Here's a breakdown of some of the highlights from the first day of the 2021 American College of Obstetricians and Gynecologists (ACOG) Annual Clinical and Scientific Meeting.
The American College of Obstetricians and Gynecologists (ACOG) 2021 Annual Clinical and Scientific Meeting kicked off with an opening presidential address and panel diving straight into personalized care. This year’s event aimed to investigate topics in preventative care, including cardiovascular disease, pre-pregnancy counseling, diabetes, obesity, and modified and genetic risk factors for cancer patients.
ACOG President Dr. Eva Chalas, NIH Associate Director for Research on Women’s Health Dr. Janine Austin Clayton, and Acting Director of the U.S. National Cancer Institute Dr. Douglas R. Lowry shared key topics to be discussed throughout the conference and provided more information on the main focus of the meeting, OB-GYNs developing personalized care to close gaps in healthcare.
Some examples of this were utilizing pregnancy history to counsel on future risks of disease and pregnancy outcomes, assist in the management of obesity, offer mitigation strategies to lower the risk of cardiovascular disease, provide screening for diabetes, and evaluate the risk of deleterious mutations and other risks of developing cancer, all to deliver the higher level of clinical knowledge and skill.
Below are some of the highlights from Day 1 of ACOG’s 2021 meeting.
Pink Problems: GYN Issues in Breast Cancer Survivors
Dr. Erin A. Keyser, breast cancer survivor, gynecologist, and Program Director for the Department of OBGYN at San Antonio Uniformed Services Health Education Consortium (SAUSHEC), explored best practices for managing the gynecologic side effects of breast cancer treatments.
In addressing some of these side effects, such as sexual health dysfunction and hot flashes, Dr. Keyser walked the audience through some of the strategies for preventing OB-GYNs from feeling ill-equipped to help their patients.
She explored the role of OB-GYNs in managing these side effects and shared a few key takeaways on caring for breast cancer survivors, like ensuring routine pap smear screenings, asking about sexual health and hot flashes, assuring bone screenings, and screening for depression/anxiety. For more resources, Dr. Keyser recommends visiting the Living Beyond Breast Cancer.
Is BMI a Valid Measure of Obesity in Post-Menopausal Women? A New Paradigm to Assess Cardio-Metabolic Risk
BMI and postmenopausal women were other highlights from the conference. In this session, Dr. Maida Beth Taylor, who works in pharmacovigilance at Biomarin, examined obesity in postmenopausal women and investigated the risk of adverse outcomes.
According to Dr. Taylor, to help maintain a healthy weight and prevent obesity in postmenopausal women, physicians must aim for a target BMI of 24.8 or under for their patients. And while that number may seem unattainable, it’s an important measure to reduce cardiometabolic risk.
Dr. Taylor also encouraged OB-GYNs to dispose of the mythology of fat and fit and emphasized that while BMI cannot accurately account for muscle mass or other factors, it remains a key component in assessing the body fat level of menopausal women. On average, women see an increase of up to 400% of visceral body mass post-menopause, and unlike men, their fat is distributed in a way that can increase their cardio-metabolic and cancer risk.
CDC STI Treatment Guideline Update
The STI epidemic was another focus at this year’s conference. In this session, Dr. Kimberly A. Workowski, a Professor of Medicine in the Division of Infectious Diseases at Emory University and a consultant to the CDC to coordinate the development of the CDC STI Treatment Guidelines, studied STI epidemiological trends and prevention challenges, provided recommendations for STI management in anticipation of the release of the 2021 STI Treatment Guidelines from the CDC, and discussed STI antimicrobial challenges.
Focused on assessing the dramatic rise in congenital syphilis, Dr. Workowski walked the audience through some of the biggest challenges in STI prevention, including health disparities and stigma, limited preventative services, lack of awareness and training, limited point of care in diagnostics, antimicrobial resistance, and suboptimal vaccination uptake. Dr. Workowski also shared tools we have available for STI screening and management, specifically the CDC STI Treatment Guidelines.
And with the upcoming 2021 STI Treatment Guidelines, Dr. Workowski highlighted new data to be included, including the efficacy of doxycycline as compared to azithromycin to treat bacterial STIs such as chlamydia and the demotion of Clindamycin to an alternative therapy to Gentamicin.
Machine Learning: What the Obstetrician-Gynecologist Needs to Know About This New Technology
Led by Dr. Alexis C. Gimovksy, who’s an obstetrician/gynecologist in Providence, Rhode Island, this session began by reviewing the definition of machine learning: a non-linear data model that can be used to make predictions and guide decisions. At the heart of the session was the idea that machine learning needs to be recognized as a pathway to providing personalized care.
To help drive that point home, Dr. Gimovksy reviewed the following key examples of machine learning in action:
- AI Predicting Pre-Term Delivery: To evaluate the risk of pre-term delivery based on a variety of factors like demographics, amniotic fluid characteristics, and protein levels, the model used what’s called “deep learning,” which is an algorithm that can handle a lot of unstructured data such as images and audio. Since it’s difficult to understand how deep learning gets to its conclusion, it’s often referred to as a black box method.
- AI Identifying Anomalies in Ultrasounds: This is another example of a deep learning model. In this instance, a clinician had to identify what’s considered normal versus abnormal on the anatomy ultrasound to help develop the deep learning model. Once that preliminary work was done, the model was shown to be a time- and cost-efficient alternative to live ultrasound scans.
- Machine Learning Predicting C-Sections in the Second Stage of Labor: What makes this example different is that an optimal decision tree model was used. Unlike the black-box model, the optimal decision tree shows you the algorithm so you can follow how it came to make a prediction. Once specific data points like demographics and fetal positions were entered into the model, it was able to predict if a patient in the second stage of labor who had received epidural anesthesia would require a C-section.
Machine learning already plays such a significant role in our daily lives, and according to Dr. Gimovksy, it’s just as important to understand its role in medicine.
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