Prediction of Low Birth Weight Delivery by Maternal Status and Its Validation: Decision Curve Analysis

Mehri Rejali, Marjan Mansourian, Zohre Babaei, Babak Eshrati

Abstract


Background: In this study, we evaluated assessed elements connected with low birth weight (LBW) and used decision curve analysis (DCA) to defne a scale to anticipate the probability of having a LBW newborn child.

Methods: This hospital‑based case–control study was led in Arak Hospital in Iran. The study included 470 mothers with LBW neonate and 470 mothers with natural neonates. Information were gathered by meeting moms utilizing preplanned organized questionnaire and from hospital records. The estimated probabilities of detecting LBW were calculated using the logistic regression and DCA to quantify the clinical consequences and its validation.

Results: Factors signifcantly associated with LBW were premature membrane rupture (odds ratio [OR] = 3.18 [1.882– 5.384]), former LBW infants (OR = 2.99 [1.510–5.932]), premature pain (OR = 2.70 [1.659– 4.415]), hypertension in pregnancy (OR = 2.39 [1.429–4.019]), last trimester of pregnancy bleeding (OR = 2.58 [1.018–6.583]), mother age >30 (OR = 2.17 [1.350–3.498]). However, with DCA, the prediction model made on these 15 variables has a net beneft (NB) of 0.3110 is best predictive with the highest NB. NB has simple clinical interpretation and utilizing the model is what might as well be called a procedure that distinguished what might as well be called 31.1 LBW per
100 cases with no superfluous recognize.

Conclusions: It is conceivable to foresee LBW utilizing a prediction model show in light of noteworthy hazard components connected with LBW. The majority of the hazard elements for LBW are preventable, and moms can be alluded amid early pregnancy to
a middle which is furnished with facilities for administration of high hazard pregnancy and LBW infant.

Keywords: Decision curve analysis, low birth weight, maternal status, validation


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