The article discusses the development of the Endometrial Malignancy Prediction System (EMPS), a nomogram that can be used to identify patients at high risk of endometrial cancer and prioritize them for hysteroscopy in Brazil's public health system.
The EMPS was developed by researchers at the Municipal Hospital of Vila Santa Catarina in São Paulo, a public unit managed by Hospital Israelita Albert Einstein. The tool was created during the COVID-19 pandemic, when the researchers noticed that patients with postmenopausal bleeding, who have a higher risk of endometrial cancer, were waiting in the same line as patients with less severe complaints for hysteroscopy.
The EMPS was developed based on a retrospective case-control study involving 1,945 patients who had undergone diagnostic hysteroscopy. The researchers identified several risk factors for endometrial cancer, including the presence or absence of hypertension, diabetes, postmenopausal bleeding, endometrial polyps, uterine volume, number of pregnancies, body mass index, age, and endometrial thickness. These factors were used to create the EMPS nomogram, which can be used by physicians to classify a patient's risk of endometrial cancer or precursor lesions.
The goal of the EMPS is not to remove low-risk patients from the hysteroscopy waiting list, but rather to prioritize high-risk patients to receive the examination sooner. This is particularly important in Brazil's public health system, where some patients can wait up to 2 years for a hysteroscopy.
The researchers plan to continue their research by conducting a prospective study to evaluate the tool within their own service and to explore ways to implement the EMPS in primary care clinics, where patients are initially referred for hysteroscopy.
To Another Language
from source content
www.medscape.com
Thông tin chi tiết chính được chắt lọc từ
by Teresa Santo... lúc www.medscape.com 05-01-2024
https://www.medscape.com/viewarticle/tool-may-help-prioritize-high-risk-patients-hysteroscopy-2024a10008emYêu cầu sâu hơn