A Preconception Nomogram to Predict Preterm Delivery

Shilpi S. Mehta-Lee, Anton Palma, Peter S. Bernstein, David W. Lounsbury, Nicolas F. Schlecht

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Objective Preterm birth is a leading cause of perinatal morbidity and mortality. Prevention strategies rarely focus on preconception care. We sought to create a preconception nomogram that identifies nonpregnant women at highest risk for preterm birth using the Pregnancy Risk Assessment Monitoring System (PRAMS) surveillance data. Methods We used PRAMS data from 2004 to 2009. The odds ratios (ORs) of preterm birth for each preconception variable was estimated and adjusted analyses were conducted. We created a validated nomogram predicting the probability of preterm birth using multivariate logistic regression coefficients. Results 192,208 cases met inclusion criteria. Demographic/maternal health characteristics and associations with preterm birth and ORs are reported. After validation, we identified the following significant predictors of preterm birth: prior history of preterm birth or low birth weight baby, prior spontaneous or elective abortion, maternal diabetes prior to conception, maternal race (e.g., non-Hispanic black), intention to get pregnant prior to conception (i.e., did not want or wanted it sooner), and smoking prior to conception (p <0.05). Overall, our preconception preterm risk model correctly classified 76.1 % of preterm cases with a negative predictive value (NPV) of 76.7 %. A nomogram using a 0–100 scale illustrates our final preconception model for predicting preterm birth. Conclusion This preconception nomogram can be used by clinicians in multiple settings as a tool to help predict a woman’s individual preterm birth risk and to triage high-risk non-pregnant women to preconception care. Future studies are needed to validate the nomogram in a clinical setting.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalMaternal and Child Health Journal
DOIs
StateAccepted/In press - Jul 26 2016

Fingerprint

Nomograms
Premature Birth
Preconception Care
Information Systems
Odds Ratio
Mothers
Pregnancy
Reproductive History
Triage
Perinatal Mortality
Low Birth Weight Infant
Logistic Models
Smoking
Demography
Morbidity

Keywords

  • Nomogram
  • PRAMS
  • Prediction tool
  • Preterm birth

ASJC Scopus subject areas

  • Epidemiology
  • Pediatrics, Perinatology, and Child Health
  • Obstetrics and Gynecology
  • Public Health, Environmental and Occupational Health

Cite this

A Preconception Nomogram to Predict Preterm Delivery. / Mehta-Lee, Shilpi S.; Palma, Anton; Bernstein, Peter S.; Lounsbury, David W.; Schlecht, Nicolas F.

In: Maternal and Child Health Journal, 26.07.2016, p. 1-10.

Research output: Contribution to journalArticle

@article{5c8cd0e8a7fc426f8b7b9a35c4d6de83,
title = "A Preconception Nomogram to Predict Preterm Delivery",
abstract = "Objective Preterm birth is a leading cause of perinatal morbidity and mortality. Prevention strategies rarely focus on preconception care. We sought to create a preconception nomogram that identifies nonpregnant women at highest risk for preterm birth using the Pregnancy Risk Assessment Monitoring System (PRAMS) surveillance data. Methods We used PRAMS data from 2004 to 2009. The odds ratios (ORs) of preterm birth for each preconception variable was estimated and adjusted analyses were conducted. We created a validated nomogram predicting the probability of preterm birth using multivariate logistic regression coefficients. Results 192,208 cases met inclusion criteria. Demographic/maternal health characteristics and associations with preterm birth and ORs are reported. After validation, we identified the following significant predictors of preterm birth: prior history of preterm birth or low birth weight baby, prior spontaneous or elective abortion, maternal diabetes prior to conception, maternal race (e.g., non-Hispanic black), intention to get pregnant prior to conception (i.e., did not want or wanted it sooner), and smoking prior to conception (p <0.05). Overall, our preconception preterm risk model correctly classified 76.1 {\%} of preterm cases with a negative predictive value (NPV) of 76.7 {\%}. A nomogram using a 0–100 scale illustrates our final preconception model for predicting preterm birth. Conclusion This preconception nomogram can be used by clinicians in multiple settings as a tool to help predict a woman’s individual preterm birth risk and to triage high-risk non-pregnant women to preconception care. Future studies are needed to validate the nomogram in a clinical setting.",
keywords = "Nomogram, PRAMS, Prediction tool, Preterm birth",
author = "Mehta-Lee, {Shilpi S.} and Anton Palma and Bernstein, {Peter S.} and Lounsbury, {David W.} and Schlecht, {Nicolas F.}",
year = "2016",
month = "7",
day = "26",
doi = "10.1007/s10995-016-2100-3",
language = "English (US)",
pages = "1--10",
journal = "Maternal and Child Health Journal",
issn = "1092-7875",
publisher = "Springer GmbH & Co, Auslieferungs-Gesellschaf",

}

TY - JOUR

T1 - A Preconception Nomogram to Predict Preterm Delivery

AU - Mehta-Lee, Shilpi S.

AU - Palma, Anton

AU - Bernstein, Peter S.

AU - Lounsbury, David W.

AU - Schlecht, Nicolas F.

PY - 2016/7/26

Y1 - 2016/7/26

N2 - Objective Preterm birth is a leading cause of perinatal morbidity and mortality. Prevention strategies rarely focus on preconception care. We sought to create a preconception nomogram that identifies nonpregnant women at highest risk for preterm birth using the Pregnancy Risk Assessment Monitoring System (PRAMS) surveillance data. Methods We used PRAMS data from 2004 to 2009. The odds ratios (ORs) of preterm birth for each preconception variable was estimated and adjusted analyses were conducted. We created a validated nomogram predicting the probability of preterm birth using multivariate logistic regression coefficients. Results 192,208 cases met inclusion criteria. Demographic/maternal health characteristics and associations with preterm birth and ORs are reported. After validation, we identified the following significant predictors of preterm birth: prior history of preterm birth or low birth weight baby, prior spontaneous or elective abortion, maternal diabetes prior to conception, maternal race (e.g., non-Hispanic black), intention to get pregnant prior to conception (i.e., did not want or wanted it sooner), and smoking prior to conception (p <0.05). Overall, our preconception preterm risk model correctly classified 76.1 % of preterm cases with a negative predictive value (NPV) of 76.7 %. A nomogram using a 0–100 scale illustrates our final preconception model for predicting preterm birth. Conclusion This preconception nomogram can be used by clinicians in multiple settings as a tool to help predict a woman’s individual preterm birth risk and to triage high-risk non-pregnant women to preconception care. Future studies are needed to validate the nomogram in a clinical setting.

AB - Objective Preterm birth is a leading cause of perinatal morbidity and mortality. Prevention strategies rarely focus on preconception care. We sought to create a preconception nomogram that identifies nonpregnant women at highest risk for preterm birth using the Pregnancy Risk Assessment Monitoring System (PRAMS) surveillance data. Methods We used PRAMS data from 2004 to 2009. The odds ratios (ORs) of preterm birth for each preconception variable was estimated and adjusted analyses were conducted. We created a validated nomogram predicting the probability of preterm birth using multivariate logistic regression coefficients. Results 192,208 cases met inclusion criteria. Demographic/maternal health characteristics and associations with preterm birth and ORs are reported. After validation, we identified the following significant predictors of preterm birth: prior history of preterm birth or low birth weight baby, prior spontaneous or elective abortion, maternal diabetes prior to conception, maternal race (e.g., non-Hispanic black), intention to get pregnant prior to conception (i.e., did not want or wanted it sooner), and smoking prior to conception (p <0.05). Overall, our preconception preterm risk model correctly classified 76.1 % of preterm cases with a negative predictive value (NPV) of 76.7 %. A nomogram using a 0–100 scale illustrates our final preconception model for predicting preterm birth. Conclusion This preconception nomogram can be used by clinicians in multiple settings as a tool to help predict a woman’s individual preterm birth risk and to triage high-risk non-pregnant women to preconception care. Future studies are needed to validate the nomogram in a clinical setting.

KW - Nomogram

KW - PRAMS

KW - Prediction tool

KW - Preterm birth

UR - http://www.scopus.com/inward/record.url?scp=84979683996&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84979683996&partnerID=8YFLogxK

U2 - 10.1007/s10995-016-2100-3

DO - 10.1007/s10995-016-2100-3

M3 - Article

SP - 1

EP - 10

JO - Maternal and Child Health Journal

JF - Maternal and Child Health Journal

SN - 1092-7875

ER -