Gestational Trophoblastic Diseases. An Overview of Patients visiting Sheikh Zayed Medical Hospital, Rahim Yar Khan
DOI:
https://doi.org/10.37018/DEFE5247Keywords:
Gestational trophoblastic disease (GTD), Partial mole (PM), Complete mole (CM), Choriocarcinoma, β-hCGAbstract
Background: Gestational trophoblastic disease is an important obstetric condition which if untreated, can result in serious and harmful consequences. Objective of this study was to determine incidence and pattern of gestational trophoblastic disease in tertiary care hospital of Rahim Yar khan.
Methods: This prospective observational cohort study was conducted at Sheikh Zayed Medical Hospital, Rahim Yar Khan, from January 2019 to December 2022. Patients presented with gestational amenorrhea and vaginal bleeding were sus-pected to be cases of molar pregnancy based on certain distinct clinical factors. These indicators included a large-for-date symphysio-fundal height (SFH), a snow storm appearance on ultrasonography, and significantly higher serum β-hCG levels. Data for these specific cases was collected from molar pregnancy registers maintained in Obstetrics and Gynecology de-partment of Sheikh Zayed Medical Hospital, Rahim Yar Khan. Final diagnosis of molar pregnancy was confirmed by histo-pathology of the tissue removed from uterine cavity by suction and evacuation.
Results: A total of 33,494 pregnant women were admitted during the study period. Among this population, 197 were diag-nosed with a molar pregnancy, representing a 0.58% rate. This equates to an overall incidence of 5.8 per 1,000 obstetric admission. Complete hydatidiform mole was the most common type, diagnosed in 127 females (64.4%). Partial mole oc-curred in 58 females (29.4%), while choriocarcinoma was found in 12 females (6.1%). Among 12 women with choriocarci-noma, four presented with pulmonary metastasis, received multiagent chemotherapy in liaison with oncology department.
Conclusion: High sensitivity pelvic ultrasonography may have led to early detection of abnormal pregnancies, before we can detect classical symptoms or identify patients with advanced disease.
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Copyright (c) 2026 Mahwish Najam, Beenish Najam, Sadia Shafiq, Farhana Shabnum

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