Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model

A comprehensive systematic literature review of the burden of illness of Lennox-Gastaut syndrome on patients, caregivers, and society

Fully elucidating the burden that Lennox-Gastaut syndrome (LGS) places on individuals with the disease and their caregivers is critical to improving outcomes and quality of life (QoL). This systematic literature review evaluated the global burden of illness of LGS, including clinical symptom burden, care requirements, QoL, comorbidities, caregiver burden, economic burden, and treatment burden (PROSPERO ID: CRD42022317413). MEDLINE, Embase, and the Cochrane Library were searched for articles that met predetermined criteria. After screening 1442 deduplicated articles and supplementary manual searches, 113 articles were included for review. A high clinical symptom burden of LGS was identified, with high seizure frequency and nonseizure symptoms (including developmental delay and intellectual disability) leading to low QoL and substantial care requirements for individuals with LGS, with the latter including daily function assistance for mobility, eating, and toileting. Multiple comorbidities were identified, with intellectual disorders having the highest prevalence. Although based on few studies, a high caregiver burden was also identified, which was associated with physical problems (including fatigue and sleep disturbances), social isolation, poor mental health, and financial difficulties. Most economic analyses focused on the high direct costs of LGS, which arose predominantly from medically treated seizure events, inpatient costs, and medication requirements. Pharmacoresistance was common, and many individuals required polytherapy and treatment changes over time. Few studies focused on the humanistic burden. Quality concerns were noted for sample representativeness, disease and outcome measures, and reporting clarity. In summary, a high burden of LGS on individuals, caregivers, and health care systems was identified, which may be alleviated by reducing the clinical symptom burden. These findings highlight the need for a greater understanding of and better definitions for the broad spectrum of LGS symptoms and development of treatments to alleviate nonseizure symptoms.

Selection and Continuation of Antiseizure Medication in Children With Epilepsy in Sweden From 2007 to 2020

BACKGROUND

Knowledge on antiseizure medication (ASM) use and retention for children with epilepsy is limited, partly because of extensive off-label use of newer drugs with limited registration. We used prescription data to study prescription patterns on a population-wide scale and compared the proportion of patients remaining on monotherapy of ASMs with and without formal indication for different age groups.

METHODS

A total of 14,681 individuals aged <18 years were included, using cross-referenced Swedish registers from 2007 to 2020. Kaplan-Meier retention rates were calculated for all ASMs. The most common pathways of the first three medications per patient were analyzed.

RESULTS

In children older than one month and up to age one year, monotherapy retention rates were the highest for oxcarbazepine, valproic acid, and carbamazepine. Among children aged one to five years, oxcarbazepine and levetiracetam were among ASMs that do not have a monotherapy indication in Sweden but still had high retention rates. In the age group five to 12 years, lamotrigine and oxcarbazepine had the highest retention rate. In males aged 12 to 18 years, valproic acid was the most common choice followed by lamotrigine, whereas lamotrigine was the first choice of ASM for females, exceeding the second and third most common options levetiracetam and oxcarbazepine by a factor of two and three, respectively.

CONCLUSION

Off-label medication is common in children with epilepsy but does not seem to be associated with lower retention. The restrictions regarding valproic acid for females of childbearing age seem to have been well implemented in Swedish neuropediatric care.

Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population

BACKGROUND

Sharing data across institutions is critical to improving care for children who are using long-term mechanical ventilation (LTMV). Mechanical ventilation data are complex and poorly standardized. This lack of data standardization is a major barrier to data sharing.

OBJECTIVE

We aimed to describe current ventilator data in the electronic health record (EHR) and propose a framework for standardizing these data using a common data model (CDM) across multiple populations and sites.

METHODS

We focused on a cohort of patients with LTMV dependence who were weaned from mechanical ventilation (MV). We extracted and described relevant EHR ventilation data. We identified the minimum necessary components, termed "Clinical Ideas," to describe MV from time of initiation to liberation. We then utilized existing resources and partnered with informatics collaborators to develop a framework for incorporating Clinical Ideas into the PEDSnet CDM based on the Observational Medical Outcomes Partnership (OMOP).

RESULTS

We identified 78 children with LTMV dependence who weaned from ventilator support. There were 25 unique device names and 28 unique ventilation mode names used in the cohort. We identified multiple Clinical Ideas necessary to describe ventilator support over time: device, interface, ventilation mode, settings, measurements, and duration of ventilation usage per day. We used Concepts from the SNOMED-CT vocabulary and integrated an existing ventilator mode taxonomy to create a framework for CDM and OMOP integration.

CONCLUSION

The proposed framework standardizes mechanical ventilation terminology and may facilitate efficient data exchange in a multisite network. Rapid data sharing is necessary to improve research and clinical care for children with LTMV dependence.

Effectiveness of the Use of Standardized Vocabularies on Epilepsy Patient Cohort Generation

Objectives

This study investigated the effectiveness of using standardized vocabularies to generate epilepsy patient cohorts with local medical codes, SNOMED Clinical Terms (SNOMED CT), and International Classification of Diseases tenth revision (ICD-10)/Korean Classification of Diseases-7 (KCD-7).

Methods

We compared the granularity between SNOMED CT and ICD-10 for epilepsy by counting the number of SNOMED CT concepts mapped to one ICD-10 code. Next, we created epilepsy patient cohorts by selecting all patients who had at least one code included in the concept sets defined using each vocabulary. We set patient cohorts generated by local codes as the reference to evaluate the patient cohorts generated using SNOMED CT and ICD-10/KCD-7. We compared the number of patients, the prevalence of epilepsy, and the age distribution between patient cohorts by year.

Results

In terms of the cohort size, the match rate with the reference cohort was approximately 99.2% for SNOMED CT and 94.0% for ICD-10/KDC7. From 2010 to 2019, the mean prevalence of epilepsy defined using the local codes, SNOMED CT, and ICD-10/KCD-7 was 0.889%, 0.891% and 0.923%, respectively. The age distribution of epilepsy patients showed no significant difference between the cohorts defined using local codes or SNOMED CT, but the ICD-9/KCD-7-generated cohort showed a substantial gap in the age distribution of patients with epilepsy compared to the cohort generated using the local codes.

Conclusions

The number and age distribution of patients were substantially different from the reference when we used ICD-10/KCD-7 codes, but not when we used SNOMED CT concepts. Therefore, SNOMED CT is more suitable for representing clinical ideas and conducting clinical studies than ICD-10/KCD-7.

Real-world treatment intensities and pathways of macular edema following retinal vein occlusion in Korea from Common Data Model in ophthalmology

Despite many studies, optimal treatment sequences or intervals are still questionable in retinal vein occlusion (RVO) macular edema. The aim of this study was to examine the real-world treatment patterns of RVO macular edema. A retrospective analysis of the Observational Medical Outcomes Partnership Common Data Model, a distributed research network, of four large tertiary referral centers (n = 9,202,032) identified 3286 eligible. We visualized treatment pathways (prescription volume and treatment sequence) with sunburst and Sankey diagrams. We calculated the average number of intravitreal injections per patient in the first and second years to evaluate the treatment intensities. Bevacizumab was the most popular first-line drug (80.9%), followed by triamcinolone (15.1%) and dexamethasone (2.28%). Triamcinolone was the most popular drug (8.88%), followed by dexamethasone (6.08%) in patients who began treatment with anti-vascular endothelial growth factor (VEGF) agents. The average number of all intravitreal injections per person decreased in the second year compared with the first year. The average number of injections per person in the first year increased throughout the study. Bevacizumab was the most popular first-line drug and steroids were considered the most common as second-line drugs in patients first treated with anti-VEGF agents. Intensive treatment patterns may cause an increase in intravitreal injections.

Big data analysis and artificial intelligence in epilepsy - common data model analysis and machine learning-based seizure detection and forecasting

There has been significant interest in big data analysis and artificial intelligence (AI) in medicine. Ever-increasing medical data and advanced computing power have enabled the number of big data analyses and AI studies to increase rapidly. Here we briefly introduce epilepsy, big data, and AI and review big data analysis using a common data model. Studies in which AI has been actively applied, such as those of electroencephalography epileptiform discharge detection, seizure detection, and forecasting, will be reviewed. We will also provide practical suggestions for pediatricians to understand and interpret big data analysis and AI research and work together with technical expertise.

Patient characteristics and antiseizure medication pathways in newly diagnosed epilepsy: Feasibility and pilot results using the common data model in a single-center electronic medical record database

INTRODUCTION

Efforts to characterize variability in epilepsy treatment pathways are limited by the large number of possible antiseizure medication (ASM) regimens and sequences, heterogeneity of patients, and challenges of measuring confounding variables and outcomes across institutions. The Observational Health Data Science and Informatics (OHDSI) collaborative is an international data network representing over 1 billion patient records using common data standards. However, few studies have applied OHDSI's Common Data Model (CDM) to the population with epilepsy and none have validated relevant concepts. The goals of this study were to demonstrate the feasibility of characterizing adult patients with epilepsy and ASM treatment pathways using the CDM in an electronic health record (EHR)-derived database.

METHODS

We validated a phenotype algorithm for epilepsy in adults using the CDM in an EHR-derived database (2001-2020) against source records and a prospectively maintained database of patients with confirmed epilepsy. We obtained the frequency of all antecedent conditions and procedures for patients meeting the epilepsy phenotype criteria and characterized ASM exposure sequences over time and by age and sex.

RESULTS

The phenotype algorithm identified epilepsy with 73.0-85.0% positive predictive value and 86.3% sensitivity. Many patients had neurologic conditions and diagnoses antecedent to meeting epilepsy criteria. Levetiracetam incrementally replaced phenytoin as the most common first-line agent, but significant heterogeneity remained, particularly in second-line and subsequent agents. Drug sequences included up to 8 unique ingredients and a total of 1,235 unique pathways were observed.

CONCLUSIONS

Despite the availability of additional ASMs in the last 2 decades and accumulated guidelines and evidence, ASM use varies significantly in practice, particularly for second-line and subsequent agents. Multi-center OHDSI studies have the potential to better characterize the full extent of variability and support observational comparative effectiveness research, but additional work is needed to validate covariates and outcomes.

Drug-drug interactions in atrial fibrillation patients receiving direct oral anticoagulants

Polypharmacy is common in patients with atrial fibrillation (AF), making these patients vulnerable to the occurrence of potential drug-drug interactions (DDIs). We assessed the risk of ischemic stroke and major bleeding in the context of concomitant treatment with potential DDIs in patients with AF prescribed direct oral anticoagulants (DOACs). Using the common data model (CDM) based on an electronic health record (EHR) database, we included new users of DOACs from among patients treated for AF between January 2014 and December 2017 (n = 1938). The median age was 72 years, and 61.8% of the patients were males, with 28.2% of the patients having a CHA₂DS₂-VASc score in category 0–1, 49.4% in category 2–3, and 22.4% in category ≥ 4. The CHA₂DS₂-VASc score was significantly associated with ischemic stroke occurrence and hospitalization for major bleeding. Multiple logistic regression analysis showed that increased risk of ischemic stroke and hospitalization for major bleeding was associated with the number of DDIs regardless of comorbidities: ≥ 2 DDIs was associated with ischemic stroke (OR = 18.68; 95% CI, 6.22–55.27, P < 0.001) and hospitalization for major bleeding (OR = 5.01; 95% CI, 1.11–16.62, P < 0.001). DDIs can cause reduced antithrombotic efficacy or increased risk of bleeding in AF patients prescribed DOACs.

Treatment Patterns of Type 2 Diabetes Assessed Using a Common Data Model Based on Electronic Health Records of 2000-2019

BACKGROUND

Real-world data analysis is useful for identifying treatment patterns. Understanding drug prescription patterns of type 2 diabetes mellitus may facilitate diabetes management. We aimed to analyze treatment patterns of type 2 diabetes mellitus using Observational Medical Outcomes Partnership Common Data Model based on electronic health records.

METHODS

This retrospective, observational study employed electronic health records of patients who visited Jeonbuk National University Hospital in Korea during January 2000-December 2019. Data were transformed into the Observational Medical Outcomes Partnership Common Data Model and analyzed using R version 4.0.3 and ATLAS ver. 2.7.6. Prescription frequency for each anti-diabetic drug, combination therapy pattern, and prescription pattern according to age, renal function, and glycated hemoglobin were analyzed.

RESULTS

The number of adults treated for type 2 diabetes mellitus increased from 1,867 (2.0%) in 2000 to 9,972 (5.9%) in 2019. In the early 2000s, sulfonylurea was most commonly prescribed (73%), and in the recent years, metformin has been most commonly prescribed (64%). Prescription rates for DPP4 and SGLT2 inhibitors have increased gradually over the past few years. Monotherapy prescription rates decreased, whereas triple and quadruple combination prescription rates increased steadily. Different drug prescription patterns according to age, renal function, and glycated hemoglobin were observed. The proportion of patients with HbA1c ≤ 7% increased from 31.1% in 2000 to 45.6% in 2019, but that of patients visiting the emergency room for severe hypoglycemia did not change over time.

CONCLUSION

Medication utilization patterns have changed significantly over the past 20 years with an increase in the use of newer drugs and a shift to combination therapies. In addition, various prescription patterns were demonstrated according to the patient characteristics in actual practice. Although glycemic control has improved, the proportion within the target is still low, underscoring the need to improve diabetes management.