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WO2003079894A1 - Methode d'analyse de rythmes circadiens - Google Patents

Methode d'analyse de rythmes circadiens Download PDF

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Publication number
WO2003079894A1
WO2003079894A1 PCT/AU2003/000358 AU0300358W WO03079894A1 WO 2003079894 A1 WO2003079894 A1 WO 2003079894A1 AU 0300358 W AU0300358 W AU 0300358W WO 03079894 A1 WO03079894 A1 WO 03079894A1
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WO
WIPO (PCT)
Prior art keywords
circadian rhythm
data
blood pressure
day
night
Prior art date
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Ceased
Application number
PCT/AU2003/000358
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English (en)
Inventor
Geoffrey Albert Head
Elena Vladimirovna Lukoshkova
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Baker Medical Research Institute
Original Assignee
Baker Medical Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Baker Medical Research Institute filed Critical Baker Medical Research Institute
Priority to AU2003212108A priority Critical patent/AU2003212108A1/en
Publication of WO2003079894A1 publication Critical patent/WO2003079894A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network

Definitions

  • the present invention relates to a method of analysis of human circadian rhythms. More particularly, the invention relates to a non-symmetrical method for analysing diurnal blood pressure and heart rate recordings.
  • Ambulatory blood pressure (ABP) monitoring devices are now routinely used throughout the world. In today's health care environment there is an ever-increasing importance on quickly and accurately identifying patient problems before they become critical and require hospitalization. More and more often, the 24 hour blood pressure variation information of a patient is being sought as a better indicator of a patient's cardiovascular health than clinically acquired blood pressure measurements.
  • the clinically acquired blood pressure measurements have a limited usefulness due to phenomena such as white coat hypertension. Many physicians use ABP monitoring to detect such phenomena and to adjust drug therapy for hypertensive patients. It has become recognised that 24 hour monitoring of blood pressure provides a better indicator of the likely impact of blood pressure on the degree of enlargement of the heart and thickening of the blood vessels. These factors are critical in the prediction of cardiovascular events.
  • Blood pressure tends to be the highest in the morning, decreasing gradually during the day and reaching its lowest values at night. Physical activity and periods of rest strongly influence the diurnal blood pressure pattern. However, a major characteristic of the diurnal variation is the fall in blood pressure and heart rate during the night.
  • the present invention provides a method of analysis of a circadian rhythm, including the steps of: receiving data indicative of the circadian rhythm; and performing a statistical fit of the data to provide a non-symmetrical approximation curve representative of the circadian rhythm.
  • the approximation curve includes a first transitional curve indicative of a transition of the circadian rhythm from a night-time level to a day-time level and a second transitional curve indicative of a transition of the circadian rhythm from a day-time level to a night-time level.
  • the first transition curve has a negative slope and the second transitional curve has a positive slope different in magnitude to that of the negative slope.
  • the step of performing a statistical fit is performed using the following equation:
  • PI is a day-time plateau level of the circadian rhythm
  • P2 is the difference between the day-time plateau and a night-time plateau
  • P3 is a rate of transition from the day to night plateaus, having units 1 /hours;
  • P4 is a centre location (in time) of a transition period from day to night;
  • P5 is a rate of transition from the night to day plateaus, having units 1/nours
  • P6 is a centre location (in time) of a transition period from night to day; x is a sample time; and y is the approximation curve.
  • the method further includes the step of using the approximation curve to facilitate assessment of the likelihood of a cardiac event.
  • the statistical fit is a least squares fit.
  • the least squares fit uses an initial parameter set to reduce the number of iterations required by the least squares fit to achieve a desired curve fitting accuracy level.
  • the initial parameter set is generated by performing a least squares fit of the data according to a sine curve model.
  • the circadian rhythm is a cardiovascular rhythm.
  • the circadian rhythm is a human cardiovascular rhythm.
  • the circadian rhythm includes one of a diastolic blood pressure level, a systolic blood pressure level, a mean blood pressure level or a heart rate.
  • the present invention further provides a system for analysing a circadian rhythm, including: means for receiving data indicative of the circadian rhythm; and means for performing a statistical fit of the data to provide a non-symmetrical approximation curve representative of the circadian rhythm.
  • the system includes means for collecting the data indicative of the circadian rhythm and transmitting the data to the means for receiving.
  • the means for collecting data is an ambulatory blood pressure monitor.
  • the means for performing a statistical fit of the data is a personal computer configured to interface with the ambulatory blood pressure monitor.
  • embodiments of the invention may assist medical practitioners in forming prognoses in relation to risk of cardiac disease and in more accurately titrating cardiovascular medication for a patient.
  • the present invention also provides a computer readable storage medium having stored thereon program code for causing a computer to execute the steps of the above method.
  • Figure 1 shows a system for analysing a circadian rhythm
  • Figure 2 is a flow diagram illustrating a method of obtaining initial parameter estimates for use in the method of an embodiment of the invention
  • Figure 3 is a flow diagram illustrating the method of an embodiment of the invention applied to diastolic blood pressure information
  • FIG. 4 shows sample approximation curves of different kinds of circadian rhythms, plotted in accordance with an embodiment of the invention.
  • Figure 5 shows a comparative analysis of approximation curves produced by an embodiment of the present invention and approximation curves produced by the Cosinor method using the same data.
  • a system for analysing a circadian rhythm including a recording device 6 and a computer 8, such as a personal computer or a manufacturer or server.
  • the recording device 6 may be an Ambulatory Blood Pressure Monitor, Model 90207 by Spacelabs Medical, preferably used in combination with a Report Management System, Model 90121-1 by Spacelabs Medical (to enable direct cabling of the blood monitor pressure to the computer 8).
  • the recording device 6 records information from a subject 4 relating to the subject's circadian rhythms and, in real time or in batch processing, feeds this information into the computer 8 for processing and analysis.
  • the recorded information may be stored in data storage, such as a disk, tape or distributed database, for archival and subsequent transmission to the computer 8.
  • the computer 8 applies a curve fitting procedure to ambulatory blood pressure (ABP) recordings in order to determine the rate of change in systolic (SBP) and diastolic blood pressure (DBP) and heart rate (HR) from the day to night values separately from the night to day transition.
  • ABP ambulatory blood pressure
  • SBP systolic
  • DBP diastolic blood pressure
  • HR heart rate
  • PI is the day time rhythm plateau
  • P2 is the difference between the day and night plateau and the night plateau is therefore P1-P2
  • the rate of transition between these plateaus is controlled by the value of P3 and RJ for the day to night and night to day transition respectively and have units of 1 /hours
  • the parameters P4 and P6 give the centre location of the transition periods with P4 being the middle of the day to night transition and P6 the middle of the night to day transition
  • x is a sample time
  • y is the approximation curve.
  • the maximum rate of change for each of these transitions is calculated as P2*P3/4 and P2*P5/4 for equations (1) and (2) respectively while an average rate of change (between inflection points) can be calculated as P2*P3/4.562 and R2*R5/4.562 for equations (1) and (2) respectively.
  • a v Rl + 1 . + . g _P «3((P «4--xx) ⁇ + ) + ⁇ 1 , + . e _-P5(P6-x) ( V3- )
  • the quasiperiodic equation is as follows:
  • R2 P2 y Pl + - ⁇ + ⁇ l + e " ( «-* ) ⁇ + e P5 P6 - ⁇ )
  • Initial estimates of the parameters may be made from a visual inspection of the data or from a simple sine wave fitting method as shown in equation (5) in order to reduce the number of iterations necessary under the least squares method to achieve the desired accuracy.
  • the parameters a, b, c and d are obtained from the least squares fitted data according to equation (5).
  • PI a + d (where a is the peak amplitude of the sine wave relative to the mean and d is the mean of the sine wave);
  • P2 2*a (twice the amplitude of the sine wave);
  • P4 1.25*b - b*c/2*7T + 6 (where b is the time between peaks of the sine wave and c is a location parameter of the sine wave peak);
  • P6 P4 + b/2 (hours).
  • Estimates for the slope coefficients P3 and P5 may be taken as -0.5 and 0.5 respectively.
  • the computer 8 performs the analysis method as shown in Figures 2 and 3.
  • the sample diastolic blood pressure measurements used to illustrate the method are shown in Table 1 below.
  • the blood pressure measurements were taken at regular hourly or half hourly intervals.
  • the data is received and input.
  • a least squares fit of a standard sine wave equation (5) is performed as illustrated at step 115.
  • the resulting four parameters from the sine wave fit at step 120 are used to calculate the initial parameters at step 125.
  • the raw data in combination with the initial parameters are used to perform the least squares fitting of the nonsymmetrical approximation equation (3) (termed a double logistic equation in Figure 3 at step 130).
  • the final parameter estimates are calculated at step 135 and are used at step 140 to plot the resulting approximation curve as shown at step 145.
  • Figure 4 shows sample plots for systolic blood pressure, mean BP and heart rate (next to the plot of diastolic pressure 145 as shown in Figure 2 and 3), together with the relevant parameter estimates. Table 1
  • Figure 5 makes a comparative illustration of the approximation curves produced by the present method (shown by the thick line) and the corresponding sine curves (shown by the thin line) for systolic arterial pressure (SAP), diastolic arterial pressure (DAP), mean arterial pressure (MAP) and heart rate (HR). Information as to the statistical fit of the respective curves to the data is also tabulated in Figure 5.
  • SAP systolic arterial pressure
  • DAP diastolic arterial pressure
  • MAP mean arterial pressure
  • HR heart rate
  • a significant advantage of the preferred method of analysis is that it can separately determine the slope of the decrease in blood pressure and heart rate when the subject goes to sleep from the increase in the same variables when the patient is waking up. Thus the method defines two plateaus (waking and sleeping) and the different rates of change from one to the other.
  • Analysis of a large number of data files taken from the Alfred Baker Medical Unit Risk Evaluation Clinic (Melbourne, Australia) has uncovered a difference in the rate at which heart rate changes as the patient goes to sleep and when the patient arises in the morning.
  • Data from over 200 patients have been analysed using the above-described curve fitting method and the fit has consistently been shown to be robust and to fit the data in a manner superior to the existing methods. It will be understood by persons skilled in the art that alterations and modifications may be made to some features of the described embodiments of the invention without departing from the spirit and scope of the invention.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Physiology (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Cardiology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Physics & Mathematics (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

L'invention concerne une méthode d'analyse d'un rythme circadien consistant à recevoir des données représentatives de ce rythme circadien et à réaliser un ajustement statistique de ces données en vue d'obtenir une courbe d'approximation non symétrique représentative du rythme circadien.
PCT/AU2003/000358 2002-03-25 2003-03-25 Methode d'analyse de rythmes circadiens Ceased WO2003079894A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2003212108A AU2003212108A1 (en) 2002-03-25 2003-03-25 Method of analysis of circadian rhythms

Applications Claiming Priority (2)

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AUPS1331 2002-03-25
AUPS1331A AUPS133102A0 (en) 2002-03-25 2002-03-25 Method of analysis of circadian rhythms

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007132379A3 (fr) * 2006-05-11 2008-08-28 Koninkl Philips Electronics Nv Dispositif d'administration de médicaments et/ou de surveillance de l'état d'un patient
CN109862826A (zh) * 2016-08-18 2019-06-07 皇家飞利浦有限公司 血压管理
CN110960199A (zh) * 2019-12-24 2020-04-07 中国人民解放军陆军军医大学第一附属医院 一种双变量测量动脉硬化程度的系统
US11103181B2 (en) 2017-08-01 2021-08-31 Samsung Electronics Co., Ltd. Apparatus and method for processing bio-information
US11672456B2 (en) 2018-04-27 2023-06-13 Medtronic Ireland Manufacturing Unlimited Company Identifying patients suited for renal denervation therapy

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3921624A (en) * 1974-08-14 1975-11-25 Orentreich Medical Group Apparatus for recording heart rate rhythm
US5861011A (en) * 1997-02-14 1999-01-19 Vitatron Medical, B.V. Pacemaker with automatic lower rate limit drop
US6128534A (en) * 1998-06-16 2000-10-03 Pacesetter, Inc. Implantable cardiac stimulation device and method for varying pacing parameters to mimic circadian cycles

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3921624A (en) * 1974-08-14 1975-11-25 Orentreich Medical Group Apparatus for recording heart rate rhythm
US5861011A (en) * 1997-02-14 1999-01-19 Vitatron Medical, B.V. Pacemaker with automatic lower rate limit drop
US6128534A (en) * 1998-06-16 2000-10-03 Pacesetter, Inc. Implantable cardiac stimulation device and method for varying pacing parameters to mimic circadian cycles

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
BROWN E.N. ET AL.: "A statistical model of the human core-temperature circadian rhythm", AMER. J. PHYSIOL. ENDOCRINOL. METAB., vol. 279, 2000, pages E669 - E683 *
REFINETTI R.: "Laboratory instrumentation and computing: comparison of six methods for the determination of the period of circadian rhythms", PHYSIOLOGY AND BEHAVIOUR, vol. 54, 1993, pages 869 - 875, XP024317594, DOI: doi:10.1016/0031-9384(93)90294-P *

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007132379A3 (fr) * 2006-05-11 2008-08-28 Koninkl Philips Electronics Nv Dispositif d'administration de médicaments et/ou de surveillance de l'état d'un patient
CN101442930B (zh) * 2006-05-11 2014-05-14 皇家飞利浦电子股份有限公司 用于给药和/或监测患者状态的设备
US9386925B2 (en) 2006-05-11 2016-07-12 MEDIMETRICS Personalized Drug Delivery B.V. Device for drug administration and/or monitoring the status of a patient
CN109862826A (zh) * 2016-08-18 2019-06-07 皇家飞利浦有限公司 血压管理
CN109862826B (zh) * 2016-08-18 2023-03-21 皇家飞利浦有限公司 血压管理
US11103181B2 (en) 2017-08-01 2021-08-31 Samsung Electronics Co., Ltd. Apparatus and method for processing bio-information
US11672456B2 (en) 2018-04-27 2023-06-13 Medtronic Ireland Manufacturing Unlimited Company Identifying patients suited for renal denervation therapy
CN110960199A (zh) * 2019-12-24 2020-04-07 中国人民解放军陆军军医大学第一附属医院 一种双变量测量动脉硬化程度的系统
CN110960199B (zh) * 2019-12-24 2022-05-27 中国人民解放军陆军军医大学第一附属医院 一种双变量测量动脉硬化程度的系统

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