UBC Theses and Dissertations
The prediction of adverse maternal outcomes in pre-eclampsia Devarakonda, Rajashree M.
Pre-eclampsia (PET) continues to contribute to maternal and perinatal morbidity and mortality. Management decisions include an evaluation of maternal risk, which is assisted by expert opinion-based guidelines, while not accounting for gestational age (GA) at diagnosis. We evaluated the feasibility of developing a severity score that can predict adverse maternal outcome. Methods: Design: retrospective chart review of 2 years' PET cases in three tertiary level units. Candidate predictors of adverse maternal outcome: admission gestational age, blood pressure, proteinuria, urine output, SaO₂, seizures, uric acid, creatinine, aspartate transaminase (AST), lactate dehydrogenase, bilirubin, albumin, platelet count, MPV, MPV:platelet ratio, and fibrinogen. Combined adverse maternal outcome: maternal death or one/more of: hepatic failure/haematoma/rupture, Glasgow coma scale lh, intubation, or transfusion of ≥10 units of blood products. Analyses: Women were classified by having achieved the combined adverse maternal outcome or not. Descriptive, parametric and non-parametric test analysis of the predictor variables was performed. The variables with sample size of >400 were selected and univariable logistic regression analysis was done. Variables with univariable p-value 10 units. GA and fibrinogen were lower and TLC, dipstick protein, bilirubin, creatinine, MPV/platelet ratio and AST were greater in those who developed the outcome. Multivariable logistic regression revealed that higher admission GA (odds ratio 0.85), higher dipstick proteinuria (OR 1.53), and higher MPV:platelet (OR 291.0) independently predicted the outcome. Discussion: Several promising markers were identified, which need to be substantiated in a large multi centre study. Such predictors included admission GA, dipstick proteinuria and the MPV:platelet ratio. Bilirubin and fibrinogen appeared to be promising. A prospective study is required to develop a clinical prediction model for PET.
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