Predictors of mortality in very low birth weight neonates in India

Singapore Med J. 2008 Jul;49(7):556-60.


Introduction: Very low birth weight (VLBW) neonates constitute approximately 4-7 percent of all live births and their mortality is very high. The objective of the present study was to determine the predictors of mortality in VLBW neonates.

Methods: A retrospective cohort of VLBW neonates admitted over three years was studied. Exclusion criteria were: (1) neonates weighing less than 500 g and with gestational age less than 26 weeks; (2) presence of lethal congenital malformations; and (3) death in the delivery room or within 12 hours of life. The outcome measure was in-hospital death. Medical records were reviewed and data was analysed. Univariate analysis and logistic regression analysis were done to determine the predictors of mortality.

Results: A total of 260 cases were enrolled, of which a total of 96 (36.9 percent) babies died. The survival rate was found to increase with the increase in birth weight and gestational age. Univariate analysis showed maternal per vaginal bleeding, failure to administer steroid antenatally, Apgar score less than or equal to 5 at one minute, apnoea, gestational age, neonatal septicaemia and shock are the factors directly responsible for neonatal mortality. Logistic regression equation showed maternal bleed (1.326), apnoea (3.159), birth weight (0.037), gestational age (0.063), hypothermia (1.132) and shock (3.49) predicted 65 percent of mortality in VLBW babies.

Conclusion: Common antenatal and perinatal predictors of mortality in VLBW infants in India include maternal bleed, failure to administer antenatal steroids, low Apgar score, apnoea, extreme prematurity, neonatal septicaemia and shock.

MeSH terms

  • Adolescent
  • Adult
  • Cohort Studies
  • Female
  • Humans
  • India
  • Infant, Newborn
  • Infant, Premature, Diseases / epidemiology*
  • Infant, Premature, Diseases / mortality
  • Infant, Very Low Birth Weight
  • Male
  • Morbidity
  • Prospective Studies
  • Regression Analysis
  • Retrospective Studies
  • Time Factors