Rebeca Silveira Rocha1, Júlio Augusto Gurgel Alves2, Sammya Bezerra Maia E Holanda Moura3, Edward Araujo Júnior4, Wellington P Martins5, Camila Teixeira Moreira Vasconcelos1, Fabricio Da Silva Costa6, Mônica Oliveira Batista Oriá1. 1. Department of Nursing, Federal University of Ceará (UFC), Fortaleza, State of Ceará, Brazil. 2. Department of Maternal and Child, Federal University of Ceará (UFC), Fortaleza, State of Ceará, Brazil. 3. Department of Obstetrics and Gynecology, University of Fortaleza (UNIFOR), Fortaleza, State of Ceará, Brazil. 4. Department of Obstetrics, Paulista School of Medicine-Federal University of São Paulo (EPM-UNIFESP), São Paulo, State of São Paulo, Brazil. Electronic address: araujojred@terra.com.br. 5. Department of Obstetrics and Gynecology, Ribeirão Preto Medical School, University of São Paulo (FMRP-USP), Ribeirão Preto, State of São Paulo, Brazil. 6. Department of Obstetrics and Gynaecology, Monash University Faculty of Medicine Nursing and Health Sciences, Clayton, Victoria, Australia.
Abstract
OBJECTIVE: To compare a new simple algorithm for preeclampsia (PE) prediction among Brazilian women with two international guidelines - National Institute for Clinical Excellence (NICE) and American College of Obstetricians and Gynecologists (ACOG). METHODS: We performed a secondary analysis of two prospective cohort studies to predict PE between 11 and 13+6weeks of gestation, developed between August 2009 and January 2014. Outcomes measured were total PE, early PE (<34weeks), preterm PE (<37weeks), and term PE (≥37weeks). The predictive accuracy of the models was assessed using the area under the receiver operator characteristic curve (AUC-ROC) and via calculation of sensitivity and specificity for each outcome. RESULTS: Of a total of 733 patients, 55 patients developed PE, 12 at early, 21 at preterm and 34 at term. The AUC-ROC values were low, which compromised the accuracy of NICE (AUC-ROC: 0.657) and ACOG (AUC-ROC: 0.562) algorithms for preterm PE prediction in the Brazilian population. The best predictive model for preterm PE included maternal factors (MF) and mean arterial pressure (MAP) (AUC-ROC: 0.842), with a statistically significant difference compared with ACOG (p<0.0001) and NICE (p=0.0002) guidelines. CONCLUSION: The predictive accuracies of NICE and ACOG guidelines to predict preterm PE were low and a simple algorithm involving maternal factors and MAP performed better for the Brazilian population.
OBJECTIVE: To compare a new simple algorithm for preeclampsia (PE) prediction among Brazilian women with two international guidelines - National Institute for Clinical Excellence (NICE) and American College of Obstetricians and Gynecologists (ACOG). METHODS: We performed a secondary analysis of two prospective cohort studies to predict PE between 11 and 13+6weeks of gestation, developed between August 2009 and January 2014. Outcomes measured were total PE, early PE (<34weeks), preterm PE (<37weeks), and term PE (≥37weeks). The predictive accuracy of the models was assessed using the area under the receiver operator characteristic curve (AUC-ROC) and via calculation of sensitivity and specificity for each outcome. RESULTS: Of a total of 733 patients, 55 patients developed PE, 12 at early, 21 at preterm and 34 at term. The AUC-ROC values were low, which compromised the accuracy of NICE (AUC-ROC: 0.657) and ACOG (AUC-ROC: 0.562) algorithms for preterm PE prediction in the Brazilian population. The best predictive model for preterm PE included maternal factors (MF) and mean arterial pressure (MAP) (AUC-ROC: 0.842), with a statistically significant difference compared with ACOG (p<0.0001) and NICE (p=0.0002) guidelines. CONCLUSION: The predictive accuracies of NICE and ACOG guidelines to predict preterm PE were low and a simple algorithm involving maternal factors and MAP performed better for the Brazilian population.
Authors: John Allotey; Kym Ie Snell; Melanie Smuk; Richard Hooper; Claire L Chan; Asif Ahmed; Lucy C Chappell; Peter von Dadelszen; Julie Dodds; Marcus Green; Louise Kenny; Asma Khalil; Khalid S Khan; Ben W Mol; Jenny Myers; Lucilla Poston; Basky Thilaganathan; Anne C Staff; Gordon Cs Smith; Wessel Ganzevoort; Hannele Laivuori; Anthony O Odibo; Javier A Ramírez; John Kingdom; George Daskalakis; Diane Farrar; Ahmet A Baschat; Paul T Seed; Federico Prefumo; Fabricio da Silva Costa; Henk Groen; Francois Audibert; Jacques Masse; Ragnhild B Skråstad; Kjell Å Salvesen; Camilla Haavaldsen; Chie Nagata; Alice R Rumbold; Seppo Heinonen; Lisa M Askie; Luc Jm Smits; Christina A Vinter; Per M Magnus; Kajantie Eero; Pia M Villa; Anne K Jenum; Louise B Andersen; Jane E Norman; Akihide Ohkuchi; Anne Eskild; Sohinee Bhattacharya; Fionnuala M McAuliffe; Alberto Galindo; Ignacio Herraiz; Lionel Carbillon; Kerstin Klipstein-Grobusch; SeonAe Yeo; Helena J Teede; Joyce L Browne; Karel Gm Moons; Richard D Riley; Shakila Thangaratinam Journal: Health Technol Assess Date: 2020-12 Impact factor: 4.014
Authors: Ziad T A Al-Rubaie; H Malcolm Hudson; Gregory Jenkins; Imad Mahmoud; Joel G Ray; Lisa M Askie; Sarah J Lord Journal: BMC Pregnancy Childbirth Date: 2020-01-06 Impact factor: 3.007