JP4968800B2 - How to predict the degree of improvement in patient prognosis - Google Patents
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Description
本発明は、医療施設や介護・養護施設などの各種施設の患者・利用者に対するＮＳＴ（Nutrition Support Team：栄養サポートチーム）や栄養管理士による栄養管理において、患者等の栄養状態評価データからＮＳＴ介入後の改善状態を予測する方法に関する。 The present invention provides NST intervention based on nutritional status evaluation data of patients in NST (Nutrition Support Team) and nutrition managers for patients and users of various facilities such as medical facilities and nursing / nursing facilities. The present invention relates to a method for predicting a later improvement state.
従来より、医療施設、介護・養護施設、訪問看護ステーション、学校施設など、栄養管理の実施を必要とする施設においては、ＮＳＴ（栄養サポートチーム）やそれに準じる栄養管理者による患者・利用者の栄養管理活動が実践されている。このような栄養管理は、適切な栄養療法の選択、適切かつ質の高い栄養管理の提供、早期栄養障害の発見と早期栄養療法の開始、栄養療法による合併症の予防（カテーテル肺血症）、感染症等による死亡率の軽減、在院日数の短縮とそれによる入院費の節減、在宅治療症例の再入院や重症化の抑制などを目的としている。 Traditionally, in facilities that require nutrition management, such as medical facilities, nursing / nursing facilities, visiting nursing stations, school facilities, etc., the nutrition of patients and users by NST (Nutrition Support Team) or equivalent nutrition managers Management activities are practiced. Such nutritional management includes selection of appropriate nutritional therapy, provision of appropriate and high quality nutritional management, detection of early malnutrition and initiation of early nutritional therapy, prevention of complications due to nutritional therapy (catheter lung disease), The purpose is to reduce mortality due to infectious diseases, shorten hospital stays and thereby reduce hospitalization costs, re-hospitalization of home treatment cases, and control of severity.
かかる目的から、例えば、医療施設では、栄養管理情報（身体計測値、生化学検査値、食事の種類など患者のデータ）から、患者ごとに必要な栄養量や栄養評価などを決定しながら最適な栄養管理活動を実行する試みが種々なされており、取得した栄養管理情報に基づいて、さらに改良を加えた栄養管理を実施する試みもなされている。 For this purpose, for example, in medical facilities, optimal nutritional information (physical measurement values, biochemical test values, meal types, etc., patient data) is determined while determining the required nutritional amount and nutritional evaluation for each patient. Various attempts have been made to execute nutritional management activities, and attempts have been made to implement further improved nutritional management based on the acquired nutritional management information.
非特許文献１には、患者の血中亜鉛（Ｚｎ）濃度を従来の栄養評価項目に加えることで、より精度の高い評価が可能になり、特に予後予測には有用であることが開示されている。しかしながら、非特許文献１の栄養評価方法によっても、患者の予後予測の精度は必ずしも十分とはいえなかった。
本発明は、患者の栄養管理情報から予後（栄養管理実施後）の健康状態改善度合の予測を高い精度で行なうことのできる予測方法の提供を課題とする。さらに詳しくは、患者が栄養管理実施後に改善（外来退院または転院など）、非改善（原疾患憎悪による栄養管理中止など）のいずれの状態になるかを、栄養管理開始時の患者の生化学的検査値を基に高い精度で予測することのできる予後健康状態改善度合の予測方法（以下、単に予後予測方法と略すことがある。）を提供することを課題とする。 An object of the present invention is to provide a prediction method capable of predicting the degree of improvement in the health state of the prognosis (after the execution of nutrition management) from the patient's nutrition management information with high accuracy. More specifically, the patient's biochemical status at the start of nutritional management will indicate whether the patient will be improved (outpatient discharge or hospital transfer, etc.) or not improved (such as discontinuation of nutritional management due to exacerbation of the primary disease). It is an object of the present invention to provide a method for predicting the degree of improvement in prognostic health that can be predicted with high accuracy based on test values (hereinafter, simply abbreviated as a prognostic method).
本発明は、栄養管理を実施した患者の予後の健康状態改善度合を予測する方法であって、
（Ａ）栄養管理の開始時に患者の生化学検査値であるヘマトクリット、血中尿素窒素濃度、血中アルブミン濃度および血中亜鉛濃度を測定し、
（Ｂ）測定した各生化学検査値をそれぞれ下記式（１）に代入することによりＳＰＩ（簡易予後指数）値を算出し、
（Ｃ）得られたＳＰＩ値があらかじめ設定した基準値以上である場合は予後に患者が改善状態となり、該基準値未満である場合は予後に患者が非改善状態となると予測する、
患者の予後健康状態改善度合の予測方法。
ＳＰＩ＝ａ0＋ａ1×Ｈｃｔ＋ａ2×ＢＵＮ＋ａ3×ＡＬＢ＋ａ4×Ｚｎ （１）
（式（１）において、Ｈｃｔはヘマトクリット、ＢＵＮは血中尿素窒素濃度、ＡＬＢは血中アルブミン濃度、Ｚｎは血中亜鉛濃度を示す。また、ａ0、ａ1、ａ2、ａ3およびａ4は、あらかじめ複数の患者について測定した栄養管理開始時のヘマトクリット、血中尿素窒素濃度、血中アルブミン濃度および血中亜鉛濃度を説明変数とし、該患者の栄養管理終了時の健康状態改善度合を数値化したものを目的変数とした線形重回帰分析を行うことにより求めた偏回帰係数である。）。
The present invention is a method for predicting the degree of prognostic health improvement of a patient who has performed nutritional management,
(A) Measure hematocrit, blood urea nitrogen concentration, blood albumin concentration and blood zinc concentration, which are biochemical test values of the patient at the start of nutritional management,
(B) The SPI (simple prognostic index) value is calculated by substituting each measured biochemical test value into the following formula (1),
(C) If the obtained SPI value is greater than or equal to a preset reference value, predict that the patient will be in an improved state, and if less than the reference value, predict the patient will be in an unimproved state,
A method for predicting the degree of improvement in the prognosis health status of patients.
SPI = a 0 + a 1 × Hct + a 2 × BUN + a 3 × ALB + a 4 × Zn (1)
(In Formula (1), Hct represents hematocrit, BUN represents blood urea nitrogen concentration, ALB represents blood albumin concentration, and Zn represents blood zinc concentration. Also, a 0 , a 1 , a 2 , a 3 and a 4 uses hematocrit, blood urea nitrogen concentration, blood albumin concentration and blood zinc concentration at the start of nutritional management measured in advance for a plurality of patients as explanatory variables, and the degree of improvement in the health status of the patient at the end of nutritional management. This is the partial regression coefficient obtained by performing linear multiple regression analysis using the numerical value as the objective variable.)
本発明の予測方法において、前記式（１）におけるＨｃｔの単位が％、ＢＵＮの単位が（ｍｇ／ｄＬ）、ＡＬＢの単位がｇ／ｄＬ、Ｚｎの単位がμｇ／ｄＬであることが好ましい。 In the prediction method of the present invention, it is preferable that the Hct unit in the formula (1) is%, the BUN unit is (mg / dL), the ALB unit is g / dL, and the Zn unit is μg / dL.
本発明の予測方法において、前記式（１）が下記式（２）であることが好ましい。
ＳＰＩ＝0.27×Ｈｃｔ−0.04×ＢＵＮ＋1.95×ＡＬＢ＋0.1×Ｚｎ−1.94 （２）
（式（２）において、Ｈｃｔはヘマトクリット（％）、ＢＵＮは血中尿素窒素濃度（ｍｇ／ｄＬ）、ＡＬＢは血中アルブミン濃度（ｇ／ｄＬ）、Ｚｎは血中亜鉛濃度（μｇ／ｄＬ）を示す。）。
In the prediction method of the present invention, the formula (1) is preferably the following formula (2).
SPI = 0.27 × Hct−0.04 × BUN + 1.95 × ALB + 0.1 × Zn−1.94 (2)
(In Formula (2), Hct is hematocrit (%), BUN is blood urea nitrogen concentration (mg / dL), ALB is blood albumin concentration (g / dL), Zn is blood zinc concentration (μg / dL) Is shown.)
また、前記基準値は好ましくは１５である。 The reference value is preferably 15.
本発明の予後予測方法によれば、患者の栄養評価データから患者の予後予測を高い精度で行なうことができる。すなわち、患者がＮＳＴ介入等の栄養管理実施後に改善（外来退院または転院）、非改善（原疾患憎悪によるＮＳＴ介入中止）のいずれの状態になるかを高い精度で予測することができる。 According to the prognosis prediction method of the present invention, the prognosis of a patient can be predicted with high accuracy from the patient's nutrition evaluation data. That is, it is possible to predict with high accuracy whether the patient will be improved (outpatient discharge or transfer) or not improved (NST intervention discontinued due to exacerbation of the primary disease) after performing nutrition management such as NST intervention.
これにより、ＮＳＴ介入等の栄養管理開始時に、あらかじめ栄養管理終了時に非改善例となりやすいハイリスク群患者を予測することができ、それらの患者に特別な栄養管理を実施するなど、非改善例を出さないための事前対応が可能となる。 As a result, at the start of nutrition management such as NST intervention, it is possible to predict the high-risk group patients who tend to be non-improvement cases at the end of nutrition management, and to implement special nutrition management for those patients. It is possible to respond in advance so that it does not come out.
本発明は、栄養管理を実施した患者の予後の健康状態改善度合の予測方法であって、下記（Ａ）〜（Ｃ）の工程からなる予測方法である。
（Ａ） 栄養管理の開始時に患者の生化学検査値であるヘマトクリット、血中尿素窒素濃度、血中アルブミン濃度および血中亜鉛濃度を測定する工程。
（Ｂ） 測定した各生化学検査値をそれぞれ下記式（１）に代入することによりＳＰＩ（簡易予後指数）値を算出する工程。
（Ｃ） 得られたＳＰＩ値があらかじめ設定した基準値以上である場合は予後に患者が改善状態となり、該基準値未満である場合は予後に患者が非改善状態となると予測する工程。
The present invention is a method for predicting the degree of improvement in the prognosis of a patient who has performed nutritional management, and is a prediction method comprising the following steps (A) to (C).
(A) A step of measuring hematocrit, blood urea nitrogen concentration, blood albumin concentration and blood zinc concentration, which are biochemical test values of the patient at the start of nutritional management.
(B) A step of calculating an SPI (simple prognostic index) value by substituting each measured biochemical test value into the following formula (1).
(C) A step of predicting that the patient has an improved prognosis if the obtained SPI value is greater than or equal to a preset reference value, and that the patient has an unimproved state if the SPI value is less than the reference value.
ＳＰＩ＝ａ0＋ａ1×Ｈｃｔ＋ａ2×ＢＵＮ＋ａ3×ＡＬＢ＋ａ4×Ｚｎ （１）
上記式（１）において、Ｈｃｔはヘマトクリット、ＢＵＮは血中尿素窒素濃度、ＡＬＢは血中アルブミン濃度、Ｚｎは血中亜鉛濃度を示す。一方、ａ0、ａ1、ａ2、ａ3およびａ4は偏回帰係数と呼ばれる数値であり、あらかじめ複数の患者について栄養管理開始時のヘマトクリット、血中尿素窒素濃度、血中アルブミン濃度および血中亜鉛濃度を測定しておき、さらに、該患者の栄養管理終了時の健康状態改善度合を判定した結果を改善なら「１」、非改善なら「２」と数値化しておいて、前者を説明変数（独立変数）、後者を目的変数（従属変数）とした線形重回帰分析を行うことにより求めた偏回帰係数である。
SPI = a 0 + a 1 × Hct + a 2 × BUN + a 3 × ALB + a 4 × Zn (1)
In the above formula (1), Hct represents hematocrit, BUN represents blood urea nitrogen concentration, ALB represents blood albumin concentration, and Zn represents blood zinc concentration. On the other hand, a 0 , a 1 , a 2 , a 3 and a 4 are numerical values called partial regression coefficients, and hematocrit, blood urea nitrogen concentration, blood albumin concentration and blood at the start of nutrition management for a plurality of patients in advance. Measure the concentration of zinc in the medium, and further determine the degree of improvement in the health status of the patient at the end of nutritional management by quantifying the result as “1” for improvement and “2” for non-improvement. This is a partial regression coefficient obtained by performing a linear multiple regression analysis using a variable (independent variable) and the latter as an objective variable (dependent variable).
本発明において、栄養管理とは、医療施設や介護・養護施設などの各種施設の患者・利用者に対するＮＳＴ（Nutrition Support Team：栄養サポートチーム）や栄養管理士による栄養管理などをいう。 In the present invention, nutrition management refers to NST (Nutrition Support Team) or nutrition management by a nutrition manager for patients and users of various facilities such as medical facilities and nursing / nursing facilities.
栄養管理を実施した患者の予後の健康状態改善度合とは、栄養管理終了した時点における患者の健康状態の改善（向上）の程度であり、特に、改善したか非改善であった（悪化した）かのどちらかを意味する。本発明の予測方法は、このように患者が栄養管理実施後（終了時）において改善するか非改善であるかを、栄養管理の開始時に予測する方法である。 The degree of improvement in the prognostic health status of patients who have undergone nutritional management is the degree of improvement (improvement) of the patient's health at the time of termination of nutritional management, and in particular, improved or not improved (deteriorated) Means either. The prediction method of the present invention is a method for predicting at the start of nutritional management whether the patient improves or does not improve after the nutritional management is performed (at the end).
＜患者の予後の健康状態改善度合の判断＞
患者の予後（栄養管理終了時）の健康状態改善度合は、主観的栄養評価と客観的栄養評価を総合して、改善または非改善のいずれかを判定する。主観的栄養評価とは、身体的活動の活発化、食欲の改善（食事の摂取が不可能から可能になることや経口摂取の増大など）、皮膚の状態の改善（褥瘡の改善、かさかさ度の軽減など）であり、客観的栄養評価とは、臨床検査データの改善、体重の増加などである。これらを総合して患者の改善・非改善を判定するが、改善例は明らかに見た目にも顔色や身体活動が活発化し、外来退院し、その後の通院での治療が可能な状態である。一方、非改善は原疾患憎悪による死亡、状態（病状）の悪化とともに栄養療法に反応しない（異化亢進）場合である。より客観的な判定を行うためにタンパク合成能（同化）の状態を示す血清アルブミン値やプレアルブミン値などを改善・非改善の判断指標としてもよい。
<Judgment of the degree of improvement in patient's prognosis>
The degree of improvement in the health status of the patient's prognosis (at the end of nutritional management) is determined by combining subjective and objective nutritional assessments to determine whether they are improved or not. Subjective nutritional assessment means increased physical activity, improved appetite (because it is impossible to eat or increase oral intake), improved skin condition (improved pressure ulcer, bulkiness) Objective nutritional assessment includes improvement of clinical laboratory data and weight gain. The improvement and non-improvement of the patient are judged by combining these, but the improvement example clearly shows that the complexion and physical activity are activated, and the patient is discharged from the outpatient and can be treated at the subsequent hospital visit. On the other hand, non-improvement is the case of death due to exacerbation of the primary disease, worsening of the condition (medical condition), and failure to respond to nutrition therapy (hypercatabolism). In order to make a more objective determination, serum albumin level or prealbumin level indicating the state of protein synthesis ability (anabolic) may be used as a determination index for improvement / non-improvement.
＜各生化学検査値の測定＞
本発明で使用される各生化学検査値は、臨床検査における種々公知の測定方法を用いて測定することができ、通常は患者等から血液を採取し、その血液を分析することで測定される。以下に各評価項目について説明する。
<Measurement of each biochemical test value>
Each biochemical test value used in the present invention can be measured using various known measurement methods in clinical tests, and is usually measured by collecting blood from a patient or the like and analyzing the blood. . Each evaluation item is demonstrated below.
（１）Ｈｃｔ（ヘマトクリット）の測定：
Ｈｃｔ(ヘマトクリット)は一定量の血液中に占める赤血球の割合（単位：％）である。Ｈｃｔの測定方法は、特に限定されず種々公知の測定方法を用いることができる。例えば、ミクロヘマトクリット法などの遠心法や、赤血球高値パルス検出方法などの電気抵抗法が挙げられる。
(1) Measurement of Hct (hematocrit):
Hct (hematocrit) is the ratio (unit:%) of red blood cells in a certain amount of blood. The method for measuring Hct is not particularly limited, and various known measuring methods can be used. Examples thereof include a centrifugal method such as a microhematocrit method and an electric resistance method such as a method for detecting a high-level red blood cell pulse.
（２）ＡＬＢ（血中アルブミン濃度）の測定：
ＡＬＢ（血中アルブミン濃度）の測定方法は、特に限定されず種々公知の測定方法を用いることができる。例えば、免疫学的方法、ＢＣＧ（ブロモクレゾールグリーン）法やＢＣＰ（ブロムクレゾールパープル）改良法を挙げることができる。ＢＣＧ法とは、検体中のＡｌｂはｐＨ４．０付近でＢＣＧと結合して、Ａｌｂ−ＢＣＧ複合体を生じ、メタクロマジー現象により色素は青色を呈する。その吸光度を測定することによってＡｌｂ濃度を換算する。ＢＣＧ法では一部の血中グロブリンも反応してしまうが、ＢＣＰはアルブミンとより特異的に反応するためＢＣＰ法の方が高い精度のＡＬＢ測定を行なうことができる。なお、ＢＣＧ法は簡便な測定方法であるが、免疫学的方法に比べて３．５ｇ／ｄＬでは０．１〜０．３ｇ／ｄＬ高めに測定される。
(2) Measurement of ALB (blood albumin concentration):
The measuring method of ALB (blood albumin concentration) is not particularly limited, and various known measuring methods can be used. For example, an immunological method, a BCG (bromocresol green) method, and a BCP (bromocresol purple) improvement method can be mentioned. In the BCG method, Alb in a specimen binds to BCG at around pH 4.0 to form an Alb-BCG complex, and the dye exhibits a blue color due to a metachromatic phenomenon. The Alb concentration is converted by measuring the absorbance. In the BCG method, some blood globulins also react. However, since BCP reacts more specifically with albumin, the BCP method can perform ALB measurement with higher accuracy. In addition, although the BCG method is a simple measuring method, it measures 0.1 to 0.3 g / dL higher at 3.5 g / dL than the immunological method.
ＢＣＰ改良法とは、試料中のアルブミンはブロムクレゾールパープルと結合し青色を呈し、６００ｎｍ付近で最大の吸光を示すことを利用し、標準物質を対照にして６００ｎｍでの吸光度変化量から試料中のアルブミン濃度を求める方法である。 The BCP modification method utilizes the fact that albumin in a sample binds to bromocresol purple and exhibits blue color, and shows the maximum absorbance near 600 nm. From the change in absorbance at 600 nm as a control, the standard substance is used as a control. This is a method for determining the albumin concentration.
（３）ＢＵＮ（血中尿素窒素濃度）の測定：
ＢＵＮ（血中尿素窒素濃度）の測定方法としては種々公知の方法を用いることができるが、例えば、ウレアーゼＧＬＤＨ法、ウレアーゼＩＣＤＨ法が挙げられる。この中でもウレアーゼＧＬＤＨ法が好敵に用いられる。ウレアーゼＧＬＤＨ法とは、以下の第一反応および第二反応を行い、補酵素（ＮＡＤＰＨ）の変化量を測定することによりＢＵＮを測定する方法である。
(3) Measurement of BUN (blood urea nitrogen concentration):
Various known methods can be used as a method for measuring BUN (blood urea nitrogen concentration), and examples thereof include urease GLDH method and urease ICDH method. Of these, the urease GLDH method is favorably used. The urease GLDH method is a method for measuring BUN by performing the following first reaction and second reaction and measuring the amount of change in coenzyme (NADPH).
（第一反応）
反応式（II）において反応式（Ｉ）で生じた内因性アンモニアをαケトグルタル酸、還元型ニコチンアミドアデニンジヌクレオチドリン酸（ＮＡＤＰＨ）、グルタミン酸脱水素酵素（ＧＬＤＨ）の作用により消去し、このとき生じた酸化型ニコチンアミドアデニンジヌクレオチドリン酸（ＮＡＤＰ）は反応式（III）においてＬ−イソクエン酸脱水素酵素（ＩＣＤＨ）の作用によって還元されＮＡＤＰＨへと変化する。
(First reaction)
In the reaction formula (II), endogenous ammonia produced in the reaction formula (I) is eliminated by the action of α-ketoglutarate, reduced nicotinamide adenine dinucleotide phosphate (NADPH), and glutamate dehydrogenase (GLDH). The produced oxidized nicotinamide adenine dinucleotide phosphate (NADP) is reduced to NADPH by the action of L-isocitrate dehydrogenase (ICDH) in the reaction formula (III).
（第二反応）
第一反応により内因性アンモニアを消去した後、尿素はウレアーゼの作用によりアンモニアと二酸化炭素に分解される。このアンモニアとα‐ケトグルタル酸（α‐ＫＧ）は、ＧＬＤＨの作用によりグルタミン酸に変化し、同時にＮＡＤＰＨはＮＡＤＰに変わる。ＮＡＤＰＨは３４０ｎｍに吸収極大をもち、この吸光度の減少速度を測定して尿素窒素値を求める。尚、このとき第一反応の反応式（III）は第二試薬に添加されているキレート剤の作用により停止している。
(Second reaction)
After eliminating endogenous ammonia by the first reaction, urea is decomposed into ammonia and carbon dioxide by the action of urease. The ammonia and α-ketoglutaric acid (α-KG) are changed to glutamic acid by the action of GLDH, and NADPH is simultaneously changed to NADP. NADPH has an absorption maximum at 340 nm, and the rate of decrease in absorbance is measured to determine the urea nitrogen value. At this time, the reaction formula (III) of the first reaction is stopped by the action of the chelating agent added to the second reagent.
尿素 ＋ Ｈ2Ｏ （＋ウレアーゼ） → ２ＮＨ3 ＋ ＣＯ2 ・・・（Ｉ）
α‐ケトグルタル酸 ＋ ＮＨ3 ＋ ＮＡＤＰＨ ＋ Ｈ+ （＋ＧＬＤＨ）
→ グルタミン酸 ＋ ＮＡＤＰ+ ＋ Ｈ2Ｏ ・・・（II）
ＮＡＤＰ+ ＋ Ｌ−イソクエン酸 （＋ＩＣＤＨ）
→ ＮＡＤＰＨ+ ＋ α-ケトグルタル酸 ＋ ＣＯ2 ・・・（III）
（４）Ｚｎ（血中亜鉛濃度）の測定：
Ｚｎ（血中亜鉛濃度）の測定としては種々公知の方法を用いることができるが、例えば、キレート法、原子吸光測定法が挙げられる。この中でも簡便性、正確性などの点でキレート法が好適に用いられる。キレート法とは、例えば、亜鉛とニトロＰＡＰＳ（下記化学式で示される化合物）がＺｎ‐ニトロＰＡＰＳ錯体（キレート化合物）を形成し、５７０ｎｍに吸収極大を持つことを利用して、標準物質を対照にして５７０ｎｍでの吸光度変化量から試料の濃度を求める方法である。
Urea + H 2 O (+ urease) → 2NH 3 + CO 2 (I)
α-ketoglutaric acid + NH 3 + NADPH + H + (+ GLDH)
→ Glutamic acid + NADP + + H 2 O (II)
NADP ++ L-isocitrate (+ ICDH)
→ NADPH + + α-ketoglutaric acid + CO 2 (III)
(4) Measurement of Zn (blood zinc concentration):
Various known methods can be used for measuring Zn (blood zinc concentration), and examples thereof include a chelate method and an atomic absorption measurement method. Among these, the chelate method is preferably used in terms of simplicity and accuracy. The chelate method uses, for example, a standard substance as a control by utilizing the fact that zinc and nitroPAPS (compound represented by the following chemical formula) form a Zn-nitroPAPS complex (chelate compound) and has an absorption maximum at 570 nm. This is a method for obtaining the concentration of the sample from the amount of change in absorbance at 570 nm.
以下に本発明で用いる統計解析の用語について説明する。
＜有意水準、ｐ値、危険率＞
有意とは、確率論・統計学の用語で、「確率的に偶然とは考えにくく、意味があると考えられる」ことを意味する。ｐ値は、帰無仮説の下で実際にデータから計算された統計量よりも極端な統計量が観測される確率をいう。
The terms of statistical analysis used in the present invention will be described below.
<Significance level, p-value, risk factor>
Significance is a term in probability theory / statistics that means "it is unlikely to be stochastic and considered meaningful". The p-value refers to the probability that a statistic more extreme than the statistic actually calculated from the data under the null hypothesis is observed.
有意水準α(０＜α＜１または０％＜α＜１００％）は、どの程度の正確さをもって帰無仮説Ｈ0を棄却するかを表す定数であり、統計的仮説検定を行う場合に、帰無仮説を棄却するかどうかを判定する基準である。有意水準αの仮説検定は、ｐ＜αの時にＨ0を棄却する。このとき、「統計量はα水準で有意である」という。Ｈ0が正しい場合に、これを棄却してしまう確率（第一種の誤り）はαに等しい。 The significance level α (0 <α <1 or 0% <α <100%) is a constant indicating how accurately the null hypothesis H 0 is rejected. When performing a statistical hypothesis test, This is a criterion for determining whether to reject the null hypothesis. The hypothesis test of significance level α rejects H 0 when p <α. At this time, “statistics are significant at the α level”. If H 0 is correct, the probability of rejecting it (the first type of error) is equal to α.
有意水準５％で検定を行うということは、第１種の過誤をおかす危険率が５％であることを意味する。すなわち、同様の調査・検定を行うと、２０回に１回は得られた結論が誤っていることを表す。「有意水準αで検定すると有意な差が認められた」ということと、「危険率αのもとで有意な差があるといえる」は同じような意味で使用される。 Performing a test at a significance level of 5% means that the risk rate of making a first type error is 5%. That is, if the same investigation / test is performed, the conclusion obtained once every 20 times is wrong. “A significant difference was found when tested at the significance level α” and “it can be said that there is a significant difference under the risk factor α” are used in the same meaning.
＜重回帰分析＞
以下に、複数（ｎ）の対象から得たデータを基に、重回帰分析を行う方法について説明する。
<Multiple regression analysis>
Hereinafter, a method for performing multiple regression analysis based on data obtained from a plurality (n) of objects will be described.
まず、回帰関係とは、異なったある変数に対して、別のパラメータに関する平均的な値が対応する関係をいう。前者の変数を独立変数、後者の変数を従属変数という。 First, the regression relationship is a relationship in which an average value for another parameter corresponds to a different variable. The former variable is called an independent variable, and the latter variable is called a dependent variable.
例えば、複数の人の身長と体重を測定した場合、身長が高い人は平均的に体重が高く、身長が低い人は平均的に体重が軽いことがわかる。このようなときに、異なった身長に対して異なった平均体重が対応するという関係を、身長に対する体重の回帰関係という。 For example, when the height and weight of a plurality of people are measured, it can be seen that a person with a high height has a high weight on average, and a person with a low height has a low weight on average. In such a case, a relationship in which different average weights correspond to different heights is referred to as a regression relationship of weight with respect to height.
一般に、２つの変数XおよびYがあるとき、Xの一定の値に対応するYの平均値のことを、YのXについての条件付平均値といい、以下、Yhatと表す。Yhatは、一般にXの値が異なれば異なるので、Xの関数であり、
式（３）： Yhat＝f(X)
と書き表すことができる。この式（３）のような関係を回帰関係といい、式（３）を回帰方程式または回帰関係式という。また、YhatをXに対するYの回帰という。そして、以上のような回帰関係の分析を回帰分析という。
In general, when there are two variables X and Y, an average value of Y corresponding to a certain value of X is called a conditional average value of X of Y, and is expressed as Y hat hereinafter. Y hat is generally a function of X because it differs for different values of X,
Formula (3): Y hat = f (X)
Can be written as: The relationship like this equation (3) is called a regression relationship, and the equation (3) is called a regression equation or a regression relationship equation. Y hat is called Y's return to X. The analysis of the regression relationship as described above is called regression analysis.
回帰方程式式（３）における関数f(X)のかたちとしては一般にいろいろなものが考えられるが、最もよく用いられるのは線形式（１次式）である。すなわち、
式（４）： Yhat＝a＋bX (a、bは定数)
である。これを線形回帰という。
Various forms of the function f (X) in the regression equation (3) can be generally considered, but the linear form (primary expression) is most often used. That is,
Formula (4): Y hat = a + bX (a and b are constants)
It is. This is called linear regression.
独立変数Xが１個の場合、線形回帰は式（４）のように簡単になり、このような回帰分析を特に単回帰分析（あるいは直線回帰）と呼ぶ。一方、重回帰分析とは、いくつかの独立変数X1、X2、・・・、Xmに基づいて、別の変数（従属変数）Yを予測するための回帰分析である。 When there is one independent variable X, linear regression is simplified as shown in Equation (4), and such regression analysis is particularly called single regression analysis (or linear regression). On the other hand, the multiple regression analysis is a regression analysis for predicting another variable (dependent variable) Y based on several independent variables X1, X2,.
重回帰分析において線形回帰を用いる場合、回帰方程式は以下のような式となる。
式（５）： Yhat＝ａ0＋ａ1X1＋ａ2X2＋・・・＋ａmXm (ａ0〜ａmは、定数)
これが線形重回帰である。なお、式（５）中の定数ａ0〜ａmは偏回帰係数と呼ばれる。
When linear regression is used in the multiple regression analysis, the regression equation is as follows.
This is linear multiple regression. Incidentally, the constant a 0 ~a m in formula (5) is called the partial regression coefficients.
次に、X1〜XmおよびYについての複数（ｎ）のデータが与えられた場合、線形重回帰分析により、どのように回帰(Yhat)を計算するか、すなわち、偏回帰係数(ａ0〜ａm)をどのように計算するかについて、以下に説明する。 Next, given multiple (n) data for X1 to Xm and Y, how to calculate regression (Y hat ) by linear multiple regression analysis, ie, partial regression coefficient (a 0 to The following describes how a m ) is calculated.
例えば、独立変数X1及びX2の２変数（患者の生化学検査値など）に対する従属変数Y（患者の予後改善度合を数値化したもの）について、ｎ個の対象（患者）について、X1、X2、Yの各データがあるとする。これらのデータに基づいて、X1、X2、Yの重回帰分析を行う場合、X1及びX2のYに対する関係が線形であるとすれば、回帰方程式は次の形で示される。
式（６）： Yhat＝ａ0＋ａ1X1＋ａ2X2
次に、ｎ個の対象（患者）のX1、X2、Yのデータから、最小二乗法により偏回帰係数(ａ0、ａ1およびａ2)を求める。すなわち、
式（７）： S＝Σ(Yi−Yhati)2 (i=1〜n)
＝Σ［Yi−(ａ0＋ａ1X1i＋ａ2X2i)］2(i=1〜n)
におけるSの値を最小とするようなａ0、ａ1およびａ2を決定する。
For example, with regard to the dependent variable Y (the numerical value of the patient's prognostic improvement) with respect to the two independent variables X1 and X2 (patient biochemical test values, etc.), for n subjects (patients), X1, X2, Suppose that there is Y data. Based on these data, when a multiple regression analysis of X1, X2, and Y is performed, assuming that the relationship between X1 and X2 with respect to Y is linear, the regression equation is expressed in the following form.
Formula (6): Y hat = a 0 + a 1 X1 + a 2 X2
Next, partial regression coefficients (a 0 , a 1 and a 2 ) are obtained from the data of X1, X2, and Y of n subjects (patients) by the least square method. That is,
Formula (7): S = Σ (Y i −Y hati ) 2 (i = 1 to n)
= Σ [Y i − (a 0 + a 1 X1 i + a 2 X2 i )] 2 (i = 1 to n)
A 0 , a 1 and a 2 are determined so as to minimize the value of S at.
Sの値を最小とするようなａ0、ａ1およびａ2は、前記式（７）について、ａ0、ａ1およびａ2の各々に関して偏微分した各式を0とおき、それらの連立方程式を解くことによって求めることができる。 A 0 , a 1, and a 2 that minimize the value of S are expressed by substituting 0 for each expression obtained by partial differentiation with respect to each of a 0 , a 1, and a 2 with respect to the expression (7). It can be obtained by solving the equation.
すなわち、各々の偏微分の式は、
式（８）： ∂S/∂ａ0 ＝Σ2［Y−(ａ0＋ａ1X1i＋ａ2X2i)］×(-1)＝0 (i=1〜n)
式（９）： ∂S/∂ａ1 ＝Σ2［Y−(ａ0＋ａ1X1i＋ａ2X2i)］×(-X1i)＝0 (i=1〜n)
式（１０）： ∂S/∂ａ2 ＝Σ2［Y−(ａ0＋ａ1X1i＋ａ2X2i)］×(-X2i)＝0 (i=1〜n)
であるから、これを整理すると、次のようになる。
式（１１）： nａ0＋ａ1ΣX1i＋ａ2ΣX2i＝ΣYi(i=1〜n)
式（１２）： ａ0ΣX1i＋ａ1ΣX1i 2＋ａ2ΣX1iX2i＝ΣX1iYi(i=1〜n)
式（１３）： ａ0ΣX2i＋ａ1ΣX1iX2i＋ａ2ΣX2i 2＝ΣX2iYi(i=1〜n)
この連立方程式（式（１１）〜式（１３））を解くことで、ａ0、ａ1およびａ2が求められる。これらの値を前記式（６）に代入することにより回帰方程式が完成する。
That is, each partial differential equation is
Expression (8): ∂S / ∂a 0 = Σ2 [Y− (a 0 + a 1 X1 i + a 2 X2 i )] × (−1) = 0 (i = 1 to n)
Expression (9): ∂S / ∂a 1 = Σ2 [Y− (a 0 + a 1 X1 i + a 2 X2 i )] × (−X1 i ) = 0 (i = 1 to n)
Expression (10): ∂S / ∂a 2 = Σ2 [Y− (a 0 + a 1 X1 i + a 2 X2 i )] × (−X2 i ) = 0 (i = 1 to n)
Therefore, when this is organized, it becomes as follows.
Expression (11): na 0 + a 1 ΣX1 i + a 2 ΣX2 i = ΣY i (i = 1 to n)
Expression (12): a 0 ΣX1 i + a 1 ΣX1 i 2 + a 2 ΣX1 i X2 i = ΣX1 i Y i (i = 1 to n)
Expression (13): a 0 ΣX2 i + a 1 ΣX1 i X2 i + a 2 ΣX2 i 2 = ΣX2 i Y i (i = 1 to n)
By solving these simultaneous equations (formulas (11) to (13)), a 0 , a 1 and a 2 are obtained. By substituting these values into the equation (6), the regression equation is completed.
以上の２変数回帰についての説明は、一般に回帰変数３個以上の多変数回帰
式（１４）： Y＝ａ0＋ａ1X1＋ａ2X2＋・・・＋ａmXm
についても拡張できる。
Above description of the two variables regression generally regressors three or more multivariate regression equation (14): Y = a 0 + a 1 X1 + a 2 X2 + ··· + a m Xm
Can also be extended.
この場合、前記式（１１）〜式（１３）にあたる連立方程式は、式（１５）〜式（１８）となり、これらの連立方程式を解くことにより、前記式（１４）の回帰方程式が求められる。
式（１５）： ∂S/∂ａ0
＝Σ2［Yi−(ａ0＋ａ1X1i＋ａ2X2i・・・＋ａmXm)］×(-1)＝0 (i＝1〜n)
式（１６）： ∂S/∂ａ1
＝Σ2［Y−(ａ0＋ａ1X1i＋ａ2X2i・・・＋ａmXm)］×(-X1i)＝0 (i＝1〜n)
式（１７）： ∂S/∂ａ2
＝Σ2［Y−(ａ0＋ａ1X1i＋ａ2X2i・・・＋ａmXm)］×(-X2i)＝0 (i=1〜n)
・・・
式（１８）： ∂S/∂ａm
＝Σ2［Y−(ａ0＋ａ1X1i＋ａ2X2i・・・＋ａmXm)］×(-Xmi)＝0 (i=1〜n)
本発明の偏回帰係数は、Ｈｃｔ（ヘマトクリット）、ＡＬＢ（血中アルブミン濃度）、ＢＵＮ（血中尿素窒素濃度）及びＺｎ（血中亜鉛濃度）を説明変数（独立変数）とした線形重回帰分析によって求められる。これら説明変数（独立変数）の単位は、本発明の式（１）により算出されるＳＰＩ値の数値範囲が変化するだけであるので、特に限定されるものではないが、臨床現場における測定項目として通常使用される単位を用いることが好ましい。具体的には、ヘマトクリットの単位が％、ＢＵＮ（血中尿素窒素濃度）の単位がｍｇ／ｄＬ、ＡＬＢ（血中アルブミン濃度）の単位がｇ／ｄＬ、Ｚｎ（血中亜鉛濃度）の単位がμｇ／ｄＬである。この偏回帰係数は、線形重回帰分析を行う際における母集団及び説明変数（独立変数）の単位に依存するが、ヒトがとり得る各測定値の平均値及びその日差変動を考慮すると、各測定項目の単位を、Ｈｃｔ（ヘマトクリット）の単位を％に、ＢＵＮ（血中尿素窒素濃度）の単位をｍｇ／ｄＬに、ＡＬＢ（血中アルブミン濃度）の単位をｇ／ｄＬに、Ｚｎ（血中亜鉛濃度）の単位をμｇ／ｄＬとした場合は、｜ａ1｜：｜ａ2｜：｜ａ3｜：｜ａ4｜＝0.27：0.04：1.95：0.1という関係に近づくものと考えられる。この場合、ａ2のみ負の値をとる。
In this case, the simultaneous equations corresponding to the equations (11) to (13) are equations (15) to (18), and the regression equations of the equation (14) can be obtained by solving these simultaneous equations.
Formula (15): ∂S / ∂a 0
= Σ2 [Y i − (a 0 + a 1 X1 i + a 2 X2 i ... + A m Xm)] × (−1) = 0 (i = 1 to n)
Formula (16): ∂S / ∂a 1
= Σ2 [Y− (a 0 + a 1 X1 i + a 2 X2 i ... + A m Xm)] × (−X1 i ) = 0 (i = 1 to n)
Formula (17): ∂S / ∂a2
= Σ2 [Y− (a 0 + a 1 X1 i + a 2 X2 i ... + A m Xm)] × (−X2 i ) = 0 (i = 1 to n)
...
Formula (18): ∂S / ∂am
= Σ2 [Y− (a 0 + a 1 X1 i + a 2 X2 i ... + A m Xm)] × (−Xm i ) = 0 (i = 1 to n)
The partial regression coefficient of the present invention is a linear multiple regression analysis with Hct (hematocrit), ALB (blood albumin concentration), BUN (blood urea nitrogen concentration) and Zn (blood zinc concentration) as explanatory variables (independent variables). Sought by. The unit of these explanatory variables (independent variables) is not particularly limited because it only changes the numerical range of the SPI value calculated by the equation (1) of the present invention. It is preferable to use a commonly used unit. Specifically, the unit of hematocrit is%, the unit of BUN (blood urea nitrogen concentration) is mg / dL, the unit of ALB (blood albumin concentration) is g / dL, and the unit of Zn (blood zinc concentration) is μg / dL. This partial regression coefficient depends on the unit of population and explanatory variables (independent variables) when performing linear multiple regression analysis, but taking into account the average value of each measurement value that humans can take and its daily fluctuation, each measurement The unit of the item is Hct (hematocrit) in%, BUN (blood urea nitrogen concentration) in mg / dL, ALB (blood albumin concentration) in g / dL, Zn (blood When the unit of zinc concentration is μg / dL, it is considered that the relation of | a 1 |: | a 2 |: | a 3 |: | a 4 | = 0.27: 0.04: 1.95: 0.1 is approached. In this case, only a 2 takes a negative value.
また、本発明の式（１）の偏回帰係数を求めるためには、複数の患者の栄養管理開始時のヘマトクリット、血中尿素窒素濃度、血中アルブミン濃度および血中亜鉛濃度をあらかじめ測定する必要があるが、本発明者らが求めた上記式（２）を用いることにより、必要なデータ数を収集する作業を省略して、予測方法を実施することができるので好ましい。 In addition, in order to obtain the partial regression coefficient of the formula (1) of the present invention, it is necessary to measure in advance hematocrit, blood urea nitrogen concentration, blood albumin concentration and blood zinc concentration at the start of nutritional management of a plurality of patients. However, it is preferable to use the above formula (2) obtained by the present inventors because the prediction method can be implemented by omitting the work of collecting the necessary number of data.
尚、式（１）及び（２）における各測定項目の濃度の単位は、線形重回帰分析を行った際の説明変数の単位と同一にしなければならないことは言うまでもない。式（２）は、Ｈｃｔ（ヘマトクリット）の単位を％に、ＢＵＮ（血中尿素窒素濃度）の単位をｍｇ／ｄＬに、ＡＬＢ（血中アルブミン濃度）の単位をｇ／ｄＬに、Ｚｎ（血中亜鉛濃度）の単位をμｇ／ｄＬとしている。 Needless to say, the unit of concentration of each measurement item in the equations (1) and (2) must be the same as the unit of explanatory variables when the linear multiple regression analysis is performed. Formula (2) is expressed as follows: Hct (hematocrit) in%, BUN (blood urea nitrogen concentration) in mg / dL, ALB (blood albumin concentration) in g / dL, Zn (blood The unit of (medium zinc concentration) is μg / dL.
さらに、式（１）によって算出されるＳＰＩ値及びその数値範囲を、例えば整数などのわかりやすい値にするために、偏回帰係数を調整することもできる。具体的には、ａ1、ａ2、ａ3、ａ4の４つの説明変数（独立変数）それぞれを一定の数値を乗じることにより、ＳＰＩ値の桁数を調整することができ、また、ａ0の値を変化させることによりＳＰＩ値が取り得る数値範囲をシフトさせることができる。例えば、式（１）の一態様である式（２）中における偏回帰係数は、後述する実施例においても説明しているが、臨床検査技師が親しみやすいように、ＳＰＩ値の小数点以下を四捨五入して整数で表せるよう、より詳細にはおよそ１０〜２０の数値範囲となるように、ａ1、ａ2、ａ3、ａ4の４つの偏回帰係数をそれぞれを１０倍している。 Furthermore, the partial regression coefficient can be adjusted in order to make the SPI value calculated by the equation (1) and its numerical range into an easy-to-understand value such as an integer. Specifically, the number of digits of the SPI value can be adjusted by multiplying each of the four explanatory variables (independent variables) a 1 , a 2 , a 3 , and a 4 by a fixed numerical value, and a By changing the value of 0, the range of values that the SPI value can take can be shifted. For example, the partial regression coefficient in equation (2), which is one aspect of equation (1), is also described in the examples described later, but rounds off the decimal point of the SPI value so that it is easy for clinical technologists to get familiar with. The four partial regression coefficients a 1 , a 2 , a 3 , and a 4 are each multiplied by 10 so that it can be expressed in integers, more specifically in a numerical range of about 10-20.
以下、実施例を挙げて本発明をより詳細に説明するが、本発明はこれらに限定されるものではない。 EXAMPLES Hereinafter, although an Example is given and this invention is demonstrated in detail, this invention is not limited to these.
＜各生化学検査値の測定＞
下記の実施例において、患者の各生化学検査値の測定は次のようにして行なった。
<Measurement of each biochemical test value>
In the following examples, each biochemical test value of a patient was measured as follows.
（１）Ｈｃｔ（ヘマトクリット）の測定：
測定方法としては電気抵抗法を用いて赤血球数（ＲＢＣ）および赤血球の平均サイズ（ＭＣＶ）から下式により計算した。
Ｈｃｔ＝（ＲＢＣ×ＭＣＶ）／１０
（式中、ＲＢＣは赤血球数（単位：106／μＬ）、ＭＣＶは赤血球の平均サイズ（単位：ｆＬ）を示す）。測定装置はABBOT（アボット）社製CELL-DYN（セルダイン）４０００を使用した。
(1) Measurement of Hct (hematocrit):
As a measurement method, the electrical resistance method was used to calculate from the red blood cell count (RBC) and the average red blood cell size (MCV) according to the following equation.
Hct = (RBC × MCV) / 10
(Where RBC is the number of red blood cells (unit: 10 6 / μL), MCV is the average size of red blood cells (unit: fL)). The measuring apparatus used was CELL-DYN 4000 manufactured by ABBOT.
（２）ＡＬＢ（血中アルブミン濃度）の測定：
測定方法としてはＢＣＰ（ブロムクレゾールパープル）改良法を使用した。測定装置としてＨ７６００（日立ハイテクノロジーズ製）、使用試薬としてアクアオートカイノスALB試薬（カイノス製）を用いて測定を行なった。
(2) Measurement of ALB (blood albumin concentration):
As a measuring method, BCP (Bromcresol Purple) improved method was used. Measurement was performed using H7600 (manufactured by Hitachi High-Technologies) as a measuring apparatus and Aqua Auto-Kinos ALB reagent (manufactured by Kainos) as a reagent used.
（３）ＢＵＮ（血液中尿素窒素濃度）の測定：
測定方法としてはウレアーゼＧＬＤＨ法を用い、測定装置としてＨ７６００（日立ハイテクノロジーズ製）、使用試薬としてクイックオートネオＵＮ（シノテスト製）を用いて測定を行なった。
(3) Measurement of BUN (blood urea nitrogen concentration):
As a measuring method, urease GLDH method was used, and measurement was performed using H7600 (manufactured by Hitachi High-Technologies) as a measuring apparatus and Quick Auto Neo UN (manufactured by Sinotest) as a reagent used.
（４）Ｚｎ（血中亜鉛濃度）の測定：
測定はニトロＰＡＰＳを用いたキレート法により行なり、亜鉛とニトロＰＡＰＳのキレート化合物であるＺｎ‐ニトロＰＡＰＳ錯体について、標準物質を対照にして５７０ｎｍでの吸光度変化量から試料の濃度を求めた。測定装置は日立ハイテクノロジーズ製のＨ７６００、測定試薬はプロ株式会社製のエスパＺｎを使用した。
(4) Measurement of Zn (blood zinc concentration):
The measurement was carried out by a chelate method using nitro PAPS, and for the Zn-nitro PAPS complex which is a chelate compound of zinc and nitro PAPS, the concentration of the sample was determined from the amount of change in absorbance at 570 nm with reference to the standard substance. The measuring device used was H7600 manufactured by Hitachi High-Technologies, and the measuring reagent used was ESPAR Zn manufactured by Pro Corporation.
＜重回帰分析＞
本発明においてＳＰＩを算出するための式（１）の偏回帰係数を求めるための重回帰分析は以下のようして行なった。
<Multiple regression analysis>
In the present invention, the multiple regression analysis for obtaining the partial regression coefficient of the equation (1) for calculating the SPI was performed as follows.
まず、栄養管理開始時の生化学検査値であるＨＣＴ（ヘマトクリット）、ＷＢＣ、ＬＹＭ、ＣＲＰ、ＡＳＴ、ＡＬＴ、Ｎａ、Ｃｌ、Ｋ、ＢＵＮ（血液尿素窒素）、クレアチニン、ＴＣ、ＴＧ、血糖、ＣｈＥ、ＡＬＢ（アルブミン）、Ｔ−Ｐ、PreＡＬＢ、Ｚｎ（血中亜鉛濃度）と栄養管理終了時の改善、非改善との相関関係を分析し、上記項目から有意水準５％（ｐ＜０．０５）以下となる項目を選択した。その結果、ＨＣＴ、ＢＵＮ、ＡＬＢ、Ｚｎは統計学上、独立した危険因子として抽出された。つまり、これらの項目が最も栄養管理終了時の改善、非改善と相関性が高い指標であると考えられる。 First, HCT (hematocrit), WBC, LYM, CRP, AST, ALT, Na, Cl, K, BUN (blood urea nitrogen), creatinine, TC, TG, blood sugar, ChE , ALB (albumin), TP, PreALB, Zn (blood zinc concentration) and the improvement and non-improvement at the end of nutritional management were analyzed. From the above items, the significance level was 5% (p <0.05). ) The following items were selected. As a result, HCT, BUN, ALB and Zn were statistically extracted as independent risk factors. In other words, these items are considered to be indicators that have the highest correlation with improvement and non-improvement at the end of nutritional management.
次に、ＨＣＴ、ＢＵＮ、ＡＬＢ、Ｚｎを説明変数（独立変数）とし、栄養管理終了時の改善、非改善を目的変数（従属変数）として線形重回帰分析を行なった。その際、改善、非改善はカテゴリーデータのためダミー変数に変換し改善「１」、非改善「２」とした。 Next, linear multiple regression analysis was performed with HCT, BUN, ALB, and Zn as explanatory variables (independent variables), and improvement and non-improvement at the end of nutritional management as objective variables (dependent variables). At that time, improvement and non-improvement were converted into dummy variables for category data, and were set as improvement “1” and non-improvement “2”.
重回帰分析（線形重回帰分析）にて求められた多項式（重回帰式）：
ｙ＝0.027×Ｈｃｔ−0.004×ＢＵＮ＋0.195×ＡＬＢ＋0.01×Ｚｎ−0.194
（式中、Ｈｃｔはヘマトクリット（％）、ＢＵＮは血中尿素窒素濃度（ｍｇ／ｄＬ）、ＡＬＢは血中アルブミン濃度（ｇ／ｄＬ）、Ｚｎは血中亜鉛濃度（μｇ／ｄＬ）を示す。）
のｙの値をもとに患者の予後健康状態の改善度合を予測する上で、使いやすい数値とするために、そのｙが理想的な栄養状態では２０以上になるように数式を設定することとした。上述のように、改善を「１」、非改善を「２」とした素の状態の重回帰分析により得られた重回帰式に各栄養評価項目の臨床検査値を代入した場合、ｙがおおよそ１〜２の間で推移するため、レンジが狭く数値の処理が困難であり、予測を行なう上で大切なカットオフ値の設定がしづらいためである。この改善策として、上記重回帰式のｙが整数となり、なおかつカットオフ値が把握しやすくなるようにするため、全ての偏回帰係数を１０倍して、算出したｙ値をＳＰＩ値とすることとした。
Polynomials (multiple regression equations) obtained by multiple regression analysis (linear multiple regression analysis):
y = 0.027 × Hct−0.004 × BUN + 0.195 × ALB + 0.01 × Zn−0.194
(In the formula, Hct represents hematocrit (%), BUN represents blood urea nitrogen concentration (mg / dL), ALB represents blood albumin concentration (g / dL), and Zn represents blood zinc concentration (μg / dL). )
To predict the degree of improvement in the patient's prognostic health based on the value of y, set a mathematical formula so that y is 20 or more in an ideal nutritional state. It was. As described above, when the clinical laboratory test value of each nutritional evaluation item is substituted into the multiple regression equation obtained by multiple regression analysis of the prime state where improvement is “1” and non-improvement is “2”, y is approximately This is because the transition is between 1 and 2, and the range is narrow, making it difficult to process numerical values, and it is difficult to set an important cutoff value for prediction. As an improvement measure, in order to make y in the multiple regression equation an integer and to make it easy to grasp the cut-off value, multiply all the partial regression coefficients by 10 and use the calculated y value as the SPI value. It was.
この結果以下の数式（２）が完成した。
ＳＰＩ＝0.27×Ｈｃｔ−0.04×ＢＵＮ＋1.95×ＡＬＢ＋0.1×Ｚｎ−1.94 （２）
（式（２）中、Ｈｃｔはヘマトクリット（％）、ＢＵＮは血中尿素窒素濃度（ｍｇ／ｄＬ）、ＡＬＢは血中アルブミン濃度（ｇ／ｄＬ）、Ｚｎは血中亜鉛濃度（μｇ／ｄＬ）を示す）。
As a result, the following formula (2) was completed.
SPI = 0.27 × Hct−0.04 × BUN + 1.95 × ALB + 0.1 × Zn−1.94 (2)
(In formula (2), Hct is hematocrit (%), BUN is blood urea nitrogen concentration (mg / dL), ALB is blood albumin concentration (g / dL), Zn is blood zinc concentration (μg / dL) Showing).
上記式（２）において、ＳＰＩ値は１０〜２０の範囲に収まり、以下のＲＯＣ解析から基準値（カットオフ値）をその中央値である１５と設定した。 In the above formula (2), the SPI value was within the range of 10 to 20, and the reference value (cutoff value) was set to 15 as its median value from the following ROC analysis.
（ＲＯＣ解析）
ＲＯＣ（receiving operating characteristic）曲線は、受信者動作特性曲線、等感受性曲線などと訳されるものであり、あるｄ’に対してフォールスアラームの比率を横軸に、ヒットの比率を縦軸にそれぞれとり、βを−∞から∞まで走査すると曲線が描かれるが、この曲線がＲＯＣ曲線である。ＲＯＣ曲線の曲線下面積はある課題での正答率とみなせる指標となることが知られている。
(ROC analysis)
ROC (receiving operating characteristic) curves are translated as receiver operating characteristic curves, isosensitivity curves, etc., and the ratio of false alarms to the horizontal axis and the ratio of hits to the vertical axis for a given d ' If β is scanned from −∞ to ∞, a curve is drawn, and this curve is an ROC curve. It is known that the area under the ROC curve is an index that can be regarded as the correct answer rate in a certain task.
本発明のＳＰＩを指標とした予測方法とＺｎ、ＡＬＢ、Ｈｃｔの単独値を指標とした予測方法について作成したＲＯＣ曲線を図１に示す。それぞれのカットオフ値とＲＯＣ曲線の曲線下面積を表１に示す。図１、表１の結果からＳＰＩのカットオフ値を１５に設定した本発明の予測方法が最も正答率が高いことが示される。 FIG. 1 shows ROC curves created for the prediction method using the SPI of the present invention as an index and the prediction method using the single values of Zn, ALB, and Hct as indices. Table 1 shows the cut-off values and the area under the ROC curve. 1 and Table 1 show that the prediction method of the present invention in which the cutoff value of SPI is set to 15 has the highest correct answer rate.
（実施例１）
男性１３名（年齢分布７４．１±９．８歳）および女性６名（年齢分布６８．８±１３．５歳）について、ＮＳＴ介入直後および終了時のＨｃｔ、ＢＵＮ、ＡＬＢ、Ｚｎを測定し上記式（２）からＳＰＩ値を求めた。各患者のＮＳＴ介入直後から終了時へのＳＰＩ値の推移を示すグラフを図２に示す。但し、男性１名は死亡してしまったために、図２は１８名分のデータを示している。
Example 1
Measure Hct, BUN, ALB, Zn immediately after and at the end of NST intervention in 13 men (age distribution 74.1 ± 9.8 years) and 6 women (age distribution 68.8 ± 13.5 years) The SPI value was determined from the above formula (2). A graph showing the transition of the SPI value from immediately after the NST intervention to the end of each patient is shown in FIG. However, since one male has died, FIG. 2 shows data for 18 persons.
図２の結果から、カットオフ値を１５に設定することで、高い精度で予後の予測を行なえることがわかる。 From the result of FIG. 2, it is understood that the prognosis can be predicted with high accuracy by setting the cutoff value to 15.
（実施例２）
男性２１名（年齢分布７０．０±９．９歳）および女性７名（年齢分布７１．４±７．５歳）について、ＮＳＴ介入直後および終了時のＨｃｔ、ＢＵＮ、ＡＬＢ、Ｚｎを測定し上記式（２）からＳＰＩ値を求めた。各患者のＮＳＴ介入直後から終了時へのＳＰＩ値の推移を示すグラフを図３に示す。
(Example 2)
Measure Hct, BUN, ALB, Zn immediately after and at the end of the NST intervention in 21 males (age distribution 70.0 ± 9.9 years) and 7 females (age distribution 71.4 ± 7.5 years). The SPI value was determined from the above formula (2). A graph showing the transition of the SPI value from immediately after the NST intervention to the end of each patient is shown in FIG.
図３の結果から、カットオフ値を１５に設定することで、高い精度で予後の予測を行なえることがわかる。 From the result of FIG. 3, it can be seen that the prognosis can be predicted with high accuracy by setting the cut-off value to 15.
（実施例３）
男性３３名（年齢分布７１．４±９．８歳）および女性１３名（年齢分布７０．２±１０．３歳）について、ＮＳＴ介入直後および終了時のＨｃｔ、ＢＵＮ、ＡＬＢ、Ｚｎを測定し上記式（２）からＳＰＩ値を求めた。得られた結果における改善群と非改善群のリスク比、オッズ比を示す。リスク比とは、ある群におけるイベント発生率と、他群におけるイベント発生率の比をいう。リスク比が１以上になるとその危険因子によりその結果が起きやすいことを意味し、１未満であれば逆にその危険因子があるとその結果が起きにくい事を意味する。また、オッズ比とは、ある事象または命題に対して、ｐをその確率としたときのｐ／（１‐ｐ）の値をいう。本発明においては、全症例に対する改善群の比率をｐとして代入した上式の値を改善群と非改善群のオッズ比とした。
(Example 3)
Hct, BUN, ALB and Zn were measured immediately after and at the end of the NST intervention in 33 males (age distribution 71.4 ± 9.8 years) and 13 females (age distribution 70.2 ± 10.3 years). The SPI value was determined from the above formula (2). The risk ratio and odds ratio of the improved group and the non-improved group in the obtained results are shown. The risk ratio refers to the ratio between the event occurrence rate in a certain group and the event occurrence rate in another group. If the risk ratio is 1 or more, it means that the result is likely to occur due to the risk factor, and if it is less than 1, it means that if the risk factor is present, the result is difficult to occur. The odds ratio is a value of p / (1-p) where p is the probability for a certain event or proposition. In the present invention, the value of the above formula obtained by substituting the ratio of the improvement group with respect to all cases as p is defined as the odds ratio of the improvement group and the non-improvement group.
表２、３の結果から、本発明の予後予測方法が高い精度を有するものであることがわかる。 From the results of Tables 2 and 3, it can be seen that the prognosis prediction method of the present invention has high accuracy.
今回開示された実施の形態および実施例はすべての点で例示であって制限的なものではないと考えられるべきである。本発明の範囲は上記した説明ではなくて特許請求の範囲によって示され、特許請求の範囲と均等の意味および範囲内でのすべての変更が含まれることが意図される。 It should be understood that the embodiments and examples disclosed herein are illustrative and non-restrictive in every respect. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.
Claims (1)
（Ａ）栄養管理の開始時に患者の生化学検査値であるヘマトクリット、血中尿素窒素濃度、血中アルブミン濃度および血中亜鉛濃度を測定し、
（Ｂ）測定した各生化学検査値をそれぞれ下記式（２）に代入することによりＳＰＩ（簡易予後指数）値を算出し、
（Ｃ）得られたＳＰＩ値が１５以上である場合は予後に患者が改善状態となり、１５未満である場合は予後に患者が非改善状態となると予測する、
患者の予後健康状態改善度合の予測方法。
ＳＰＩ＝0.27×Ｈｃｔ−0.04×ＢＵＮ＋1.95×ＡＬＢ＋0.1×Ｚｎ−1.94 （２）
（式（２）において、Ｈｃｔはヘマトクリット（％）、ＢＵＮは血中尿素窒素濃度（ｍｇ／ｄＬ）、ＡＬＢは血中アルブミン濃度（ｇ／ｄＬ）、Ｚｎは血中亜鉛濃度（μｇ／ｄＬ）を示す。） A method for predicting the degree of improvement in prognostic health of patients who have undergone nutritional management,
(A) Measure hematocrit, blood urea nitrogen concentration, blood albumin concentration and blood zinc concentration, which are biochemical test values of the patient at the start of nutritional management,
(B) The SPI (simple prognostic index) value is calculated by substituting each measured biochemical test value into the following formula ( 2 ),
(C) If the obtained SPI value is 15 or more, predict that the patient will be in an improved state, and if it is less than 15 , predict the patient will be in an unimproved state,
A method for predicting the degree of improvement in the prognosis health status of patients.
SPI = 0.27 × Hct−0.04 × BUN + 1.95 × ALB + 0.1 × Zn−1.94 (2)
(In Formula (2), Hct is hematocrit (%), BUN is blood urea nitrogen concentration (mg / dL), ALB is blood albumin concentration (g / dL), Zn is blood zinc concentration (μg / dL) Is shown.)
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