WO2023000530A1 - Procédé d'amélioration de la précision d'une tendance au changement de la concentration en temps réel pendant une surveillance continue de la concentration d'un analyte dans le corps d'un animal - Google Patents

Procédé d'amélioration de la précision d'une tendance au changement de la concentration en temps réel pendant une surveillance continue de la concentration d'un analyte dans le corps d'un animal Download PDF

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WO2023000530A1
WO2023000530A1 PCT/CN2021/126936 CN2021126936W WO2023000530A1 WO 2023000530 A1 WO2023000530 A1 WO 2023000530A1 CN 2021126936 W CN2021126936 W CN 2021126936W WO 2023000530 A1 WO2023000530 A1 WO 2023000530A1
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time
analyte concentration
real
analyte
concentration
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PCT/CN2021/126936
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English (en)
Chinese (zh)
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韩洋
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苏州百孝医疗科技有限公司
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G06F17/12—Simultaneous equations, e.g. systems of linear equations
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/40—Animals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/42—Evaluating a particular growth phase or type of persons or animals for laboratory research

Definitions

  • This application relates to the research field of the change of analyte concentration data and the real-time trend of analyte concentration change in the continuous monitoring process of animal body analyte concentration, especially involves judging whether the analyte concentration data is abnormal or suspicious, and judging whether to output analyte
  • the real-time trend of concentration change is a method for improving the accuracy of the real-time trend of analyte concentration change.
  • the aforementioned analytes include, but are not limited to: acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase, sarcosine, creatinine, DNA, fructosamine, glucose, glutamate, growth hormone Classes, hormones, ketone bodies, lactic acid, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, and troponin, etc.
  • Analytes can also include drugs such as antibiotics (eg, gentamicin, vancomycin, etc.), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, among others.
  • the analyte concentration data is usually obtained by an analyte sensor, wherein the analyte sensor may be a glucose sensor, a ketone sensor or other sensors corresponding to the analyte.
  • the continuous analyte concentration monitoring system will output the analyte concentration data and concentration change trend according to a certain (collection) period (as shown in Figure 1, where the concentration change trend adopts representation of an arrow trend). It is relatively easy to obtain more accurate analyte concentration data through the analyte sensor, but to obtain relatively accurate analyte concentration change trends, especially the real-time trend of analyte concentration changes (referring to the current acquisition time given the analyte concentration data At the same time, the changing state of the analyte concentration) becomes relatively difficult.
  • the amount of change in the analyte concentration data D 2 (D 2 C(T)-C(T-n ⁇ t), n is a positive integer greater than 1)
  • the direction of concentration change can be obtained by the sign and size of D 1 and D 2 and the amount of change, according to the threshold interval classification of dD 1 and D 2 , the real-time arrow trend of concentration change is obtained.
  • one collection period with the current collection time T as the end point refers to 11:57 ⁇ 12:00, which contains 2 analyte concentration data;
  • the three acquisition periods with time T as the end point refer to 11:51-12:00, which include 4 analyte concentration data.
  • the longest time is determined according to different analyte types, analyte concentration change events, medical knowledge or characteristics of physiological characteristics.
  • a continuous 15-minute blood sugar trend is a blood sugar event; Under normal circumstances, it is generally considered that a certain period of time within three hours or less after a meal is a blood glucose event.
  • the change D 2 can be calculated, and the real-time arrow trend of the concentration change is given according to the threshold interval corresponding to dD 1 and D 2 (Table 1).
  • the upward angle and quantity of the arrow represent the rate of increase in the concentration of the analyte.
  • the method of judging the real-time trend of the above-mentioned analyte concentration change is no problem; however, when the analyte concentration data suddenly appears abnormal in a certain continuous and stable trend (for example, the analyte concentration data changes from rising to falling or falling to rising, the abnormality of the analyte sensor, the difference in individual metabolic function, the abnormal rise or fall of the concentration data caused by human intervention, etc.), which makes the analyte concentration data doubtful at this moment (
  • the data is also referred to as untrustworthy data or untrustworthy points hereinafter
  • the above method for judging the real-time trend of analyte concentration changes cannot accurately output the real-time trend of analyte concentration changes (according to the above method, the speed and direction of blood sugar changes can be calculated , can output the real-time trend of analyte concentration changes, but the accuracy rate is problematic), and even classify based on
  • the blood glucose concentration drops suddenly. According to the above judgment method, the blood glucose concentration is slowly declining at this moment. Because the concentration is close to the edge of the threshold interval, it may mislead the patient to take measures to avoid the blood glucose concentration from continuing to drop; or At the first two acquisition moments (when the acquisition period is 102 and 103), the blood glucose concentration is rising and falling, and at this moment (when the acquisition period is 104), the blood glucose concentration is rising, that is to say, at three consecutive acquisition moments (acquisition The period is 102 to 104 hours) respectively give the real-time trends of blood glucose concentration as rising, falling and rising, which will lead to poor user experience for patients. Therefore, the accuracy of the above method for judging the real-time trend of analyte concentration changes needs to be improved, and it is hoped to obtain a real-time trend summary chart as shown in FIG. 4 .
  • the present application introduces a method based on least square polynomial fitting (such as Equation 1) to fit the analyte concentration data C(t) with the current collection time T as the end point.
  • the above-mentioned first-order linear equation is the linear equation of the relationship between time t and analyte concentration C(t), so that at the acquisition time t, the analyte concentration data calculated by fitting and the analyte concentration output by the continuous analyte concentration monitoring system The cumulative difference between the concentration data satisfies the minimum.
  • the positive and negative sign of P corresponds to whether the concentration of the analyte is rising or falling
  • the absolute value of P corresponds to the fast or slow rate of change of the concentration of the analyte
  • the signs of P1 and P2 it can be quickly judged whether to output the real - time trend of the change of the analyte concentration. Specifically, if the signs are inconsistent (any one of which is 0 is also considered as inconsistent signs), it means that the analyte concentration data obtained at the collection time T is abnormal, or at least doubtful, and is untrustworthy data, and it is judged that the analyte is not output Real-time trend of concentration changes.
  • the sign of P 2 is positive, it means that the overall trend has been on the rise for a long period of time in the past (the duration of Tm 2 ⁇ t to T); and if the sign of P 1 is negative or equal to 0 at this moment , it means that in a short period of time in the past (the duration from Tm 1 ⁇ t to T), it is in a sudden decline or a sudden steady trend.
  • the analyte concentration data at the future collection time (T+ ⁇ t) is unknown, it is impossible to determine whether the current data belongs to the turning point from rising to falling, or the abrupt point of the overall rising stage. Therefore, no real-time trend of analyte concentration change is output at the acquisition time T.
  • the following method is further used to judge whether to output the real-time trend of the change of the analyte concentration.
  • FIG. 5 shows a schematic flow chart of the method for improving the accuracy of the real-time trend of the concentration change in the continuous monitoring process of the concentration of the analyte in the animal body.
  • P 3 represents the accumulation trend of the analyte concentration data in the m x collection periods before the current time T (that is, excluding the partial data including the current data). If the current data point is abnormally large (that is, the absolute value of P 1 is large) and does not have clinical significance at all, the previous period (T-(m 3 +m x ) ⁇ t to Tm x ⁇ t)m 3 acquisitions can be used Cumulative trending of analyte concentration data with high confidence within a period analyzes normal trends over past time periods. In addition, because the concentration of analytes in animals has a certain continuity, the cumulative trend of data in the previous period of m3 collection cycles can reflect the current trend to a certain extent.
  • m 2 is based on the analytes judged by medical research and experience The maximum time for which the concentration event lasts is determined.
  • a continuous 15-minute blood sugar trend is a blood sugar event, so preferably, m 2 ⁇ t ⁇ 15min.
  • the analyte is glucose in the blood
  • a certain period of time within three hours or less after a meal is One glycemic event, therefore preferably, m 2 ⁇ t ⁇ 180 min. .
  • the real-time trend of analyte concentration change is an important part of the information obtained by the user.
  • the real-time trend output without concentration change for a long time will reduce the user's experience and the monitoring effect, and the real-time trend output without concentration change for a long time will make itself Correct peak error judgment. Therefore, a reasonable choice of m2 can eliminate the real-time trend output without concentration changes to the greatest possible extent without affecting the user's experience.
  • m 2 can be obtained by machine learning, that is, using machine learning to obtain different m 2 through the sample size, and comparing the real-time trend of the analyte concentration change based on the m 2
  • the number of continuous cycles the less the number of continuous cycles, the more suitable m2 is.
  • the same m2 can correspond to different acquisition cycles that do not output the real-time trend of analyte concentration changes.
  • the retrospective method it is possible to traverse all the sets A that do not output the real-time trend of the analyte concentration change within a period of data under the artificially set threshold.
  • methods such as supervised machine learning or K-means clustering algorithm, the results that do not output real-time trends of analyte concentration changes are classified as set B.
  • the optimal solution of m2 can be obtained by comparing A and B.
  • the difference between P 1 and P 3 (P 1 -P 3 ) or the ratio of P 1 and P 3 (P 1 /P 3 ) exceeds the set threshold interval can be obtained from medical common sense, that is, through Medical research and experience determine the theoretical maximum value per unit time for the change in analyte concentration.
  • a reasonable threshold interval of P 1 /P 3 is ⁇ 10.
  • the method for improving the accuracy of the real-time trend of concentration changes disclosed in this application does not need to obtain a large amount of previous analyte concentration data for modeling like a retrospective analysis method, but only needs to call a small amount of previous analyte concentration data , which greatly simplifies the workload and complexity, and improves the accuracy of the real-time trend of concentration changes.
  • FIG. 1 is a schematic diagram of analyte concentration data and concentration change arrow trend output.
  • Figure 2 is a schematic diagram of the trend output with analyte concentration data but without concentration change arrows.
  • Fig. 3 is a real-time trend summary diagram obtained by simulating blood glucose concentration changes based on the glucose content data (a certain period of time) in the blood of normal healthy people by using the change rate dD 1 and the change amount D 2 judgment method.
  • Fig. 4 is a real-time trend summary diagram obtained by simulating blood glucose concentration changes based on the glucose content data (a certain period of time) in normal healthy human blood using the scheme disclosed in the present application.
  • Fig. 5 is a schematic flowchart of a method for improving the accuracy of the real-time trend of concentration changes in the process of continuous monitoring of animal body analyte concentrations disclosed in the present application.
  • Fig. 6 is another real-time trend summary diagram obtained by simulating blood glucose concentration changes based on the glucose content data (a certain period of time) in normal healthy human blood using the scheme disclosed in the present application.
  • FIG. 7 is another real-time trend summary diagram obtained by simulating blood glucose concentration changes based on the glucose content data (a certain period of time) in normal healthy human blood using the scheme disclosed in the present application.
  • the cited drawings are based on the blood sugar concentration data (a certain period of time) of normal healthy people, simulating the scheme disclosed in the application and outputting a real-time trend summary of the real-time trend of concentration changes picture.
  • the abscissa is the collection period, which can be understood as the signal collection at that time and output the concentration data, and the ordinate is the concentration of the degree of analysis (blood sugar), where the interval between the signal collection period and the output concentration data is 3 minutes.
  • the blood glucose concentration data is the data within a certain period of time within three hours after the meal
  • the current collection time is the 1796th collection cycle time
  • the blood sugar concentration at this time has slightly increased .
  • the sign of P2 is negative. If the signs of P 1 and P 2 are different, it is determined that the blood glucose concentration at the collection time (the 1796th collection cycle time) is suspicious, and only the concentration data is output, and the real-time trend of the analyte concentration change is not output.
  • the blood glucose concentration at this time has slightly increased compared with the 1796th and 1795th collection cycle time points.
  • the sign of P2 is negative. If the signs of P 1 and P 2 are different, it is determined that the blood glucose concentration at the collection time (the 1797th collection cycle time) is suspicious, and only the concentration data is output, and the real-time trend of the analyte concentration change is not output.
  • the blood glucose concentration still rises slightly compared with the 1796th collection period at this time.
  • the sign of P2 is negative. If the signs of P 1 and P 2 are different, it is determined that the blood glucose concentration at the collection time (the 1797th collection cycle time) is suspicious, and only the concentration data is output, and the real-time trend of the analyte concentration change is not output.
  • the obtained P 1 and P 2 have the same sign and are both negative.
  • the blood glucose concentration it is preferable to compare the proportional relationship between P 1 and P 3 , and if P 1 /P 3 >10, it is determined that the blood glucose concentration at the collection time (273th collection cycle number) is suspicious , only the concentration data is output, and the real-time trend of the analyte concentration change is not output.
  • the threshold value of the difference between P 1 and P 3 or the threshold value of the ratio of P 1 to P 3 can be obtained from medical common sense, that is, the theoretical maximum value per unit time of analyte concentration change determined through medical research and experience. Specifically, for the blood glucose concentration, if the ratio between P 1 and P 3 exceeds 10, the blood glucose concentration at the collection moment is considered to be suspicious.
  • This kind of judging method is mainly aimed at data abnormality in the case of short circuit (small data) or open circuit (large data) of signal sensor equipment.

Abstract

Est divulgué dans la présente demande un procédé d'amélioration de la précision d'une tendance au changement de la concentration en temps réel pendant une surveillance continue de la concentration d'un analyte dans le corps d'un animal. Le procédé consiste à : effectuer, à l'aide d'un ajustement polynomial, un ajustement linéaire sur des données de concentration d'analyte C au cours de différentes périodes d'acquisition avant le moment d'acquisition en cours, de façon à obtenir une équation d'ajustement linéaire de premier ordre C(t) = P·t + X (P étant la pente d'une droite ajustée et X étant une constante) ; et, enfin, en comparant des symboles, des différences ou des rapports de P au cours de différentes périodes d'acquisition, ne pas délivrer une tendance au changement de la concentration de l'analyte en temps réel, ce qui permet de réduire la possibilité d'introduction d'un schéma thérapeutique erroné. Grâce à la présente demande, par comparaison avec les procédés d'analyse rétrospective existants, il n'est pas nécessaire d'acquérir une grande quantité de données antérieures de concentration de l'analyte avant la modélisation, et il suffit de faire appel à une petite quantité de données antérieures de concentration de l'analyte, ce qui permet de réduire considérablement la charge de travail et la complexité, et d'améliorer la précision d'une tendance au changement de la concentration en temps réel.
PCT/CN2021/126936 2021-07-19 2021-10-28 Procédé d'amélioration de la précision d'une tendance au changement de la concentration en temps réel pendant une surveillance continue de la concentration d'un analyte dans le corps d'un animal WO2023000530A1 (fr)

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CN113380411B (zh) * 2021-07-19 2024-03-01 苏州百孝医疗科技有限公司 一种提高动物体分析物浓度连续监测过程中浓度变化实时趋势准确率的方法
CN113951879B (zh) * 2021-12-21 2022-04-05 苏州百孝医疗科技有限公司 血糖预测方法和装置、监测血糖水平的系统
CN114166913B (zh) * 2022-02-10 2022-05-27 苏州百孝医疗科技有限公司 自动校准方法和装置、监测分析物浓度水平的系统
CN114145738B (zh) * 2022-02-10 2022-06-24 苏州百孝医疗科技有限公司 分析物浓度数据生成方法和装置、监测分析物水平的系统

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