心不全患者の肺詰まりを検出するためのウェアラブルデバイスベースの呼吸器の複雑性分析:観察調査研究
Mengwei Li1,2, Yu Kang3, Shuanglin Zhao4
1Department of Medical Engineering, Beidaihe Rest and Recuperation Center of PLA, Qinhuangdao, China.
Journal of medical Internet research
|August 27, 2025
まとめ
ウェアラブルデバイスは,心不全 (HF) の肺詰まり (PC) の患者の呼吸器の変化を検出することができます. この技術は早期の介入と PCに関連した特定の呼吸パターンを特定することによって入院を減らすことを約束しています.
科学分野:
- 心臓病と呼吸器医学
- バイオメディカルエンジニアリングとウェアラブル・テクノロジー
- デジタル・ヘルスと予測診断
背景:
- 過剰な肺詰まり (PC) は,心不全 (HF) の悪化に大きく寄与し,しばしば緊急入院につながります.
- PCに関連した呼吸器異常をウェアラブルデバイスを使用して早期に検出することで,迅速な介入を容易にし,入院を減らすことができます.
- PCの特定の呼吸器バイオマーカーを検出するためのウェアラブルの実現可能性については,さらなる調査が必要です.
研究 の 目的:
- 入院中のHF患者の呼吸データをモニタリングするためのウェアラブルデバイスの使用の可能性を評価する.
- PC 患者と非 PC 患者の呼吸信号を区別できるかどうかを判断する.
主な方法:
- 重度の肺疾患または集中治療を必要とする患者を除いて,62人の入院したHF患者 (44人) を対象とした観察調査.
- 参加者は入院後 ≥24時間装置を身につけ,夜間呼吸パターンを呼吸サイクル,振幅,多次元エントロピー (MSE) について分析した.
- PCの定義のための肺超音波を含む包括的な臨床評価は24時間以内に実施された.
主要な成果:
- PC患者では,PC患者と比較して,平均発煙時間 (TE) が著しく延長され,発煙時間比 (TE_ ratio) が増加した.
- 呼吸幅 (RA) のマルチスケールエントロピー (MSE) 値の上昇は,PCグループではスケール1〜5 (area_ 1_ 5) とスケール6〜20 (area_ 6_ 20) で観察されました.
- ロジスティック回帰では,TE_ratio,RA area_1_5,RA area_6_20がPCの有意な予測指標として特定され,多変量モデルではAUCが0. 91に達した.
結論:
- ウェアラブルベースのMSE分析は,PCとPCのない入院したHF患者を効果的に区別しました.
- 呼吸幅の複雑性が増加したのがPC群の特徴でした.
- これらの発見は,HFにおける肺詰まりの非侵襲的なモニタリングと早期発見のためのウェアラブルの可能性を示唆しています.
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