Development and validation of a claims-based algorithm to identify patients with Neuromyelitis Optica Spectrum disorder.

INTRODUCTION: No validated algorithm exists to identify patients with neuromyelitis optica spectrum disorder (NMOSD) in healthcare claims data. We developed and tested the performance of a healthcare claims-based algorithm to identify patients with NMOSD. METHODS: Using medical record data of 101 adults with NMOSD, multiple sclerosis (MS), or myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), we tested the sensitivity and specificity of claims-based algorithms developed through interviews with neurologists. We tested the best-performing algorithm's face validity using 2016-2019 data from IBM MarketScan Commercial and Medicare Supplemental databases. Demographics and clinical characteristics were reported. RESULTS: Algorithm inclusion criteria were age ≥ 18 years and (≥1 NMO diagnosis [or ≥ 1 transverse myelitis (TM) and ≥ 1 optic neuritis (ON) diagnosis] and ≥ 1 NMOSD drug) or (≥2 NMO diagnoses ≥90 days apart). Exclusion criteria were MS diagnosis or use of MS-specific drug after last NMO diagnosis or NMOSD drug; sarcoidosis diagnosis after last NMO diagnosis; or use of ≥1 immune checkpoint inhibitor. In medical record billing data of 50 patients with NMOSD, 30 with MS, and 21 with MOGAD, the algorithm had 82.0% sensitivity and 70.6% specificity. When applied to healthcare claims data, demographic and clinical features of the identified cohort were similar to known demographics of NMOSD. CONCLUSIONS: This clinically derived algorithm performed well in medical records. When tested in healthcare claims, demographics and clinical characteristics were consistent with previous clinical findings. This algorithm will enable a more accurate estimation of NMOSD disease burden using insurance claims datasets.

Processing and validation of inpatient Medicare Advantage data for use in hospital outcome measures.

OBJECTIVE: To determine the feasibility of integrating Medicare Advantage (MA) admissions into the Centers for Medicare & Medicaid Services (CMS) hospital outcome measures through combining Medicare Advantage Organization (MAO) encounter- and hospital-submitted inpatient claims. DATA SOURCES AND STUDY SETTING: Beneficiary enrollment data and inpatient claims from the Integrated Data Repository for 2018 Medicare discharges. STUDY DESIGN: We examined timeliness of MA claims, compared diagnosis and procedure codes for admissions with claims submitted both by the hospital and the MAO (overlapping claims), and compared demographic characteristics and principal diagnosis codes for admissions with overlapping claims versus admissions with a single claim. DATA COLLECTION/EXTRACTION METHODS: We combined hospital- and MAO-submitted claims to capture MA admissions from all hospitals and identified overlapping claims. For admissions with only an MAO-submitted claim, we used provider history data to match the National Provider Identifier on the claim to the CMS Certification Number used for reporting purposes in CMS outcome measures. PRINCIPAL FINDINGS: After removing void and duplicate claims, identifying overlapped claims between the hospital- and MAO-submitted datasets, restricting claims to acute care and critical access hospitals, and bundling same admission claims, we identified 5,078,611 MA admissions. Of these, 76.1% were submitted by both the hospital and MAO, 14.2% were submitted only by MAOs, and 9.7% were submitted only by hospitals. Nearly all (96.6%) hospital-submitted claims were submitted within 3 months after a one-year performance period, versus 85.2% of MAO-submitted claims. Among the 3,864,524 admissions with overlapping claims, 98.9% shared the same principal diagnosis code between the two datasets, and 97.5% shared the same first procedure code. CONCLUSIONS: Inpatient MA data are feasible for use in CMS claims-based hospital outcome measures. We recommend prioritizing hospital-submitted over MAO-submitted claims for analyses. Monitoring, data audits, and ongoing policies to improve the quality of MA data are important approaches to address potential missing data and errors.

Duration of effectiveness of the COVID-19 vaccine in Japan: a retrospective cohort study using large-scale population-based registry data.

BACKGROUND: Most evidence of the waning of vaccine effectiveness is limited to a relatively short period after vaccination. METHODS: Data obtained from a linked database of healthcare administrative claims and vaccination records maintained by the municipality of a city in the Kanto region of Japan were used in this study. The study period extended from April 1, 2020, to December 31, 2022. The duration of the effectiveness of the COVID-19 vaccine was analyzed using a time-dependent piecewise Cox proportional hazard model using the age, sex and history of cancer, diabetes, chronic obstructive pulmonary disease, asthma, chronic kidney disease, and cardiovascular disease as covariates. RESULTS: Among the 174,757 eligible individuals, 14,416 (8.3%) were diagnosed with COVID-19 and 936 (0.54%) were hospitalized for COVID-19. Multivariate analysis based on the time-dependent Cox regression model with reference of non-vaccine group revealed a lower incidence of COVID-19 in the one-dose group (hazard ratio, 0.76 [95% confidence interval, 0.63-0.91]), two-dose (0.89 [0.85-0.93]), three-dose (0.80 [0.76-0.85]), four-dose (0.93 [0.88-1.00]), and five-dose (0.72 [0.62-0.84]) groups. A lower incidence of COVID-19-related hospitalization was observed in the one-dose group (0.42 [0.21-0.81]), two-dose (0.44 [0.35-0.56]), three-dose (0.38 [0.30-0.47]), four-dose (0.20 [0.14-0.28]), and five-dose (0.11 [0.014-0.86]) groups. Multivariable analyses based on the time-dependent piecewise Cox proportional hazard model with reference of non-vaccine group revealed significant preventive effects of the vaccine for 4 months for the incidence of COVID-19 and ≥ 6 months for hospitalization. CONCLUSIONS: Vaccine effectiveness showed gradual attenuation with time after vaccination; however, protective effects against the incidence of COVID-19 and hospitalization were maintained for 4 months and ≥ 6 months, respectively. These results may aid in formulating routine vaccination plans after the COVID-19 pandemic.

Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source.

Background: With an increasing interest in using large claims databases in medical practice and research, it is a meaningful and essential step to efficiently identify patients with the disease of interest. Objectives: This study aims to establish a machine learning (ML) approach to identify patients with congenital heart disease (CHD) in large claims databases. Methods: We harnessed data from the Quebec claims and hospitalization databases from 1983 to 2000. The study included 19,187 patients. Of them, 3,784 were labeled as true CHD patients using a clinician developed algorithm with manual audits considered as the gold standards. To establish an accurate ML-empowered automated CHD classification system, we evaluated ML methods including Gradient Boosting Decision Tree, Support Vector Machine, Decision tree, and compared them to regularized logistic regression. The Area Under the Precision Recall Curve was used as the evaluation metric. External validation was conducted with an updated data set to 2010 with different subjects. Results: Among the ML methods we evaluated, Gradient Boosting Decision Tree led the performance in identifying true CHD patients with 99.3% Area Under the Precision Recall Curve, 98.0% for sensitivity, and 99.7% for specificity. External validation returned similar statistics on model performance. Conclusions: This study shows that a tedious and time-consuming clinical inspection for CHD patient identification can be replaced by an extremely efficient ML algorithm in large claims database. Our findings demonstrate that ML methods can be used to automate complicated algorithms to identify patients with complex diseases.

Invited Commentary: Raising a High-Pressure Alarm About Pediatric Hypertension.

Hypertension is a common "silent killer" in adult medicine, but epidemiologic estimates of elevated blood pressure in children and adolescents are challenged by under-diagnosis and resultant low utilization of relevant administrative or billing codes. In the article by Horgan et al (Am J Epidemiol 2024), children and adolescents with hypertension and elevated blood pressure were identified using direct assessment of blood pressure measurements available in the electronic health record from both inpatient and outpatient visits ("clinical cohort") in comparison to diagnosis codes ("claims-based cohort"). The study population included 3.75 million pediatric healthcare visits available in the US Food and Drug Administration's Sentinel System. While the study applied a relatively novel methodology to interrogate available clinical data within the EHR to better understand the prevalence of pediatric hypertension and raised concern for a higher occurrence of hypertension among children and adolescents than previously realized using claims codes, the utility of the prevalence estimates may be limited by the potential for misclassification bias inherent in EHR data. However, these data raise important concerns about relaying solely on ICD-9-CM/ICD-10-CM codes to quantify the epidemiology of pediatric hypertension and highlight opportunities to address elevated blood pressure in children that could improve long-term cardiovascular health.

Incidence and Potential Risk Factors of Human Cytomegalovirus Infection in Patients with Severe and Critical Coronavirus disease 2019.

BACKGROUND: Human cytomegalovirus (HCMV) infection occurs in immunosuppressed individuals and is known to increase mortality. Patients with coronavirus disease 2019 (COVID-19) are often treated with steroids, require intensive care unit (ICU) treatment, and may therefore be at risk for HCMV infection. However, which factors predispose severely ill patients with COVID-19 to HCMV infection and the prognostic value of such infections remain largely unexplored. This study aimed to examine the incidence and potential risk factors of HCMV infection in patients with severe or critical COVID-19 and evaluate the relationship between HCMV infection and mortality. METHODS AND FINDINGS: We used administrative claims data from advanced treatment hospitals in Japan to identify and analyze patients with severe or critical COVID-19. We explored potential risk factors for HCMV infection using multivariable regression models and their contribution to mortality in patients with COVID-19. Overall, 33,151 patients who progressed to severe or critical COVID-19 illness were identified. The incidence of HCMV infection was 0.3-1.7% depending on the definition of HCMV infection. Steroids, immunosuppressants, ICU admission, and blood transfusion were strongly associated with HCMV infection. Furthermore, HCMV infection was associated with patient mortality independent of the observed risk factors for death. CONCLUSIONS: HCMV infection is a notable complication in patients with severe or critical COVID-19 who are admitted to the ICU or receive steroids, immunosuppressants, and blood transfusion and can significantly increase mortality risk.

Investigating the association between metabolic syndrome conditions and perinatal mental illness: a national administrative claims study.

BACKGROUND: Although the association between mental disorder and metabolic syndrome as a bidirectional relationship has been demonstrated, there is little knowledge of the cumulative and individual effect of these conditions on peripartum mental health. This study aims to investigate the association between metabolic syndrome conditions (MetS-C) and maternal mental illness in the perinatal period, while exploring time to incident mental disorder diagnosis in postpartum women. METHODS: This observational study identified perinatal women continuously enrolled 1 year prior to and 1 year post-delivery using Optum's de-identified Clinformatics® Data Mart Database (CDM) from 2014 to 2019 with MetS-C i.e. obesity, diabetes, high blood pressure, high triglycerides, or low HDL (1-year prior to delivery); perinatal comorbidities (9-months prior to and 4-month postpartum); and mental disorder (1-year prior to and 1-year post-delivery). Additionally, demographics and the number of days until mental disorder diagnosis were evaluated in this cohort. The analysis included descriptive statistics and multivariable logistic regression. MetS-C, perinatal comorbidities, and mental disorder were assessed using the International Classification of Diseases, Ninth, and Tenth Revision diagnosis codes. RESULTS: 372,895 deliveries met inclusion/exclusion criteria. The prevalence of MetS-C was 13.43%. Multivariable logistic regression revealed prenatal prevalence (1.64, CI = 1.59-1.70) and postpartum incident (1.30, CI = 1.25-1.34) diagnosis of mental health disorder were significantly higher in those with at least one MetS-C. Further, the adjusted odds of having postpartum incident mental illness were 1.51 times higher (CI = 1.39-1.66) in those with 2 MetS-C and 2.12 times higher (CI = 1.21-4.01) in those with 3 or more MetS-C. Young women (under the age of 18 years) were more likely to have an incident mental health diagnosis as opposed to other age groups. Lastly, time from hospital discharge to incident mental disorder diagnosis revealed an average of 157 days (SD = 103 days). CONCLUSION: The risk of mental disorder (both prenatal and incident) has a significant association with MetS-C. An incremental relationship between incident mental illness diagnosis and the number of MetS-C, a significant association with younger mothers along with a relatively long period of diagnosis mental illness highlights the need for more screening and treatment during pregnancy and postpartum.

Patterns of primary and specialty care among children with sickle cell anemia.

BACKGROUND AND OBJECTIVE: National guidelines recommend that children with sickle cell anemia (SCA) be seen regularly by primary care providers (PCPs) as well as hematologists to receive comprehensive, multidisciplinary care. The objective is to characterize the patterns of primary and hematology care for children with SCA in Michigan. METHODS: Using validated claims definitions, children ages 1-17 years with SCA were identified using Michigan Medicaid administrative claims from 2010 to 2018. We calculated the number of outpatient PCP and hematologist visits per person-year, as well as the proportion of children with at least one visit to a PCP, hematologist, or both a PCP and hematologist annually. Negative binomial regression was used to calculate annual rates of visits for each provider type. RESULTS: A total of 875 children contributed 2889 person-years. Of the total 22,570 outpatient visits, 52% were with a PCP and 34% with a hematologist. Annually, 87%-93% of children had a visit with a PCP, and 63%-85% had a visit with a hematologist. Approximately 66% of total person-years had both visit types within a year. The annual rate ranged from 2.3 to 2.5 for hematologist visits and from 3.7 to 4.1 for PCP visits. CONCLUSIONS: Substantial gaps exist in the receipt of annual hematology care. Given that the majority of children with SCA see a PCP annually, strategies to leverage primary care visits experienced by this population may be needed to increase receipt of SCA-specific services.

Target trial emulation of carfilzomib safety among patients with relapsed/refractory multiple myeloma using a nationwide observational data in Korea.

PURPOSE: Carfilzomib, commonly used for relapsed/refractory multiple myeloma (RRMM), has been associated with various adverse events in randomized controlled trials (RCTs). However, real-world safety data for a more diverse population are needed, as carfilzomib received expedited approval. This study aimed to evaluate carfilzomib's safety in Korea by comparing new users of KRd (carfilzomib, lenalidomide, and dexamethasone) to Rd (lenalidomide and dexamethasone) using a nationwide administrative claims database. METHODS: The retrospective cohort study utilized target trial emulation, focusing on adverse events in various organ systems similar to the ASPIRE trial. RESULTS: This study included 4,580 RRMM patients between 2007 and 2020, and the KRd group showed significantly higher risks of hematologic adverse events (anemia, neutropenia, thrombocytopenia) and some non-hematologic adverse events (cough, hypokalemia, constipation, hypertension, heart failure) compared to the Rd group. Among non-hematologic adverse events, cardiovascular events (heart failure [HR 2.04; 95% CI 1.24-3.35], hypertension [HR 1.58; 95% CI 1.15-2.17]) had the highest risk in the KRd group. CONCLUSION: The safety profile of carfilzomib in Korean patients was similar to previous RCTs. Therefore, caution should be exercised when using carfilzomib in Asian individuals with RRMM due to the increased risk of cardiovascular adverse events.

Using Claims Data to Predict Pre-Operative BMI Among Bariatric Surgery Patients: Development of the BMI Before Bariatric Surgery Scoring System (B3S3).

Background: Lack of body mass index (BMI) measurements limits the utility of claims data for bariatric surgery research, but pre-operative BMI may be imputed due to existence of weight-related diagnosis codes and BMI-related reimbursement requirements. We used a machine learning pipeline to create a claims-based scoring system to predict pre-operative BMI, as documented in the electronic health record (EHR), among patients undergoing a new bariatric surgery. Methods: Using the Optum Labs Data Warehouse, containing linked de-identified claims and EHR data for commercial or Medicare Advantage enrollees, we identified adults undergoing a new bariatric surgery between January 2011 and June 2018 with a BMI measurement in linked EHR data ≤30 days before the index surgery (n=3226). We constructed predictors from claims data and applied a machine learning pipeline to create a scoring system for pre-operative BMI, the B3S3. We evaluated the B3S3 and a simple linear regression model (benchmark) in test patients whose index surgery occurred concurrent (2011-2017) or prospective (2018) to the training data. Results: The machine learning pipeline yielded a final scoring system that included weight-related diagnosis codes, age, and number of days hospitalized and distinct drugs dispensed in the past 6 months. In concurrent test data, the B3S3 had excellent performance (R2 0.862, 95% confidence interval [CI] 0.815-0.898) and calibration. The benchmark algorithm had good performance (R2 0.750, 95% CI 0.686-0.799) and calibration but both aspects were inferior to the B3S3. Findings in prospective test data were similar. Conclusion: The B3S3 is an accessible tool that researchers can use with claims data to obtain granular and accurate predicted values of pre-operative BMI, which may enhance confounding control and investigation of effect modification by baseline obesity levels in bariatric surgery studies utilizing claims data.

Pre-operative BMI is an important potential confounder in comparative effectiveness studies of bariatric surgeries.Claims data lack clinical measurements, but insurance reimbursement requirements for bariatric surgery often result in pre-operative BMI being coded in claims data.We used a machine learning pipeline to create a model, the B3S3, to predict pre-operative BMI, as documented in the EHR, among bariatric surgery patients based on the presence of certain weight-related diagnosis codes and other patient characteristics derived from claims data.Researchers can easily use the B3S3 with claims data to obtain granular and accurate predicted values of pre-operative BMI among bariatric surgery patients.

Assessing Patterns of Telehealth Use Among People with Sickle Cell Disease Enrolled in Medicaid During the Start of the COVID-19 Pandemic.

Background: Telehealth can be defined as using remote technologies to provide health care. It may increase access to care among people with sickle cell disease (SCD). This study examined (1) telehealth use, (2) characteristics of telehealth use, and (3) differences between telehealth users and nonusers among people with SCD during the COVID-19 pandemic. Methods: This was a retrospective analysis of Medicaid claims among four states [California (CA), Georgia (GA), Michigan (MI), Tennessee (TN)] participating in the Sickle Cell Data Collection program. Study participants were individuals ≥1 year old with SCD enrolled in Medicaid September 2019-December 2020. Telehealth encounters during the pandemic were characterized by provider specialty. Health care utilization was compared between those who did (users) and did not (nonusers) use telehealth, stratified by before and during the pandemic. Results: A total of 8,681 individuals with SCD (1,638 CA; 3,612 GA; 1,880 MI; and 1,551 TN) were included. The proportion of individuals with SCD that accessed telehealth during the pandemic varied across states from 29% in TN to 80% in CA. During the pandemic, there was a total of 21,632 telehealth encounters across 3,647 users. In two states (MI and GA), over a third of telehealth encounters were with behavioral health providers. Telehealth users had a higher average number of health care encounters during the pandemic: emergency department (pooled mean = 2.6 for users vs. 1.5 for nonusers), inpatient (1.2 for users vs. 0.6 for nonusers), and outpatient encounters (6.0 for users vs. 3.3 for nonusers). Conclusions: Telehealth was frequently used at the beginning of the COVID-19 pandemic by people with SCD. Future research should focus on the context, facilitators, and barriers of its implementation in this population.

Database profile of the Fukuoka-City Information Platform for Community-based Integrated Care.

BACKGROUND: The Fukuoka-City Information Platform for Community-based Integrated Care is an advanced big data platform that aggregates information on the health and medical services of Fukuoka citizens. Fukuoka City is engaged in a joint project with Kyushu University to promote policy making through a large-scale real-world data analysis. This paper describes the framework for this cooperative effort and the features of the analytical platform. METHODS: Fukuoka City is the fifth most populous ordinance-designated city in Japan, with an estimated population of approximately 1.6 million. Under an agreement with Fukuoka City, Kyushu University was granted access to a portion of the city's anonymized healthcare database as secondary-use information. The database contains information on resident registration, health insurance claims, specific health checkups and health checkups for the older adults, specific health guidance, long-term care insurance data, and cancer screenings collected after fiscal year 2012. Each of these constituent datasets can be interlinked using anonymized hashed key variables, allowing individuals to be followed across databases and over time. CONCLUSIONS: The platform allows longitudinal investigation of the complex association between various aspects of healthcare, such as medical procedures, examinations, interviews, medical costs, long-term care certifications, and care costs. The platform can provide valuable public-health information because it is relatively large for a single database, and because it allows analysis of data across multiple domains and tracing of individuals over time.

Influence of Incomplete Death Information on Cumulative Risk Estimates in United States Claims Data.

Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event-equivalent to other withdrawal reasons-rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment-weighted cumulative risks of a composite cardiovascular outcome among 34,527 initiators of telmisartan (exposure) and ramipril (referent) ages ≥55 in Optum claims from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared to treating death as a competing event (sub-distribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users (selected results), 5-year cause-specific and sub-distribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI 15.3, 17.5) and 16.2 (95% CI 15.1, 17.3) among ages 55-64 (difference=0.2) and were 43.2 (95% CI 41.3, 45.2) and 39.7 (95% CI 37.9, 41.4) among ages ≥75 (difference=3.6). Plasmode simulation results demonstrated the differences in cause-specific versus sub-distribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern.

Evaluating the association of body mass index with COVID-19 severity and mortality using Japanese administrative claims data.

INTRODUCTION: Obesity is a risk factor for aggravation of and mortality from coronavirus disease 2019 (COVID-19). We aimed to investigate the relationship between COVID-19 and Body Mass Index (BMI) in the Japanese population. METHODS: We used administrative claims data from an advanced treatment hospital in Japan and extracted data from patients hospitalized for COVID-19. The exposure variable was BMI measured at the time of admission, and the study outcomes were progression to critical illness and death. Analyses were performed for each age group. RESULTS: Overall, 58,944 patients met the inclusion criteria. The risk of critical illness increased monotonically with higher BMI. In contrast, the relationship between BMI and mortality follows a J-shaped curve; being underweight and obese are risk factors for mortality. When stratified by age, similar trends were observed for both critical illness and mortality. CONCLUSION: A higher BMI is a risk factor for the progression of COVID-19 severity, whereas both lower and higher BMIs are risk factors for mortality in the Japanese population.

Predictive Modeling of Factors Influencing Adherence to SGLT-2 Inhibitors in Ambulatory Care: Insights from Prescription Claims Data Analysis.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are novel oral anti-hyperglycemic drugs that demonstrate cardiovascular and metabolic benefits for patients with type 2 diabetes (T2D), heart failure (HF), and chronic kidney disease (CKD). There is limited knowledge of real-world data to predict adherence to SGLT-2i in an ambulatory setting. The study aims to predict SGLT-2i adherence in patients with T2D and/or HF and/or CKD by building a prediction model using electronic prescription claims data presented within EPIC datasets. This is a retrospective study of 174 adult patients prescribed SGLT-2i at UC San Diego Health ambulatory pharmacies between 1 January 2020 to 30 April 2021. Adherence was measured by the proportion of days covered (PDC). R packages were used to identify regression and non-linear regression predictive models to predict adherence. Age, gender, race/ethnicity, hemoglobin A1c, and insurance plan were included in the model. Diabetes control based on hemoglobin A1c (HbA1c) and the glomerular filtration rate (GFR) was also evaluated using Welch t-test with a p-value of 0.05. The best predictive model for measuring adherence was the simple decision tree. It had the highest area under the curve (AUC) of 74% and accuracy of 82%. The model accounted for 21 variables with the main node predictors, including glycated hemoglobin, age, gender, and insurance plan payment amount. The adherence rate was inversely proportional to HbA1c and directly proportional to the plan payment amount. As for secondary outcomes, HbA1c values from baseline till 90 days post-treatment duration were consistently higher in the non-compliant group: 7.4% vs. 9.6%, p < 0.001 for the PDC ≥ 0.80 and PDC < 0.80, respectively. Baseline eGFR was 55.18 mL/min/1.73m2 vs. 54.23 mL/min/m2 at 90 days. The mean eGFR at the end of the study (minimum of 90 days of treatment) was statistically different between the groups: 53.1 vs. 59.6 mL/min/1.73 m2, p < 0.001 for the PDC ≥ 0.80 and PDC < 0.80, respectively. Adherence predictive models will help clinicians to tailor regimens based on non-adherence risk scores.

Use of Healthcare Claims Data to Generate Real-World Evidence on Patients With Drug-Resistant Epilepsy: Practical Considerations for Research.

Objectives: Regulatory bodies, health technology assessment agencies, payers, physicians, and other decision-makers increasingly recognize the importance of real-world evidence (RWE) to provide important and relevant insights on treatment patterns, burden/cost of illness, product safety, and long-term and comparative effectiveness. However, RWE generation requires a careful approach to ensure rigorous analysis and interpretation. There are limited examples of comprehensive methodology for the generation of RWE on patients who have undergone neuromodulation for drug-resistant epilepsy (DRE). This is likely due, at least in part, to the many challenges inherent in using real-world data to define DRE, neuromodulation (including type implanted), and related outcomes of interest. We sought to provide recommendations to enable generation of robust RWE that can increase knowledge of "real-world" patients with DRE and help inform the difficult decisions regarding treatment choices and reimbursement for this particularly vulnerable population. Methods: We drew upon our collective decades of experience in RWE generation and relevant disciplines (epidemiology, health economics, and biostatistics) to describe challenges inherent to this therapeutic area and to provide potential solutions thereto within healthcare claims databases. Several examples were provided from our experiences in DRE to further illustrate our recommendations for generation of robust RWE in this therapeutic area. Results: Our recommendations focus on considerations for the selection of an appropriate data source, development of a study timeline, exposure allotment (specifically, neuromodulation implantation for patients with DRE), and ascertainment of relevant outcomes. Conclusions: The need for RWE to inform healthcare decisions has never been greater and continues to grow in importance to regulators, payers, physicians, and other key stakeholders. However, as real-world data sources used to generate RWE are typically generated for reasons other than research, rigorous methodology is required to minimize bias and fully unlock their value.

Identifying pregnancy episodes and estimating the last menstrual period using an administrative database in Korea: an application to patients with systemic lupus erythematosus.

OBJECTIVES: This study developed an algorithm for identifying pregnancy episodes and estimating the last menstrual period (LMP) in an administrative claims database and applied it to investigate the use of pregnancy-incompatible immunosuppressants among pregnant women with systemic lupus erythematosus (SLE). METHODS: An algorithm was developed and applied to a nationwide claims database in Korea. Pregnancy episodes were identified using a hierarchy of pregnancy outcomes and clinically plausible periods for subsequent episodes. The LMP was estimated using preterm delivery, sonography, and abortion procedure codes. Otherwise, outcome-specific estimates were applied, assigning a fixed gestational age to the corresponding pregnancy outcome. The algorithm was used to examine the prevalence of pregnancies and utilization of pregnancy-incompatible immunosuppressants (cyclophosphamide [CYC]/mycophenolate mofetil [MMF]/methotrexate [MTX]) and non-steroidal anti-inflammatory drugs (NSAIDs) during pregnancy in SLE patients. RESULTS: The pregnancy outcomes identified in SLE patients included live births (67%), stillbirths (2%), and abortions (31%). The LMP was mostly estimated with outcome-specific estimates for full-term births (92.3%) and using sonography procedure codes (54.7%) and preterm delivery diagnosis codes (37.9%) for preterm births. The use of CYC/MMF/MTX decreased from 7.6% during preconception to 0.2% at the end of pregnancy. CYC/MMF/MTX use was observed in 3.6% of women within 3 months preconception and 2.5% during 0-7 weeks of pregnancy. CONCLUSIONS: This study presents the first pregnancy algorithm using a Korean administrative claims database. Although further validation is necessary, this study provides a foundation for evaluating the safety of medications during pregnancy using secondary databases in Korea, especially for rare diseases.

One-year emergency department visits for children < 18 years of age, associated factors and frequency of primary general practitioner or pediatrician visits before: a French observational study (2018-19).

BACKGROUND: This study was designed to identify factors associated with at least one emergency department (ED) visit and those associated without consultation by a general practitioner or paediatrician (GPP) before ED visit. Levels of annual consumption of healthcare services as a function of the number of ED visit were reported. METHODS: This retrospective study focused on children < 18 years of age living in mainland France and followed for one-year after their birth or birthday in 2018. Children were selected from the national health data system, which includes data on healthcare reimbursements, long-term chronic diseases (LTD) eligible for 100% reimbursement, and individual complementary universal insurance (CMUc) status granted to households with a low annual income. Adjusted odds ratios (OR) were estimated using multivariate logistic regression. RESULTS: There were 13.211 million children included (94.2% of children; girls 48.8%). At least one annual ED visit was found for 24% (1: 16%, 2: 5%, 3 or more: 3%) and 14% of visits led to hospitalization. Factors significantly associated with at least one ED visit were being a girl (47.1%; OR = 0.92), age < 1 year (9.1%; OR = 2.85), CMUc (22.7%, OR = 1.45), an ED in the commune of residence (33.3%, OR = 1.15), type 1 diabetes (0.25%; OR = 2.4), epilepsy (0.28%; OR = 2.1), and asthma (0.39%; OR = 2.0). At least one annual short stay hospitalisation (SSH) was found for 8.8% children of which 3.4% after an ED visit. A GPP visit the three days before or the day of the ED visit was found for 19% of children (< 1 year: 29%, 14-17 years: 13%). It was 30% when the ED was followed by SSH and 17% when not. Significant factors associated with the absence of a GPP visit were being a girl (OR = 0.9), age (1 year OR = 1.4, 14-17 years OR = 3.5), presence of an ED in the commune of residence (OR = 1.12), epilepsy LTD (OR = 1.1). CONCLUSION: The low level of visits to GPP prior to a visit to the ED and the associated factors are the elements to be taken into account for appropriate policies to limit ED overcrowding. The same applies to factors associated with a visit to the ED, in order to limit daily variations.

Prescription of Opioid Analgesics for Chronic Non-Cancer Pain in Germany despite Contraindications: Administrative Claims Data Analysis.

In Germany, long-term opioid treatment (L-TOT) for chronic non-tumor pain (CNTP) is discussed as not being performed according to the German guideline on L-TOT for CNTP. In the present analysis, the occurrence and predictors of inappropriate care/overuse in a cohort of German insureds with L-TOT for CNTP by the presence of a contraindication with concurrent opioid analgesic (OA) therapy were investigated. We also analyzed whether prescribing physicians themselves diagnosed a contraindication. The retrospective cohort study was based on administrative claims data from a German statutory health insurance. Eight contraindication groups were defined based on the German guideline. Logistic regressions were performed in order to identify predictors for OA prescriptions despite contraindications. The possible knowledge of the prescribing physician about the contraindication was approximated by analyzing concordant unique physician identification numbers of OA prescriptions and contraindication diagnoses. A total of 113,476 individuals (75% female) with a mean age of 72 years were included. The most common documented contraindications were primary headaches (8.7%), severe mood disorders (7.7%) and pain in somatoform disorders (4.5%). The logistic regressions identified a younger age, longer history of OA therapy, opioid related psychological problems, and outpatient psychosomatic primary care as positive predictors for all contraindication groups.

Validation of administrative claims to identify ultrasound enhancing agent use.

BACKGROUND: Ultrasound enhancing agents (UEAs) are an invaluable adjunct to stress and transthoracic echocardiography (STE) to improve left ventricular visualization. Despite multiple single center studies evaluating UEA use, investigation into the rates, sources of variation, and outcomes of UEA use on a national level in the United States (US) has been limited by lack of validation of UEA codes for claims analyses. METHODS: We conducted a retrospective cross-sectional study, 2019-2022, using linked multicenter electronic medical record (EMR) data from > 30 health systems linked to all-payor claims data representing > 90% of the US population. Individuals receiving STE in both EMR and claims data on the same day during the study window were included. UEA receipt as identified by presence of a Current Procedural Terminology (CPT) or National Drug Code (NDC) for UEA use within 1-day of the index STE event. We evaluated the performance of claims to identify UEA use, using EMR data as the gold standard, stratified by inpatient and outpatient status. RESULTS: Amongst 54,525 individuals receiving STE in both EMR and claims data, 12,853 (23.6%) had a UEA claim in EMR, 10,461 (19.2%) had a UEA claim in claims, and 9140 (16.8%) had a UEA claim in both within the 1-day window. The sensitivity, specificity, accuracy, positive, and negative predictive values for UEA claims were 71.1%, 96.8%, 90.8%, 87.4%. and 91.6% respectively. However, amongst inpatients, the sensitivity of UEA claims was substantially lower (6.8%) compared to outpatients (79.7%). CONCLUSIONS: While the overall accuracy of claims to identify UEA use was high, there was substantial under-capture of UEA use by claims amongst inpatients. These results call into question published rates of UEA use amongst inpatients in studies using administrative claims, and highlight ongoing need to improve inpatient coding for UEA use.