Impact of Obstetric History on Preterm Birth: An Observational Study at a Tertiary Care Hospital in North India
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background Preterm birth, defined as delivery before 37 weeks of gestation, is a leading cause of neonatal morbidity and mortality worldwide. Obstetric history factors such as prior preterm birth, miscarriages, stillbirths, neonatal deaths, and short interpregnancy intervals have been identified as important predictors, but data from North India remain limited. Objective This study aimed to evaluate the association between adverse obstetric history and risk of preterm birth in women delivering at a tertiary care hospital in Lucknow, India. Methods This observational study included 200 women aged 18-40 years with singleton live births: 100 preterm (<37 weeks) and 100 term (≥37 weeks). Data on gravidity, parity, abortions, interpregnancy interval, and prior adverse outcomes were collected using a structured proforma. Statistical analysis included chi-squared tests, t-tests, and multivariable logistic regression. Results Mean gestational age and birth weight were significantly lower in the preterm group (32.6±2.1 weeks; 1840±510 g) compared with the term group (38.2±1.1 weeks; 2630±520 g; p<0.001). Independent predictors of preterm birth included prior preterm birth (adjusted odds ratio (aOR): 6.41; 95% CI: 2.61-15.72), neonatal death (aOR: 4.66), stillbirth (aOR: 3.87), recurrent abortions (aOR: 3.08), short interpregnancy interval <18 months (aOR: 3.04), and higher gravidity ≥3 (aOR: 2.19). The risk increased cumulatively with multiple adverse factors (aOR: 12.98 for ≥2 risk factors). Model performance was strong (Hosmer-Lemeshow: p=0.57; area under the receiver operating characteristic curve (AUC)=0.80). Conclusion Adverse obstetric history is a significant predictor of preterm birth. Systematic documentation of prior obstetric events and targeted antenatal surveillance can help identify high-risk pregnancies and reduce preterm birth burden in resource-limited settings.
Source: Cureus
DOI: 10.7759/cureus.94558
Authors: Iman Baig, Pavitra K Rastogi, Sujata Deo, Nitu Nigam, Nand Lal, Shilpi Gupta, Nabila Sharif, Rameshwari Singhal, Aisha S Baig, Paridhi Rastogi
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4415154209
