Platelet Count and Its Prognostic Value in Pregnancy-Induced Hypertension
DOI:
https://doi.org/10.61581/mjsp.v6i01.380Keywords:
Thrombocytopenia, Prognostic marker, Preeclampsia, Platelet count, EclampsiaAbstract
Objective: To determine the pattern of decline in platelet count across the spectrum of pregnancy-induced hypertension in preeclampsia and eclampsia.
Methods: This prospective observational study was conducted at the Obstetrics and Gynecology Department of CMH Hospital, Multan, Pakistan, from 3rd April 2024 to 4th October 2024. A total of 200 pregnant women beyond 28 weeks of gestation were enrolled, comprising 100 women diagnosed with PIH (study group) and 100 normotensive women (control group). Platelet counts were monitored at four-week intervals for the control group and weekly for the study group until delivery. Statistical analyses, including Chi-square tests, independent t-tests, and one-way ANOVA, were utilized to assess the correlation between platelet dynamics and PIH severity.
Results: The mean platelet counts demonstrated a significant progressive decline correlating with disease severity: 2.09 ± 0.43 ×10?/mm³ in mild preeclampsia, 1.58 ± 0.42 ×10?/mm³ in severe preeclampsia, and 0.96 ± 0.38 ×10?/mm³ in eclampsia. The overall mean platelet count for the PIH group (1.74 ± 0.56 ×10?/mm³) was significantly lower than that of the normotensive controls (2.28 ± 0.46 ×10?/mm³, p < 0.001). Severe thrombocytopenia (<1 ×10?/mm³) was predominantly observed in eclampsia cases (33.3%). Adverse maternal and fetal outcomes were disproportionately clustered among patients with platelet counts ?1.5 ×10?/mm³.
Conclusion: Platelet count serves as a highly effective, economical, and accessible biomarker for predicting the progression of PIH and anticipating adverse maternal and fetal outcomes. Routine antenatal platelet monitoring should be integrated into the standard management protocols for hypertensive pregnancies to facilitate early intervention and mitigate severe complications.
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Copyright (c) 2025 Syeda Zainab Gillani, Nida Yaseen, Lubna Noor, Maryam Arif, Sadia Majeed, Najia Kasi

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