# SCH: Prediction of Preterm Birth in Nulliparous Women

> **NIH NIH R01** · COLUMBIA UNIV NEW YORK MORNINGSIDE · 2020 · $172,531

## Abstract

Program Director/Principal Investigator (Last, First, Middle): Salleb-Aouissi, Ansaf
Summary Prediction of Preterm Birth (PTB) has been an exceedingly challenging problem, predom-
inantly due to the inherent complexity of its multifactorial etiology and the lack of approaches capable of
integrating and interpreting large multidisciplinary data. It is a major long-lasting public health problem
with heavy emotional and financial consequences to families and society [ , ]. PTB is the leading
cause of mortality and long-term disabilities among neonates. Most studies to date have examined
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individual risk factors through univariate analyses of their coincidence with PTB.
 Our previous work [NSF Eager 1454855, 1454814] developed predictive models for PTB based on
non-genetic maternal attributes [ ,
30
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]. A particularly challenging population to determine PTB risk is
first time mothers (nulliparous women) due to the lack of prior pregnancy history. An important question
is to know whether factors other than history of PTB can be used to identify a nullipara patient at risk.
Specific aims of the original project Our basic specific aims are as follows: (1) Longitudinal
Preterm Birth Prediction: We will first build a series of accurate prediction models for PTB using
the nuMoM2b dataset. Such models will handle the challenges common to medical datasets including
(a) imbalance in the classes, (b) missing data, and (c) disparity in data collection. We will achieve this
by designing an objective function for Support Vector Machines that captures and corrects for these
issues. Second, by leveraging the availability of patient future data, our Learning Under Privileged Infor-
mation (LUPI)-based approach [ ] will significantly increase the rate of convergence of the algorithms
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and improve prediction with less data. Our transformative approach is well-suited for medical datasets
that are both limited by the number of patients and inherently include the challenges mentioned above.
(2) Combining clinical and genetic features for risk prediction: In this aim we tackle questions of
causality between the genetic information and its various forms of phenotypic implications by leveraging
the phenotypically rich nuMoM2b dataset. We will first apply standard GWAS analysis to apply new
insight regarding the changing patterns of genetic association as additional phenotypic data is accumu-
lated as well as serve as a baseline. We will then seek to develop improved analysis of involvement of
genetic contributions in PTB. (3) Clinical and social impact: We plan to assess the effectiveness of
the methods in clinical practice by: (a) testing the effectiveness of the longitudinal models produced in
objective 1 and 2 on existing clinical data at the New York Presbyterian Hospital. (b) building a sequential
decision making model; this includes optimizing the scheduling of patient visits and diagnostic testing
tailored for different classes of patients.
Specific aims for this NIH...

## Key facts

- **NIH application ID:** 10148467
- **Project number:** 3R01LM013327-02S1
- **Recipient organization:** COLUMBIA UNIV NEW YORK MORNINGSIDE
- **Principal Investigator:** Alexander M Friedman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $172,531
- **Award type:** 3
- **Project period:** 2019-09-16 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10148467

## Citation

> US National Institutes of Health, RePORTER application 10148467, SCH: Prediction of Preterm Birth in Nulliparous Women (3R01LM013327-02S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10148467. Licensed CC0.

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