Perinatal Death: Epidemiology and Etiology

Perinatal mortality rate is an important mark to evaluate women and perinatal health care. It is of utmost importance to know causes and the evolution of its two components aiming to improve health care in different fields – sanitary conditions, diagnosis and treatment of infectious disease, immunisations, diagnosing and caring for medical diseases induced by pregnancy or directly related to it, providing skilled birth attendance, preventing birth asphyxia, preventing preterm birth complications and infections. In high-income countries the epidemiology varies mainly with social and economic conditions; in low-income countries, paired with poverty, undernutrition, superstition, lack of medical care, deficient basic sanitary conditions are also found. Also, in rich countries, responsible for 1% of deaths, data are published and improvements evaluated, while in low-income countries responsible for 99% of deaths numbers and causes are unknown, making difficult to implement cost effective interventions, a reason why “stillbirth rates in low-income countries are now where they were in high-income countries 50 to 100 years ago”. Knowledge on causes of death are very important as often what is needed are “simple” measures as improvement of sanitary conditions and immunisation programmes rather than high technologies. About four million babies dye each year in the first 28 days of life and another 3 million dye before birth in the third-trimester, with 98% occurring in low-income and middle income countries and more than 1 million occurring during labour and delivery. Classically stillbirths are the major component of perinatal mortality rate. Causes of death are even more difficult to know. In low-income countries a great proportion of women give birth at home. Worldwide the main causes of stillbirth are asphyxia due to obstructed labour, eclampsia, abruption placenta and umbilical cord complications - making valid the assumption that skilled birth attendance would decrease stillbirth; and infection - chorioamnioitis, syphilis and malaria. In high-income countries placental pathology and infection, congenital anomalies, complications of preterm birth and post term delivery, are the most common. If in low-income countries famine and lack of provisions and health care are common, in high-income countries, advanced maternal age and diabetes, obesity, hypertension, smoking, are frequent findings.

Modeling in-Hospital Patient Survival During the First 28 Days After Intensive Care Unit Admission: a Prognostic Model for Clinical Trials in General Critically Ill Patients

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.