madman
Super Moderator
Approach to Managing a Postmenopausal Patient
Richard J. Santen , Daniel F. Heitjan , Anne Gompel , Mary Ann Lumsden, JoAnn V. Pinkerton , Susan R. Davis, Cynthia A. Stuenkel
Abstract
The case of a symptomatic, postmenopausal woman is presented and a full discussion of the approach to her management is discussed. Pertinent guidelines and scientific evidence are emphasized as support for the recommendations.
Case presentation: A 51-year-old, white woman presents with symptoms of hot flashes starting 2 years ago and now occurring every three hours during the daytime and 2 to 3 times at night. The hot flashes awaken her; she has difficulty getting back to sleep and wakes up feeling unrested. She thinks that her work as an office manager is suffering, perhaps due to fatigue from poor sleep. She began having irregular periods starting 5 years ago which were attributed to perimenopause. Her last menstrual period occurred one year ago, with no subsequent bleeding. She has intercourse with her partner weekly and has no symptoms of dyspareunia, vaginal dryness, or recent urinary tract infections. Her first menstrual period was at age 11 and her first child was born when she was 23 years old. Further questioning reveals symptoms of low mood and some mental ‘fog’. She has no symptoms related to cardiac, pulmonary, gastrointestinal, musculoskeletal, skin, or neurologic systems.
Medical history is negative for hypertension, diabetes, or hyperlipidemia. She has had lifelong overweight/obesity with a weight of 140 pounds (63.5 kg) at age 13, 170 pounds (77kg) on graduation from high school at age 18, 190 pounds (86.3 kg) when she married at age 22, and 198 pounds (90kg) after the last pregnancy. Her pregnancies were uneventful and without miscarriages; she has three children, ages 28, 26, and 25. She drinks on average a glass of wine twice per month. She previously smoked less than 1 pack per day, stopping 20 years ago. She has no history of deep venous thrombosis (DVT) or pulmonary embolus. She used oral contraceptives for around 3 years, followed by a tubal ligation after her last pregnancy.
Family history is positive for breast cancer in a second cousin diagnosed at age 72. There is no family history of a cardiovascular event before age 60 or of thromboembolic disease. Her mother and maternal grandmother were both obese.
The patient relates that she has a satisfying job. She has not experienced major conflicts in her marriage, which she characterizes as successful.
On physical exam, blood pressure is 122/72, pulse 72 and regular, height 5’4 (1.62m), and body mass index (BMI) 34 kg/m2. Her waist circumference is 36 inches (90 cm) with a waist/hip ratio of 1.2. The remainder of her physical exam is normal including the breast exam. The gynecologic exam was deferred.
Laboratory data included a total cholesterol of 230mg/dl (5.9mmol/L), LDL cholesterol 125 mg/dl (3.2 mmol/L), HDL cholesterol 60 mg/dl (1.6 mmol/L), and hemoglobin A1C 5.2%. Complete blood count, comprehensive metabolic panel, and thyroid testing were normal.
Her primary care physician ordered a mammogram that revealed no abnormalities and average breast density categorized on the BiRads density system as category B (otherwise called category 2) containing scattered fibro-glandular tissue.
Discussion with the patient: When asked how much the hot flashes bothered her, she said that they are very bothersome and she would like some type of treatment for them. She expressed a willingness to consider menopausal hormone therapy (MHT). However, she had read that MHT often causes breast cancer and less often a heart attack. She desired an opinion on the side effects and harms related to MHT individualized for her and a discussion of other treatment options. She had read in the newspaper that high breast density can increase breast cancer risk, but she did not understand why or what ‘normal’ breast density means.
Principles of Personalized Medicine: Each woman has a unique set of underlying factors that influence the harms and benefits of a treatment and the process of making a decision 1,2. The term “Personalized Medicine” conveys the notion of tailoring treatment to the individual patient based on such factors 2. Readily available factors include knowledge of blood pressure; BMI; lipid profile; alcohol intake; smoking history; age at menarche, menopause, and first live birth; contraceptive and reproductive history; breast density on a most recent mammogram; family history of heart disease, cancer or genetic risks of cardiovascular disease or thromboembolic disease; and physical activity 3. Knowledge of the woman’s genetic makeup would be helpful 4 but its use is not currently feasible for most patients based on cost and availability.
An integrated assessment of these factors enables the evaluation of the harms and benefits of strategies for managing menopause. Prediction tools, while imperfect 5, are available for estimation of the underlying risks of breast cancer and cardiovascular disease. Examples include the IBIS Breast Cancer Risk Evaluation Tool (ibis.ikonopedia.com) and the heart disease ACC/AHA Pooled Cohort Equation (tools.acc/org/ASCVD-Risk-Estimator). We favor these models for women living in North America. Other available methods include the Gail model6 and models created for use in other countries 5. Pooling all available information to predict harm and benefit enables the provider to make decisions based on the principles of Personalized Medicine. Notably, the Endocrine Society guidelines for the management of menopause 7 and the American Heart Association/ American College of Cardiology guidelines for the treatment of hypertension 8 and hyperlipidemia 9 now incorporate the principles of Personalized Medicine in their recommendations.
Our prior manuscript discussed in detail why women at low underlying risk of breast cancer can expect to experience a lesser increase in breast cancer from MHT than women at higher underlying risk 1. The data supporting this assessment of underlying risk are based on published data including randomized controlled data, extrapolations, and assumptions of linearity 1. However, these data involve post-hoc analyses and no head-to-head, prospective, randomized controlled trials that have been conducted enrolling women based on low versus higher baseline risk. The data regarding the use of the underlying risk of cardiovascular disease to stratify individual risk are based on the assessment of biomarkers in the Women’s Health Initiative Studies 2,10,11. The use of systemic MHT in women with active or non-traumatic DVT and pulmonary emboli is generally contraindicated and a personalized approach to identify these risk factors minimizes this risk.
General Approach to Menopause Management:
Step 1.0:
Step 2.0:
Step 3.0:
Understanding Risk:
Underlying and Excess Risk of Breast Cancer:
How does one apply the data obtained in the tables?
The Patient:
General principles: cardiovascular risk:
The Patient:
Deep vein thrombosis and pulmonary emboli:
The patient:
Step 4:
Step 5:
Practical approaches to the management of refractory symptoms and side effects:
Limitations of Data Interpretation: Only one randomized, controlled trial (the WHI) has compared the use of placebo to E alone or E+SP, and the only agents tested were conjugated equine estrogen and medroxyprogesterone acetate. Accordingly, the validity of the application of these data to all forms of estrogen and progestogens (synthetic progestogens or progesterone) and to women with moderate to severe vasomotor symptoms lacks high-level scientific support. However, a compilation of all available data from the large WHI RCT and observational data suggest that these databases are probably reasonable for making clinical decisions. When comparing data from this RCT and observational studies, the data appear reasonably congruent if confounding factors are accounted for 21,90-93. For this reason, the authors of this treatise and the Endocrine Society Clinical Practice guidelines have used both data from RCTs and observational studies to support our recommendations.
Conclusions: The management of menopause depends on the application of concepts of personalized medicine and the individualized tailoring of therapy. This requires a careful assessment of benefits and harms based on individual factors. While clinical judgment can intuitively integrate risk factors, predictive models that estimate the underlying risk of breast cancer and heart disease provide more objective information. These models are useful in planning therapy and in educating women who are confused by the information they find on the internet, in the media, or from their friends and family. The use of risk tables, as first described in this manuscript, saves clinician time and provides an effective way for women to understand the roles of various risk factors. The systematic, step-by-step approach to treating menopausal women provides a framework for the busy clinician to make appropriate recommendations.
Richard J. Santen , Daniel F. Heitjan , Anne Gompel , Mary Ann Lumsden, JoAnn V. Pinkerton , Susan R. Davis, Cynthia A. Stuenkel
Abstract
The case of a symptomatic, postmenopausal woman is presented and a full discussion of the approach to her management is discussed. Pertinent guidelines and scientific evidence are emphasized as support for the recommendations.
Case presentation: A 51-year-old, white woman presents with symptoms of hot flashes starting 2 years ago and now occurring every three hours during the daytime and 2 to 3 times at night. The hot flashes awaken her; she has difficulty getting back to sleep and wakes up feeling unrested. She thinks that her work as an office manager is suffering, perhaps due to fatigue from poor sleep. She began having irregular periods starting 5 years ago which were attributed to perimenopause. Her last menstrual period occurred one year ago, with no subsequent bleeding. She has intercourse with her partner weekly and has no symptoms of dyspareunia, vaginal dryness, or recent urinary tract infections. Her first menstrual period was at age 11 and her first child was born when she was 23 years old. Further questioning reveals symptoms of low mood and some mental ‘fog’. She has no symptoms related to cardiac, pulmonary, gastrointestinal, musculoskeletal, skin, or neurologic systems.
Medical history is negative for hypertension, diabetes, or hyperlipidemia. She has had lifelong overweight/obesity with a weight of 140 pounds (63.5 kg) at age 13, 170 pounds (77kg) on graduation from high school at age 18, 190 pounds (86.3 kg) when she married at age 22, and 198 pounds (90kg) after the last pregnancy. Her pregnancies were uneventful and without miscarriages; she has three children, ages 28, 26, and 25. She drinks on average a glass of wine twice per month. She previously smoked less than 1 pack per day, stopping 20 years ago. She has no history of deep venous thrombosis (DVT) or pulmonary embolus. She used oral contraceptives for around 3 years, followed by a tubal ligation after her last pregnancy.
Family history is positive for breast cancer in a second cousin diagnosed at age 72. There is no family history of a cardiovascular event before age 60 or of thromboembolic disease. Her mother and maternal grandmother were both obese.
The patient relates that she has a satisfying job. She has not experienced major conflicts in her marriage, which she characterizes as successful.
On physical exam, blood pressure is 122/72, pulse 72 and regular, height 5’4 (1.62m), and body mass index (BMI) 34 kg/m2. Her waist circumference is 36 inches (90 cm) with a waist/hip ratio of 1.2. The remainder of her physical exam is normal including the breast exam. The gynecologic exam was deferred.
Laboratory data included a total cholesterol of 230mg/dl (5.9mmol/L), LDL cholesterol 125 mg/dl (3.2 mmol/L), HDL cholesterol 60 mg/dl (1.6 mmol/L), and hemoglobin A1C 5.2%. Complete blood count, comprehensive metabolic panel, and thyroid testing were normal.
Her primary care physician ordered a mammogram that revealed no abnormalities and average breast density categorized on the BiRads density system as category B (otherwise called category 2) containing scattered fibro-glandular tissue.
Discussion with the patient: When asked how much the hot flashes bothered her, she said that they are very bothersome and she would like some type of treatment for them. She expressed a willingness to consider menopausal hormone therapy (MHT). However, she had read that MHT often causes breast cancer and less often a heart attack. She desired an opinion on the side effects and harms related to MHT individualized for her and a discussion of other treatment options. She had read in the newspaper that high breast density can increase breast cancer risk, but she did not understand why or what ‘normal’ breast density means.
Principles of Personalized Medicine: Each woman has a unique set of underlying factors that influence the harms and benefits of a treatment and the process of making a decision 1,2. The term “Personalized Medicine” conveys the notion of tailoring treatment to the individual patient based on such factors 2. Readily available factors include knowledge of blood pressure; BMI; lipid profile; alcohol intake; smoking history; age at menarche, menopause, and first live birth; contraceptive and reproductive history; breast density on a most recent mammogram; family history of heart disease, cancer or genetic risks of cardiovascular disease or thromboembolic disease; and physical activity 3. Knowledge of the woman’s genetic makeup would be helpful 4 but its use is not currently feasible for most patients based on cost and availability.
An integrated assessment of these factors enables the evaluation of the harms and benefits of strategies for managing menopause. Prediction tools, while imperfect 5, are available for estimation of the underlying risks of breast cancer and cardiovascular disease. Examples include the IBIS Breast Cancer Risk Evaluation Tool (ibis.ikonopedia.com) and the heart disease ACC/AHA Pooled Cohort Equation (tools.acc/org/ASCVD-Risk-Estimator). We favor these models for women living in North America. Other available methods include the Gail model6 and models created for use in other countries 5. Pooling all available information to predict harm and benefit enables the provider to make decisions based on the principles of Personalized Medicine. Notably, the Endocrine Society guidelines for the management of menopause 7 and the American Heart Association/ American College of Cardiology guidelines for the treatment of hypertension 8 and hyperlipidemia 9 now incorporate the principles of Personalized Medicine in their recommendations.
Our prior manuscript discussed in detail why women at low underlying risk of breast cancer can expect to experience a lesser increase in breast cancer from MHT than women at higher underlying risk 1. The data supporting this assessment of underlying risk are based on published data including randomized controlled data, extrapolations, and assumptions of linearity 1. However, these data involve post-hoc analyses and no head-to-head, prospective, randomized controlled trials that have been conducted enrolling women based on low versus higher baseline risk. The data regarding the use of the underlying risk of cardiovascular disease to stratify individual risk are based on the assessment of biomarkers in the Women’s Health Initiative Studies 2,10,11. The use of systemic MHT in women with active or non-traumatic DVT and pulmonary emboli is generally contraindicated and a personalized approach to identify these risk factors minimizes this risk.
General Approach to Menopause Management:
Step 1.0:
Step 2.0:
Step 3.0:
Understanding Risk:
Underlying and Excess Risk of Breast Cancer:
How does one apply the data obtained in the tables?
The Patient:
General principles: cardiovascular risk:
The Patient:
Deep vein thrombosis and pulmonary emboli:
The patient:
Step 4:
Step 5:
Practical approaches to the management of refractory symptoms and side effects:
Limitations of Data Interpretation: Only one randomized, controlled trial (the WHI) has compared the use of placebo to E alone or E+SP, and the only agents tested were conjugated equine estrogen and medroxyprogesterone acetate. Accordingly, the validity of the application of these data to all forms of estrogen and progestogens (synthetic progestogens or progesterone) and to women with moderate to severe vasomotor symptoms lacks high-level scientific support. However, a compilation of all available data from the large WHI RCT and observational data suggest that these databases are probably reasonable for making clinical decisions. When comparing data from this RCT and observational studies, the data appear reasonably congruent if confounding factors are accounted for 21,90-93. For this reason, the authors of this treatise and the Endocrine Society Clinical Practice guidelines have used both data from RCTs and observational studies to support our recommendations.
Conclusions: The management of menopause depends on the application of concepts of personalized medicine and the individualized tailoring of therapy. This requires a careful assessment of benefits and harms based on individual factors. While clinical judgment can intuitively integrate risk factors, predictive models that estimate the underlying risk of breast cancer and heart disease provide more objective information. These models are useful in planning therapy and in educating women who are confused by the information they find on the internet, in the media, or from their friends and family. The use of risk tables, as first described in this manuscript, saves clinician time and provides an effective way for women to understand the roles of various risk factors. The systematic, step-by-step approach to treating menopausal women provides a framework for the busy clinician to make appropriate recommendations.
