up to 50 cases per thrombosis site was conducted, with positive predictive values (PPV) and 95% confidence intervals (95% CI) calculated using pathology or imaging results as the gold standard. Results: The algorithm identified 278 unusual site TTS cases, of which 117 (42.1%) were selected for validation. In both the algorithm-identified and validation cohorts, over 60% of patients were 56 years or older. The positive predictive value (PPV) for unusual site TTS was 76.1% (95% CI 67.2-83.2%) and at least 80% for all but one individual thrombosis diagnosis code. PPV for thrombocytopenia was 98.3% (95% CI 92.1-99.5%). Discussion: This study represents the first report of a validated ICD-10-CM-based algorithm for unusual site TTS. A validation effort found that the algorithm performed at an intermediate-to-high PPV, suggesting that the algorithm can be used in observational studies including active surveillance of COVID-19 vaccines and other medical products.

Characterization of COVID-19 Hospitalized Patients in Three United States Electronic Health Record Databases.

COVID-19 infections have contributed to substantial increases in hospitalizations. This study describes demographics, baseline clinical characteristics and treatments, and clinical outcomes among U.S. patients admitted to hospitals with COVID-19 during the prevaccine phase of the pandemic. A total of 20,446 hospitalized patients with a positive COVID-19 nucleic acid amplification test were identified from three large electronic health record databases during 5 February-30 November 2020 (Academic Health System: n = 4504; Explorys; n = 7492; OneFlorida: n = 8450). Over 90% of patients were ≥30 years of age, with an even distribution between sexes. At least one comorbidity was recorded in 84.6-96.1% of patients; cardiovascular and respiratory conditions (28.8-50.3%) and diabetes (25.6-44.4%) were most common. Anticoagulants were the most frequently reported medications on or up to 28 days after admission (44.5-81.7%). Remdesivir was administered to 14.1-24.6% of patients and increased over time. Patients exhibited higher COVID-19 severity 14 days following admission than the 14 days prior to and on admission. The length of in-patient hospital stay ranged from a median of 4 to 6 days, and over 85% of patients were discharged alive. These results promote understanding of the clinical characteristics and hospital-resource utilization associated with hospitalized COVID-19 over time.

Immune globulin usage trends in commercially insured and Medicare populations, 2009-2019.

BACKGROUND: Longitudinal patterns of immune globulins (IG) use have not been described in large populations. Understanding IG usage is important given potential supply limitations impacting individuals for whom IG is the sole life-saving/health-preserving therapy. The study describes US IG utilization patterns from 2009 to 2019. STUDY DESIGN AND METHODS: Using IBM MarketScan commercial and Medicare claims data, we examined four metrics overall and by condition-specific categories during 2009-2019: (1) IG administrations per 100,000 person-years, (2) IG recipients per 100,000 enrollees, (3) average annual administrations per recipient, and (4) average annual dose per recipient. RESULTS: In the commercial and Medicare populations respectively: IG administrations per 100,000 person-years increased by 120% (213-470) and 144% (692-1693); IG recipients per 100,000 enrollees grew by 71% (24-42) and 102% (89-179); average annual administrations per recipient rose by 28% (8-10) and 19% (8-9); and average annual dose (grams) per recipient increased by 29% (384-497) and 34% (317-426). IG administrations associated with immunodeficiency (per 100,000 person-years) increased by 154% (from 127 to 321) and 176% (from 365 to 1007). Autoimmune and neurologic conditions were associated with higher annual average administrations and dose than other conditions. DISCUSSION: IG use increased, coinciding with a growth in the IG recipient population in the United States. Several conditions contributed to the trend, with the largest increase observed among immunodeficient individuals. Future investigations should assess changes in the demand for IVIG by disease state or indication and consider treatment effectiveness.

Background rates of adverse events of special interest for COVID-19 vaccine safety monitoring in the United States, 2019-2020.

BACKGROUND: The U.S. Food and Drug Administration (FDA) Biologics Effectiveness and Safety (BEST) Initiative conducts active surveillance of adverse events of special interest (AESI) after COVID-19 vaccination. Historical incidence rates (IRs) of AESI are comparators to evaluate safety. METHODS: We estimated IRs of 17 AESI in six administrative claims databases from January 1, 2019, to December 11, 2020: Medicare claims for adults ≥ 65 years and commercial claims (Blue Health Intelligence®, CVS Health, HealthCore Integrated Research Database, IBM® MarketScan® Commercial Database, Optum pre-adjudicated claims) for adults < 65 years. IRs were estimated by sex, age, race/ethnicity (Medicare), and nursing home residency (Medicare) in 2019 and for specific periods in 2020. RESULTS: The study included >100 million enrollees annually. In 2019, rates of most AESI increased with age. However, compared with commercially insured adults, Medicare enrollees had lower IRs of anaphylaxis (11 vs 12-19 per 100,000 person-years), appendicitis (80 vs 117-155), and narcolepsy (38 vs 41-53). Rates were higher in males than females for most AESI across databases and varied by race/ethnicity and nursing home status (Medicare). Acute myocardial infarction (Medicare) and anaphylaxis (all databases) IRs varied by season. IRs of most AESI were lower during March-May 2020 compared with March-May 2019 but returned to pre-pandemic levels after May 2020. However, rates of Bell's palsy, Guillain-Barré syndrome, narcolepsy, and hemorrhagic/non-hemorrhagic stroke remained lower in multiple databases after May 2020, whereas some AESI (e.g., disseminated intravascular coagulation) exhibited higher rates after May 2020 compared with 2019. CONCLUSION: AESI background rates varied by database and demographics and fluctuated in March-December 2020, but most returned to pre-pandemic levels after May 2020. It is critical to standardize demographics and consider seasonal and other trends when comparing historical rates with post-vaccination AESI rates in the same database to evaluate COVID-19 vaccine safety.

Assessment of performance characteristics of COVID-19 ICD-10-CM diagnosis code U07.1 using SARS-CoV-2 nucleic acid amplification test results.

The Food and Drug Administration's Biologics Effectiveness and Safety Initiative conducts active surveillance to protect public health during the coronavirus disease 2019 (COVID-19) pandemic. This study evaluated performance of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code U07.1 in identifying COVID-19 cases in claims compared with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid amplification test results in linked electronic health records (EHRs). Care episodes in three populations were defined using COVID-19-related diagnoses (population 1), SARS-CoV-2 nucleic acid amplification test procedures (population 2), and all-cause hospitalizations (population 3) in two linked claims-EHR databases: IBM® MarketScan® Explorys® Claims-EMR Data Set (commercial) and OneFlorida Data Trust linked Medicaid-EHR. Positive and negative predictive values were calculated. Respectively, populations 1, 2, and 3 included 26,686, 26,095, and 2,564 episodes (commercial) and 29,117, 23,412, and 9,629 episodes (Florida Medicaid). The positive predictive value was >80% and the negative predictive value was >95% in each population, with the highest positive predictive value in population 3 (commercial: 91.9%; Medicaid: 93.1%). Findings did not vary substantially by patient age. Positive predictive values in populations 1 and 2 fluctuated during April-June 2020. They then stabilized in the commercial but not the Medicaid population. Negative predictive values were consistent over time in all populations and databases. Our findings indicate that U07.1 has high performance in identifying COVID-19 cases and noncases in claims databases. Performance may vary across populations and periods.

Characterization of test positivity among patients with coronavirus disease 2019 (COVID-19) in three electronic health records databases, February-November 2020.

BACKGROUND: Monitoring COVID-19 testing volumes and test positivity is an integral part of the response to the pandemic. We described the characteristics of individuals who were tested and tested positive for SARS-CoV-2 during the pre-vaccine phase of the pandemic in the United States (U.S.). METHODS: This descriptive study analyzed three U.S. electronic health record (EHR) databases (Explorys, Academic Health System, and OneFlorida) between February and November 2020, identifying patients who received an interpretable nucleic acid amplification test (NAAT) result. Test-level data were used to characterize the settings in which tests were administered. Patient-level data were used to calculate test positivity rates and characterize the demographics, comorbidities, and hospitalization rates of COVID-19-positive patients. RESULTS: Over 40% of tests were conducted in outpatient care settings, with a median time between test order and result of 0-1 day for most settings. Patients tested were mostly female (55.6-57.7%), 18-44 years of age (33.9-41.2%), and Caucasian (44.0-66.7%). The overall test positivity rate was 13.0% in Explorys, 8.0% in Academic Health System, and 8.9% in OneFlorida. The proportion of patients hospitalized within 14 days of a positive COVID-19 NAAT result was 24.2-33.1% across databases, with patients over 75 years demonstrating the highest hospitalization rates (46.7-69.7% of positive tests). CONCLUSIONS: This analysis of COVID-19 testing volume and positivity patterns across three large EHR databases provides insight into the characteristics of COVID-19-tested, COVID-19-test-positive, and hospitalized COVID-19-test-positive patients during the early phase of the pandemic in the U.S.

Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database.

INTRODUCTION: Pregnancy outcome identification and precise estimates of gestational age (GA) are critical in drug safety studies of pregnant women. Validated pregnancy outcome algorithms based on the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) have not previously been published. METHODS: We developed algorithms to classify pregnancy outcomes and estimate GA using ICD-10-CM/PCS and service codes on claims in the 2016-2018 IBM® MarketScan® Explorys® Claims-EMR Data Set and compared the results with ob-gyn adjudication of electronic medical records (EMRs). Obstetric services were grouped into episodes using hierarchical and spacing requirements. GA was based on evidence with the highest clinical accuracy. Among pregnancies with obstetric EMRs, 100 full-term live births (FTBs), 100 preterm live births (PTBs), 100 spontaneous abortions (SAs), and 24 stillbirths were selected for review. Physicians adjudicated cases using Global Alignment of Immunization safety Assessment in pregnancy (GAIA) definitions applied to structured EMRs. RESULTS: The claims-based algorithms identified 34,204 pregnancies, of which 9.9% had obstetric EMRs. Of sampled pregnancies, 92 FTBs, 93 PTBs, 75 SAs, and 24 stillbirths were adjudicated. Among these pregnancies, the percent agreement was 97.8%, 62.4%, 100.0%, and 70.8% for FTBs, PTBs, SAs, and stillbirths, respectively. The percent agreement on GA within 7 and 28 days, respectively, was 85.9% and 100.0% for FTBs, 81.7% and 98.9% for PTBs, 61.3% and 94.7% for SAs, and 66.7% and 79.2% for stillbirths. CONCLUSIONS: The pregnancy outcome algorithms had high agreement with physician adjudication of EMRs and may inform post-market maternal safety surveillance.

Vaccine exposure during pregnancy among privately and publicly insured women in the United States, 2016-2018.

BACKGROUND: Vaccine use during pregnancy affects maternal and infant health. Many women do not receive vaccines recommended during pregnancy; conversely, inadvertent exposure to vaccines contraindicated or not recommended during pregnancy may occur. We assessed exposure to two recommended vaccines and two vaccines not recommended during pregnancy among privately and Medicaid-insured women in the United States. METHODS: This study includes a retrospective cohort of pregnancies in women aged 12-55 years resulting in live birth, spontaneous abortion, or stillbirth identified in the IBM® MarketScan® Commercial, Blue Health Intelligence® (BHI®) Commercial, and IBM MarketScan Multi-State Medicaid Databases from August 1, 2016, to December 31, 2018. Gestational age at vaccination was determined using a validated algorithm. We examined vaccines (1) recommended by the Centers for Disease Control and Prevention Advisory Committee on Immunization Practices (ACIP) (tetanus, diphtheria, and acellular pertussis [Tdap]; inactivated influenza) and (2) not recommended (human papillomavirus [HPV]) or contraindicated (measles, mumps, and rubella [MMR]). RESULTS: We identified 496,771 (MarketScan Commercial), 858,961 (BHI), and 289,573 (MarketScan Medicaid) pregnancies (approximately 75% aged 20-34 years). Across these three databases, 52.1%, 50.3%, and 31.3% of pregnancies, respectively, received Tdap, most often at a gestational age of 28 weeks, and influenza vaccination occurred in 32.1%, 30.8%, and 18.0% of pregnancies, respectively. HPV vaccination occurred in < 0.2% of pregnancies, mostly in the first trimester among women aged 12-19 years, and MMR was administered in < 0.1% of pregnancies. Use of other contraindicated vaccines per ACIP (e.g., varicella, live attenuated influenza) was rare. CONCLUSION: Maternal vaccination with ACIP-recommended vaccines was suboptimal among privately and Medicaid-insured patients, with lower vaccination coverage among Medicaid-insured pregnancies than their privately insured counterparts. Inadvertent exposure to contraindicated vaccines during pregnancy was rare. This study evaluated only vaccinations reimbursed among insured populations and may have limited generalizability to uninsured populations.

Acute myocardial infarction: Development and application of an ICD-10-CM-based algorithm to a large U.S. healthcare claims-based database.

BACKGROUND: Healthcare administrative claims data hold value for monitoring drug safety and assessing drug effectiveness. The U.S. Food and Drug Administration Biologics Effectiveness and Safety Initiative (BEST) is expanding its analytical capacity by developing claims-based definitions-referred to as algorithms-for populations and outcomes of interest. Acute myocardial infarction (AMI) was of interest due to its potential association with select biologics and the lack of an externally validated International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) algorithm. OBJECTIVE: Develop and apply an ICD-10-CM-based algorithm in a U.S. administrative claims database to identify and characterize AMI populations. METHODS: A comprehensive literature review was conducted to identify validated AMI algorithms. Building on prior published methodology and consistent application of ICD-9-CM codes, an ICD-10-CM algorithm was developed via forward-backward mapping using General Equivalence Mappings and refined with clinical input. An AMI population was then identified in the IBM® MarketScan® Research Databases and characterized using descriptive statistics. RESULTS AND DISCUSSION: Between 2014-2017, 2.83-3.16 individuals/1,000 enrollees/year received ≥1 AMI diagnosis in any healthcare setting. The 2015 transition to ICD-10-CM did not result in a substantial change in the proportion of patients identified. Average patient age at first AMI diagnosis was 64.9 years, and 61.4% of individuals were male. Unspecified chest pain, hypertension, and coronary atherosclerosis of native coronary vessel/artery were most commonly reported within one day of AMI diagnosis. Electrocardiograms were the most common medical procedure and beta-blockers were the most commonly ordered cardiac medication in the one day before to 14 days following AMI diagnosis. The mean length of inpatient stay was 5.6 days (median 3 days; standard deviation 7.9 days). Findings from this ICD-10-CM-based AMI study were internally consistent with ICD-9-CM-based findings and externally consistent with ICD-9-CM-based studies, suggesting that this algorithm is ready for validation in future studies.

Electronic phenotyping of health outcomes of interest using a linked claims-electronic health record database: Findings from a machine learning pilot project.

OBJECTIVE: Claims-based algorithms are used in the Food and Drug Administration Sentinel Active Risk Identification and Analysis System to identify occurrences of health outcomes of interest (HOIs) for medical product safety assessment. This project aimed to apply machine learning classification techniques to demonstrate the feasibility of developing a claims-based algorithm to predict an HOI in structured electronic health record (EHR) data. MATERIALS AND METHODS: We used the 2015-2019 IBM MarketScan Explorys Claims-EMR Data Set, linking administrative claims and EHR data at the patient level. We focused on a single HOI, rhabdomyolysis, defined by EHR laboratory test results. Using claims-based predictors, we applied machine learning techniques to predict the HOI: logistic regression, LASSO (least absolute shrinkage and selection operator), random forests, support vector machines, artificial neural nets, and an ensemble method (Super Learner). RESULTS: The study cohort included 32 956 patients and 39 499 encounters. Model performance (positive predictive value [PPV], sensitivity, specificity, area under the receiver-operating characteristic curve) varied considerably across techniques. The area under the receiver-operating characteristic curve exceeded 0.80 in most model variations. DISCUSSION: For the main Food and Drug Administration use case of assessing risk of rhabdomyolysis after drug use, a model with a high PPV is typically preferred. The Super Learner ensemble model without adjustment for class imbalance achieved a PPV of 75.6%, substantially better than a previously used human expert-developed model (PPV = 44.0%). CONCLUSIONS: It is feasible to use machine learning methods to predict an EHR-derived HOI with claims-based predictors. Modeling strategies can be adapted for intended uses, including surveillance, identification of cases for chart review, and outcomes research.

Kilauea lava fuels phytoplankton bloom in the North Pacific Ocean.

From June to August 2018, the eruption of Kilauea volcano on the island of Hawai'i injected millions of cubic meters of molten lava into the nutrient-poor waters of the North Pacific Subtropical Gyre. The lava-impacted seawater was characterized by high concentrations of metals and nutrients that stimulated phytoplankton growth, resulting in an extensive plume of chlorophyll a that was detectable by satellite. Chemical and molecular evidence revealed that this biological response hinged on unexpectedly high concentrations of nitrate, despite the negligible quantities of nitrogen in basaltic lava. We hypothesize that the high nitrate was caused by buoyant plumes of nutrient-rich deep waters created by the substantial input of lava into the ocean. This large-scale ocean fertilization was therefore a unique perturbation event that revealed how marine ecosystems respond to exogenous inputs of nutrients.