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医療関連データベースの解析によるアミオダロンの心外副作用に関する研究

木下 佐昌子 近畿大学

2022.03.02

概要

アミオダロン(AMD)はVaughan Williams分類のクラスⅢ群に分類される抗不整脈薬であり、致死性不整脈治療に用いられる。その作用機序は、心筋において主にK+チャネル遮断作用を示し活動電位持続時間を延長することにより抗不整脈作用を示す。非持続性心室頻拍および期外収縮を有する心筋梗塞後の患者を対象とした研究では、対照群との比較においてAMDを使用した患者で全死亡率と突然死が減少することが報告されている1-3)。一方で、AMDには多様な副作用が知られており、循環器関連以外の重篤な副作用として間質性肺炎、肺線維症、肝障害、甲状腺機能異常がみられることがある4,5)。AMDでの治療をする上でこれらの副作用の発症が治療継続の障害となることがあり、臨床において副作用の予防、早期発見は治療を継続する上で重要である。しかしながら、その発症機序は非常に複雑であり、いまなお不明な点が多い。また、AMDは脂溶性が高く様々な組織および臓器に濃縮され蓄積されるため、半減期は55日と極めて長期であることが知られている。このような特徴により、服用を中止した以降も長期に渡り体内に残留しているため長期間の観察が必要となる。また、AMD服用中止後に副作用が発現する例も報告されているため服用中止後も注意深く観察する必要がある。

近年、情報技術の発展によりビッグデータを用いた研究が可能となった。医薬品は臨床試験を経て厳格に規定された承認までのプロセスを経て市販に至るが、一定の条件のもとに被験者を選出した臨床試験では有害事象は十分に検出できていないとの報告がある6,7)。近年のデジタル技術の発展に伴い、リアルワールドデータと呼ばれる電子化された実臨床における医療に関する情報はビッグデータとして蓄積され、臨床試験のみでは得られない現実世界の医療を反映したデータとして注目されている。リアルワールドデータには身近な病院情報システムまたは電子カルテに蓄積されている医療情報や、全世界から副作用情報が登録可能な有害事象自発報告データベースなど様々なものが存在する。有害事象自発報告データベースについては、その活用によりこれまで検討が難しかった比較的使用患者数の少ない薬剤についても全世界の情報をもとに有害事象の検討が可能となっている。また、膨大なデータから有益な情報を探索する技術であるデータマイニング手法の発展により、ビッグデータから効率よく未知の関係性を抽出することが可能となり、多くの研究に応用されている。

今回、AMDの心外副作用について検討するために、循環器疾患の高度専門病院である国立循環器病研究センター(NCVC)の病院情報システムに蓄積された医療情報と米国の有害事象自発報告データベースであるFood and Drug Administration Adverse Event Reporting System(FAERS)を用いてデータマイニング手法により検討をした。

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