Montse Palacio1, Elisenda Bonet-Carne2, Teresa Cobo3, Alvaro Perez-Moreno2, Joan Sabrià4, Jute Richter5, Marian Kacerovsky6, Bo Jacobsson7, Raúl A García-Posada8, Fernando Bugatto9, Ramon Santisteve10, Àngels Vives11, Mauro Parra-Cordero12, Edgar Hernandez-Andrade13, José Luis Bartha14, Pilar Carretero-Lucena15, Kai Lit Tan16, Rogelio Cruz-Martínez17, Minke Burke18, Suseela Vavilala19, Igor Iruretagoyena20, Juan Luis Delgado21, Mauro Schenone22, Josep Vilanova23, Francesc Botet3, George S H Yeo16, Jon Hyett18, Jan Deprest5, Roberto Romero24, Eduard Gratacos3. 1. BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (Centro de Investigación Biomédica en Red Enfermedades Raras), Barcelona, Spain. Electronic address: mpalacio@clinic.cat. 2. Transmural Biotech SL, Barcelona, Spain. 3. BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain; Centre for Biomedical Research on Rare Diseases (Centro de Investigación Biomédica en Red Enfermedades Raras), Barcelona, Spain. 4. BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), IDIBAPS, University of Barcelona, Barcelona, Spain. 5. Department of Obstetrics and Gynaecology, University Hospitals Leuven, and Academic Department of Development and Regeneration, Organ System Cluster, KU Leuven, Leuven, Belgium. 6. Department of Obstetrics and Gynecology, University Hospital Hradec Kralove, and Charles University in Prague, Faculty of Medicine in Hradec Kralove, Hradec Kralove, Czech Republic. 7. Department of Obstetrics and Gynecology, Sahlgrenska University Hospital/Ostra, Gothenburg University, Gothenburg, Sweden; Department of Genetics and Bioinformatics, Area of Health Data and Digitalization, Norwegian Institute of Public Health, Oslo, Norway. 8. Clínica del Prado. Medellín, Antioquía, Colombia. 9. Division of Fetal-Maternal Medicine, Department of Obstetrics and Gynecology, University Hospital Puerta del Mar, Cadiz, Spain. 10. Althaia Xarxa Assistencial Universitària de Manresa, Hospital de Sant Joan de Déu, Manresa, Spain. 11. Department of Obstetrics and Gynaecology, Consorci Sanitari de Terrassa, Terrassa, Spain. 12. Maternal-Fetal Medicine Unit, Department of Obstetrics and Gynecology, University of Chile Hospital, Santiago de Chile, Chile. 13. Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, and Detroit, MI; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Wayne State University Detroit, MI. 14. Division of Maternal and Fetal Medicine, University Hospital La Paz, Madrid, Spain. 15. Maternal-Fetal Medicine Unit, Department of Obstetrics and Gynaecology, University Hospital of Granada (CHUG), Granada, Spain. 16. Department of Maternal-Fetal Medicine, KK Women's and Children's Hospital, Singapore. 17. Fetal Medicine Research Unit, Children's and Women's Specialty Hospital of Queretaro, Unidad de Investigación en Neurodesarrollo, Instituto de Neurobiología, UNAM-Juriquilla, Queretaro, Mexico. 18. Royal Prince Alfred Hospital Sydney, University of Sydney, Sydney, New South Wales, Australia. 19. Fernández Hospital, Hyberabad, India. 20. Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynaecology, University of Wisconsin, Madison, WI. 21. Fetal Medicine Unit, Clinic University Hospital, Virgen de la Arrixaca, Murcia, Spain. 22. Department of Obstetrics and Gynecology, University of Tennessee Health Science Center, Memphis, TN. 23. Hospital Nostra Senyora de Meritxell, Escaldes-Engordany, Andorra. 24. Perinatology Research Branch, Program for Perinatal Research and Obstetrics, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, and Detroit, MI; Center for Molecular Medicine and Genetics, Wayne State University Detroit, MI; Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI.
Abstract
BACKGROUND: Prediction of neonatal respiratory morbidity may be useful to plan delivery in complicated pregnancies. The limited predictive performance of the current diagnostic tests together with the risks of an invasive procedure restricts the use of fetal lung maturity assessment. OBJECTIVE: The objective of the study was to evaluate the performance of quantitative ultrasound texture analysis of the fetal lung (quantusFLM) to predict neonatal respiratory morbidity in preterm and early-term (<39.0 weeks) deliveries. STUDY DESIGN: This was a prospective multicenter study conducted in 20 centers worldwide. Fetal lung ultrasound images were obtained at 25.0-38.6 weeks of gestation within 48 hours of delivery, stored in Digital Imaging and Communication in Medicine format, and analyzed with quantusFLM. Physicians were blinded to the analysis. At delivery, perinatal outcomes and the occurrence of neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, were registered. The performance of the ultrasound texture analysis test to predict neonatal respiratory morbidity was evaluated. RESULTS: A total of 883 images were collected, but 17.3% were discarded because of poor image quality or exclusion criteria, leaving 730 observations for the final analysis. The prevalence of neonatal respiratory morbidity was 13.8% (101 of 730). The quantusFLM predicted neonatal respiratory morbidity with a sensitivity, specificity, positive and negative predictive values of 74.3% (75 of 101), 88.6% (557 of 629), 51.0% (75 of 147), and 95.5% (557 of 583), respectively. Accuracy was 86.5% (632 of 730) and positive and negative likelihood ratios were 6.5 and 0.3, respectively. CONCLUSION: The quantusFLM predicted neonatal respiratory morbidity with an accuracy similar to that previously reported for other tests with the advantage of being a noninvasive technique.
BACKGROUND: Prediction of neonatal respiratory morbidity may be useful to plan delivery in complicated pregnancies. The limited predictive performance of the current diagnostic tests together with the risks of an invasive procedure restricts the use of fetal lung maturity assessment. OBJECTIVE: The objective of the study was to evaluate the performance of quantitative ultrasound texture analysis of the fetal lung (quantusFLM) to predict neonatal respiratory morbidity in preterm and early-term (<39.0 weeks) deliveries. STUDY DESIGN: This was a prospective multicenter study conducted in 20 centers worldwide. Fetal lung ultrasound images were obtained at 25.0-38.6 weeks of gestation within 48 hours of delivery, stored in Digital Imaging and Communication in Medicine format, and analyzed with quantusFLM. Physicians were blinded to the analysis. At delivery, perinatal outcomes and the occurrence of neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, were registered. The performance of the ultrasound texture analysis test to predict neonatal respiratory morbidity was evaluated. RESULTS: A total of 883 images were collected, but 17.3% were discarded because of poor image quality or exclusion criteria, leaving 730 observations for the final analysis. The prevalence of neonatal respiratory morbidity was 13.8% (101 of 730). The quantusFLM predicted neonatal respiratory morbidity with a sensitivity, specificity, positive and negative predictive values of 74.3% (75 of 101), 88.6% (557 of 629), 51.0% (75 of 147), and 95.5% (557 of 583), respectively. Accuracy was 86.5% (632 of 730) and positive and negative likelihood ratios were 6.5 and 0.3, respectively. CONCLUSION: The quantusFLM predicted neonatal respiratory morbidity with an accuracy similar to that previously reported for other tests with the advantage of being a noninvasive technique.
Authors: Stuart R Dalziel; Vanessa K Lim; Anthony Lambert; Dianne McCarthy; Varsha Parag; Anthony Rodgers; Jane E Harding Journal: BMJ Date: 2005-09-05
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