Integrating real-world data to assess cardiac ablation device outcomes in a multicenter study using the OMOP common data model for regulatory decisions: implementation and evaluation.

The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient outcomes and covariates from electronic health record data. Several data quality assurance (QA) analyses were implemented on the OMOP CDM to validate the data extraction, transformation, and load (ETL) process. OMOP CDM-based data of relevant patient encounters were successfully established to support studies for FDA regulatory submissions. QA analyses verified that the data transformation was robust between data sources and OMOP CDM. Our efforts provided useful insights in real-world data integration using OMOP CDM for medical device evaluation coordinated across multiple healthcare systems.

An Algorithm to Characterize a Dementia Population by Disease Subtype.

PURPOSE: Identification of Alzheimer disease and related dementias (ADRD) subtypes is important for pharmacologic treatment and care planning, yet inaccuracies in dementia diagnoses make ADRD subtypes hard to identify and characterize. The objectives of this study were to (1) develop a method to categorize ADRD cases by subtype and (2) characterize and compare the ADRD subtype populations by demographic and other characteristics. METHODS: We identified cases of ADRD occurring during 2008 to 2014 from the OptumLabs Database using diagnosis codes and antidementia medication fills. We developed a categorization algorithm that made use of temporal sequencing of diagnoses and provider type. RESULTS: We identified 36,838 individuals with ADRD. After application of our algorithm, the largest proportion of cases were nonspecific dementia (41.2%), followed by individuals with antidementia medication but no ADRD diagnosis (15.6%). Individuals with Alzheimer disease formed 10.2% of cases. Individuals with vascular dementia had the greatest burden of comorbid disease. Initial documentation of dementia occurred primarily in the office setting (35.1%). DISCUSSION: Our algorithm identified 6 dementia subtypes and three additional categories representing unique diagnostic patterns in the data. Differences and similarities between groups provided support for the approach and offered unique insight into ADRD subtype characteristics.

Increased Health Care Utilization in Dementia Subtypes Before Diagnosis.

INTRODUCTION: Prior studies have reported higher health care utilization (HCU) leading up to diagnosis of the Alzheimer disease and related dementia (ADRD), but none have assessed variation in HCU by ADRD subtype or examined disease-specific HCU. The objectives of this study were to identify ADRD subtypes and: (1) characterize all-cause and (2) disease-specific HCU during the 3 years preceding diagnosis, and (3) determine if HCU varied by ADRD subtype. METHODS: We used data from the OptumLabs Data Warehouse 2008 to 2014 to identify ADRD subtypes (total N=36,838) using an algorithm based on temporal sequencing of diagnoses and provider type. Annual counts of all-cause and disease-specific HCU in each of the 3 years preceding ADRD diagnosis were regressed on ADRD subtypes with mild cognitive impairment (MCI) as the reference group, year, and other variables. RESULTS: HCU increased over time, was highest in the outpatient setting, and varied by ADRD subtype. Compared with MCI, highest HCU was observed in vascular and nonspecific dementia. Compared with MCI, most subtypes had elevated disease-specific HCU. DISCUSSION: Variation in HCU by ADRD subtype points to different pathways to diagnosis and patterns of use.

Predicting Diagnosis of Alzheimer's Disease and Related Dementias Using Administrative Claims.

BACKGROUND: Predictive models for earlier diagnosis of Alzheimer's disease and related dementias (ADRD) that rely on variables requiring assessment during an office visit, such as cognitive function, body mass index, or lifestyle factors, may not be broadly applicable, since that level of data may be inaccessible or inefficient. OBJECTIVE: To build a predictive model for earlier diagnosis of ADRD using only administrative claims data to enhance applicability at the health care-system level. Building on the strength of this approach and knowledge that health care utilization (HCU) is increased before dementia diagnosis, it was hypothesized that previous HCU history would improve predictive ability of the model. METHODS: We conducted a case-control study using data from the OptumLabs Data Warehouse. ADRD was defined using ICD-9-CM codes and prescription fills for antidementia medications. We included individuals with mild cognitive impairment. Cases aged ≥ 18 years with a diagnosis between 2011-2014 were matched to controls without ADRD. HCU variables were incorporated into regression models along with comorbidities and symptoms. RESULTS: The derivation cohort comprised 24,521 cases and 95,464 controls. Final adjusted models were stratified by age. We obtained moderate accuracy (c-statistic = 0.76) for the model among younger (aged < 65 years) adults and poor discriminatory ability (c-statistic = 0.63) for the model among older adults (aged ≥ 65 years). Neurological and psychological disorders had the largest effect estimates. CONCLUSIONS: We created age-stratified predictive models for earlier diagnosis of dementia using information available in administrative claims. These models could be used in decision support systems to promote targeted cognitive screening and earlier dementia recognition for individuals aged < 65 years. These models should be validated in other cohorts. DISCLOSURES: This research was supported by AstraZeneca, Global CEO Initiative, Janssen, OptumLabs, and Roche. Albrecht was supported by Agency for Healthcare Quality and Research grant K01HS024560. Perfetto is employed by the National Health Council, which accepts membership dues and sponsorships from a variety of organizations and companies. The authors declare no other potential conflicts of interest.