Noninvasive prediction models of intra-amniotic infection in women with preterm labor
Por:
Cobo, Teresa, Burgos-Artizzu, Xavier P, Collado, M Carmen, Andreu, Vicente, Sanchez-Garcia, Ana B, Filella, Xavier, Marin, Silvia, Cascante, Marta, Bosch, Jordi, Ferrero-Martinez SI, Boada, David, Murillo, Clara, Rueda, Claudia, Ponce, Julia, Palacio, Montse and Gratacós E
Publicada:
1 ene 2023
Ahead of Print:
1 dic 2022
Categoría:
Obstetrics and gynecology
Resumen:
BACKGROUND: Among women with preterm labor, those with intra-amniotic infection present the highest risk of early delivery and the most adverse outcomes. The identification of intra-amniotic infection requires amniocentesis, perceived as too invasive by women and physicians. Noninvasive methods for identifying intra-amniotic infection and/or early delivery are crucial to focus early efforts on high-risk preterm labor women while avoiding unnecessary interventions in low-risk preterm labor women. OBJECTIVE: This study modeled the best performing models, inte-grating biochemical data with clinical and ultrasound information to predict a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days. STUDY DESIGN: From 2015 to 2020, data from a cohort of women, who underwent amniocentesis to rule in or rule out intra-amniotic infection or inflammation, admitted with a diagnosis of preterm labor at <34 weeks of gestation at the Hospital Clinic and Hospital Sant Joan de D & ccaron;u, Bar-celona, Spain, were used. At admission, transvaginal ultrasound was performed, and maternal blood and vaginal samples were collected. Using high-dimensional biology, vaginal proteins (using multiplex immunoassay), amino acids (using high-performance liquid chromatography), and bac-teria (using 16S ribosomal RNA gene amplicon sequencing) were explored to predict the composite outcome. We selected ultrasound, maternal blood, and vaginal predictors that could be tested with rapid diagnostic techniques and developed prediction models employing machine learning that was applied in a validation cohort. RESULTS: A cohort of 288 women with preterm labor at <34 weeks of gestation, of which 103 (35%) had a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days, were included in this study. The sample was divided into derivation (n=116) and validation (n=172) cohorts. Of note, 4 prediction models were proposed, including ultrasound transvaginal cervical length, maternal C-reactive protein, vaginal interleukin 6 (using an automated immunoanalyzer), vaginal pH (using a pH meter), vaginal lactic acid (using a reflectometer), and vaginal Lactobacillus genus (using quantitative polymerase chain reaction), with areas under the receiving operating characteristic curve ranging from 82.2% (95% confidence interval, +3.1%) to 85.2% (95% confidence interval, +3.1%), sensitivities ranging from 76.1% to 85.9%, and spec-ificities ranging from 75.2% to 85.1%. CONCLUSION: The study results have provided proof of principle of how noninvasive methods suitable for point-of-care systems can select high-risk cases among women with preterm labor and might substantially aid in clinical management and outcomes while improving the use of resources and patient experience.
Filiaciones:
BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clinic and Hospital Sant Joan de Deu), Institut Clinic de Ginecologia, Obstetricia I Neonatologia, Fetal i+D Fetal Medicine Research Center, Barcelona, Spain; Institut d'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), University of Barcelona. Barcelona, Spain; Center for Biomedical Research on Rare Diseases (CIBER-ER), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain.; MovumTech, Madrid, Spain.; De
BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clinic and Hospital Sant Joan de Deu), Institut Clinic de Ginecologia, Obstetricia I Neonatologia, Fetal i+D Fetal Medicine Research Center, Barcelona, Spain; Institut d'Investigacions Biomediques August Pi I Sunyer, University of Barcelona. Barcelona, Spain; Center for Biomedical Research on Rare Diseases, Institute of Health Carlos III, Madrid, Spain.; MovumTech, Madrid, Spain.; Department of Biotechnology, Institute
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