OBJECTIVE: To investigate the value of adding second-trimester uterine artery Doppler ultrasound to patient characteristics in the identification of nulliparous women at risk for pre-eclampsia. METHODS: For this individual patient data meta-analysis, studies published between January 1995 and December 2009 were identified in MEDLINE and EMBASE. Studies were eligible in which Doppler assessment of the uterine arteries had been performed among pregnant women and in which gestational age at ultrasound, Doppler ultrasound findings and data on the occurrence of pre-eclampsia were available. We invited corresponding authors to share their original datasets. Data were included of nulliparous women who had had a second-trimester uterine artery Doppler ultrasound examination. Shared data were checked for consistency, recoded to acquire uniformity and merged into a single dataset. We constructed random intercept logistic regression models for each of the patient and Doppler characteristics in isolation and for combinations. We compared goodness of fit, discrimination and calibration. RESULTS: We analyzed eight datasets, reporting on 6708 nulliparous women, of whom 302 (4.5%) developed pre-eclampsia. Doppler findings included higher, lower and mean pulsatility index (PI) and resistance index (RI) and any or bilateral notching. Of these, the best predictors were combinations of mean PI or RI and bilateral notching, with areas under the receiver-operating characteristics curve (AUC) of 0.75 (95% confidence interval (CI), 0.56-0.95) and 0.70 (95% CI, 0.66-0.74), respectively. Addition of Doppler findings to the patient characteristics blood pressure or body mass index (BMI) significantly improved discrimination. A model with blood pressure, PI and bilateral notching had an AUC of 0.85 (95% CI, 0.67-1.00). CONCLUSIONS: The addition of Doppler characteristics of mean PI or RI and bilateral notching to patient characteristics of blood pressure or BMI improves the identification of nulliparous women at risk for pre-eclampsia.
OBJECTIVE: To investigate the value of adding second-trimester uterine artery Doppler ultrasound to patient characteristics in the identification of nulliparous women at risk for pre-eclampsia. METHODS: For this individual patient data meta-analysis, studies published between January 1995 and December 2009 were identified in MEDLINE and EMBASE. Studies were eligible in which Doppler assessment of the uterine arteries had been performed among pregnant women and in which gestational age at ultrasound, Doppler ultrasound findings and data on the occurrence of pre-eclampsia were available. We invited corresponding authors to share their original datasets. Data were included of nulliparous women who had had a second-trimester uterine artery Doppler ultrasound examination. Shared data were checked for consistency, recoded to acquire uniformity and merged into a single dataset. We constructed random intercept logistic regression models for each of the patient and Doppler characteristics in isolation and for combinations. We compared goodness of fit, discrimination and calibration. RESULTS: We analyzed eight datasets, reporting on 6708 nulliparous women, of whom 302 (4.5%) developed pre-eclampsia. Doppler findings included higher, lower and mean pulsatility index (PI) and resistance index (RI) and any or bilateral notching. Of these, the best predictors were combinations of mean PI or RI and bilateral notching, with areas under the receiver-operating characteristics curve (AUC) of 0.75 (95% confidence interval (CI), 0.56-0.95) and 0.70 (95% CI, 0.66-0.74), respectively. Addition of Doppler findings to the patient characteristics blood pressure or body mass index (BMI) significantly improved discrimination. A model with blood pressure, PI and bilateral notching had an AUC of 0.85 (95% CI, 0.67-1.00). CONCLUSIONS: The addition of Doppler characteristics of mean PI or RI and bilateral notching to patient characteristics of blood pressure or BMI improves the identification of nulliparous women at risk for pre-eclampsia.
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