JP2017500076A - 鬱血性心不全リスク状態判定方法及び関連する装置 - Google Patents
鬱血性心不全リスク状態判定方法及び関連する装置 Download PDFInfo
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Abstract
Description
1.測定値と履歴時間窓にわたって測定される値とを比較することによって生体インピーダンス指標フラグを形成することができる。現在の時間窓における生体インピーダンスに基づく値、例えば流動状態が履歴値の平均を特定のパーセンテージ上回る或いは下回る場合には、生体インピーダンス指標フラグがアサートされる。フラグは、パーセンテージ差が減少する数時間でデアサートされ得る。例えば、生体インピーダンス流動状態値の48時間窓のメジアンと立ち下がり48時間窓のメジアンとを比較する。値間のパーセンテージ差が10%よりも大きい場合には、生体インピーダンス指標フラグがアサートされる。指標フラグは、パーセンテージ差が10%を下回った24時間後にデアサートされ得る。
2.前述の方法に基づいて心拍数指標フラグを形成することができ、その場合、移動窓に関してリスク数が形成される。移動窓リスク数が閾値(例えば、2)を超える場合には、HR指標フラグがアサートされる。指標フラグは、リスク数が所定の閾値を下回った後、数時間で、例えば24時間でデアサートされ得る。
3.前述した他の方法に基づいて心拍数指標フラグを形成することもでき、その場合には、閾値と比較した心拍数変動に基づいてリスクが測定される。心拍数変動が閾値、例えば100msを超える場合に指標フラグをアサーすることができ、また、心拍数変動が所定の閾値を下回った後、数時間で、例えば24時間でフラグをデアサートすることができる。
4.指標フラグの組み合わせ、例えば呼吸速度と姿勢に基づく指標フラグとの組み合わせは、移動窓呼吸速度リスク数に基づいて前述の心拍数指標フラグに類似する態様で形成され得る。移動窓呼吸速度リスク数を決定することができる(例えば、バンドk=3及びリスクバンドB={[<6brpm],[6〜20brpm],[>20brpm]}の数、及び、バンドR={2,1,2}ごとに割り当てられるリスク値を規定する)。前述したように呼吸速度指標フラグをアサート及びデアサートすることができる。患者が1日当たり所定時間未満を安静状態に費やす場合には、姿勢に基づくフラグをアサートすることができる。例えば、患者が1日の40%未満を背臥位安静姿勢に費やす場合には、指標フラグをアサートすることができる。組み合わされた指標フラグは、呼吸速度指標フラグ又は姿勢に基づく指標フラグのいずれかがアサートされるときにアサートされ得る。そのような組み合わされた指標フラグは、複数の個々の指標フラグに基づいてもよい。
5.不整脈リスクフラグを例えば心房性細動負荷に基づいて形成できる。例えば、24時間AF負荷が所定の閾値よりも大きく且つその24時間窓において手動オーバーリードにより少なくとも1つのAF発症が特定された場合には、AF負荷指標フラグをアサートできる。
Claims (17)
- 患者の健康状態を判定するための方法であって、
前記患者の1つ以上の生理学的値を測定するステップと、
前記1つ以上の生理学的値に基づいて前記患者の安静状態を判定するステップと、
患者安静状態に基づいて前記生理学的値のうちの1つ以上をフィルタ処理するステップと、
前記1つ以上のフィルタ処理された値に基づいて前記患者の鬱血性心不全(CHF)リスクを判定するステップと
を備える方法。 - 1つ以上の生理学的値を測定する前記ステップが、心電図(ECG)記録、生体インピーダンス測定値、及び、3軸加速度計測定値を得る工程を備える、請求項1に記載の方法。
- 前記1つ以上の生理学的値が、流動状態、心拍数、心拍リズム、呼吸速度、呼吸量、身体姿勢、活動強度、及び、活動持続時間を含む、請求項1に記載の方法。
- 患者の安静状態を判定する前記ステップが、3軸加速度計測定値を得る工程を備え、x軸及びy軸が患者の身体平面上にあり、z軸が前記身体平面に対して垂直である、請求項1に記載の方法。
- 患者の安静状態を判定する前記ステップが、
z軸測定値の分布範囲、
前記x軸測定値及び前記y軸測定値に対する前記z軸測定値の相対的な大きさ、
前記x軸測定値の平方と前記y軸測定値の平方との和に対する前記z軸測定値の相対的な大きさ、
前記x軸測定値及び前記z軸測定値に対する前記y軸測定値の相対的な大きさ、並びに、
前記x軸測定値の平方と前記z軸測定値の平方との和に対する前記y軸測定値の相対的な大きさ
のうちの1つ以上を測定する工程を更に備える、請求項4に記載の方法。 - 患者が安静状態にあるかどうかを判定する前記ステップが、
患者睡眠スケジュール、
前記患者により与えられる睡眠スケジュールデータ、及び、
時刻
のうちの1つ以上を決定する工程を更に備える、請求項1に記載の方法。 - 生理学的値をフィルタ処理する前記ステップが、
前記患者が安静状態にない間に測定される心拍数を無視する工程、
前記患者が安静状態にない間に測定される呼吸速度を無視する工程、
前記患者が安静状態にない間に測定される呼吸量を無視する工程、及び、
前記患者が安静状態にある間に測定される活動強度を無視する工程
のうちの1つ以上を備える、請求項1に記載の方法。 - CHFリスクを判定する前記ステップが、フィルタ処理された生理学的値とベースライン生理学的値とを比較して比較出力を生成する工程を備える、請求項1に記載の方法。
- 前記ベースライン生理学的値が、患者人口統計データ、患者入院中に決定される生理学的値、患者入院後に決定される履歴的な生理学的値、臨床実験結果、及び、患者病歴のうちの1つ以上に基づいて決定される、請求項8に記載の方法。
- 原文になし
- 前記比較出力は、フィルタ処理された生理学的値がベースライン生理学的値を下回る又は上回ることを示す発生フラグ、及び、フィルタ処理された生理学的値がベースラインメトリックを下回る又は上回る比較パーセンテージのうちの1つ以上を備える、請求項8に記載の方法。
- CHFリスクを判定する前記ステップは、発生フラグが所定の時間窓に関して生成される発生頻度を決定する工程を備える、請求項11に記載の方法。
- CHFリスクを判定する前記ステップが、連続する時間窓における発生頻度の動向を決定する工程を更に備える、請求項12に記載の方法。
- CHFリスクを判定する前記ステップが、所定の又が連続する時間窓における比較パーセンテージの大きさの動向を決定する工程を更に備える、請求項11に記載の方法。
- 複数の前記比較出力を組み合わせることによって複合リスク因子を形成するステップを更に備える、請求項8に記載の方法。
- 鬱血性心不全(CHF)入院のリスクを監視して判定するための装置であって、
患者の生理学的値を測定するように構成される3軸加速度計と、
患者の生理学的値を測定するように構成される1つ以上の生体インピーダンスセンサと、
患者の生理学的値を測定するように構成される心電図と、
前記測定された生理学的値を受信するための1つ以上のプロセッサと
を備える装置。 - 患者の身体に取り外し可能に取り付くことができるパッチを更に備える、請求項16に記載の装置。
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KR20210107428A (ko) * | 2020-02-24 | 2021-09-01 | 주식회사 에이티센스 | 기계 학습 기반의 심장질환 예측 모델 구축 장치, 심장질환 예측 모델 구축 방법, 기계 학습 기반으로 구축된 모델을 이용하여 심장질환 여부를 출력하는 심장질환 측정 장치, 방법 및 상기 방법들을 실행하기 위한 컴퓨터 프로그램들 |
KR102427287B1 (ko) | 2020-02-24 | 2022-08-01 | 주식회사 에이티센스 | 기계 학습 기반의 심장질환 예측 모델 구축 장치, 심장질환 예측 모델 구축 방법, 기계 학습 기반으로 구축된 모델을 이용하여 심장질환 여부를 출력하는 심장질환 측정 장치, 방법 및 상기 방법들을 실행하기 위한 컴퓨터 프로그램들 |
Also Published As
Publication number | Publication date |
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EP3065635A1 (en) | 2016-09-14 |
WO2015066430A1 (en) | 2015-05-07 |
CN105873504A (zh) | 2016-08-17 |
US20150126822A1 (en) | 2015-05-07 |
EP3065635A4 (en) | 2017-08-30 |
JP6455843B2 (ja) | 2019-01-23 |
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