Using the common data model to identify antiseizure drug-related adverse reactions

Choi SA, Kim H, Kim S, Yoo S, Yi S, Jeon Y, Hwang H, Kim KJ. Analysis of antiseizure drug-related adverse reactions from the electronic health record using the common data model. Epilepsia. 2020 Apr;61(4):610-616. doi:10.1111/epi.16472.

Abstract

Objective

Antiseizure drugs (ASDs) are known to cause a wide range of adverse drug reactions (ADRs). Recently, electronic health care data using the common data model (CDM) have been introduced and commonly adopted in pharmacovigilance research. We aimed to analyze ASD‐related ADRs using CDM and to assess the feasibility of CDM analysis in monitoring ADR in a single tertiary hospital.

Methods

We selected five ASDs: oxcarbazepine (OXC), lamotrigine (LTG), levetiracetam (LEV), valproic acid (VPA), and topiramate (TPM). Patients diagnosed with epilepsy and exposed to monotherapy with one of the ASDs before age 18 years were included. We measured four ADR outcomes: (1) hematologic abnormality, (2) hyponatremia, (3) elevation of liver enzymes, and (4) subclinical hypothyroidism. We performed a subgroup analysis to exclude the effects of concomitant medications.

Results

From the database, 1344 patients were included for the study. Of the 1344 patients, 436 were receiving OXC, 293 were receiving LTG, 275 were receiving LEV, 180 were receiving VPA, and 160 were receiving TPM. Thrombocytopenia developed in 14.1% of patients taking VPA. Hyponatremia occurred in 10.5% of patients taking OXC. Variable ranges of liver enzyme elevation were detected in 19.3% of patients taking VPA. Subclinical hypothyroidism occurred in approximately 21.5% to 28% of patients with ASD monotherapy, which did not significantly differ according to the type of ASD. In a subgroup analysis, we observed similar ADR tendencies, but with less thrombocytopenia in the TPM group.

Significance

The incidence and trends of ADRs that were evaluated by CDM were similar to the previous literature. CDM can be a useful tool for analyzing ASD‐related ADRs in a multicenter study. The strengths and limitations of CDM should be carefully addressed.

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The information on the appearance and frequency of side effects is essential for all drugs, especially those used for an extended period. Although patients generally experience relatively mild abnormalities, sometimes they can suffer life-threatening severe side effects. However, side effects are assessed only in a very limited number of patients in most cases, such as pre-marketing clinical trials or post-marketing investigations. It is nearly impossible to investigate all patients who use drugs.

The research team, led by Professors Hwang Hee, Kim Hun-min, and Yoo Soo-young, analyzed the big data of medical information systems that have already been de-identified and structured, the hospital said in a news release on Wednesday.

They used the Observational Medical Outcomes Partnership (OMOP)-CDM database of about 170 million patients for the study. OMOP-CDM is a data model that transforms electronic medical records information, such as different terms and formats for each medical institution, into a standardized structure.

The study used data from blood tests conducted during the period of drugs and anticonvulsants used by 1,344 out of around 5,000 patients treated at the hospital’s Department of Pediatric Neurology, specializing in epilepsy, from 2003 to 2017.

Based on the results of blood tests conducted during the five most commonly used anticonvulsants, the research team confirmed abnormalities such as anemia, thrombocytopenia, leukopenia, hyponatremia, thyroid dysfunction, and liver dysfunction.

Using the CDM data, the researchers could analyze the overall information of abnormalities in blood tests due to anticonvulsants in all children with epilepsy, the hospital said. Furthermore, they confirmed the exact prevalence of side effects that could be caused by the drugs already known, as well as the previously unknown side effects.

"We were able to complete the study in months with the CDM model while similar observation of drug side effects typically usually requires more than a year," Professor Hwang said.

The study was meaningful as it showed the possibility of replacing some of the existing post-marketing surveys in a short period with less expense if it is spread to multi-agency research in the future due to the nature of CDM, he noted.

Professor Kim also said, “CDM model is fast and accurate, but careful design is also significant because there are some points that can be missed out in the process of specifying search conditions.”

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