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このページは機械翻訳されています。他のページは英語で表示される場合があります。View in English
  1. ホーム
  2. 研究分野
  3. 生物医学と臨床科学
  4. 心血管医学と血液学
  5. 心臓病 (心血管疾患を含む)
  6. 訪問ごとに血圧の変動と心血管のアウトカム: システマティック・レビューと用量反応メタ解析

訪問ごとに血圧の変動と心血管のアウトカム: システマティック・レビューと用量反応メタ解析

Mifetika Lukitasari1,2, Jitendra Jonnagaddala1,3,4, Siaw-Teng Liaw1,4

  • 1School of Population Health, UNSW Sydney, NSW 2052  Australia.

European journal of preventive cardiology
|August 28, 2025

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PubMed で要約を見る

まとめ
この要約は機械生成です。

訪問毎の血圧変動 (VVV BPV) は,電子医療記録 (EHR) を使用して,心血管疾患 (CVD) のリスクを確実に予測します. 高いVVBBVは,心血管疾患のリスクの増加を示し,臨床統合を支持する.

科学分野:

  • 心臓病と心血管疾患
  • 医療情報と電子医療記録
  • バイオ統計学と疫学

背景:

  • 訪問ごとに血圧の変動 (VVV BPV) は,心血管疾患 (CVD) のリスク因子として十分に活用されていない.
  • BPVの推定とCVDの予測のための電子医療記録 (EHR) データの信頼性は不確実です.
  • この研究は,BPVの推定方法とCVDの予測精度を比較する必要性に対処しています.

研究 の 目的:

  • EHRデータと非EHRデータを用いたBPV推定方法を比較する.
  • VVV BPVとCVDのリスクとの間での用量反応の関連性を調べる.
  • BPVの評価のためのEHRデータの臨床的有用性を評価する.

主な方法:

  • 2012年1月から2024年8月までの間に発表された研究の体系的なレビューとメタ解析.
  • 成人のVVVBPVとCVDアウトカムとの関連を評価した研究を含める.
  • CVDリスクに関連したBPV (SDとCV) 値の評価のための用量反応メタ解析 (DRMA).

主要な成果:

  • 対象となった4, 926件のうち,49件が含有基準を満たした.
  • VVV BPVは,EHR (HR: 1.17) と非EHR (HR: 1.14) データの間の比較可能な効果サイズで,CVDのアウトカムを予測することが判明しました.
キーワード:
絶対リスク評価心血管疾患電子医療記録訪問ごとに血圧の変動

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Measuring the Carotid to Femoral Pulse Wave Velocity Cf-PWV to Evaluate Arterial Stiffness
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Published on: May 3, 2018

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Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

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Published on: June 27, 2025

382
Author Spotlight: Assessing the Cardiovascular Profile of Patients with Metabolic Syndrome
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  • SD ≥6. 72mmHgまたはCV ≥9. 05%のBPVの値はCVDのリスクが10%高かった.
  • 結論:

    • EHRデータは,VVBVを信頼性のある見積もりで,EHR以外の情報源と同様にCVDの予測力を示しています.
    • 線形ではない投与量反応関係があり,高レベルのVVBVが心血管疾患のリスクを大幅に増加させる.
    • VVV BPVは臨床実務に統合されるべきであり,実施戦略に関するさらなる研究が必要である.