急性腎臓損傷後の進行性慢性腎臓疾患の予測モデルの推移と外部検証
Matthew T James1,2,3,4, Neesh Pannu5, Brenda R Hemmelgarn1,2,3,4
1Department of Medicine, Cumming School of Medicine, University of Calgary, Alberta, Canada.
JAMA
|November 15, 2017
まとめ
急性腎臓損傷の入院後に進行した慢性腎臓疾患を ルーティンデータを使って 予測する新しいモデルです このツールは,ターゲット化されたフォローアップケアのための高リスク患者を特定します.
科学分野:
- 腎臓科
- 臨床流行病学
- バイオ統計学
背景:
- 急性腎臓損傷 (AKI) の入院は慢性腎臓病 (CKD) に繋がる可能性があります.
- 現在,AKI後のCKD進行のリスクが高い患者を予測する有効なツールはありません.
研究 の 目的:
- AKIの進行を予測するモデルを開発し,検証する.
- AKI入院後のCKD発症の主な予測要因を特定する.
主な方法:
- カナダ (アルバータ州とオンタリオ州) の2つの大きな集団のデータを利用した.
- AKI入院を生き残った,入院前の推定グルメルフィルタレーションレート (eGFR) > 45 mL/ min/ 1. 73 m2の患者も含まれました.
- 人口統計,実験室,併発性データを用いて多変量予測モデルを導出,検証した.
主要な成果:
- 6つの変数モデル (年齢,性別,ベースラインクレアチニン,アルブミヌリア,AKIの重症度,排出クレアチニン) は,進行したCKDを正確に予測した.
- このモデルは,外部検証において良好な差別性を示した (C統計値0.81).
- このモデルはよりシンプルなモデルと比較して リスク予測を大幅に改善しました
結論:
- 常規の検査データを組み込んだ多変量モデルは,AKI後の進行性CKDを予測できます.
- この予測ツールは,AKIの生存者の臨床管理とフォローアップ戦略を導く可能性があります.
- 臨床的有用性を確認するためにさらなる研究が必要である.
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