OBJECTIVE: To evaluate the predictive value of a combination of sonographic, clinical and demographic data for detecting fetal macrosomia compared to ultrasound fetal weight estimation alone. METHODS: Retrospective cohort data were obtained from 1062 pregnancies in an unselected population. Estimated fetal sonographic weight was obtained within the last week prior to delivery. Two different combination models-published by Mazouni et al. and Nahum and Stanislaw-were employed to predict the presence of macrosomia at birth in these infants. Receiver-operating characteristics (ROC) curves were generated to compare the prediction of macrosomia when using different observation methods and sensitivity, specificity, positive predictive value, negative predictive value (NPV) and accuracy were calculated. RESULTS: Macrosomia (birth weight >or= 4000 g) was present in 135/1062 (12.7%) newborns. ROC curve analysis revealed the prediction of macrosomia using ultrasound alone to be significantly superior to the combined method of Mazouni et al. (area under the curve (AUC) 0.922, 95% CI 0.902-0.943 vs. 0.747, 95% CI 0.700-0.794, respectively; P < 0.0005), whereas the performance of the Nahum and Stanislaw equation was similar but not superior to ultrasound alone (AUC 0.895, 95% CI 0.839-0.950 vs. 0.912, 95% CI 0.867-0.958, respectively; P > 0.05). The accuracy of macrosomia prediction was similar for ultrasound alone and the Nahum and Stanislaw equation (approximately 90%), whereas the nomogram of Mazouni et al. reached only 51.7% accuracy (using a probability cut-off level of 50%). The NPV was found to be over 90% for all methods. CONCLUSIONS: Combination of sonographic estimates with clinical and demographic variables does not improve the prediction of macrosomia at delivery in comparison with a routine ultrasound scan within a week before delivery, at least in unselected populations. (c) 2009 ISUOG. Published by John Wiley & Sons, Ltd.
OBJECTIVE: To evaluate the predictive value of a combination of sonographic, clinical and demographic data for detecting fetal macrosomia compared to ultrasound fetal weight estimation alone. METHODS: Retrospective cohort data were obtained from 1062 pregnancies in an unselected population. Estimated fetal sonographic weight was obtained within the last week prior to delivery. Two different combination models-published by Mazouni et al. and Nahum and Stanislaw-were employed to predict the presence of macrosomia at birth in these infants. Receiver-operating characteristics (ROC) curves were generated to compare the prediction of macrosomia when using different observation methods and sensitivity, specificity, positive predictive value, negative predictive value (NPV) and accuracy were calculated. RESULTS:Macrosomia (birth weight >or= 4000 g) was present in 135/1062 (12.7%) newborns. ROC curve analysis revealed the prediction of macrosomia using ultrasound alone to be significantly superior to the combined method of Mazouni et al. (area under the curve (AUC) 0.922, 95% CI 0.902-0.943 vs. 0.747, 95% CI 0.700-0.794, respectively; P < 0.0005), whereas the performance of the Nahum and Stanislaw equation was similar but not superior to ultrasound alone (AUC 0.895, 95% CI 0.839-0.950 vs. 0.912, 95% CI 0.867-0.958, respectively; P > 0.05). The accuracy of macrosomia prediction was similar for ultrasound alone and the Nahum and Stanislaw equation (approximately 90%), whereas the nomogram of Mazouni et al. reached only 51.7% accuracy (using a probability cut-off level of 50%). The NPV was found to be over 90% for all methods. CONCLUSIONS: Combination of sonographic estimates with clinical and demographic variables does not improve the prediction of macrosomia at delivery in comparison with a routine ultrasound scan within a week before delivery, at least in unselected populations. (c) 2009 ISUOG. Published by John Wiley & Sons, Ltd.
Authors: Gordon Cs Smith; Alexandros A Moraitis; David Wastlund; Jim G Thornton; Aris Papageorghiou; Julia Sanders; Alexander Ep Heazell; Stephen C Robson; Ulla Sovio; Peter Brocklehurst; Edward Cf Wilson Journal: Health Technol Assess Date: 2021-02 Impact factor: 4.014
Authors: Alexandros A Moraitis; Norman Shreeve; Ulla Sovio; Peter Brocklehurst; Alexander E P Heazell; Jim G Thornton; Stephen C Robson; Aris Papageorghiou; Gordon C Smith Journal: PLoS Med Date: 2020-10-13 Impact factor: 11.069