OBJECTIVE: To develop a statistically derived but clinically usable antenatal risk scoring system. METHODS: Data from 20,985 pregnancies were statistically analyzed to identify significant risk factors. Logistic regression analysis was then used to produce a final scoring system, which was subsequently tested for validity on a separate population of 3120 pregnancies. RESULTS: Twenty-seven significant antenatal variables were included in the final scoring system. Application of the system in early pregnancy resulted in a predictive accuracy of 0.73; at the onset of labor, predictive accuracy was 0.91. At the time of labor, 87% of poor outcomes were accurately identified by allocation of only 16% of the women to the high-risk group. CONCLUSIONS: It was possible to develop a risk scoring system with a predictive accuracy higher than any previously reported statistically derived score. Summation of the logistic coefficients provides a score that by comparison with a chosen threshold identifies a high-risk pregnancy. In this way, despite the complexity of statistical analysis, all clinicians can quickly apply this scoring system.
OBJECTIVE: To develop a statistically derived but clinically usable antenatal risk scoring system. METHODS: Data from 20,985 pregnancies were statistically analyzed to identify significant risk factors. Logistic regression analysis was then used to produce a final scoring system, which was subsequently tested for validity on a separate population of 3120 pregnancies. RESULTS: Twenty-seven significant antenatal variables were included in the final scoring system. Application of the system in early pregnancy resulted in a predictive accuracy of 0.73; at the onset of labor, predictive accuracy was 0.91. At the time of labor, 87% of poor outcomes were accurately identified by allocation of only 16% of the women to the high-risk group. CONCLUSIONS: It was possible to develop a risk scoring system with a predictive accuracy higher than any previously reported statistically derived score. Summation of the logistic coefficients provides a score that by comparison with a chosen threshold identifies a high-risk pregnancy. In this way, despite the complexity of statistical analysis, all clinicians can quickly apply this scoring system.
Authors: Rachel A Haws; Mohammad Yawar Yakoob; Tanya Soomro; Esme V Menezes; Gary L Darmstadt; Zulfiqar A Bhutta Journal: BMC Pregnancy Childbirth Date: 2009-05-07 Impact factor: 3.007
Authors: Nir Melamed; Ahmet Baschat; Yoav Yinon; Apostolos Athanasiadis; Federico Mecacci; Francesc Figueras; Vincenzo Berghella; Amala Nazareth; Muna Tahlak; H David McIntyre; Fabrício Da Silva Costa; Anne B Kihara; Eran Hadar; Fionnuala McAuliffe; Mark Hanson; Ronald C Ma; Rachel Gooden; Eyal Sheiner; Anil Kapur; Hema Divakar; Diogo Ayres-de-Campos; Liran Hiersch; Liona C Poon; John Kingdom; Roberto Romero; Moshe Hod Journal: Int J Gynaecol Obstet Date: 2021-03 Impact factor: 3.561
Authors: Amber A Vos; Mieke J van Veen; Erwin Birnie; Semiha Denktaş; Eric A P Steegers; Gouke J Bonsel Journal: Int J Integr Care Date: 2015-03-06 Impact factor: 5.120