Predictors of maternal mortality among critically ill obstetric patients

Aim Evaluation of the predictors of maternal mortality among critically ill obstetric patients managed at the intensive care unit (ICU). Methods A case control study to evaluate the predictors of maternal mortality among critically ill obstetric patients managed at the intensive care unit (ICU) of the University of Ilorin Teaching Hospital, Ilorin, Nigeria from 1st January 2010 to 30th June 2013. Participants were critically ill obstetric patients who were admitted and managed at the ICU during the study period. Subjects were those who died while controls were age and parity matched survivors. Statistical analysis was with SPSS-20 to determine chi square, Cox-regression and odds ratio; p value < 0.05 was significant. Results The mean age of subjects and controls were 28.92 ± 5.09 versus 29.44 ± 5.74 (p = 0.736), the level of education was higher among controls (p = 0.048) while more subjects were of low social class (p = 0.321), did not have antenatal care (p = 0.131) and had partners with lower level of education (p = 0.156) compared to controls. The two leading indications for admission among subjects and controls were massive postpartum haemorrhage and severe preeclampsia or eclampsia. The mean duration of admission was higher among controls (3.32 ± 2.46 versus 3.00 ± 2.58; p = 0.656) while the mean cost of ICU care was higher among the subjects (p = 0.472). The statistical significant predictors of maternal deaths were the patient’s level of education, Glasgow Coma Scale (GCS) score, oxygen saturation, multiple organ failure at ICU admission and the need for mechanical ventilation or inotrophic drugs after admission. Conclusion The clinical state at ICU admission of the critically ill obstetric patients is the major outcome determinant. Therefore, early recognition of the need for ICU care, adequate pre-ICU admission supportive care and prompt transfer will improve the outcome.

Smoothing a Discrete Hazard Function for the Number of Patients Colonized with Methicillin-Resistance Staphylococcus Aureus in an Intensive Care Unit

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.