HomeAboutusEditorial BoardCurrent issuearchivesSearch articlesInstructions for authorsSubscription detailsAdvertise

  Login  | Users online: 754

   Ahead of print articles    Bookmark this page Print this page Email this page Small font sizeDefault font size Increase font size  

ORIGINAL ARTICLE Table of Contents   
Year : 2019  |  Volume : 44  |  Issue : 2  |  Page : 102-106
Development of risk scoring scale tool for prediction of preterm birth

1 Department of Nursing, Krishna Institute of Nursing Sciences, Karad, Maharashtra, India
2 Department of Community Medicine, Krishna Institute of Medical Sciences, Krishna Institute of Medical Sciences “Deemed to be University”, Karad, Maharashtra, India

Correspondence Address:
Prof. Avinash Hindurao Salunkhe
“Gajanan Prasad” Near Malai Bungalow Scheme, Koyana Vasaha, Malakapur Karad, Satara - 415 539, Maharashtra
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijcm.IJCM_262_18

Rights and Permissions

Background: Prediction of preterm births in the early stage during pregnancy may reduce prevalence of preterm births by appropriate interventions. Aims/Objective: The aim of the study is to develop an antenatal risk scoring system/scale for prediction of preterm births. Subjects and Methods: From a cohort of 1876 and subset of 380 pregnant women attending Krishna Hospital Karad, Maharashtra, routine antenatal and in-depth information on diet, occupation, and the rest were collected and analyzed using SPSS version 16. A scoring system was developed by multivariate analysis based on the relative risk (RR) and tested on separate set of 251 mothers. Statistical Analysis Used: Bivariate analysis by Chi-square test, backward multivariate regression model, receiver operating characteristic curve (ROC) curve analysis, and calculation of RR for identified risk factors. Sensitivity and specificity of newly developed risk scoring scale. Results: Out of six risk factors from whole cohort (n = 1876) and three risk factors from subsample (n = 380) identified by bivariate analysis. Further four and three risk factors were retained after multivariate analysis from whole and part of cohort, respectively, and risk scores of “7” and “9” were assigned based on RR cutoff levels of three and five were identified separately for whole and part data by ROC curve analyses together making it “8” with 75.5% sensitivity and 85.5% specificity when tested on 251 independent patients. Based on the prevalence of preterm births, low-, moderate-, and high-risk grading was done by identifying as second cutoff value. Conclusions: Identification of low-, moderate-, and high-risk of preterm births was possible at <8, 8, and 9 and equal to ≥10 with high sensitivity at lower cutoff and high specificity at upper cutoff.

Print this article  Email this article

  Similar in PUBMED
    Search Pubmed for
    Search in Google Scholar for
  Related articles
   Citation Manager
  Access Statistics
   Reader Comments
   Email Alert *
   Add to My List *
 * Requires registration (Free)

 Article Access Statistics
    PDF Downloaded166    
    Comments [Add]    

Recommend this journal


  Sitemap | What's New | Feedback | Copyright and Disclaimer
  2007 - Indian Journal of Community Medicine | Published by Wolters Kluwer - Medknow
  Online since 15th September, 2007