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Hamilton EF, Zhoroev T, Warrick PA, Tarca AL, Garite TJ, Caughey AB, Melillo J, Prasad M, Neilson D, Singson P, McKay K, Romero R. New labor curves of dilation and station to improve the accuracy of predicting labor progress. Am J Obstet Gynecol 2024; 231:1-18. [PMID: 38423450 DOI: 10.1016/j.ajog.2024.02.289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND The diagnosis of failure to progress, the most common indication for intrapartum cesarean delivery, is based on the assessment of cervical dilation and station over time. Labor curves serve as references for expected changes in dilation and fetal descent. The labor curves of Friedman, Zhang et al, and others are based on time alone and derived from mothers with spontaneous labor onset. However, labor induction is now common, and clinicians also consider other factors when assessing labor progress. Labor curves that consider the use of labor induction and other factors that influence labor progress have the potential to be more accurate and closer to clinical decision-making. OBJECTIVE This study aimed to compare the prediction errors of labor curves based on a single factor (time) or multiple clinically relevant factors using two modeling methods: mixed-effects regression, a standard statistical method, and Gaussian processes, a machine learning method. STUDY DESIGN This was a longitudinal cohort study of changes in dilation and station based on data from 8022 births in nulliparous women with a live, singleton, vertex-presenting fetus ≥35 weeks of gestation with a vaginal delivery. New labor curves of dilation and station were generated with 10-fold cross-validation. External validation was performed using a geographically independent group. Model variables included time from the first examination in the 20 hours before delivery; dilation, effacement, and station recorded at the previous examination; cumulative contraction counts; and use of epidural anesthesia and labor induction. To assess model accuracy, differences between each model's predicted value and its corresponding observed value were calculated. These prediction errors were summarized using mean absolute error and root mean squared error statistics. RESULTS Dilation curves based on multiple parameters were more accurate than those derived from time alone. The mean absolute error of the multifactor methods was better (lower) than those of the single-factor methods (0.826 cm [95% confidence interval, 0.820-0.832] for the multifactor machine learning and 0.893 cm [95% confidence interval, 0.885-0.901] for the multifactor mixed-effects method and 2.122 cm [95% confidence interval, 2.108-2.136] for the single-factor methods; P<.0001 for both comparisons). The root mean squared errors of the multifactor methods were also better (lower) than those of the single-factor methods (1.126 cm [95% confidence interval, 1.118-1.133] for the machine learning [P<.0001] and 1.172 cm [95% confidence interval, 1.164-1.181] for the mixed-effects methods and 2.504 cm [95% confidence interval, 2.487-2.521] for the single-factor [P<.0001 for both comparisons]). The multifactor machine learning dilation models showed small but statistically significant improvements in accuracy compared to the mixed-effects regression models (P<.0001). The multifactor machine learning method produced a curve of descent with a mean absolute error of 0.512 cm (95% confidence interval, 0.509-0.515) and a root mean squared error of 0.660 cm (95% confidence interval, 0.655-0.666). External validation using independent data produced similar findings. CONCLUSION Cervical dilation models based on multiple clinically relevant parameters showed improved (lower) prediction errors compared to models based on time alone. The mean prediction errors were reduced by more than 50%. A more accurate assessment of departure from expected dilation and station may help clinicians optimize intrapartum management.
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Affiliation(s)
- Emily F Hamilton
- Faculty of Medicine and Health Sciences, Department of Obstetrics and Gynecology, McGill University, Montreal, Quebec, Canada; PeriGen, Inc, Cary, NC.
| | - Tilekbek Zhoroev
- PeriGen, Inc, Cary, NC; Faculty of Science, Department of Applied Mathematics, North Carolina State University, Raleigh, NC
| | - Philip A Warrick
- PeriGen, Inc, Cary, NC; Faculty of Medicine and Health Sciences, Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Adi L Tarca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI; Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI; Department of Computer Science, Wayne State University College of Engineering, Detroit, MI
| | - Thomas J Garite
- Department of Obstetrics and Gynecology, University of California, Irvine, Irvine, CA; Sera Prognostics, The Pregnancy Company, Salt Lake City, UT
| | - Aaron B Caughey
- Department of Obstetrics and Gynecology, Oregon Health & Science University School of Medicine, Portland, OR
| | - Jason Melillo
- Department of Obstetrics and Gynecology, OhioHealth, Columbus, OH
| | - Mona Prasad
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, OhioHealth, Columbus, OH
| | | | - Peter Singson
- Women's Health Services, Legacy Health, Portland, OR
| | - Kimberlee McKay
- PeriGen, Inc, Cary, NC; Sanford School of Medicine at the University of South Dakota, Vermillion, SD; Perinatal Quality and Obstetrics and Gynecology Service Line, Avera Health, Sioux Falls, SD
| | - Roberto Romero
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD; Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI.
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Parasiliti M, Vidiri A, Perelli F, Scambia G, Lanzone A, Cavaliere AF. Cesarean section rate: navigating the gap between WHO recommended range and current obstetrical challenges. J Matern Fetal Neonatal Med 2023; 36:2284112. [PMID: 37989541 DOI: 10.1080/14767058.2023.2284112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 11/13/2023] [Indexed: 11/23/2023]
Abstract
The cesarean section (CS) rate is very heterogeneous all over the world, reflecting the differences in the access to healthcare services. In higher-income countries, changes observed in the obstetrical population brought to an increased rate of cesarean section for maternal request. Besides, clinicians are facing an increasing number of induction of labor, with the consequent risk of CS if the management is inappropriate. Analyzing the rate of primary CS, the interpretation of intrapartum CTG and a tailored management of labor are also red flags that must be considered. In this optic, the implementation of obstetrics training and simulation programs and the improvement of clinical protocols with the latest evidence can lead to the reduction of unnecessary CS.
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Affiliation(s)
- Marco Parasiliti
- Department of Gynecology and Obstetrics, ASST Crema - Ospedale Maggiore, Crema, Italy
| | - Annalisa Vidiri
- Department of Gynecology and Obstetrics, Isola Tiberina Hospital - Gemelli Isola, Rome, Italy
| | - Federica Perelli
- Division of Gynaecology and Obstetrics, Santa Maria Annunziata Hospital, USL Toscana Centro, Florence, Italy
| | - Giovanni Scambia
- Department of Science of Woman, Child and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Antonio Lanzone
- Department of Science of Woman, Child and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Roma, Italy
| | - Anna Franca Cavaliere
- Department of Gynecology and Obstetrics, Isola Tiberina Hospital - Gemelli Isola, Rome, Italy
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