JP6858751B2 - 糖尿病管理療法アドバイザ - Google Patents
糖尿病管理療法アドバイザ Download PDFInfo
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Images
Classifications
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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Description
インスリンの食事ボーラス={食事中の炭水化物のグラム}/CIR (1)
CB=(BG−BGTarget)/CF (2)
EMAt=α(BGt)+(1−α)EMAt-1 (3)
式中、
α=2/(n+1)であり、
nは、相当する日数の平均である。他の実施形態では、n日における全てのBGの合計を関連する全ての値の総数(n)で除算したものを計算する算術移動平均が利用される。
TDD=QuickTransitionConstant*MTrans (4A)
式中のQuickTransitionConstantは、通常は1000に等しく、MTransは、SubQ遷移プロセスの開始時の患者の乗数である。他の実装では、TDDが、体重の関数としてのTDDの統計的相関によって計算される。使用される相関は以下の通りである。
TDD=0.5*体重(kg) (4B)
他の実装では、患者の1日の総投与量TDDが以下の式に従って計算される。
TDD=(BGTarget−K)*(MTrans)*24 (4C)
式中のMTransは、SubQ遷移プロセスの開始時の患者の乗数である。
Err%=ABS[(BGnext−BGTC)/BGTC] (5)
式中のBGTCは、SubQ情報208cから取得されたBG目標範囲BGTRの目標中心である。
BrkErr%=ABS[(BGBreakfastNext−BGTC)/BGTC] (6)
P=(子ボックス内のN)/(親ボックス内のN) (7)
式中の子ボックスは、現在の属性に対応する。
20 ネットワーク
24 ストレージ
40 医師/看護師/医療提供者(HCP)
42 診療所又は病院のコールセンター、電話医療センター
100 臨床判断支援システム
110a デスクトップコンピュータ
110b ポータブル電子装置
112a〜e データプロセッサ
114a〜e 非一時的メモリ
116a〜e ディスプレイ
118 キーボード
120 警報
122 スピーカ
123 皮下インスリン装置
123a インスリンポンプ
123b インスリンペ
124 血糖値測定器
130 サービス提供者
132 データプロセッサ
134 非一時的メモリ
140 医療提供者電子医療記録システム
142 データプロセッ
144 非一時的メモリ
146 ディスプレイ
150 警報
152 スピーカ
160 投与コントローラ
190 計器メーカー
196 ダウンロードプログラム
198 ウェブベースアプリケーション
Claims (24)
- 治療患者(10)のための治療量(336)を決定する方法(500)であって、
データ処理ハードウェア(112、132、142、192)によって、該データ処理ハードウェア(112、132、142、192)と通信する記憶ハードウェア(24、114、134、144、194)から患者集団の複数の患者(10)の訓練データ(310)を取得するステップを含み、前記訓練データ(310)は、許可された医療専門家(40)に関連する医療専門家コンピューティングデバイスによって前記記憶ハードウェア(24、114、134、144、194)に入力され、前記訓練データ(310)は、前記患者集団の各患者(10)の訓練血糖履歴データ(208b)と、訓練患者状態情報(208a)とを含み、前記訓練血糖履歴データ(208b)は、前記患者集団の前記患者(10)に投与されたインスリンの治療量(316、336)と、前記患者集団の前記患者(10)に投与されたインスリンの各治療量(316、336)に関連する1又は2以上の結果属性(318、338)とを含み、前記治療量(316、336)は、1日を通じて複数の予定される時間間隔のそれぞれの間に前記患者集団のそれぞれの患者(10)に投与されるインスリンの食事ボーラス、及び
前記患者集団のそれぞれの患者(10)に投与される基礎インスリン量、を含み、
前記方法(500)は、
前記患者集団の各患者(10)について、前記データ処理ハードウェア(112、132、142、192)によって、好ましい結果属性(318、338)をもたらす前記インスリンの治療量(316、336)から最適なインスリンの治療量(340)を識別するステップであって、前記識別された最適なインスリンの治療量は、各予定される時間間隔に関連する最適なインスリンの食事ボーラス、及び最適な基礎インスリン量を含むものであるステップと、
前記データ処理ハードウェア(112、132、142、192)によって、前記治療患者(10)の患者状態情報(208a)を受け取るステップと、
前記データ処理ハードウェア(112、132、142、192)によって、前記患者集団のうちの、前記治療患者(10)の前記患者状態情報(208a)と同様の訓練患者状態情報(208a)を有する前記患者(10)に関連する前記識別された最適な治療量(340)のうちの1つ又は2つ以上に基づいて、前記治療患者(10)のための次回の推奨インスリン治療量(336)を決定するステップと、
前記データ処理ハードウェア(112、132、142、192)から投与装置(123)に、前記次回の推奨インスリン治療量(336)を送信するステップであって、前記投与装置(123)は、前記治療患者に前記次回の推奨インスリン治療量(336)を投与するように構成された投薬器(223a)を含むものであるステップと、
をさらに含む、ことを特徴とする方法(500)。 - 前記訓練データ(310)を取得するステップは、繰り返し生じる構成可能な時間間隔の終了時に前記訓練データ(310)を自動的に取得するステップを含む、
請求項1に記載の方法(500)。 - 前記訓練データ(310)を取得するステップは、前記データ処理ハードウェア(112、132、142、192)と通信するディスプレイ(116、146)上に表示された即時開始ボタン(305)を選択するユーザ入力に応答して、前記訓練データ(310)を即座に取得するステップを含む、
請求項1に記載の方法(500)。 - 前記訓練データ(310)を取得するステップは、前記訓練データ(310)を選択日に取得するステップを含む、
請求項1に記載の方法(500)。 - 前記治療患者(10)のための前記次回の推奨インスリン治療量(336)を決定するステップは、
前記治療患者(10)の前記患者状態情報(208a)に基づいて、前記治療患者(10)がインスリンを必要としていると判断するステップと、
予定される時間間隔中に前記治療患者(10)に以前に投与されたインスリンの食事ボーラスを受け取るステップと、
前記予定される時間間隔に関連する、前記識別された最適なインスリンの食事ボーラス、及び前記受け取った、前記予定される時間間隔中に前記治療患者(10)に以前に投与されたインスリンの食事ボーラスに基づいて、前記治療患者(10)のための、前記予定される時間間隔中における次回の推奨治療量(430)を決定するステップと、
を含む、請求項1に記載の方法(500)。 - 前記予定される時間間隔は、朝食前の時間間隔、昼食前の時間間隔、夕食前の時間間隔、就寝時の時間間隔、又は睡眠中の時間間隔を含む、
請求項5に記載の方法(500)。 - 前記訓練血糖履歴データ(208b)の前記1又は2以上の結果属性(318、338)は、次回の予定血糖測定値(BGnext)と血糖目標範囲(BGTR)との関数に基づく血糖パーセント誤差(Err%)を含み、前記次回の予定血糖測定値は、対応するインスリンの治療量(316、336)の投与後に生じる血糖測定値に対応する、
請求項1に記載の方法(500)。 - 前記治療患者(10)のための前記次回の推奨インスリン治療量(336)を決定するステップは、
前記治療患者(10)の前記患者状態情報(208a)に基づいて、前記治療患者(10)がインスリンを必要としていると判断するステップと、
前記治療患者(10)に以前に投与された基礎インスリン量を受け取るステップと、
前記識別された最適な基礎量、及び前記受け取った基礎インスリン量のうちの少なくとも一方に基づいて、前記治療患者(10)のための次回の推奨基礎量(458)を決定するステップと、
を含む、請求項1に記載の方法(500)。 - 前記次回の推奨インスリン治療量(336)を前記治療患者に関連する、前記次回の推奨インスリン治療量を表示するように構成されたポータブル装置に送信するステップをさらに含む、
請求項1に記載の方法(500)。 - 前記データ処理ハードウェア(112、132、142、192)によって、新しい治療患者のための患者状態情報(208a)を受信するステップと、
前記データ処理ハードウェア(112、132、142、192)によって、前記新しい治療患者(10)の前記患者状態情報(208a)に基づいて、前記新しい治療患者(10)の治療のために抗糖尿病薬が使用可能であると判断するステップと、
前記データ処理ハードウェア(112、132、142、192)によって、前記新しい治療患者(10)の糖化ヘモグロビン測定値(A1c)を受け取るステップと、
前記データ処理ハードウェア(112、132、142、192)によって、前記糖化ヘモグロビン測定値(A1c)と前記訓練データ(310)とに基づいて、前記新しい治療患者(10)のための抗糖尿病薬投与計画を決定するステップと、
をさらに含む、請求項1に記載の方法(500)。 - 前記訓練データ(310)は、前記患者集団の患者(10)に投与された抗糖尿病薬併用量、及び各抗糖尿病薬投与計画に関連する糖化ヘモグロビン測定値(A1c)をさらに含む、
請求項10に記載の方法(500)。 - 前記患者状態情報(208a)は、前記患者(10)に関連する複数の患者状態属性(314)を含み、該患者状態属性(314)は、年齢属性、性別属性、医療履歴属性、肥満度指数属性、医療履歴属性、リスク因子属性及び/又は財務属性のうちの1つ又は2つ以上を含む、
請求項1に記載の方法(500)。 - システム(100)であって、
データ処理ハードウェア(112、132、142、192)と、該データ処理ハードウェア(112、132、142、192)と通信する記憶ハードウェア(24、114、134、144、194)とを含む投与量コントローラ(160)を備え、該投与量コントローラ(160)は、
前記記憶ハードウェア(24、114、134、144、194)から患者集団の複数の患者(10)の訓練データ(310)を取得し、前記訓練データ(310)は、許可された医療専門家(40)に関連する医療専門家コンピューティングデバイスによって前記記憶ハードウェア(24、114、134、144、194)に入力され、前記訓練データ(310)は、前記患者集団の各患者(10)の訓練血糖履歴データ(208b)と、訓練患者状態情報(208a)とを含み、前記訓練血糖履歴データ(208b)は、前記患者集団の前記患者(10)に投与されたインスリンの治療量(316、336)と、前記患者集団の前記患者(10)に投与されたインスリンの各治療量(316、336)に関連する1又は2以上の結果属性(318、338)とを含み、前記治療量(316、336)は、1日を通じて複数の予定される時間間隔のそれぞれの間に前記患者集団のそれぞれの患者(10)に投与されるインスリンの食事ボーラス、及び
前記患者集団のそれぞれの患者(10)に投与される基礎インスリン量、を含み、
前記投与量コントローラ(160)は、
前記患者集団の各患者(10)について、好ましい結果属性(318、338)をもたらす前記インスリンの治療量から1又は2以上の最適なインスリンの治療量(340)を識別し、前記識別された最適なインスリンの治療量は、各予定される時間間隔に関連する最適なインスリンの食事ボーラス、及び最適な基礎インスリン量を含み、
治療患者(10)の患者状態情報(208a)を受け取り、
前記患者集団のうちの、前記治療患者(10)の前記患者状態情報(208a)と同様の訓練患者状態情報(208a)を有する前記患者(10)に関連する前記識別された最適な治療量(340)のうちの1つ又は2つ以上に基づいて、前記治療患者(10)のための次回の推奨インスリン治療量(336)を決定し、
前記データ処理ハードウェア(112、132、142、192)から投与装置(123)に、前記次回の推奨インスリン治療量(336)を送信し、前記投与装置(123)は、前記治療患者に前記次回の推奨インスリン治療量を投与するように構成された投薬器(223a)を含むものであり、
ことを特徴とするシステム(100)。 - 前記訓練データ(310)を取得することは、繰り返し生じる構成可能な時間間隔の終了時に前記訓練データ(310)を自動的に取得することを含む、
請求項13に記載のシステム(100)。 - 前記訓練データ(310)を取得することは、前記データ処理ハードウェア(112、132、142、192)と通信するディスプレイ(116、146)上に表示された即時開始ボタン(305)を選択するユーザ入力に応答して、前記訓練データ(310)を即座に取得することを含む、
請求項13に記載のシステム(100)。 - 前記訓練データ(310)を取得することは、前記訓練データ(310)を選択日に取得することを含む、
請求項13に記載のシステム(100)。 - 前記治療患者(10)のための前記次回の推奨インスリン治療量(336)を決定することは、
前記治療患者(10)の前記患者状態情報(208a)に基づいて、前記治療患者(10)がインスリンを必要としていると判断することと、
予定される時間間隔中に前記治療患者(10)に以前に投与されたインスリンの食事ボーラスを受け取ることと、
前記予定される時間間隔に関連する、前記識別された最適なインスリンの食事ボーラス、及び前記受け取った、前記予定される時間間隔中に前記治療患者(10)に以前に投与されたインスリンの食事ボーラスに基づいて、前記治療患者(10)のための、前記予定される時間間隔中における次回の推奨治療量(430)を決定することと、
を含む、請求項13に記載のシステム(100)。 - 前記予定される時間間隔は、朝食前の時間間隔、昼食前の時間間隔、夕食前の時間間隔、就寝時の時間間隔、又は睡眠中の時間間隔を含む、
請求項17に記載のシステム(100)。 - 前記訓練血糖履歴データ(208b)の前記1又は2以上の結果属性(318、338)は、次回の予定血糖測定値(BGnext)と血糖目標範囲(BGTR)との関数に基づく血糖パーセント誤差(Err%)を含み、前記次回の予定血糖測定値は、対応するインスリンの治療量(316、336)の投与後に生じる血糖測定値に対応する、
請求項13に記載のシステム(100)。 - 前記治療患者(10)のための前記次回の推奨インスリン治療量(336)を決定することは、
前記治療患者(10)の前記患者状態情報(208a)に基づいて、前記治療患者(10)がインスリンを必要としていると判断することと、
前記治療患者(10)に以前に投与された基礎インスリン量を受け取ることと、
前記識別された最適な基礎量、及び前記受け取った基礎インスリン量のうちの少なくとも一方に基づいて、前記治療患者(10)のための次回の推奨基礎量(458)を決定することと、
を含む、請求項13に記載のシステム(100)。 - 前記投与量コントローラは、前記次回の推奨インスリン治療量(336)を前記治療患者に関連する、前記次回の推奨インスリン治療量を表示するように構成されたポータブル装置に送信する、
請求項13に記載のシステム(100)。 - 前記投与量コントローラは、
新しい治療患者のための患者状態情報(208a)を受信し、
前記新しい治療患者(10)の前記患者状態情報(208a)に基づいて、前記新しい治療患者(10)の治療のために抗糖尿病薬が使用可能であると判断し、
前記新しい患者(10)の糖化ヘモグロビン測定値(A1c)を受け取り、
前記糖化ヘモグロビン測定値(A1c)と前記訓練データ(310)とに基づいて、前記新しい治療患者(10)のための抗糖尿病薬投与計画を決定する、
請求項13に記載のシステム(100)。 - 前記訓練データ(310)は、前記患者集団の患者(10)に投与された抗糖尿病薬併用量、及び各抗糖尿病薬投与計画に関連する糖化ヘモグロビン測定値(A1c)をさらに含む、
請求項22に記載のシステム(100)。 - 前記患者状態情報(208a)は、前記患者(10)に関連する複数の患者状態属性(314)を含み、該患者状態属性(314)は、年齢属性、性別属性、医療履歴属性、肥満度指数属性、医療履歴属性、リスク因子属性及び/又は財務属性のうちの1つ又は2つ以上を含む、
請求項13に記載のシステム(100)。
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