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Risk scores for predicting small for gestational age infants in Japan: The TMM birthree cohort study

Noriyuki Iwama1,2, Taku Obara3,4,5, Mami Ishikuro3,4

  • 1Department of Obstetrics and Gynecology, Tohoku University Hospital, 1-1, Seiryomachi, Sendai, Miyagi, 980-8574, Japan. noriyuki.iwama@med.tohoku.ac.jp.

Scientific Reports
|May 26, 2022
PubMed

Insights

This study developed a pregnancy risk score to predict small-for-gestational-age (SGA) infants in Japan. Mid-gestation risk scores showed acceptable predictive ability for SGA, outperforming early-gestation scores.

Area of Science:

  • Obstetrics and Gynecology
  • Perinatal Medicine
  • Public Health

Background:

  • Small-for-gestational-age (SGA) infants face increased risks.
  • Accurate prediction models are crucial for timely interventions.
  • Existing prediction tools may lack precision in specific populations.

Purpose of the Study:

  • To develop and validate a risk score for predicting SGA infants in Japan.
  • To compare the predictive performance of early versus mid-gestation risk scores.
  • To identify key predictive factors for SGA during pregnancy.

Main Methods:

  • A prospective cohort study of 17,073 subjects (Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study).
  • Multiple logistic regression was used to construct risk scores at early (11-17 weeks) and mid-gestation (18-21 weeks).
  • Risk scores included maternal demographics, lifestyle factors, medical history, and fetal measurements.

Main Results:

  • The mid-gestation risk score achieved a C-statistic of 0.725 (95% CI: 0.710-0.740).
  • The early-gestation risk score achieved a C-statistic of 0.658 (95% CI: 0.642-0.675).
  • Mid-gestation scores demonstrated superior predictive ability for SGA compared to early-gestation scores.

Conclusions:

  • Risk scores developed during mid-gestation provide acceptable prediction for SGA infants.
  • Predictive performance is significantly better in mid-gestation than in early gestation.
  • These findings can aid in early identification and management of SGA pregnancies.

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