Salunkhe Avinash Hindurao, Salunkhe Jyoti A, Mohite Vaishali R, More Ujawala, Pratinidhi Asha K, Kakade Satish V
Department of Nursing, Krishna Institute of Nursing Sciences, Karad, Maharashtra, India.
Department of Community Medicine, Krishna Institute of Medical Sciences, Krishna Institute of Medical Sciences "Deemed to be University", Karad, Maharashtra, India.
Indian J Community Med. 2019 Apr-Jun;44(2):102-106. doi: 10.4103/ijcm.IJCM_262_18.
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.
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.
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.
Out of six risk factors from whole cohort ( = 1876) and three risk factors from subsample ( = 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.
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.
孕期早期预测早产可能通过适当干预降低早产发生率。
本研究旨在开发一种用于预测早产的产前风险评分系统/量表。
从马哈拉施特拉邦卡拉德克里希纳医院就诊的1876名孕妇队列及380名孕妇子集中，收集常规产前信息以及关于饮食、职业等的详细信息，并使用SPSS 16版进行分析。基于相对风险（RR）通过多变量分析开发了一个评分系统，并在另外251名母亲的数据集上进行测试。
采用卡方检验进行双变量分析、向后多变量回归模型、受试者工作特征曲线（ROC）分析以及计算已识别风险因素的RR。新开发的风险评分量表的敏感性和特异性。
通过双变量分析从整个队列（n = 1876）中识别出6个风险因素，从子样本（n = 380）中识别出3个风险因素。多变量分析后，分别从整个队列和部分队列中保留了4个和3个风险因素，并根据RR临界值分别为整个队列和部分数据分配了“7”和“9”的风险评分，通过ROC曲线分析共同确定为“8”，在对251名独立患者进行测试时，敏感性为75.5%，特异性为85.5%。根据早产发生率，通过确定第二个临界值进行低、中、高风险分级。
在<8、8和9以及等于≥10时可以识别早产的低、中、高风险，在较低临界值时具有高敏感性，在较高临界值时具有高特异性。
