[go: up one dir, main page]

WO2011021163A1 - Assiduité à un médicament et/ou à un régime de traitement - Google Patents

Assiduité à un médicament et/ou à un régime de traitement Download PDF

Info

Publication number
WO2011021163A1
WO2011021163A1 PCT/IB2010/053743 IB2010053743W WO2011021163A1 WO 2011021163 A1 WO2011021163 A1 WO 2011021163A1 IB 2010053743 W IB2010053743 W IB 2010053743W WO 2011021163 A1 WO2011021163 A1 WO 2011021163A1
Authority
WO
WIPO (PCT)
Prior art keywords
patient
medication
compliance
treatment regimen
level
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/IB2010/053743
Other languages
English (en)
Inventor
Matthew Harris
Cristina Bescos Del Castillo
Elke Naujokat
Richard Daniel Willmann
Harald Reiter
Pradyumna Dutta
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.)
Philips Intellectual Property and Standards GmbH
Koninklijke Philips NV
Original Assignee
Philips Intellectual Property and Standards GmbH
Koninklijke Philips Electronics NV
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 Philips Intellectual Property and Standards GmbH, Koninklijke Philips Electronics NV filed Critical Philips Intellectual Property and Standards GmbH
Publication of WO2011021163A1 publication Critical patent/WO2011021163A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • 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
    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the invention relates to a method and system for providing an indication of a patient's compliance with a particular medication and/or treatment regimen.
  • Compliance describes the extent to which a patient follows recommendations from a physician, nurse or other health practitioner to take a medication or follow a treatment regimen (which could include taking medication, following a diet, performing particular exercises and/or making other lifestyle changes).
  • failure of a patient to adhere or comply with a specified medication or treatment regimen is a significant problem, and results in a suboptimal clinical benefit to the patient from the medication or treatment. Failures can range from missing a single dose of a medication to taking the medication at the wrong time of day to failing to follow the treatment regimen at all.
  • Compliance is a particular problem in long term treatments, and this problem will only increase as the population ages and chronic diseases become more prominent. There is strong evidence that increasing the compliance may have a far greater impact on the health of the population than any improvement in specific medical treatments. For example, in "A question of choice - compliance in medicine taking, a preliminary review" by S. Carter and D. Taylor, it was found that patients with, or at risk for, coronary artery disease or congestive heart failure and who were classified as non-compliant were twice as likely to die from these conditions as those who were compliant.
  • the World Health Organization has identified five factors contributing to non-compliance: social and economic factors, the healthcare system, characteristics of the disease, disease therapies and patient-related factors, and it is thought that the most promising methods of improving compliance use a combination of patient education, behavioral skills, self-rewards, social support and telephone follow-up.
  • patient compliance is determined by a healthcare professional during a patient visit.
  • the professional asks the patient about their compliance with the medication or treatment regimen, and then estimates the patient compliance, factoring in the professional's estimate on how truthful the patient was in answering the questions. Most patients overestimate their level of compliance to a medication or treatment.
  • a method of measuring a patient's compliance to a medication or treatment regimen comprising obtaining measurements of one or more physiological characteristics of the patient; and determining a level of compliance of the patient with the medication or treatment regimen using the obtained measurements and a model estimating the effect of the medication or treatment regimen on values of the physiological characteristics.
  • a system for measuring a patient's compliance with a medication or treatment regimen comprising one or more physiological characteristic sensors for taking measurements of one or more physiological characteristics of the patient; processing means for determining a level of compliance of the patient with the medication or treatment regimen using the measurements and a model estimating the effect of the medication or treatment regimen on values of the physiological characteristics.
  • a computer program product comprising computer-readable code that, when executed on a suitable computer or processor, is configured to cause the computer or processor to perform the steps in the method described above.
  • Fig. 1 is a block diagram of a system for determining a level of compliance in accordance with the invention
  • Fig. 2 is a flow chart of a method of determining a level of compliance in accordance with the invention
  • Fig. 3 indicates the inputs and output of the model in accordance with the invention
  • Fig. 4 is a graph indicating an exemplary change in a physiological
  • Fig. 5 is an exemplary model of the effect of a beta blocker on blood pressure in a patient.
  • the user of the system according to the invention whose compliance is being measured is referred to as a patient.
  • an objective measure of the level of a patient's compliance to a medication or treatment regimen is determined by obtaining and analyzing measurements of one or more physiological characteristics of the patient.
  • a model of the effect of the prescribed medication or treatment on the physiological characteristic(s) it is possible to determine if the patient has taken the medication or followed the treatment plan (and if the treatment is effective) by comparing the measurements to those predicted by the model.
  • Figure 1 shows a system 2 for determining a level of compliance in accordance with the invention.
  • the system 2 comprises one or more physiological characteristic sensors 4 for obtaining measurements of physiological characteristics of the patient.
  • the physiological characteristic(s) measured by the physiological characteristic sensor(s) 4 can include, but is not limited to, any of the following: blood pressure, heart rate, blood sugar levels, blood oxygen levels, breathing rate, heart function (including an electrocardiogram), motion or weight. Those skilled in the art will be aware of other physiological characteristics of a patient that can be measured, and the appropriate sensors 4 for doing so.
  • the numbers and types of physiological characteristic sensors 4 present in the system 2 can be determined by the medication or treatment regimen specified for the patient, or whether the system 2 is intended to be suitable for use with more than one type of medication and/or treatment regimen.
  • the underlying principle is that in order for the system 2 to provide a level of compliance for the patient, it is necessary to obtain measurements of the physiological characteristic(s) of the patient that are affected by taking or not taking the medication(s) or following or not following the treatment regimen(s).
  • the physiological characteristic sensors 4 will include at least a blood pressure sensor.
  • the system 2 can be provided with sensors 4 for many or all of the physiological characteristics indicated above.
  • the system 2 also comprises a processor 6 that is connected to, and receives the outputs from, the physiological characteristic sensor(s) 4, a memory 8 that is connected to the processor 6, a user interface 10 (that can comprise means for receiving a patient input, such as a keypad, keyboard, mouse, a touch screen, etc., and/or means for presenting information to the patient, such as a display, one or more lights, a loudspeaker, etc.) that is connected to the processor 6 and a terminal 12 used by a healthcare professional.
  • the processor 6 can communicate and exchange data with the terminal 12, as indicated by dashed arrow 14.
  • the system 2 may take many forms, and may include many more components than those shown in Figure 1.
  • the sensor(s) 4, processor 6, memory 8 and user interface 10 may be integrated into a small handheld unit that has some means (for example wireless Internet access or mobile telephony circuitry) for communicating with the terminal 12, while in another implementation the processor 6, memory 8 and user interface 10 may be in the form of a personal computer or laptop, with the sensor(s) 4 being peripheral components that can be attached to the computer or laptop (in this implementation the computer or laptop can again communicate with the terminal 12 via the Internet).
  • the processor 6 that determines the level of compliance for the patient may located remotely from the sensor(s) 4 (and therefore the patient). The processing may be carried out in a remote server before being communicated to the terminal 12, or the processing may be carried out in the terminal 12 itself.
  • step 101 measurements of the required physiological characteristics of the patient are collected using the sensor(s) 4.
  • the system 2 can prompt the patient to take a measurement using the user interface 10. Such a prompt could be issued in accordance with a schedule that indicates when measurements should be taken. The time between measurements can be set based on the particular medication or treatment regimen that the patient is undergoing.
  • the processor 6 compares the measurements to a model that predicts the effects of the medication or treatment regimen on the particular physiological characteristics (step 103).
  • the data for the model may be stored in the memory 8, along with data for models relating to other medication or treatment regimens if the system 2 is to be able to determine a level of compliance for different medication and treatment types.
  • the processor 105 can then determine a level of compliance for the patient with the medication or treatment regimen. Clearly, the closer that the physiological characteristics get to the values predicted by the model, the more compliant the patient has been with the medication or treatment regimen.
  • the level of compliance can be given at any desired level of granularity.
  • the processor 6 can provide a positive or negative indication of the compliance of the patient (for example "compliant" or "non-compliant") based on whether the deviation of the patient's physiological characteristic(s) from those predicted by the model is above or below a threshold.
  • multiple thresholds can be used, with each one indicating a different level of compliance for the patient.
  • the level of compliance can be given as a percentage value of the number of doses of medication or exercise routines, etc. completed by the patient.
  • the model in order for the processor 6 to improve the estimate of the level of compliance, the model also receives initial and/or previous measurements of the physiological characteristics of the patient.
  • measurements can also be stored in the memory 8. This allows the processor 6 to determine the change in the physiological characteristic(s) of the patient in response to the medication or treatment regimen over time, and the processor 6 can compare this or these changes to those predicted by the model to determine if the patient is complying with the medication or treatment regimen.
  • the patient can also be presented with a question or series of questions by the user interface 10 in order to obtain information relevant to the medication or treatment regimen from the patient that cannot otherwise be obtained by sensor(s) 4.
  • This information can also be input to the model by the processor 6 (optionally along with initial and/or previous responses stored in the memory 8) and used to determine the level of compliance in conjunction with the collected measurements.
  • the patient can answer the question(s) at the same time or around the same time that the measurements of the physiological characteristics are taken.
  • the question or series of questions could ascertain information about the patient's symptoms, such as whether the patient is in any pain, whether the pain has improved or worsened, its location, their mental state, etc. Questions could also be asked about whether the patient believes they have complied with the medication or treatment regimen so far. This information could be used to provide an indication of the confidence of the determined level of compliance (for example if the patient suggests that they have taken every dose of the medication so far and the comparison agrees, the confidence in the determined level of compliance will be high). This information can also highlight cases where the medication or treatment regimen is not having the desired effect (for example the patient will indicate that they have followed the regimen properly but the model will indicate that there is a low level of compliance).
  • Figure 3 illustrates the different types of information that can be input to the model.
  • the model can receive information about the medication or treatment regimen (for example a specific dosage amount and/or frequency for a medication), the initial and/or previous measurements of the physiological characteristics of the patient, the new/current measurements of the physiological characteristics of the patient and responses to any questions presented to the patient.
  • the output of the model, or the result of a comparison of the new measurements of the physiological characteristics to the predicted values for the physiological characteristics is a level of compliance of the patient to the medication or treatment regimen.
  • the system 2 may take measurements of physiological characteristics of the patient that are not directly affected by the medication or treatment regimen, but which are used to adapt or personalize the model to the patient.
  • the system 2 may measure the weight of the patient (either initially or on a regular basis), and this weight measurement can be used to calibrate the model to the particular size of the patient since it is known that the effect of various medications can depend on the size of the patient.
  • the model may also be able to predict the effect of several medications and/or treatment regimens taken in parallel or in combination, even where doses of particular medications are not taken at the same time. Where appropriate, the effect of the different medications on the physiological characteristics can simply be added, otherwise, a more involved medication combination function must be used. Those skilled in the art will be aware of suitable functions for implementing this.
  • the model may also be able to predict the effect on the physiological characteristics of under or over dosage of the medication with respect to the prescription. For example, the comparison of the measurements of the physiological characteristic(s) to the model may indicate that the medication is too effective on the patient (indicating to the healthcare professional that the medication regimen should be reduced) or insufficiently effective (indicating to the healthcare professional that the medication regimen should be increased).
  • the model shown in Figure 4 represents relatively slow working medication whose effects are cumulative over the course of the medication or treatment regimen. However, there are relatively fast working medications whose effect can be noticed after just a single dose (and therefore the effect of missing a dose can also be noticed). In both (and intermediate) cases, the level of compliance to the medication or treatment regimen can be determined by comparing the observed progression of the physiological characteristic ⁇ ) to that predicted by the model.
  • Beta blockers are one example of a class of medication that is relatively fast working on blood pressure. It has been found that the effect of a beta blocker on reducing blood pressure can be seen after each dose. Therefore, it is also possible to see when the patient has missed just a single dose of the beta blocker. In addition, it can also be possible to see an increase in blood pressure between regular doses of the medication (i.e. regular doses in accordance with the medication regimen), which can allow a healthcare professional to determine that the medication regimen may need to be changed (for example by increasing the frequency of the dose, increasing the dosage or changing to a stronger medication).
  • Figure 5 is an example of a model of the effect of a beta blocker on blood pressure in a given patient.
  • Line 200 shows the model's prediction of the change in blood pressure of the patient over time, given regular doses of the beta blocker.
  • the model shows a "safe" range for the blood pressure which is bounded by lines 202 and 204.
  • the patient takes a dose of beta blocker in accordance with the medication regimen, and it can be seen that the measurements of the blood pressure of the patient (line 210) initially track that predicted by the model. However, the patient misses the next dose of the beta blocker at t 2 and the measurements of the blood pressure indicate a substantial deviation from that predicted by the model. In this case, the blood pressure measurements indicate that the blood pressure is outside of the "safe" range.
  • the system 2 compares the current measurements to those predicted by the model and determines a level of compliance for the patient. The level of compliance can be given by the magnitude of the deviation between the current measurements and those predicted by the model or by evaluating the integrated absolute difference between the model and actual blood pressure curves (200, 210) with respect to a threshold.
  • the indication of the level of compliance provided by the system 2 can be reviewed by a healthcare professional during a patient visit to determine if the medication or treatment regimen is effective or between patient visits to allow the professional to intervene earlier than with conventional systems if the patient is not complying with the regimen. It will also be appreciated that the fact that the patient may be aware that the system 2 is providing the healthcare professional with an indication of their level of compliance with the regimen could induce the patient to try harder to follow the regimen fully.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Pathology (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Molecular Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Biophysics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Medicinal Chemistry (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Physics & Mathematics (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

L'invention concerne un procédé de mesure de l'assiduité d'un patient à un médicament ou à un régime de traitement, le procédé comportant l'obtention de mesures d'une ou de plusieurs caractéristiques physiologiques du patient, et la détermination d'un niveau d'assiduité du patient au médicament ou au régime de traitement à l'aide des mesures obtenues et d'un modèle estimant l'effet du médicament ou du régime de traitement sur les valeurs des caractéristiques physiologiques.
PCT/IB2010/053743 2009-08-20 2010-08-19 Assiduité à un médicament et/ou à un régime de traitement Ceased WO2011021163A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP09168246.8 2009-08-20
EP09168246 2009-08-20

Publications (1)

Publication Number Publication Date
WO2011021163A1 true WO2011021163A1 (fr) 2011-02-24

Family

ID=43383518

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2010/053743 Ceased WO2011021163A1 (fr) 2009-08-20 2010-08-19 Assiduité à un médicament et/ou à un régime de traitement

Country Status (1)

Country Link
WO (1) WO2011021163A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017146525A1 (fr) 2016-02-25 2017-08-31 Samsung Electronics Co., Ltd. Appareil et procédé de dosage de médicament
WO2019177395A1 (fr) * 2018-03-16 2019-09-19 삼성전자 주식회사 Procédé et dispositif permettant de déterminer la cause d'une tendance dans des données de signes vitaux
JP7177540B1 (ja) 2021-12-21 2022-11-24 Ｅｓｓｅｎｃｅ ｒｅｓｅａｒｃｈ株式会社 血圧予測装置、血圧予測プログラム、及び、血圧予測方法
WO2024146753A1 (fr) * 2023-01-03 2024-07-11 Bayer Aktiengesellschaft Surveillance de la prise de médicaments antihypertenseurs

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2000022437A1 (fr) * 1998-10-14 2000-04-20 Hybritech Incorporated Methode de pronostic pour traitement anti-resorption
US20030135392A1 (en) * 2002-01-11 2003-07-17 Bernard Vrijens Systems and methods for medication monitoring
WO2003082096A1 (fr) * 2002-03-28 2003-10-09 Abbott Laboratories Systeme et procede de gestion d'un programme de traitement
US20070179355A1 (en) * 2005-12-30 2007-08-02 Howard Rosen Mobile self-management compliance and notification method, system and computer program product
WO2007103474A2 (fr) * 2006-03-07 2007-09-13 University Of Florida Research Foundation, Inc. Système de suivi d'adhésion aux médicaments
US20080109252A1 (en) * 2006-11-08 2008-05-08 Lafountain Andrea Predicting patient compliance with medical treatment

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2000022437A1 (fr) * 1998-10-14 2000-04-20 Hybritech Incorporated Methode de pronostic pour traitement anti-resorption
US20030135392A1 (en) * 2002-01-11 2003-07-17 Bernard Vrijens Systems and methods for medication monitoring
WO2003082096A1 (fr) * 2002-03-28 2003-10-09 Abbott Laboratories Systeme et procede de gestion d'un programme de traitement
US20070179355A1 (en) * 2005-12-30 2007-08-02 Howard Rosen Mobile self-management compliance and notification method, system and computer program product
WO2007103474A2 (fr) * 2006-03-07 2007-09-13 University Of Florida Research Foundation, Inc. Système de suivi d'adhésion aux médicaments
US20080109252A1 (en) * 2006-11-08 2008-05-08 Lafountain Andrea Predicting patient compliance with medical treatment

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017146525A1 (fr) 2016-02-25 2017-08-31 Samsung Electronics Co., Ltd. Appareil et procédé de dosage de médicament
US20170246086A1 (en) * 2016-02-25 2017-08-31 Samsung Electronics Co., Ltd. Chronotherapeutic dosing of medication and medication regimen adherence
CN108697336A (zh) * 2016-02-25 2018-10-23 三星电子株式会社 用于给药的装置和方法
US11039986B2 (en) 2016-02-25 2021-06-22 Samsung Electronics Co., Ltd. Chronotherapeutic dosing of medication and medication regimen adherence
WO2019177395A1 (fr) * 2018-03-16 2019-09-19 삼성전자 주식회사 Procédé et dispositif permettant de déterminer la cause d'une tendance dans des données de signes vitaux
JP7177540B1 (ja) 2021-12-21 2022-11-24 Ｅｓｓｅｎｃｅ ｒｅｓｅａｒｃｈ株式会社 血圧予測装置、血圧予測プログラム、及び、血圧予測方法
JP2023091942A (ja) * 2021-12-21 2023-07-03 Ｅｓｓｅｎｃｅ ｒｅｓｅａｒｃｈ株式会社 血圧予測装置、血圧予測プログラム、及び、血圧予測方法
WO2024146753A1 (fr) * 2023-01-03 2024-07-11 Bayer Aktiengesellschaft Surveillance de la prise de médicaments antihypertenseurs

Similar Documents

Publication Publication Date Title
Brahmbhatt et al. Remote management of heart failure: an overview of telemonitoring technologies
Mohebali et al. Remote monitoring in heart failure: current and emerging technologies in the context of the pandemic
JP6466422B2 (ja) 医療支援システム及び方法
JP7600197B2 (ja) ウェアラブル装置を利用した甲状腺機能亢進症の予測システム及び予測コンピュータプログラム
Antoniades et al. Pilot study of remote telemonitoring in COPD
US11033455B2 (en) Tools for case review performance analysis and trending of treatment metrics
RU2603052C2 (ru) Обнаружение нарушений у пациента
US11551793B2 (en) Systems and methods for optimizing management of patients with medical devices and monitoring compliance
US11596335B2 (en) System and methods for remotely monitoring lean muscle mass
US20160117469A1 (en) Healthcare support system and method
KR20130082698A (ko) 건강 관리 서버 및 그것의 동작 방법
US20140358571A1 (en) Healthcare support system and method for scheduling a clinical visit
CN104871162B (zh) 用于监测用户的系统
US20240194314A1 (en) Medical care assistance system, medical care assistance device, and program
WO2011021163A1 (fr) Assiduité à un médicament et/ou à un régime de traitement
KR20250114123A (ko) 혈류 및 응고를 모니터링하기 위한 비침습적 장치, 시스템 및 방법
Sano et al. HeartMapp: a mobile application to improve CHF outcomes and reduce hospital readmissions
US10390772B1 (en) Scale-based on-demand care system
WO2021122124A1 (fr) Système et procédé pour déclencher une action sur la base d'une gravité de maladie ou d'un état affectif d'un sujet
CN118412083A (zh) 检测报告数据的处理方法及装置
CN116913512A (zh) 一种血友病居家康复系统
US12249424B2 (en) Decision-support tools for pediatric obesity
WO2015044859A1 (fr) Méthodologie de contrôle de patient hospitalisé et de prévision de risque en unité de soins intensifs avec système d'alerte avancée basé sur la physiologie
US12283376B2 (en) Systems and methods to monitor patient devices
US20240274293A1 (en) Medical care assistance system, medical care assistance device, and recording medium

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 10752627

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 10752627

Country of ref document: EP

Kind code of ref document: A1