JP2009222610A - Automatic analyzer - Google Patents

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JP2009222610A
JP2009222610A JP2008068657A JP2008068657A JP2009222610A JP 2009222610 A JP2009222610 A JP 2009222610A JP 2008068657 A JP2008068657 A JP 2008068657A JP 2008068657 A JP2008068657 A JP 2008068657A JP 2009222610 A JP2009222610 A JP 2009222610A
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time
automatic analyzer
measurement
value
abnormality
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Takahiro Sasaki
孝浩 佐々木
Taku Sakazume
卓 坂詰
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Hitachi High Tech Corp
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Hitachi High Tech Corp
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<P>PROBLEM TO BE SOLVED: To solve a problem wherein there are the possibility that a material other than an analyte, which is represented by a material contained in a system reagent for example, emits light and the possibility that the material disturbs the transmission of light, and an appropriate maintenance is required to be performed at a suitable time in order to improve the detection accuracy. <P>SOLUTION: The light emission amount obtained in a reaction process or the transmission amount of light to the material obtained in the reaction process are measured, analysis results are verified based on the measured value of multi-dimensional arrangement consisting of a plurality of items and a plurality of pieces obtained by measurement. The automatic analyzer has a function of storing the transition of the measured value obtained during the analysis, estimating a time when the measured value exceeds a predetermined value based on the trend of the variation, and providing the appropriate maintenance time, and a function of providing the appropriate maintenance contents based on the transition of the measured value. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

本発明は血液，尿などの生体サンプルの定性・定量分析を行う自動分析装置に係り、特にメンテナンスの時期，内容などを自動的に表示する機能を備えた自動分析装置に関する。   The present invention relates to an automatic analyzer that performs qualitative / quantitative analysis of biological samples such as blood and urine, and more particularly to an automatic analyzer that has a function of automatically displaying the time and content of maintenance.

自動分析装置は、試料と試薬を反応させ反応液の色の変化を吸光度変化として測定したり、試薬に外部からの化学的，物理的刺激により発光する発光体を用い、発光体の発光強度を測定することで、試料中の測定対象物の定性・定量分析を行うものである。   The automatic analyzer reacts the sample with the reagent, measures the change in color of the reaction solution as a change in absorbance, or uses a luminescent material that emits light from an external chemical or physical stimulus as the reagent. By measuring, qualitative and quantitative analysis of the measurement object in the sample is performed.

自動分析装置による分析では、測定が正しく行われたことを保証するための精度管理が重要である。この点、特許文献１には、各成分の濃度が既知の精度管理試料を測定し、測定結果が既知濃度の精度を保証する範囲内かどうかを判定することにより、精度管理を行う方法が記載されている。また、特許文献２には、生化学分野において、測定した吸光度のデータを、最小二乗法を用いて反応速度論から導き出される化学反応モデルで近似する手法を用い、精度の良い測定を行う方法が記載されている。更に、特許文献３には、測定時に発生する異常を検出する方法が記載されている。   In the analysis by the automatic analyzer, it is important to manage the accuracy in order to ensure that the measurement is correctly performed. In this regard, Patent Document 1 describes a method of performing quality control by measuring a quality control sample with a known concentration of each component and determining whether the measurement result is within a range that guarantees the accuracy of the known density. Has been. Further, Patent Document 2 discloses a method of performing high-precision measurement in a biochemical field using a method of approximating measured absorbance data with a chemical reaction model derived from reaction kinetics using a least square method. Are listed. Furthermore, Patent Document 3 describes a method for detecting an abnormality that occurs during measurement.

特開２００５−１２７７５７号公報JP 2005-127757 A 特表平６−１９４３１３号公報Japanese National Patent Publication No. 6-194313 特開２００４−３４７３８５号公報JP 2004-347385 A

前記の方法では、精度管理試料の測定結果が、それぞれの精度管理試料毎に定められた仕様範囲内かを判定することは可能であるが、仕様範囲外の結果が生じるか、あるいは生じる危険性がある時、その原因を追究し対策へ繋げるまでには至っていなかった。さらに、光の検出においては、例えばシステム試薬中の含有物質に代表されるような被検体以外の物質が発光する可能性や当該物質が光の透過の障害となる可能性があり、検出精度を向上させるためには、自動分析装置の状態を一定に保つことが重要で、自動分析装置の状態をコントロールするためには、的確な時期に適切なメンテナンスを実施する必要がある。   In the above method, it is possible to determine whether the measurement result of the quality control sample is within the specification range defined for each quality control sample, but the result out of the specification range or the risk of occurrence There was a time when the cause was investigated and connected to countermeasures. Furthermore, in the detection of light, for example, a substance other than the analyte, such as a substance contained in a system reagent, may emit light or the substance may become an obstacle to light transmission. In order to improve, it is important to keep the state of the automatic analyzer constant, and in order to control the state of the automatic analyzer, it is necessary to perform appropriate maintenance at an appropriate time.

本発明の目的は、異常反応が生じた場合には、予め装置上に記憶してあるデータベースを基に速やかに異常の内容を把握し、測定値の変動傾向から、測定値がある所定の値を超える時期を予測して的確なメンテナンス時期を示すことにある。また、精度管理試料や標準液等を測定することで取得する結果の変動傾向を用いて、自動分析装置の状態を把握することで、適切なメンテナンス内容を示すことにある。   The object of the present invention is to quickly grasp the contents of an abnormality based on a database stored in advance on the apparatus when an abnormal reaction occurs, and from the fluctuation tendency of the measured value, the measured value has a predetermined value. It is to predict the maintenance time by predicting the time exceeding. Moreover, it is to show appropriate maintenance contents by grasping the state of the automatic analyzer using the fluctuation tendency of the results obtained by measuring the quality control sample, standard solution, and the like.

上記目的を達成するために本発明による自動分析装置は以下の特徴を有する。
（１）自動分析装置による複数種類の管理試料の測定で取得される光量データについて、 各種類毎に平均値及び標準偏差を算出して時系列に記憶する。この時系列データを １種類以上の係数を含む時間関数で近似し、最小二乗法にて係数を同定する手段を 有することを特徴とする。
（２）（１）の自動分析装置であって、前記導出された係数からなる時間関数が、予め記 憶されている所定の値に達する時間を算出することで、測定値が所定の値を超える か否かを判定する機能を備えたことを特徴とする。
（３）（１）の自動分析装置であって、前記導出される時間の精度をあげるために、分析 対象として母集団に取り入れるデータの判断を、単数項目に対する正規分布による 方法のみならず、二項目に対する二次元正規分布の方法を用いることを特徴とする 。
（４）（１）の自動分析装置であって、（２）の機能により、正常反応と異なる反応が行 われた場合、自動分析装置に生じた異常の原因を推定し、必要に応じた適切なメン テナンス時期を提供する機能を備えたことを特徴とする。 In order to achieve the above object, an automatic analyzer according to the present invention has the following features.
(1) For light quantity data acquired by measuring a plurality of types of control samples by an automatic analyzer, an average value and a standard deviation are calculated for each type and stored in time series. The time series data is approximated by a time function including one or more types of coefficients, and has a means for identifying the coefficients by the least square method.
(2) The automatic analyzer according to (1), wherein the time function of the derived coefficient reaches a predetermined value stored in advance, so that the measured value becomes a predetermined value. It is characterized by having a function to determine whether or not it exceeds.
(3) In the automatic analyzer of (1), in order to increase the accuracy of the derived time, the determination of the data to be included in the population as the analysis target is not limited to the method based on the normal distribution for the singular item. It is characterized by using a two-dimensional normal distribution method for items.
(4) If the automatic analyzer in (1) performs a reaction different from the normal reaction by the function in (2), the cause of the abnormality that occurred in the automatic analyzer is estimated and It is characterized by providing a function to provide a proper maintenance period.

本発明によれば次の効果がある。ユーザーに提供される測定データの精度が向上し、結果として自動分析における検査項目に対する信頼性が向上する。   The present invention has the following effects. The accuracy of measurement data provided to the user is improved, and as a result, the reliability of inspection items in automatic analysis is improved.

前記、信頼性向上により、再検査にかかるラニングコストの削減が可能となる。   By improving the reliability, the running cost for re-inspection can be reduced.

また、測定時に異常が発生した場合、異常の種類毎に分類されているデータベースを基に、異常の原因を推定することが可能になり、異常の原因調査の時間が省略できる。   Further, when an abnormality occurs during measurement, it becomes possible to estimate the cause of the abnormality based on the database classified for each type of abnormality, and the time for investigating the cause of the abnormality can be omitted.

以下に図面を用いて本発明の実施例を説明する。   Embodiments of the present invention will be described below with reference to the drawings.

測定値の統計精度向上による適切なメンテナンス時期提供実施例
図１は本発明に関わるデータ解析の処理フローを示す。試料と試薬を所定の反応領域で混合させ化学的な発光を引き起こさせ、あるいは試料と試薬を所定の混合液体に対して所定の強度で光をあて、所定の時間内に所定の回数，測光を行う。ここで算出される測光データを基本単位として、測定時に毎回測定データを取得する。取得するデータは１００にて取り込む。これにより、精度管理試料や標準液等の測定値Ｉn(ｘ1，ｘ2，Λ，ｘn)が得られる。 Example of Providing Appropriate Maintenance Time by Improving Statistical Accuracy of Measurement Values FIG. 1 shows a processing flow of data analysis according to the present invention. A sample and a reagent are mixed in a predetermined reaction region to cause chemical luminescence, or the sample and a reagent are irradiated with light at a predetermined intensity to a predetermined liquid mixture, and photometry is performed a predetermined number of times within a predetermined time. Do. Using the photometric data calculated here as a basic unit, measurement data is acquired every time measurement is performed. Data to be acquired is captured at 100. As a result, measured values I n (x 1 , x 2 , Λ, x n ) of the quality control sample and standard solution are obtained.

次に、前記で取得した多次元配列の測定値について、統計的な精度を向上させる。当演算は、データ精度向上演算部１０１で行う。具体的な演算処理の方法を以下に示す。まず、項目毎に平均値および標準偏差を算出する。前記で算出された値をもとに、予測精度向上を目的としたデータの質向上を図るため、解析対象とする母集団への帰属の有無を評価する基準を挙げる。一つ目は、一種類の項目を対象に提供される基準である。一種類の項目では、測定値の集合は正規分布に従うものとする。正規分布は、ｘを一種類の測定値、ｆ(ｘ)を確率密度、μを当該測定値が属する測定値の集合における平均値、σを当該測定値が属する測定値の集合における標準偏差として、次の式で与えられる。   Next, statistical accuracy is improved for the measurement values of the multidimensional array acquired above. This calculation is performed by the data accuracy improvement calculation unit 101. A specific calculation processing method is shown below. First, an average value and a standard deviation are calculated for each item. In order to improve the quality of data for the purpose of improving the prediction accuracy based on the values calculated above, criteria for evaluating the presence / absence of belonging to a population to be analyzed are listed. The first is a standard provided for one type of item. For one type of item, the set of measurements shall follow a normal distribution. In the normal distribution, x is one type of measurement value, f (x) is a probability density, μ is an average value in a set of measurement values to which the measurement value belongs, and σ is a standard deviation in a set of measurement values to which the measurement value belongs. Is given by:

Figure 2009222610
Figure 2009222610

個々の測定値が、当該測定値が属する測定値の集合における平均値を中心としたある一定の範囲から外れた場合は、当該測定値を異常な測定値であるとする。前記範囲は各項目の測定値の特徴により決定されるものであり、当範囲を平均値から標準偏差（σ）のｋ倍と定義する。ｋ値は、項目毎に決められ、データベースとして、予め記憶機構に記憶されている。項目毎に、個々の測定値が、当該測定値が属する測定値の集合の平均値を中心としたある一定の範囲の内か外かを判定し、外であれば統計的な例外値として、以後、処理作業の対象となる母集団から除外する。   When an individual measurement value deviates from a certain range centered on an average value in a set of measurement values to which the measurement value belongs, the measurement value is regarded as an abnormal measurement value. The range is determined by the characteristic of the measured value of each item, and this range is defined as k times the standard deviation (σ) from the average value. The k value is determined for each item, and is stored in advance in the storage mechanism as a database. For each item, determine whether each measurement value is within or outside a certain range centered on the average value of the set of measurement values to which the measurement value belongs. Thereafter, it is excluded from the population to be processed.

二つ目は、二種類の項目を対象に提供される基準である。図４に例を示す。二種類の項目（４０１，４０２）は、測定値の集合は二次元正規分布に従うものとする。二次元的正規分布はｘ，ｙを異なる項目の測定値、μx，μyを当該測定値が属する測定値の集合におけるそれぞれの平均値、σxおよびσyを当該測定値が属する測定値の集合におけるそれぞれの標準偏差、ρを当該測定値が属する測定値の集合における母共標準偏差、ｆ(ｘ，ｙ)を確率密度として、 The second is a standard provided for two types of items. An example is shown in FIG. In the two types of items (401, 402), a set of measurement values is assumed to follow a two-dimensional normal distribution. In the two-dimensional normal distribution, x and y are measured values of different items, μ x and μ y are average values in a set of measured values to which the measured values belong, σ x and σ y are measured values to which the measured values belong. Each standard deviation in the set of ρ, ρ as the population standard deviation in the set of measurements to which the measurement belongs, and f (x, y) as the probability density,

Figure 2009222610
Figure 2009222610

で与えられる。この指数部分を−ｃ2／２とおくと、 Given in. When put this index part and -c 2/2,

Figure 2009222610
Figure 2009222610

という楕円が描かれ、この楕円上の点は全て等しい確率、 The ellipse is drawn, and all the points on this ellipse have the same probability,

Figure 2009222610
Figure 2009222610

を持つ（４０４，４０５，４０６）。任意の二種類の項目からなる個々の測定値４０３が、この楕円内に存在する点の確率は、１−exp(−ｃ2／２)で示される。当楕円の外側にある測定値を、当該集合における異常な測定値とした。ｃ値は、二種類の項目の組合せ毎に決められ、データベース１０５として、予め記憶機構に記憶されている。任意の項目毎に、個々の測定値が、当該当基準となる範囲の内か外かを判定し、外４０７であれば統計的な例外値として、以後、処理作業の対象となる母集団から除外する。 (404, 405, 406). Individual measurements 403 made of any two items, the probability of points present in this ellipse is represented by 1-exp (-c 2/2 ). The measurement value outside the ellipse was defined as an abnormal measurement value in the set. The c value is determined for each combination of two types of items, and is stored in advance in the storage mechanism as the database 105. For each arbitrary item, it is determined whether each measurement value is within or outside the standard range. If it is outside 407, it is determined as a statistical exception value, and from the population to be processed thereafter. exclude.

上記のルールに従い、データの統計的な精度を向上させた後、次は１０２にてメンテナンスが必要な時期を予測する演算を行う。図２に具体例を示す。時間軸２０１に対して発光感度測定値２０２の推移２０３が算出されている。当該発光感度は検出部や検出部まで試料を運搬する流路系、試薬や試料その他消耗品の劣化の状態を反映する測定項目である。発光感度の推移の変動を予測し２０５、予測した値とあらかじめデータベース１０５の記憶機構に記憶されている所定値２０４を比較し、所定値を下回るまたは超える時点２０６をメンテナンスが必要な時期と判断する。また、メンテナンスの緊急度合いに応じてその旨をユーザー側に表示する機能を備える。   After improving the statistical accuracy of the data according to the above rules, the next calculation is performed to predict when maintenance is required at 102. A specific example is shown in FIG. A transition 203 of the light emission sensitivity measurement value 202 is calculated with respect to the time axis 201. The light emission sensitivity is a measurement item that reflects the state of deterioration of the detection unit, the flow path system for transporting the sample to the detection unit, reagents, the sample, and other consumables. The variation of the light emission sensitivity is predicted 205, and the predicted value is compared with the predetermined value 204 stored in advance in the storage mechanism of the database 105, and a time point 206 that falls below or exceeds the predetermined value is determined as a time when maintenance is necessary. . In addition, a function for displaying the fact to the user according to the urgent degree of maintenance is provided.

次に、メンテナンスの内容を推測する１０３。目的成分のみが正常に反応し、成分の定量性に見合う量の発光あるいは透過が行われた理想的な状態における測定値で構成される測定値の集合がデータベース１０６として記憶機構に記憶されている。同時に、装置・試薬・試料それぞれを前記で定義された理想状態と異なる特徴を有する状態の場合に取得する測定値の集合がデータベースとして記憶機構に記憶されている。測定項目における変動傾向がデータベース上のどの部類に属する傾向があるかを判断することで、装置の状態を推測して把握することが可能となる。例えば、光電子倍増管に異常があった場合、予めデータベースとして装置に記憶してある光電子倍増管に異常があった場合に見出される測定値の集合に属するような傾向を持ち、「光電子倍増管異常」のような表示をすることでユーザー側に異常の種類ないし原因を知らしめさせることが可能となる。また、流路系に異常があった場合、磁性粒子が含まれる特定項目の測定値における傾向が同程度に変動するため、この変動をみつけ、「流路洗浄」のような表示をすることで、適切なメンテナンス箇所を指摘することが可能となる。同様に、試料の混合不十分による異常、試薬の劣化による異常などが前記の方法で検出された場合、それぞれ「試料の混合不十分による異常」「試薬の劣化」と表示する１０４ことで、ユーザー側に異常の種類ないし原因を知らせることが可能となる。図３は、二項目に共通の変動から、自動分析装置に生じた異常を特定した例を示す。測定に共通した要素を持つ二つの項目（発光感度３０１，磁性粒子擬似サンプル３０６）をそれぞれの時間軸３０２，３０７に対して記録する。自動分析装置以外の条件（例えば、試薬状態，検体サンプル状態，測定環境）を一致させた時取得する発光感度および磁性粒子擬似サンプル測定値の平均推移３０３，３０８に対し、特定装置で取得する発光感度および磁性粒子擬似サンプル測定値の平均推移３０４，３０９をそれぞれ比較する。この時、特定装置で取得する発光感度および磁性粒子擬似サンプル測定値で変化が生じているとする３０５，３１０と、発光感度および磁性粒子擬似サンプルの測定に共通した要素である流路系または検出部の磁石稼動機構に異常が生じていることがいることが推定できる。共通した要素に対し、洗浄・部品交換などのメンテナンスを実施するように表示することが可能になる１０４。   Next, the content of maintenance is estimated 103. A set of measured values composed of measured values in an ideal state in which only the target component reacts normally and light emission or transmission corresponding to the quantitative property of the component is performed is stored in the storage mechanism as the database 106. . At the same time, a set of measurement values obtained when the apparatus, reagent, and sample are in a state having characteristics different from the ideal state defined above is stored in the storage mechanism as a database. By determining to which category on the database the variation tendency of the measurement item belongs, it becomes possible to estimate and grasp the state of the apparatus. For example, when there is an abnormality in the photomultiplier tube, it has a tendency to belong to a set of measurement values found when there is an abnormality in the photomultiplier tube stored in the apparatus as a database in advance. It is possible to let the user know the type or cause of the abnormality. In addition, when there is an abnormality in the flow path system, the trend in the measured values of specific items that contain magnetic particles fluctuate to the same extent. This makes it possible to point out appropriate maintenance points. Similarly, when abnormalities due to insufficient sample mixing, abnormalities due to reagent deterioration, etc. are detected by the above methods, the user can display 104 “abnormality due to insufficient sample mixing” and “degradation of reagent”, respectively. It is possible to inform the side of the type or cause of the abnormality. FIG. 3 shows an example in which an abnormality that has occurred in the automatic analyzer is specified based on a variation common to two items. Two items (emission sensitivity 301, magnetic particle pseudo sample 306) having elements common to the measurement are recorded on the respective time axes 302 and 307. Luminescence acquired by a specific device with respect to the average transitions 303 and 308 of the luminescence sensitivity and magnetic particle pseudo sample measured values obtained when the conditions other than the automatic analyzer (for example, reagent state, specimen sample state, measurement environment) are matched. The average transitions 304 and 309 of the sensitivity and the magnetic particle pseudo sample measurement values are compared. At this time, it is assumed that the luminescence sensitivity and the magnetic particle pseudo sample measurement value obtained by the specific device have changed, and the flow path system or detection that is a common element in the measurement of the luminescence sensitivity and the magnetic particle pseudo sample. It can be estimated that an abnormality has occurred in the magnet operating mechanism of the part. The common elements can be displayed 104 for maintenance such as cleaning and parts replacement.

精度のよい分析データを提供する実施例を示す。前記実施例１の方法により、目的とする反応以外からの発光量が同定できれば、測光した全データから目的とする反応以外からの発光量を除外することで、最終的に得られる項目に関する分析データ精度の向上が実現される。さらには、実施例１の方法により異常を来たす原因を同定することにより、装置から得られる発光量の時系列データから、異常な状態に起因する波形，共鳴点，ピーク値、など緒々の特徴を取り除くことで、これまでの技術である発光量の積分値のみを対象とする分析と比して、精度のよい分析データの提供が可能となる。図５に例を表す。時間軸５０２に対して、例として発光標識を測定した結果の標準偏差５０１の推移５０４を示している。異常データが入っていた場合５０５、統計精度を向上させる演算により、精度が向上する５１０。   An embodiment for providing highly accurate analysis data will be described. If the amount of luminescence from other than the target reaction can be identified by the method of Example 1, analysis data on the items finally obtained by excluding the amount of luminescence from other than the target reaction from all the measured light data Improved accuracy is achieved. Furthermore, by identifying the cause of the abnormality by the method of Example 1, various characteristics such as the waveform, resonance point, peak value, etc. resulting from the abnormal state are obtained from the time series data of the light emission amount obtained from the apparatus. By removing the analysis data, it is possible to provide analysis data with higher accuracy than in the conventional analysis only for the integrated value of the light emission amount. An example is shown in FIG. A transition 504 of the standard deviation 501 as a result of measuring the luminescent label is shown as an example with respect to the time axis 502. If abnormal data is entered 505, the accuracy is improved 510 by an operation that improves the statistical accuracy.

次に、上記のほか、装置が自動でメンテナンスできる実施例について示す。実施例１の方法により異常を来たす原因を同定することにより、再検査にかかるラニングコストの削減が可能となる。例えば当該機能を応用し、汚れの度合いに応じて洗浄液の濃度をコントロールする機能を加えることで、消耗品に対する無駄なコストの削減が可能になる。さらには、的確な時期に適切なメンテナンスが実施できることにより、自動分析装置自身が、自動でメンテナンスできる自己メンテナンス機能の実現が可能になる。   Next, in addition to the above, an embodiment in which the apparatus can be automatically maintained will be described. By identifying the cause of the abnormality by the method of the first embodiment, it is possible to reduce the running cost for re-inspection. For example, by applying this function and adding a function of controlling the concentration of the cleaning liquid in accordance with the degree of contamination, it is possible to reduce wasteful costs for consumables. Furthermore, since appropriate maintenance can be performed at an appropriate time, a self-maintenance function that can be automatically maintained by the automatic analyzer itself can be realized.

異常データを特定するシステムの構成例を説明する図。The figure explaining the example of composition of the system which specifies abnormal data. メンテナンスが必要な時期を表す例。An example of when maintenance is needed. 相関のある特定の二項目の変動からメンテナンス内容を推定する例。An example of estimating maintenance contents from changes in two specific correlated items. 予測精度の向上に用いる二項目の相関。Correlation between two items used to improve prediction accuracy. 統計精度が向上した例。An example of improved statistical accuracy.

符号の説明Explanation of symbols

１００ 分析データ取り込み部
１０１ データ精度向上演算部
１０２ メンテナンス時期予測演算部
１０３ メンテナンス内容判断部
１０４ メンテナンス内容出力部
１０５，１０６ データベース
２０１，３０２，３０７，５０２ 時間軸（単位は週）
２０２ 濃度の異なる二種類の標準液を測定することで得られる発光感度の軸
２０３ ２０２記載発光感度の推移
２０４ 所定（下限）値
２０５ 測定値の推移傾向を表現する矢印
２０６ メンテナンス必要時期を示す矢印
３０１ 発光感度測定値の標準偏差を表す軸
３０３ 発光感度測定値の平均値推移
３０４ 特定の装置で取得した発光感度の測定値推移
３０５ 特定の装置で取得した発光感度の異常値
３０６ 磁性粒子擬似サンプルの標準偏差を表す軸
３０８ 磁性粒子擬似サンプル測定値の平均値推移
３０９ 特定の装置で取得した磁性粒子擬似サンプルの測定値推移
３１０ 特定の装置で取得した磁性粒子擬似サンプル測定値の異常値
４０１ 溶液系標準発光物質の測定値を表す軸
４０２ ビオチン化発光標識の測定値を表す軸
４０３ 溶液系標準発光物質とビオチン化発光標識の相関を記録した点の集合
４０４ 二項目の相関において等確率を表す楕円曲線（確率±６６％）
４０５ 二項目の相関において等確率を表す楕円曲線（確率±９５％）
４０６ 二項目の相関において等確率を表す楕円曲線（確率±９９％）
４０７ ４０６記載の楕円曲線から外れる点
５０１ 発光標識測定値の標準偏差を表す軸
５０３ 発光標識における使用装置毎の平均値の推移
５０４ 発光標識における平均値の推移
５０５ 統計処理実施前の状態を示す例
５１０ 統計処理実施後の状態を示す例 100 Analysis Data Acquisition Unit 101 Data Accuracy Improvement Calculation Unit 102 Maintenance Time Prediction Calculation Unit 103 Maintenance Content Determination Unit 104 Maintenance Content Output Units 105, 106 Databases 201, 302, 307, 502 Time Axis (Unit: Week)
202 Luminous sensitivity axis 203 obtained by measuring two types of standard solutions having different concentrations 203 Luminous sensitivity transition 204 Predetermined (lower limit) value 205 Arrow 206 representing the trend of measured value transition 206 Arrow indicating maintenance required time 301 Axis representing standard deviation of luminescence sensitivity measurement value 303 Average value transition of luminescence sensitivity measurement value 304 Measured value transition of luminescence sensitivity acquired by a specific device 305 Abnormal value of luminescence sensitivity acquired by a specific device 306 Magnetic particle pseudo sample Axis 308 representing the standard deviation of the magnetic particle pseudo sample measured value average value transition 309 of the magnetic particle pseudo sample Measured value transition 310 of the magnetic particle pseudo sample acquired by the specific apparatus Abnormal value 401 of the magnetic particle pseudo sample measured value acquired by the specific apparatus Axis 402 representing measured value of system standard luminescent substance Axis 403 representing measured value of biotinylated luminescent label Solution-based standard luminescent substance Collection of points 404 recording the correlation between quality and biotinylated luminescent label. Elliptic curve representing probability of equality in two-item correlation (probability ± 66%)
405 Elliptic curve representing the probability of equality in the correlation between two items (probability ± 95%)
406 Elliptic curve showing probability of equality in correlation between two items (probability ± 99%)
Point 501 deviating from elliptic curve described in 407 406 Axis 503 indicating standard deviation of measured value of luminescent label 503 Transition of average value for each device used in luminescent label 504 Transition of average value in luminescent label 505 Example showing state before statistical processing 510 Example of status after statistical processing

Claims (5)

濃度既知の試料の測定結果を時系列に記憶する記憶手段と、
該記憶手段に記憶された前記試料の測定結果の時系列変化に基づき、該測定結果が予め定めた値を超える時期を予測する予測手段と、
該予測手段が予測した時期を表示する表示手段と、
を備えたことを特徴とする自動分析装置。 Storage means for storing the measurement results of samples of known concentrations in time series;
Prediction means for predicting a time when the measurement result exceeds a predetermined value based on a time-series change of the measurement result of the sample stored in the storage means;
Display means for displaying the time predicted by the prediction means;
An automatic analyzer characterized by comprising: 請求項１記載の自動分析装置において、
前記予測手段は、更に前記時系列の測定結果に基づき必要なメンテナンスの内容をも予測し、前記表示手段は、予測した時期とともに該予測したメンテナンスの内容も表示することを特徴とする自動分析装置。 The automatic analyzer according to claim 1, wherein
The prediction means further predicts necessary maintenance contents based on the time-series measurement results, and the display means displays the predicted maintenance contents together with the predicted time. . 請求項１記載の自動分析装置において、
前記記憶手段に記憶された前記試料の測定結果の時系列変化に基づき、測定値の傾向が正常か異常かを判定する判定手段を備え、測定値の傾向が異常な場合につき異常をきたした原因を推定する異常原因推定手段と、該異常原因推定手段の推定結果を前記表示手段に表示することを特徴とする自動分析装置。 The automatic analyzer according to claim 1, wherein
A determination means for determining whether the tendency of the measurement value is normal or abnormal based on a time-series change of the measurement result of the sample stored in the storage means, and the cause of the abnormality when the tendency of the measurement value is abnormal An automatic analysis apparatus characterized by displaying an abnormality cause estimation means for estimating the abnormality cause estimation result on the display means. 請求項３記載の自動分析装置において、
前記異常原因推定手段が推定した異常原因に基づき、適切なメンテナンス内容を予測する予測手段を備え、該予測手段が予測したメンテナンス内容を前記表示手段に表示することを特徴とする自動分析装置。 The automatic analyzer according to claim 3,
An automatic analyzer comprising prediction means for predicting appropriate maintenance contents based on an abnormality cause estimated by the abnormality cause estimation means, and displaying the maintenance contents predicted by the prediction means on the display means. 請求項１記載の自動分析装置において、
メンテナンス時期を予測する精度を向上させるために、個々のデータに対して解析対象となる母集団へ帰属の有無を判断する基準が、単数項目に対して施される正規分布による方法のみならず、二項目に対して施される二次元手法に基づくものであることを特徴とする自動分析装置。 The automatic analyzer according to claim 1, wherein
In order to improve the accuracy of predicting the maintenance time, not only the method based on the normal distribution applied to the singular items, but also the criteria for determining whether each data belongs to the analysis target population, An automatic analyzer characterized by being based on a two-dimensional method applied to two items.
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