the influence of progestogen types
play

The influence of progestogen types on the risk of venous thrombosis - PowerPoint PPT Presentation

Feb 02, 2023 •130 likes •585 views

The influence of progestogen types on the risk of venous thrombosis An up-date jvind Lidegaard European Congress on Menopuse and Andropause. Athens March 2012 Gynaecological Clinic, Rigshospitalet University of Copenhagen OC generations

  1. The influence of progestogen types on the risk of venous thrombosis An up-date Øjvind Lidegaard European Congress on Menopuse and Andropause. Athens March 2012 Gynaecological Clinic, Rigshospitalet University of Copenhagen

  2. OC generations according to oestrogen dose and progestogen type Progestogen generation ”2” 3 1 2 3 4 Estrans Levonor- Norges- Deso- Gesto- Dros- NETA gestrel timate gestrel dene pirenone 50 high High dose EVRA NuvaRing - - 30-40 mid 1st + 2nd + + + +4th 20 low - 3rd + + - - E2/DNG + - - - - POP + + + Li/12

  3. Venous thrombosis in DK 2001-2009* Pregnant and puerperal women excluded Incidence per 100,000 years ( including users of hormonal contraception) 70 60 3/4 Deep venous Venous thrombosis 46 50 thrombosis alone 1/4 Pulmonary embolism 37 40 with or without DVT 28 30 Increase in incidence rate 20 of 5% per year 20 14 And 6.8 fold by age from 9 7 10 15 to 45 years. 0 Lidegaard et al BMJ 2011 Li/11

  4. CT, AMI and VT in DK 2001-2009* Pregnant and puerperal women excluded Incidence per 100,000 years ( including users of hormonal contraception) 70 64 60 CT Arterial Venous thrombosis 46 50 diseases 39 38 37 40 28 30 25 23 20 AMI 20 15 14 11 12 9 7 6 10 5 3 2 0,7 0,4 0 Lidegaard et al 2012 (submitted) and BMJ 2011 Li/12

  5. VT: Genetic risk factors Risk factor Prevalence RR Leiden fact V hetero 6% 8 Leiden fact V homoz 0.2% 64 Protein C insufficiency 0.2% 15 Protein S insufficiency <0.1% >10 Antithrombin III insuff. 0.02% 50 Prothrombin 20210A 2% 3 Hyperhomocysteinaemia 3% 3 Li/12

  6. VT: Acquired risk factors Prevalence RR Age ≥ 30 vs <30 50% 2.5 Pregnancy 4% 8 Adiposity (BMI>25) 30% 2 Varicose veins 8% 2 Immobilisation/trauma ? 2-10 Oral contraceptives 30% 3-6 Medical diseases 5%? 2-5 Li/12

  7. VT: Acquired risk factors Prevalence RR Age ≥ 30 vs <30 50% 2.5 Pregnancy 4% 8 Adiposity (BMI>25) 30% 2 Varicose veins 8% 2 Immobilisation/trauma ? 2-10 Oral contraceptives 30% 3-6 Medical diseases 5%? 2-5 Li/12

  8. Venous thrombosis in pregnant and puerperal women, DK 1995-2005. N=709 80 Incidence of VT per 10,000 exposure years 59 60 60 Puerperium Pregnancy (n=218) 48 (n=491) 39 Delivery 37 40 31 23 12 16 17 16 20 11 11 6 5 4 2 Gestational week Weeks after delivery 0 1-11 12- 24- 28- 32- 36 37 38 39 40+ 1 2 3 4 5-6 7-8 9-12 23 27 31 35 Li/12 Virkus et al. Thromb Haemost 2011; 106: 304-9

  9. VT: Acquired risk factors Prevalence RR Age ≥ 30 vs <30 50% 2.5 Pregnancy 4% 8 Adiposity (BMI>25) 30% 2 Varicose veins 8% 2 Immobilisation/trauma ? 2-10 Oral contraceptives 30% 3-6 Medical diseases 5%? 2-5 Li/12

  10. Adiposity in Danish women and men in 1994 and 2010 70 94 women 2010 women 94 men 2010 men 60 Adiposity: BMI >25 50 40 64 61 30 52 46 41 20 37 37 25 21 20 10 13 12 0 16-24 25-44 45-66 Li/12 www.si-folkesundhed.dk

  11. VT: Acquired risk factors Prevalence RR Age ≥ 30 vs <30 50% 2.5 Pregnancy 4% 8 Adiposity (BMI>25) 30% 2 Varicose veins 8% 2 Immobilisation/trauma ? 2-10 Oral contraceptives 30% 3-6 Medical diseases 5%? 2-5 Li/12

  12. 0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 3 rd versus 2 nd generation COC Rate ratio Blomen- 126 2,3 kamp 88-92 WHO 433 2,1 1989-93 Jick 1,8 80 1991-94 Spitzer 471 1,5 1991-95 Lewis 502 0,8 1993-95 Farmer 1,6 85 1991-95 Todd 1,4 99 1992-97 Bloemen- 185 1,9 kamp 94-98 Parkin 2,9 26 1990-1998 Lidegaard 987 1,4 1994-98 Dinger 118 1,3 2000-04

  13. VT and COC drospirenone (4 th ) VT Risk Rate ratio no / 10,000 DRSP/2nd gen Dinger 07 118 9.1 1.0 (0.6-1.8) 4th/2nd Seeger 07 57 13.0 * 0.9 (0.5-1.6) 4th/???

  14. VT and COC drospirenone (4 th ) VT Risk Rate ratio no / 10,000 DRSP/2nd gen Dinger 07 118 9.1 1.0 (0.6-1.8) 4th/2nd Seeger 07 57 13.0 * 0.9 (0.5-1.6) 4th/???

  16. VT and drospirenone VT Risk Rate ratio no / 10,000 DRSP/2nd gen Dinger 07 118 9.1 1.0 (0.6-1.8) 4th/2nd Seeger 07 57 13.0 * 0.9 (0.5-1.6) 4th/??? 09 Vlieg 1,524 na 1.7 (0.7-3.9) 4th/2nd Lidegaard 09 4.213 7.8 1.6 (1.3-2.1) 4th/2nd

  17. OC and VT: Methods National Registry of National Registry of Patients (>1977) Medicinal products (>1995): OC use VT diagnoses, Medication against Previous CaVD/canc. BP , DM, Hyperchol. Pregnancies, surgery 1995 2005 Cause of Deaths Statistics of Denmark Registry (>1977) PIN-codes, education Lethal VT vital status, emigration Lidegaard et al. BMJ 2009

  18. OC and VT: Progestagen type adjusted for duration of use ug EE Neta Lng NGM Deso Gest Drsp CPA 50 1.4 1.2 na na na na na 1.0-2.1 0.9-1.7 30-40 1.0 1 1.2 1.8 1.9 1.64 1.9 0.7-1.4 Ref 1.0-1.5 1.5-2.2 1.6-2.2 1.3-2.1 1.5-2.4 20 na na na 1.5 1.5 na na 1.3-1.8 1.2-1.9 POP na 0.3 0.2-0.5 0.5 0.2-1.7 Mirena na 0.4 0.3-0.6 Lidegaard et al. BMJ 2009; 339; b2890 Li/09

  19. VT and drospirenone VT Risk Rate ratio no / 10,000 DRSP/2nd gen Dinger 07 118 9.1 1.0 (0.6-1.8) 4th/2nd Seeger 07 57 13.0 * 0.9 (0.5-1.6) 4th/??? 09 Vlieg 1,524 na 1.7 (0.7-3.9) 4th/2nd Lidegaard 09 4.213 7.8 1.6 (1.3-2.1) 4th/2nd

  20. Research story • 2010, Jan: Shapiro-Dinger critique*

  21. OC and VT: Methods National Registry of National Registry of Patients (>1977) Medicinal products (>1995): OC use VT diagnoses, Medication against Previous CaVD/canc. BP , DM, Hyperchol. Pregnancies, surgery 1995 2005 Cause of Deaths Statistics Denmark Registry (>1977) PIN-codes, education Lethal VT vital status, emigration Lidegaard et al. BMJ 2009

  22. Research story • 2010, Jan: Shapiro-Dinger critique* • 2010, Jan: EMA request • 2010, March: Agreement PI, EMA, and MH • 2010, June: Steering Committee established • 2010, Oct: Protocol agreement • 2010, New case-control study by Dinger

  23. VT and drospirenone VT Risk Rate ratio no / 10,000 DRSP/2nd gen Dinger 07 118 9.1 1.0 (0.6-1.8) 4th/2nd Seeger 07 57 13.0 * 0.9 (0.5-1.6) 4th/??? 09 Vlieg 1,524 na 1.7 (0.7-3.9) 4th/2nd Lidegaard 09 4.213 7.8 1.6 (1.3-2.1) 4th/2nd Dinger 10 680 na 1.0 (0.5-1.8) 4th/2nd

  24. Research story • 2010, Jan: Shapiro-Dinger critique* • 2010, Jan: EMA request • 2010, March: Agreement PI, EMA, Bayer • 2010, June: Steering Committee established • 2010, Oct: Protocol agreement • 2010, New case-control study by Dinger • 2011, March: Submission BMJ

  25. OC and VT: Methods National Registry of Registry of Medicinal Patients (>1977) products (>1995): OC use (>1995) VT diagnoses, Anticoagulation therapy Previous CaVD/canc. BP , DM, Hyperchol. Pregnancies, surgery 1995 2001 2005 2009 1.3 million women Cause of Deaths Statistics Denmark Registry (>1977) PIN-codes, education Lethal VT vital status, emigration Lidegaard et al. BMJ 2011

  27. Results 2001-2009 • Identified 15-49 years: 1,436,130 women • Excluded: 140,010 women • Included in analysis: 1,296,120 women • Observation years: 8,010,290 womenyrs • First time VT (all): 4,307 events First time DVT: 2,738 (64%) First time PE: 1,130 (26%) First time other VT: 357 (10%) Lidegaard et al. BMJ 2011; 343: d6423

  28. OC and VT: Progestogen type Confirmed versus non-use ug EE Neta Lng NGM Deso Gest Drsp Cypr 50 6.2 4.5 Patch na na na na 3.0-13.2 2.9-6.9 30-40 2.2 3.0 3.5 6.6 6.2 6.4 6.4 1.1-4.5 2.4-4.0 2.9-4.3 5.6-7.8 5.6-7.0 5.4-7.5 5.4-7.5 20 na na na 4.8 5.1 6.9 na 4.1-5.6 4.4-5.9 4.2-11.5 Vg. Ring POP 0.7 0.3-1.5 0.6 0.2-1.9 Mirena 0.7 0.5-1.1 Lidegaard et al. BMJ 2011; 343: d6423

  29. OC and VT: Progestogen type Confirmed versus non-use ug EE Neta Lng NGM Deso Gest Drsp Cypr 50 6.2 4.5 7.9 Patch na na na 3.0-13.2 2.9-6.9 3.5-17.7 30-40 2.2 3.0 3.5 6.6 6.2 6.4 6.4 1.1-4.5 2.4-4.0 2.9-4.3 5.6-7.8 5.6-7.0 5.4-7.5 5.4-7.5 20 na na na 4.8 5.1 6.9 na 4.1-5.6 4.4-5.9 4.2-11.5 6.5 4.5-8.9 Vaginal Ring POP 0.7 0.3-1.5 0.6 0.2-1.9 Lng-IUS 0.7 0.5-1.1 Lidegaard et al. In press

  30. Relative risk versus non-use Confirmed events only 10 Rate ratio 8 6 4 7,9 6,6 6,5 6,4 6,4 6,2 2 3,5 3 0 Lidegaard et al. BMJ 2011; 343: d6423 + new in press

  31. Relative risk versus non-use 10 Rate ratio Vs non use 8 6 4 7,3 6,8 6,3 5,9 5,6 2 3,6 0 Vlieg et al. BMJ 2009; 339; b2921

  32. VT and drospirenone VT Risk Rate ratio no / 10,000 DRSP/2nd gen Dinger 07 118 9.1 1.0 (0.6-1.8) 4th/2nd Seeger 07 57 13.0 * 0.9 (0.5-1.6) 4th/??? 09 Vlieg 1,524 na 1.7 (0.7-3.9) 4th/2nd Lidegaard 09 4.213 7.8 1.6 (1.3-2.1) 4th/2nd Dinger 10 680 na 1.0 (0.5-1.8) 4th/2nd Lidegaard 11 4,246 2.1 (1.6-2.8) 4th/2nd 9.3 IR = incidence per 10,000 women years

  33. VT and drospirenone VT Risk Rate ratio no / 10,000 DRSP/2nd gen Dinger 07 118 9.1 1.0 (0.6-1.8) 4th/2nd 09 Vlieg 1,524 na 1.7 (0.7-3.9) 4th/2nd Lidegaard 09 4.213 7.8 1.6 (1.3-2.1) 4th/2nd Dinger 10 680 na 1.0 (0.5-1.8) 4th/2nd Parkin 11 61 2.3 2.7 (1.5-4-7) 4th/2nd Jick 11 186 3.1 2.8 (2.1-3.8) 4th/2nd Lidegaard 11 4,246 9.3 2.1 (1.6-2.8) 4th/2nd IR = incidence per 10,000 women years

Recommend

The influence of various types of boundary conditions on the liquid side boundary on the results

The influence of various types of boundary conditions on the liquid side boundary on the results

Marine Hydrophysical Institute of RAS The influence of various types of boundary conditions on the liquid side boundary on the results of numerical modeling near the southern coast of Crimea M. V. Senderov A. I. Mizyuk Moskow 2019

317 views • 21 slides
Working across the food environment: How can local authorities actively influence the types of

Working across the food environment: How can local authorities actively influence the types of

Working across the food environment: How can local authorities actively influence the types of food available in their locality? Whole Systems Obesity National Conference 18th October 2016, Leeds Town Hall Jenny Morris (CIEH) Sue Bagwell

448 views • 17 slides
Social influence Conformity Informational influence Influence that produces conformity when a

Social influence Conformity Informational influence Influence that produces conformity when a

What is a norm? Informal rule for acceptable and expected behavior Scripts Rituals Customs &amp; traditions Social influence Conformity Informational influence Influence that produces conformity when a person feels that others are correct

234 views • 4 slides
Module 5 Positive Influence Module Five: Positive Influence Objectives Understand the need

Module 5 Positive Influence Module Five: Positive Influence Objectives Understand the need

In the last module we discussed why Character and Ethics are important, we now turn to how to make those results happen. Your positive influence on others daily. 0 Module 5 Positive Influence Module Five: Positive Influence Objectives

250 views • 13 slides
Influencer Influence Challenge THE THREE KEYS TO INFLUENCE 1. Focus and measure 2. Find vital

Influencer Influence Challenge THE THREE KEYS TO INFLUENCE 1. Focus and measure 2. Find vital

Influencer Influence Challenge THE THREE KEYS TO INFLUENCE 1. Focus and measure 2. Find vital behaviours 3. Engage all 6 sources of influence KEY 1: Focus and measure Identify the results you want to achieve An effective result should be

361 views • 32 slides
The Importance of Influence Definition: influence (n) the capacity to have an effect on the

The Importance of Influence Definition: influence (n) the capacity to have an effect on the

The Importance of Influence Definition: influence (n) the capacity to have an effect on the character, development, or behavior of someone or something, or the effect itself. Synonyms: effect, impact Especially relevant to APTA ECP members,

324 views • 15 slides
Influence in Social Networks 2 1 4/23/18 Overview Influence Networks 3 2 4/23/18

Influence in Social Networks 2 1 4/23/18 Overview Influence Networks 3 2 4/23/18

4/23/18 CSCI 3210: Computational Game Theory Influence Games Ref: Irfan &amp; Ortiz, AI (2014) Reading: Sections 13(up to pg. 86), Sections 4.5, 5 (no proof), 6 bowdoin.edu/~mirfan/papers/Irfan_Ortiz_Influence_Games_AI2014.pdf Mohammad T.

512 views • 19 slides
INFLUENCE OF AND INFLUENCE ON THE EXECUTIVE BRANCH: COOPERATION AND INDEPENDENCE Dr Veljko

INFLUENCE OF AND INFLUENCE ON THE EXECUTIVE BRANCH: COOPERATION AND INDEPENDENCE Dr Veljko

REPUBLIC OF SERBIA COMMISSION FOR PROTECTION OF COMPETITION CONFERENCE ON INSTITUTION-BUILDING OF THE COMPETITION AUTHORITIES IN SOUTH-EAST EUROPE INFLUENCE OF AND INFLUENCE ON THE EXECUTIVE BRANCH: COOPERATION AND INDEPENDENCE Dr Veljko

432 views • 13 slides
Data Types Gabriele Keller Ron Vanderfeesten Compound types What are types? So far, we

Data Types Gabriele Keller Ron Vanderfeesten Compound types What are types? So far, we

Concepts of Program Design Data Types Gabriele Keller Ron Vanderfeesten Compound types What are types? So far, we only looked at basic types, such as Int, Boolean, as well as function types Int Bool 1 True 2 False 3 4

651 views • 35 slides
Types Classification of Values cs3723 1 Values and Types Basic types: types of atomic

Types Classification of Values cs3723 1 Values and Types Basic types: types of atomic

Types Classification of Values cs3723 1 Values and Types Basic types: types of atomic values int, bool, character, real, symbol Compound types: types of compound values List, record, array, tuple, struct, ref, pointer Built

450 views • 16 slides
#Influence in an attention economy Influence Noun invisible or insensible action exerted by

#Influence in an attention economy Influence Noun invisible or insensible action exerted by

#Influence in an attention economy Influence Noun invisible or insensible action exerted by one thing or person on another, especially by people in power Verb to modify, affect, or sway (Macquarie Dictionary, 6 th edition, 2013) The

362 views • 21 slides
Foreign Ownership, Control or Influence and Foreign Ownership, Control or Influence and Government

Foreign Ownership, Control or Influence and Foreign Ownership, Control or Influence and Government

Presenting a live 90 minute webinar with interactive Q&amp;A Foreign Ownership, Control or Influence and Foreign Ownership, Control or Influence and Government Contractor Security Clearance Mitigating FOCI and Meeting Requirements for National

469 views • 17 slides
On social influence, topics, and communities Francesco Bonchi www.francescobonchi.com Plan of

On social influence, topics, and communities Francesco Bonchi www.francescobonchi.com Plan of

On social influence, topics, and communities Francesco Bonchi www.francescobonchi.com Plan of the talk Some background on social influence Some background on influence maximization Topic-aware social influence propagation models

755 views • 61 slides
Types of Types Types of Types natural numbers. A type is a (possibly infinite) set of values.

Types of Types Types of Types natural numbers. A type is a (possibly infinite) set of values.

One-Slide Summary In lazy evaluation , expressions are not evaluated until their values are needed. We can use lazy evaluation to program with infinite data structures , such as a list of all Types of Types Types of Types natural numbers.

367 views • 8 slides
If we control these we can monitor &amp; influence these If we control these

If we control these we can monitor &amp; influence these If we control these

If we control these we can monitor &amp; influence these If we control these we can monitor &amp; influence these Spam conversion experiment Instrumented ~1.5% of Storms total output (2008) Pharmacy E-card Campaigns

833 views • 43 slides
Outlines Topic-aware Social Influence Propagation Models by N Barbieri and et al. , ICDM

Outlines Topic-aware Social Influence Propagation Models by N Barbieri and et al. , ICDM

Outlines Topic-aware Social Influence Propagation Models by N Barbieri and et al. , ICDM 2012 Scalable topic-specific influence analysis on microblogs by Bin Bi and et al., WSDM 2014 Introduction The problem of influence maximization

705 views • 29 slides
INFLUENTIAL COMMUNICATION How to increase your influence by asking better questions and

INFLUENTIAL COMMUNICATION How to increase your influence by asking better questions and

INFLUENTIAL COMMUNICATION How to increase your influence by asking better questions and listening well. For this workshop: Make sure you have a blank sheet of paper and a pen. Roi Ben-Yehuda What is influence? How would you define it?

909 views • 78 slides
! TYPES &amp; STATIC ANALYSIS TYPES ARE GOOD, I PROMISE. SAM GREENWOOD @SAMTGREENWOOD

! TYPES &amp; STATIC ANALYSIS TYPES ARE GOOD, I PROMISE. SAM GREENWOOD @SAMTGREENWOOD

! TYPES &amp; STATIC ANALYSIS TYPES ARE GOOD, I PROMISE. SAM GREENWOOD @SAMTGREENWOOD TECHNOLOGY MANAGER @ UNITI GROUP I LIKE TYPES. WITHOUT TYPES WITH TYPES WHAT ARE TYPES? DYNAMIC TYPES DEFINED WHEN WE USE THEM STATIC TYPES DEFINED

756 views • 40 slides
Presentation Outline Speakers Experience 1. Opening questions 2. Systems management Types

Presentation Outline Speakers Experience 1. Opening questions 2. Systems management Types

9/16/2019 Presentation Outline Speakers Experience 1. Opening questions 2. Systems management Types of systems; study of systems; evidence based 3. practise Main influence on Climate Change - Small Water Cycle 4. Key features of

184 views • 17 slides
Generic Views on Data Types Problem Towards a solution Generic Views Detour into GH The

Generic Views on Data Types Problem Towards a solution Generic Views Detour into GH The

Introduction Generic Views on Data Types Problem Towards a solution Generic Views Detour into GH The influence of S. Holdermans 1 J. Jeuring 1 oh 2 A. Rodriguez 1 A. L views Formal definition Validity Implementation 1 Department of

677 views • 56 slides
Influence Maximization in Dynamic Social Networks Honglei Zhuang, Yihan Sun, Jie Tang, Jialin

Influence Maximization in Dynamic Social Networks Honglei Zhuang, Yihan Sun, Jie Tang, Jialin

Influence Maximization in Dynamic Social Networks Honglei Zhuang, Yihan Sun, Jie Tang, Jialin Zhang, Xiaoming Sun Influence Maximiza.on Influence threshold How to find influen4al users to help promote a

400 views • 21 slides
Under Understandi anding ng Blac ack-bo box x Pr Predictions via Influence Fu Funct

Under Understandi anding ng Blac ack-bo box x Pr Predictions via Influence Fu Funct

Under Understandi anding ng Blac ack-bo box x Pr Predictions via Influence Fu Funct ctions Pang Wei Koh &amp; Perry Liang Presented by Theo, Aditya, Patrick 1 Roadmap 1.Influence functions: definitions and theory 2.Efficiently

1.12k views • 93 slides
Framework of Six Types of Parental Involvement Theoretical Model OVERLAPPING SPHERES OF

Framework of Six Types of Parental Involvement Theoretical Model OVERLAPPING SPHERES OF

Framework of Six Types of Parental Involvement Theoretical Model OVERLAPPING SPHERES OF INFLUENCE OF FAMILY, SCHOOL, AND COMMUNITY ON CHILDRENS LEARNING External Structure Force C Force B Experience, Experience, Philosophy,

655 views • 11 slides
On the Influence of Input Noise On the Influence of Input Noise on a Generalization Error

On the Influence of Input Noise On the Influence of Input Noise on a Generalization Error

IASTED-AIA2004 Feb. 16-18, 2004 On the Influence of Input Noise On the Influence of Input Noise on a Generalization Error Estimator on a Generalization Error Estimator (1,2) Masashi Sugiyama (2) Yuta Okabe (2) Hidemitsu Ogawa (1)

460 views • 21 slides

More recommend