Sex Differences in Diagnosis and Treatment Timing of Comorbid Depression/Anxiety and Disease Subtypes in Patients With ADHD: A Database Study.

OBJECTIVE: Our study examined the impact of sex, ADHD subtype, and comorbid illnesses (depression/anxiety) on the timing of diagnosis and treatment for ADHD. METHOD: To analyze ADHD patients, four health databases were used to assess subtype, comorbid mood, and antidepressant or anxiolytic drug exposure. Analyses were stratified by sex and age. Standardized mean differences measured intergroup differences. RESULTS: Females with ADHD were identified at older ages and had higher rates of depression and anxiety diagnoses and treatments before and after their initial ADHD diagnosis. Predominantly inattentive ADHD patients were diagnosed later and more likely to receive mood disorder diagnosis and treatment than hyperactive impulsive ADHD patients. CONCLUSIONS: Results suggest a more complex ADHD presentation in females, potentially causing late diagnosis and delayed treatment.

Evaluating the impact of alternative phenotype definitions on incidence rates across a global data network.

Objective: Developing accurate phenotype definitions is critical in obtaining reliable and reproducible background rates in safety research. This study aims to illustrate the differences in background incidence rates by comparing definitions for a given outcome. Materials and Methods: We used 16 data sources to systematically generate and evaluate outcomes for 13 adverse events and their overall background rates. We examined the effect of different modifications (inpatient setting, standardization of code set, and code set changes) to the computable phenotype on background incidence rates. Results: Rate ratios (RRs) of the incidence rates from each computable phenotype definition varied across outcomes, with inpatient restriction showing the highest variation from 1 to 11.93. Standardization of code set RRs ranges from 1 to 1.64, and code set changes range from 1 to 2.52. Discussion: The modification that has the highest impact is requiring inpatient place of service, leading to at least a 2-fold higher incidence rate in the base definition. Standardization showed almost no change when using source code variations. The strength of the effect in the inpatient restriction is highly dependent on the outcome. Changing definitions from broad to narrow showed the most variability by age/gender/database across phenotypes and less than a 2-fold increase in rate compared to the base definition. Conclusion: Characterization of outcomes across a network of databases yields insights into sensitivity and specificity trade-offs when definitions are altered. Outcomes should be thoroughly evaluated prior to use for background rates for their plausibility for use across a global network.

European Health Data & Evidence Network-learnings from building out a standardized international health data network.

OBJECTIVE: Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful. MATERIALS AND METHODS: Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data. Three surveys, DataQualityDashboard results, and statistics from the conversion process were analyzed qualitatively and quantitatively. Our measures of success were the total number of days to transform source data into the OMOP CDM and participation in network research. RESULTS: The health data converted to CDM represented more than 133 million patients. 100%, 88%, and 84% of DPs took Surveys 1, 2, and 3. The median duration of the 6 key extract, transform, and load (ETL) processes ranged from 4 to 115 days. Of the 25 DPs, 21 DPs were considered applicable for analysis of which 52% standardized their data on time, and 48% participated in an international collaborative study. DISCUSSION: This study shows that the consistent workflow used by EHDEN proves appropriate to support the successful standardization of observational data across Europe. Over the 25 successful transformations, we confirmed that getting the right people for the ETL is critical and vocabulary mapping requires specific expertise and support of tools. Additionally, we learned that teams that proactively prepared for data governance issues were able to avoid considerable delays improving their ability to finish on time. CONCLUSION: This study provides guidance for future DPs to standardize to the OMOP CDM and participate in distributed networks. We demonstrate that the Observational Health Data Sciences and Informatics community must continue to evaluate and provide guidance and support for what ultimately develops the backbone of how community members generate evidence.

Transforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP Common Data Model and Its Use for COVID-19 Research.

Purpose: The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population. Patients and Methods: We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022. Results: After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died. Conclusion: We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond.

Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study.

Background: Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population. Methods: A multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study's evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain- Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia. Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases. Findings: Substantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1.32 (1.05 to 1.66) for narcolepsy to 11.70 (10.10 to 13.70) for pulmonary embolism. Interpretation: Our findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so. More research is needed to contextualise post-COVID-19 complications in the longer term. Funding: None.

Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study.

OBJECTIVE: Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations' variability on patient characteristics. MATERIALS AND METHODS: Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams' cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. RESULTS: On average, the teams' interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts' size varied from one-third of the master cohort size to 10 times the cohort size (2159-63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3-16.2%). The teams' cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. CONCLUSIONS: Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.

Social determinants of health associated with COVID-19 severity during pregnancy: a multinational cohort study (in the International Registry of Coronavirus Exposure in Pregnancy).

BACKGROUND: The COVID-19 pandemic has caused morbidity and mortality, particularly among vulnerable populations. We aimed to assess social and demographic characteristics associated with COVID-19 severity among symptomatic participants during pregnancy. METHODS: The International Registry of Coronavirus Exposure in Pregnancy is a multinational, longitudinal observational cohort study of adult participants tested for SARS-CoV-2 or who received clinical diagnosis of COVID-19 during pregnancy (NCT04366986). Disease severity status of mild, moderate, or severe was determined based on symptoms and healthcare utilization. Stratified by current versus recent pregnancy at enrollment, univariate mixed-effects logistic regression modeling was used to characterize association between social and demographic characteristics with COVID-19 severity, using a cumulative mixed effect model with country as a random effect. RESULTS: The odds of developing more severe COVID-19 (odds ratio [95% confidence interval]) were higher among participants with lower socioeconomic status (poor: 2.72 [2.01,3.69]; lower-middle class: 2.07 [1.62,2.65] vs wealthy), among participants with lower educational attainment (high school: 1.68 [1.39,2.03]; < high school (1.77 [1.25,2.51] vs graduate education). Participants over 25 years of age had lower odds of severe COVID-19 versus participants < 25 years (25-34: 0.69 [0.56,0.85]; 35-50: 0.62 [0.48,0.80]). Employment in food services was also associated with increased odds of more severe COVID-19, whereas employment in healthcare and within home, and primiparity were associated with lower severity. CONCLUSIONS: Findings suggest that employment setting and economic status have strong associations with COVID-19 severity, which warrants considering social determinants of health in the context of assessing risk factors of more severe COVID-19 during pregnancy. TRIAL REGISTRATION: IRCEP was registered with the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) [EUPAS37360] and clinicaltrials.gov [NCT04366986].

Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDMs) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500 000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM. MATERIALS AND METHODS: We converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHRs) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data. RESULTS: We identified 502 505 participants (3086 with COVID-19) and transformed 690 fields (1 373 239 555 rows) to the OMOP CDM using 8 different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported noncancer illnesses 946 053 (83.91% of all source entries), cancers 37 802 (70.81%), medications 1 218 935 (88.25%), and prescriptions 864 788 (86.96%). In EHR, we transformed 13 028 182 (99.95%) hospital diagnoses, 6 465 399 (89.2%) procedures, 337 896 333 primary care diagnoses (CTV3, SNOMED-CT), 139 966 587 (98.74%) prescriptions (dm+d) and 77 127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data. DISCUSSION AND CONCLUSION: Our study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK Biobank resource is a valuable tool that can enable federated research, like COVID-19 studies.

Hip Fracture Risk After Treatment with Tramadol or Codeine: An Observational Study.

INTRODUCTION: Hip fractures among older people are a major public health issue, which can impact quality of life and increase mortality within the year after they occur. A recent observational study found an increased risk of hip fracture in subjects who were new users of tramadol compared with codeine. These drugs have somewhat different indications. Tramadol is indicated for moderate to severe pain and can be used for an extended period; codeine is indicated for mild to moderate pain and cough suppression. OBJECTIVE: In this observational study, we compared the risk of hip fracture in new users of tramadol or codeine, using multiple databases and analytical methods. METHODS: Using data from the Clinical Practice Research Datalink and three US claims databases, we compared the risk of hip fracture after exposure to tramadol or codeine in subjects aged 50-89 years. To ensure comparability, large-scale propensity scores were used to adjust for confounding. RESULTS: We observed a calibrated hazard ratio of 1.10 (95% calibrated confidence interval 0.99-1.21) in the Clinical Practice Research Datalink database, and a pooled estimate across the US databases yielded a calibrated hazard ratio of 1.06 (95% calibrated confidence interval 0.97-1.16). CONCLUSIONS: Our results did not demonstrate a statistically significant difference between subjects treated for pain with tramadol compared with codeine for the outcome of hip fracture risk.

Baseline risk characterization of early versus later adopters of long-acting paliperidone palmitate formulations.

Early Post-Marketing Phase Vigilance (EPPV) is a unique system that encourages reporting of serious adverse reactions for medications newly introduced to Japan. When a once-monthly paliperidone palmitate formulation (PP1M) was introduced in Japan in 2013, EPPV detected a signal of increased mortality, but this signal was not subsequently confirmed. To clarify whether that signal reflected increased adverse event reporting or an atypically high baseline mortality risk among early adopters of PP1M, we evaluated the baseline risk characteristics of early, mid, and later adopters of PP1M in a Japanese database and did a similar evaluation of PP1M and the three-monthly formulation (PP3M) in two US databases. In Japan, early adopters compared with later adopters were older (mean 39.16 vs 33.70 years) but had a lower proportion of male patients (32.0% vs 44.44%), and a lower mean number of antipsychotic medications (distinct active medical substances) other than paliperidone (2.62 vs 2.85). In the United States, the baseline characteristics of early adopters of PP1M and PP3M did not suggest higher mortality risk than later adopters. These results offer no convincing evidence that the unconfirmed early signal of increased mortality with PP1M was due to increased baseline mortality risk among early adopters.

Phenotype Algorithms for the Identification and Characterization of Vaccine-Induced Thrombotic Thrombocytopenia in Real World Data: A Multinational Network Cohort Study.

INTRODUCTION: Vaccine-induced thrombotic thrombocytopenia (VITT) has been identified as a rare but serious adverse event associated with coronavirus disease 2019 (COVID-19) vaccines. OBJECTIVES: In this study, we explored the pre-pandemic co-occurrence of thrombosis with thrombocytopenia (TWT) using 17 observational health data sources across the world. We applied multiple TWT definitions, estimated the background rate of TWT, characterized TWT patients, and explored the makeup of thrombosis types among TWT patients. METHODS: We conducted an international network retrospective cohort study using electronic health records and insurance claims data, estimating background rates of TWT amongst persons observed from 2017 to 2019. Following the principles of existing VITT clinical definitions, TWT was defined as patients with a diagnosis of embolic or thrombotic arterial or venous events and a diagnosis or measurement of thrombocytopenia within 7 days. Six TWT phenotypes were considered, which varied in the approach taken in defining thrombosis and thrombocytopenia in real world data. RESULTS: Overall TWT incidence rates ranged from 1.62 to 150.65 per 100,000 person-years. Substantial heterogeneity exists across data sources and by age, sex, and alternative TWT phenotypes. TWT patients were likely to be men of older age with various comorbidities. Among the thrombosis types, arterial thrombotic events were the most common. CONCLUSION: Our findings suggest that identifying VITT in observational data presents a substantial challenge, as implementing VITT case definitions based on the co-occurrence of TWT results in large and heterogeneous incidence rate and in a cohort of patints with baseline characteristics that are inconsistent with the VITT cases reported to date.

Medications for attention-deficit/hyperactivity disorder in Japan: A retrospective cohort study of label compliance.

AIM: To assess label compliance in prescription of medications approved for treatment of attention-deficit/hyperactivity disorder (ADHD) in Japan at the time of this study: methylphenidate (MPH), atomoxetine, and guanfacine. METHODS: Retrospective descriptive study was conducted in prevalent-user cohorts from the Japan Medical Data Center database. Patients who were prescribed a study drug between January 1, 2013 and September 30, 2018 and were in the database for ≥30 days were included. A prescription was considered compliant if all 4 criteria were satisfied: appropriate age, daily dose not exceeding the approved maximum, no contraindicated concurrent medications, and no contraindicated conditions. RESULTS: Among 17 418 patients who were prescribed a study drug during 2013-2018, 73% were male and 53% were children (aged <18 years). Fewer than 2% of prescriptions were for patients outside the approved age, 10%-13% of patients in the atomoxetine and MPH cohorts received ≥1 prescription exceeding maximum approved dose, no patients were co-prescribed a contraindicated medication, and 16%-18% of patients in the MPH cohorts had ≥1 contraindicated condition. During their first 500 days of use, for approximately 73%-86% of patients, all prescriptions were compliant with all label requirements. CONCLUSIONS: Among patients exposed to ADHD medications in Japan during 2013-2018, nearly all prescriptions for these medications were label-compliant for age. For >85% of patients, all prescriptions were label-compliant for dose, and for approximately 80%, all prescriptions were label-compliant for contraindicated conditions. We did not find evidence of widespread abuse or noncompliant use of prescribed ADHD medications.

Understanding patient journey in ulcerative colitis prior to biologic initiation: a 5-year exploration.

BACKGROUND: There has been a more pronounced shift toward earlier, more aggressive therapies in Crohn's disease than in ulcerative colitis (UC). The aim of this study was to describe the pre-biologic treatment and health care experience, including co-morbidities and overall health care utilization, for UC patients who initiated biologic therapies, in the 5 years prior to the initiation of the first biologic agent. METHODS: UC patients who initiated a biologic agent approved for UC between 9/15/2005 and 1/30/2018 were identified from the IBM® MarketScan® Commercial Database, a large US database. The date of the first recorded UC biologic exposure was defined as the index date, and ≥ 5 years of pre-index records were required to evaluate patients' treatment, disease progression and overall health care utilization prior to initiating biologic agents. RESULTS: Among the 1891 eligible patients, treatment with oral corticosteroids, 5-aminosalicylates, and other non-biologic immunomodulators, all increased progressively across the 5 years prior to the index. From within year-five to within year-one prior to the index, the median duration of oral corticosteroid treatment increased from 34 to 88 days per year and the proportion of patients who experienced more extensive/pancolitis disease increased from 16 to 59%. Overall, the frequency of all-cause health care visits also increased. CONCLUSIONS: Patients with UC experienced increasing morbidity and treatment burden in the 5 years prior to initiating biologic therapy. To achieve reduced corticosteroids in UC management, better risk stratification is needed to help identify patients for more timely biologic treatment.

Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network.

BACKGROUND AND PURPOSE: Hemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis. Thus, identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction. The purpose of this study was to develop and validate a model to predict a patient's risk of HT within 30 days of initial ischemic stroke. METHODS: We utilized a retrospective multicenter observational cohort study design to develop a Lasso Logistic Regression prediction model with a large, US Electronic Health Record dataset which structured to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). To examine clinical transportability, the model was externally validated across 10 additional real-world healthcare datasets include EHR records for patients from America, Europe and Asia. RESULTS: In the database the model was developed, the target population cohort contained 621,178 patients with ischemic stroke, of which 5,624 patients had HT within 30 days following initial ischemic stroke. 612 risk predictors, including the distance a patient travels in an ambulance to get to care for a HT, were identified. An area under the receiver operating characteristic curve (AUC) of 0.75 was achieved in the internal validation of the risk model. External validation was performed across 10 databases totaling 5,515,508 patients with ischemic stroke, of which 86,401 patients had HT within 30 days following initial ischemic stroke. The mean external AUC was 0.71 and ranged between 0.60-0.78. CONCLUSIONS: A HT prognostic predict model was developed with Lasso Logistic Regression based on routinely collected EMR data. This model can identify patients who have a higher risk of HT than the population average with an AUC of 0.78. It shows the OMOP CDM is an appropriate data standard for EMR secondary use in clinical multicenter research for prognostic prediction model development and validation. In the future, combining this model with clinical information systems will assist clinicians to make the right therapy decision for patients with acute ischemic stroke.

Diabetic ketoacidosis in patients with type 2 diabetes treated with sodium glucose co-transporter 2 inhibitors versus other antihyperglycemic agents: An observational study of four US administrative claims databases.

PURPOSE: To compare the incidence of diabetic ketoacidosis (DKA) among patients with type 2 diabetes mellitus (T2DM) who were new users of sodium glucose co-transporter 2 inhibitors (SGLT2i) versus other classes of antihyperglycemic agents (AHAs). METHODS: Patients were identified from four large US claims databases using broad (all T2DM patients) and narrow (intended to exclude patients with type 1 diabetes or secondary diabetes misclassified as T2DM) definitions of T2DM. New users of SGLT2i and seven groups of comparator AHAs were matched (1:1) on exposure propensity scores to adjust for imbalances in baseline covariates. Cox proportional hazards regression models, conditioned on propensity score-matched pairs, were used to estimate hazard ratios (HRs) of DKA for new users of SGLT2i versus other AHAs. When I2 <40%, a combined HR across the four databases was estimated. RESULTS: Using the broad definition of T2DM, new users of SGLT2i had an increased risk of DKA versus sulfonylureas (HR [95% CI]: 1.53 [1.31-1.79]), DPP-4i (1.28 [1.11-1.47]), GLP-1 receptor agonists (1.34 [1.12-1.60]), metformin (1.31 [1.11-1.54]), and insulinotropic AHAs (1.38 [1.15-1.66]). Using the narrow definition of T2DM, new users of SGLT2i had an increased risk of DKA versus sulfonylureas (1.43 [1.01-2.01]). New users of SGLT2i had a lower risk of DKA versus insulin and a similar risk as thiazolidinediones, regardless of T2DM definition. CONCLUSIONS: Increased risk of DKA was observed for new users of SGLT2i versus several non-SGLT2i AHAs when T2DM was defined broadly. When T2DM was defined narrowly to exclude possible misclassified patients, an increased risk of DKA with SGLT2i was observed compared with sulfonylureas.

Liver Test Monitoring: Real-World Compliance for Drugs with Monitoring Requirements at 2-Week Intervals or More Frequently.

BACKGROUND: Monitoring risk is often an important component of therapy. Some compounds require liver test (LT) monitoring, with the frequency detailed in the product label. Compliance with these instructions is generally unknown. OBJECTIVE: The goal of this short study was to describe LT compliance for compounds with monitoring recommended at 2-week intervals or more frequently in three US administrative claims databases. METHODS: The sample was drawn from three US claims databases during the period 1 January 2015 through 30 June 2018. This study examined nine compounds and five types of LTs. We looked at compounds in a published list of drugs requiring LTs at 2-week intervals or more frequently. Descriptive statistics about the days between tests were reported, as were the number and proportion of tests associated with each drug that met the recommended frequency. RESULTS: Compliance was < 33% with four drugs (ketoconazole, succimer, pentamidine, and felbamate) and > 60% with five drugs (oxaliplatin, rifampin, tolcapone, albendazole, and azathioprine). Among drugs with more than 1000 drug eras observed (all but succimer and tolcapone), LT compliance was highest for oxaliplatin (75.3%) and lowest for pentamidine (20.6%), with little difference in overall compliance by type of test (range 41-46). CONCLUSION: Compliance with frequent LT monitoring differed for the drugs examined. Two strata were found: compliance > 60% (oxaliplatin, rifampin, tolcapone, albendazole, and azathioprine) and compliance 20-30% (ketoconazole, succimer, pentamidine, and felbamate). No drug reached 80% compliance.

Risk Prediction for Ischemic Stroke and Transient Ischemic Attack in Patients Without Atrial Fibrillation: A Retrospective Cohort Study.

BACKGROUND: Stroke mainly occurs in patients without atrial fibrillation (AF). This study explored risk prediction models for ischemic stroke and transient ischemic attack (TIA) in patients without AF. METHODS: Three US-based healthcare databases (Truven MarketScan Commercial Claims and Encounters [CCAE], Medicare Supplemental [MDCR], and Optum Clinformatics [Optum]) were used to establish patient cohorts without AF during the index period of 2008-2012. The performance of 2 existing models (CHADS2 and CHA2DS2-VASc) for predicting stroke and TIA was examined by fitting a logistic regression to a training dataset and evaluating predictive accuracy in a validation dataset (area under the curve, AUC) using patients with complete follow-up of 1 or 3 years, separately. RESULTS: The commercial populations were younger and had fewer comorbidities than Medicare-eligible population. The incidence proportions of ischemic stroke and TIA during 1 and 3 years of follow-up were .5% and 1.9% (CCAE), .6% and 2.2% (Optum), and 4.6% and 13.1% (MDCR), respectively. The models performed consistently across all 3 databases, with the AUC ranging from .69 to .77 and from .68 to .73 for 1- and 3-year prediction, respectively. Predictive accuracy was lower than the initial work of CHADS2 evaluation in patients with AF (AUC: .82), but consistent with a subsequent meta-analysis of CHADS2 (.60-.80) and CHA2DS2-VASc performance (.64-.79). CONCLUSION: Although the existing schemes for predicting ischemic stroke and TIA in patients with AF can be applied to patients without AF with comparable predictive accuracy, the evidence suggests that there is room for improvement in these models' performance.

Prevalence of Chronic Metoclopramide Use and Associated Diagnoses in the US Pediatric Population.

OBJECTIVE: Metoclopramide is the only medication widely used to promote gastrointestinal motility in the USA. Despite its appreciable risk of central nervous system complications, it continues to be prescribed to children for chronic use. We sought to estimate the prevalence of chronic metoclopramide use among US children and identify the diagnoses that may have prompted this use. The US metoclopramide label lists only two indications in adults: symptomatic gastroesophageal reflux (GERD) and diabetic gastroparesis. The latter is rare in children so, in examining the indications likely to have prompted chronic metoclopramide use, we focused on GERD. METHODS: From two health services databases representing privately and publically insured children, respectively, we estimated the number of US children who used metoclopramide chronically and identified the diagnoses recorded at approximately the time when the chronic use began. We defined chronic use liberally as ≥ 35 days' supply, or conservatively as ≥ 130 days' supply in a 6-month period. For each chronic-use definition, insurance type, and age group, we estimated the proportion of children using metoclopramide chronically. We applied these proportions to US population estimates. RESULTS: Under the liberal and conservative definitions, respectively, 89,020 and 28,222 US children used metoclopramide chronically. CONCLUSION: In spite of its risk, substantial numbers of US children use metoclopramide chronically for symptoms suggestive of GERD.

The impact of standardizing the definition of visits on the consistency of multi-database observational health research.

BACKGROUND: Use of administrative claims from multiple sources for research purposes is challenged by the lack of consistency in the structure of the underlying data and definition of data across claims data providers. This paper evaluates the impact of applying a standardized revenue code-based logic for defining inpatient encounters across two different claims databases. METHODS: We selected members who had complete enrollment in 2012 from the Truven MarketScan Commercial Claims and Encounters (CCAE) and the Optum Clinformatics (Optum) databases. The overall prevalence of inpatient conditions in the raw data was compared to that in the common data model (CDM) with the standardized visit definition applied. RESULTS: In CCAE, 87.18% of claims from 2012 that were classified as part of inpatient visits in the raw data were also classified as part of inpatient visits after the data were standardized to CDM, and this overlap was consistent from 2006 to 2011. In contrast, Optum had 83.18% concordance in classification of 2012 claims from inpatient encounters before and after standardization, but the consistency varied over time. The re-classification of inpatient encounters substantially impacted the observed prevalence of medical conditions occurring in the inpatient setting and the consistency in prevalence estimates between the databases. On average, before standardization, each condition in Optum was 12% more prevalent than that same condition in CCAE; after standardization, the prevalence of conditions had a mean difference of only 1% between databases. Amongst 7,039 conditions reviewed, the difference in the prevalence of 67% of conditions in these two databases was reduced after standardization. CONCLUSIONS: In an effort to improve consistency in research results across database one should review sources of database heterogeneity, such as the way data holders process raw claims data. Our study showed that applying the Observational Medical Outcomes Partnership (OMOP) CDM with a standardized approach for defining inpatient visits during the extract, transfer, and load process can decrease the heterogeneity observed in disease prevalence estimates across two different claims data sources.

Switching from risperidone long-acting injectable to paliperidone long-acting injectable or oral antipsychotics: analysis of a Medicaid claims database.

This report examines relapse risk following a switch from risperidone long-acting injectable (RLAI) to another long-acting injectable antipsychotic [paliperidone palmitate (PP)] versus a switch to oral antipsychotics (APs). Truven Health's MarketScan Multistate Medicaid Database compared relapses following switches from RLAI. New user cohorts for these two groups were created on the basis of first incidence of exposure to the 'switched to' drug. Groups were balanced using 1:1 propensity score matching. Time-to-event analysis assessed schizophrenia-related hospital/emergency department visits. A total of 188 patients switched from RLAI to PP, and 131 patients switched from RLAI to oral AP. Propensity score-matched cohort included 109 patients who switched to PP and 109 patients who switched to an oral AP. Patients who switched from RLAI to PP had fewer events (26 vs. 32), longer time to an event (mean 70 vs. 47 days), and lower risk of relapse (hazard ratio, 0.54; 95% confidence interval, 0.32-0.92; P=0.024) compared with those who switched from RLAI to oral AP. Switching from RLAI to PP may be associated with a lower risk for relapse and longer duration of therapy compared with switching to oral AP. Given the limitations of observational studies, these results should be confirmed by other prospective evaluations.