Prediction of low birth weight delivery by maternal status and its validation: Decision curve analysis
Background: In this study, we evaluated assessed elements connected with low birth weight (LBW) and used decision curve analysis (DCA) to define 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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2017-01-01
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| Series: | International Journal of Preventive Medicine |
| Subjects: | |
| Online Access: | http://www.ijpvmjournal.net/article.asp?issn=2008-7802;year=2017;volume=8;issue=1;spage=53;epage=53;aulast=Rejali |
| _version_ | 1819275078263635968 |
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| author | Mehri Rejali Marjan Mansourian Zohre Babaei Babak Eshrati |
| author_facet | Mehri Rejali Marjan Mansourian Zohre Babaei Babak Eshrati |
| author_sort | Mehri Rejali |
| collection | DOAJ |
| description | Background: In this study, we evaluated assessed elements connected with low birth weight (LBW) and used decision curve analysis (DCA) to define 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 significantly 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 benefit (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. |
| first_indexed | 2024-12-23T23:18:36Z |
| format | Article |
| id | doaj.art-c75ef9dc330d4849acb638c26fcb0bc0 |
| institution | Directory Open Access Journal |
| issn | 2008-7802 2008-8213 |
| language | English |
| last_indexed | 2024-12-23T23:18:36Z |
| publishDate | 2017-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | International Journal of Preventive Medicine |
| spelling | doaj.art-c75ef9dc330d4849acb638c26fcb0bc02022-12-21T17:26:25ZengWolters Kluwer Medknow PublicationsInternational Journal of Preventive Medicine2008-78022008-82132017-01-0181535310.4103/ijpvm.IJPVM_146_16Prediction of low birth weight delivery by maternal status and its validation: Decision curve analysisMehri RejaliMarjan MansourianZohre BabaeiBabak EshratiBackground: In this study, we evaluated assessed elements connected with low birth weight (LBW) and used decision curve analysis (DCA) to define 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 significantly 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 benefit (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.http://www.ijpvmjournal.net/article.asp?issn=2008-7802;year=2017;volume=8;issue=1;spage=53;epage=53;aulast=RejaliDecision curve analysislow birth weightmaternal statusvalidation |
| spellingShingle | Mehri Rejali Marjan Mansourian Zohre Babaei Babak Eshrati Prediction of low birth weight delivery by maternal status and its validation: Decision curve analysis International Journal of Preventive Medicine Decision curve analysis low birth weight maternal status validation |
| title | Prediction of low birth weight delivery by maternal status and its validation: Decision curve analysis |
| title_full | Prediction of low birth weight delivery by maternal status and its validation: Decision curve analysis |
| title_fullStr | Prediction of low birth weight delivery by maternal status and its validation: Decision curve analysis |
| title_full_unstemmed | Prediction of low birth weight delivery by maternal status and its validation: Decision curve analysis |
| title_short | Prediction of low birth weight delivery by maternal status and its validation: Decision curve analysis |
| title_sort | prediction of low birth weight delivery by maternal status and its validation decision curve analysis |
| topic | Decision curve analysis low birth weight maternal status validation |
| url | http://www.ijpvmjournal.net/article.asp?issn=2008-7802;year=2017;volume=8;issue=1;spage=53;epage=53;aulast=Rejali |
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