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Medjedovic E, Stanojevic M, Jonuzovic-Prosic S, Ribic E, Begic Z, Cerovac A, Badnjevic A. Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics. Technol Health Care 2024; 32:1273-1287. [PMID: 38073356 DOI: 10.3233/thc-231482] [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] [Indexed: 05/12/2024]
Abstract
BACKGROUND Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. OBJECTIVE The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. METHODS A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. RESULTS According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. CONCLUSION The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.
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Affiliation(s)
- Edin Medjedovic
- Clinic of Gynecology and Obstetrics, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina
- Department of Gynecology, Obstetrics and Reproductive Medicine, School of Medicine, Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Milan Stanojevic
- Department of Obstetrics and Gynecology, University Hospital "Sveti Duh", Zagreb, Croatia
| | - Sabaheta Jonuzovic-Prosic
- Clinic of Gynecology and Obstetrics, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Emina Ribic
- Public Institution Department for Health Care of Women and Maternity of Sarajevo Canton, Sarajevo, Bosnia and Herzegovina
| | - Zijo Begic
- Department of Cardiology, Pediatric Clinic, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Anis Cerovac
- Department of Gynecology and Obstetrics Tesanj, General Hospital Tesanj, Bosnia and Herzegovina
| | - Almir Badnjevic
- International Burch University, Sarajevo, Bosnia and Herzegovina
- Genetics and Bioengineering Department, Faculty of Engineering and Natural Sciences, Sarajevo, Bosnia and Herzegovina
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Liu X, Liu L, Zhang J, Meng X, Huang C, Zhang M. Construction and evaluation of nursing-sensitive quality indicators for vaginal birth after cesarean: A Delphi study based on Chinese population. Heliyon 2023; 9:e21389. [PMID: 37885709 PMCID: PMC10598526 DOI: 10.1016/j.heliyon.2023.e21389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023] Open
Abstract
Aim To develop scientific, systematic and clinically applicable nursing-sensitive quality indicators for vaginal birth after cesarean in obstetrics, which provide a theoretical and clinical basis for monitoring and improving the nursing quality of vaginal birth after cesarean in China. Methods A modified Delphi-consensus technique was used in this study. Based on literature retrieval published between January 2012 and December 2022 and group discussion, the preliminary nursing-sensitive quality indicators were selected using a structural-process-outcome model. Then a questionnaire was designed on the preliminary indicators. The modified Delphi method was used to conduct two rounds of expert consultation among 26 hospitals in China. The survey data of experts' opinions were collected and analyzed to determine the final nursing-sensitive quality indicators. The importance of indicators, rationality of calculation formula and operability of data collection were analyzed and discussed. Results A total of 33 nursing-sensitive quality indicators were determined. The indicators were composed of 3-level ones, including 3 first-level indicators (structural, process and outcome indicators), 9 s-level ones and 33 third-level ones. The positive coefficients in the two rounds of expert consultation were 95.56 % and 97.67 %, respectively, and the authoritative coefficients were 0.88 and 0.94. The coefficients of variation ranged from 0.05 to 0.28. Conclusion The nursing-sensitive quality indicators were successfully developed using the modified Delphi method. The indicators are scientific, systematic and clinically operable, and play an important role in improving the nursing quality for pregnant women with vaginal birth after cesarean.
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Affiliation(s)
- Xian Liu
- Endoscopy Room, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Ling Liu
- Department of Obstetrics, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Junshuang Zhang
- Department of Obstetrics, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xin Meng
- Department of Obstetrics, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Congcong Huang
- Department of Obstetrics, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Meng Zhang
- Department of Obstetrics, The Affiliated Hospital of Qingdao University, Qingdao, China
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Lodi M, Poterie A, Exarchakis G, Brien C, Lafaye de Micheaux P, Deruelle P, Gallix B. Prediction of cesarean delivery in class III obese nulliparous women: An externally validated model using machine learning. J Gynecol Obstet Hum Reprod 2023; 52:102624. [PMID: 37321400 DOI: 10.1016/j.jogoh.2023.102624] [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: 05/11/2023] [Revised: 06/11/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND class III obese women, are at a higher risk of cesarean section during labor, and cesarean section is responsible for increased maternal and neonatal morbidity in this population. OBJECTIVE the objective of this project was to develop a method with which to quantify cesarean section risk before labor. METHODS this is a multicentric retrospective cohort study conducted on 410 nulliparous class III obese pregnant women who attempted vaginal delivery in two French university hospitals. We developed two predictive algorithms (a logistic regression and a random forest models) and assessed performance levels and compared them. RESULTS the logistic regression model found that only initial weight and labor induction were significant in the prediction of unplanned cesarean section. The probability forest was able to predict cesarean section probability using only two pre-labor characteristics: initial weight and labor induction. Its performances were higher and were calculated for a cut-point of 49.5% risk and the results were (with 95% confidence intervals): area under the curve 0.70 (0.62,0.78), accuracy 0.66 (0.58, 0.73), specificity 0.87 (0.77, 0.93), and sensitivity 0.44 (0.32, 0.55). CONCLUSIONS this is an innovative and effective approach to predicting unplanned CS risk in this population and could play a role in the choice of a trial of labor versus planned cesarean section. Further studies are needed, especially a prospective clinical trial. FUNDING French state funds "Plan Investissements d'Avenir" and Agence Nationale de la Recherche.
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Affiliation(s)
- Massimo Lodi
- Obstetrics and Gynaecology Department, Strasbourg University Hospitals, 1 Avenue Molière, 67000 Strasbourg, France; Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), CNRS, UMR7104 INSERM U964, Université de Strasbourg, France.
| | - Audrey Poterie
- IHU Strasbourg, France; Laboratoire de Mathématiques de Bretagne Atlantique (LMBA) - UMR 6205, France
| | | | - Camille Brien
- Obstetrics and Gynaecology Department, Strasbourg University Hospitals, 1 Avenue Molière, 67000 Strasbourg, France
| | - Pierre Lafaye de Micheaux
- AMIS, Université Paul Valéry Montpellier 3, France; Desbrest Institute of Epidemiology and Public Health, Université de Montpellier, France; PREMEDICAL - Médecine de précision par intégration de données et inférence causale, CRISAM, Inria Sophia Antipolis, Méditerranée, France
| | - Philippe Deruelle
- Obstetrics and Gynaecology Department, Strasbourg University Hospitals, 1 Avenue Molière, 67000 Strasbourg, France
| | - Benoît Gallix
- IHU Strasbourg, France; ICube, CNRS, University of Strasbourg, France
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4
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Elammary MN, Zohiry M, Sayed A, Atef F, Ali N, Hussein I, Mahran MA, Said AE, Elassall GM, Radwan AA, Shazly SA. Middle eastern college of obstetricians and gynecologists (MCOG) practice guidelines: Role of prediction models in management of trial of labor after cesarean section. Practice guideline no. 05-O-22 ✰,✰✰,★,★★. J Gynecol Obstet Hum Reprod 2023; 52:102598. [PMID: 37087045 DOI: 10.1016/j.jogoh.2023.102598] [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: 02/01/2023] [Revised: 04/11/2023] [Accepted: 04/19/2023] [Indexed: 04/24/2023]
Abstract
Cesarean delivery rates have been steadily rising since the beginning of the 21st century. The growing incidence is even more prominent in developing countries owing to lack of evidence-based guidance and audit, and the expansion of private practice. The uprise in Cesarean delivery rate has been associated with considerable financial burden and has increased the risk otherwise uncommon serious complications such as placenta accreta disorders and uterine rupture. In addition to primary prevention of Cesarean delivery, trial of labor after cesarean section is one of the most successful strategies to reduce Cesarean deliveries and minimize risks associated with higher order Cesarean deliveries. This guideline appraises patient selection strategies and use of prediction model to promote counseling and enhance safety in women considering vaginal birth after Cesarean.
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Affiliation(s)
| | - Mariam Zohiry
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Asmaa Sayed
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Fatma Atef
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Nada Ali
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Islam Hussein
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Manar A Mahran
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Aliaa E Said
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Gena M Elassall
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Ahmad A Radwan
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom
| | - Sherif A Shazly
- Middle Eastern College of Obstetricians and Gynecologists (MCOG) Practice Office. Leeds, United Kingdom; Department of Obstetrics and Gynecology, Leeds Teaching Hospitals, Leeds, United Kingdom.
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5
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Johansson K, Granfors M, Petersson G, Bolk J, Altman M, Cnattingius S, Liu X, Sandström A, Stephansson O. The Stockholm-Gotland perinatal cohort-A population-based cohort including longitudinal data throughout pregnancy and the postpartum period. Paediatr Perinat Epidemiol 2022; 37:276-286. [PMID: 36560891 DOI: 10.1111/ppe.12945] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/23/2022] [Accepted: 11/23/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Register-based reproductive and perinatal databases rarely contain detailed information from medical records or repeated measurements throughout pregnancy and delivery. This lack of enriched pregnancy and birth data led to the initiation of the Swedish Stockholm-Gotland Perinatal Cohort (SGPC). OBJECTIVES To describe the strengths of the SGPC, as well as the unique research questions that can be addressed using this cohort. POPULATION The SGPC is a prospectively collected, population-based cohort that includes all births (from 22 completed gestational weeks onwards) between 1 January 2008 and 15 June 2020 in the Stockholm and Gotland regions of Sweden (N 335,153 singleton and N 11,025 multiple pregnancies). DESIGN Descriptive study. METHODS The SGPC is based on the electronic medical records of women and their infants. The medical record system is used for all antenatal clinic visits and admissions, delivery and neonatal admissions, as well as postpartum clinical visits. SGPC has been further enriched with data linkages to 10 Swedish National Health Care and Quality Registers. PRELIMINARY RESULTS In contrast to other reproductive and perinatal databases available in Sweden, including the Medical Birth Register and the Pregnancy Register, SGPC contains highly detailed medical record data, including time-varying serial measurements for physiological parameters throughout pregnancy, delivery, and postpartum, for both mother and infant. These strengths have enabled studies that were previously inconceivable; the effects of serial measurements of pregnancy weight gain, changes in haemoglobin counts and blood pressure during pregnancy, fetal weight estimations by ultrasound, duration of stages and phases of labour, cervical dilatation and oxytocin use during delivery, and constructing reference curves for umbilical cord pH. CONCLUSIONS The SGPC-with its rich content, repeated measurements and linkages to numerous health care and quality registers-is a unique cohort that enables high-quality perinatal studies that would otherwise not be possible.
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Affiliation(s)
- Kari Johansson
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.,Department of Women's and Children's Health, Division of Obstetrics, Karolinska University Hospital, Stockholm, Sweden
| | - Michaela Granfors
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.,Department of Women's and Children's Health, Division of Obstetrics, Karolinska University Hospital, Stockholm, Sweden
| | - Gunnar Petersson
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Jenny Bolk
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.,Sachs´ Children and Youth Hospital, Stockholm, Sweden.,Department of Clinical Science and Education Södersjukhuset Karolinska Institutet, Stockholm, Sweden
| | - Maria Altman
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.,Pediatric Rheumatology Unit, Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden
| | - Sven Cnattingius
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Xingrong Liu
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.,Department of Women's and Children's Health, Division of Obstetrics, Karolinska University Hospital, Stockholm, Sweden
| | - Anna Sandström
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.,Department of Women's and Children's Health, Division of Obstetrics, Karolinska University Hospital, Stockholm, Sweden
| | - Olof Stephansson
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.,Department of Women's and Children's Health, Division of Obstetrics, Karolinska University Hospital, Stockholm, Sweden
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6
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Predictive Models for Estimating the Probability of Successful Vaginal Birth After Cesarean Delivery. Obstet Gynecol 2022; 140:821-841. [DOI: 10.1097/aog.0000000000004940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/30/2022] [Indexed: 11/15/2022]
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7
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Stark M. Introduction to the cesarean section articles. J Perinat Med 2021; 49:759-762. [PMID: 34407330 DOI: 10.1515/jpm-2021-0381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Michael Stark
- The New European Surgical Academy (NESA), Berlin, Germany
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8
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Ullah Z, Saleem F, Jamjoom M, Fakieh B. Reliable Prediction Models Based on Enriched Data for Identifying the Mode of Childbirth by Using Machine Learning Methods: Development Study. J Med Internet Res 2021; 23:e28856. [PMID: 34085938 PMCID: PMC8214183 DOI: 10.2196/28856] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 03/30/2021] [Accepted: 04/30/2021] [Indexed: 11/30/2022] Open
Abstract
Background The use of artificial intelligence has revolutionized every area of life such as business and trade, social and electronic media, education and learning, manufacturing industries, medicine and sciences, and every other sector. The new reforms and advanced technologies of artificial intelligence have enabled data analysts to transmute raw data generated by these sectors into meaningful insights for an effective decision-making process. Health care is one of the integral sectors where a large amount of data is generated daily, and making effective decisions based on these data is therefore a challenge. In this study, cases related to childbirth either by the traditional method of vaginal delivery or cesarean delivery were investigated. Cesarean delivery is performed to save both the mother and the fetus when complications related to vaginal birth arise. Objective The aim of this study was to develop reliable prediction models for a maternity care decision support system to predict the mode of delivery before childbirth. Methods This study was conducted in 2 parts for identifying the mode of childbirth: first, the existing data set was enriched and second, previous medical records about the mode of delivery were investigated using machine learning algorithms and by extracting meaningful insights from unseen cases. Several prediction models were trained to achieve this objective, such as decision tree, random forest, AdaBoostM1, bagging, and k-nearest neighbor, based on original and enriched data sets. Results The prediction models based on enriched data performed well in terms of accuracy, sensitivity, specificity, F-measure, and receiver operating characteristic curves in the outcomes. Specifically, the accuracy of k-nearest neighbor was 84.38%, that of bagging was 83.75%, that of random forest was 83.13%, that of decision tree was 81.25%, and that of AdaBoostM1 was 80.63%. Enrichment of the data set had a good impact on improving the accuracy of the prediction process, which supports maternity care practitioners in making decisions in critical cases. Conclusions Our study shows that enriching the data set improves the accuracy of the prediction process, thereby supporting maternity care practitioners in making informed decisions in critical cases. The enriched data set used in this study yields good results, but this data set can become even better if the records are increased with real clinical data.
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Affiliation(s)
- Zahid Ullah
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Farrukh Saleem
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mona Jamjoom
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Bahjat Fakieh
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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9
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Luo ZC, Liu X, Wang A, Li JQ, Zheng ZH, Guiyu S, Lou T, Pang J, Bai XL. Obstetricians' perspectives on trial of labor after cesarean (TOLAC) under the two-child policy in China: a cross-sectional study. BMC Pregnancy Childbirth 2021; 21:89. [PMID: 33509100 PMCID: PMC7841882 DOI: 10.1186/s12884-021-03559-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 01/13/2021] [Indexed: 11/16/2022] Open
Abstract
Background As the birth policy has been adjusted from one-child-one-couple to universal two-child-one-couple in China, there is an increasing number of women undergoing a second pregnancy after a previous cesarean section (CS). Undertaking an elective repeat CS (ERCS) has been taken for granted and has thus become a major contributor to the increasing CS rate in China. Promoting trial of labor after CS (TOLAC) can reduce the CS rate without compromising delivery outcomes. This study aimed to investigate Chinese obstetricians’ perspectives regarding TOLAC, and the factors associated with their decision-making regarding recommending TOLAC to pregnant women with a history of CS under the two-child policy. Methods A cross-sectional survey was carried out between May and July 2018. Binary logistic regression was used to determine the factors associated with the obstetricians’ intention to recommend TOLAC to pregnant women with a history of CS. The independent variables included sociodemographic factors and perceptions regarding TOLAC (selection criteria for TOLAC, basis underlying the selection criteria for TOLAC, and perceived challenges regarding promoting TOLAC). Results A total of 426 obstetricians were surveyed, with a response rate of ≥83%. The results showed that 31.0% of the obstetricians had no intention to recommend TOLAC to pregnant women with a history of CS. Their decisions were associated with the perceived lack of confidence regarding undergoing TOLAC among pregnant women with a history of CS and their families (odds ratio [OR] = 2.31; 95% CI: 1.38–1.38); obstetricians’ uncertainty about the safety of TOLAC for pregnant women with a history of CS (OR = 0.49; 95% CI: 0.27–0.96), and worries about medical lawsuits due to adverse delivery outcomes (OR = 0.14; 95% CI: 0.07–0.31). The main reported challenges regarding performing TOLAC were lack of clear guidelines for predicting or avoiding the risks associated with TOLAC (83.4%), obstetricians’ uncertainty about the safety of TOLAC for women with a history of CS (81.2%), pregnant women’s unwillingness to accept the risks associated with TOLAC (81.0%) or demand for ERCS (80.7%), and the perceived lack of confidence (77.5%) or understanding (69.7%) regarding undergoing TOLAC among pregnant women and their families. Conclusion A proportion of Chinese obstetricians did not intend to recommend TOLAC to pregnant women with a history of CS. This phenomenon was closely associated with obstetricians’ concerns about TOLAC safety and perceived attitudes of the pregnant women and their families regarding TOLAC. Effective measures are needed to help obstetricians predict and reduce the risks associated with TOLAC, clearly specify the indications for TOLAC, improve labor management, and popularize TOLAC in China. Additionally, public health education on TOLAC is necessary to improve the understanding of TOLAC among pregnant women with a history of CS and their families, and to improve their interactions with their obstetricians regarding shared decision making. Supplementary Information The online version contains supplementary material available at 10.1186/s12884-021-03559-1.
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Affiliation(s)
- Zhong-Chen Luo
- School of Nursing, Guizhou Medical University, Guiyang, China
| | - Xu Liu
- The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Anni Wang
- School of Nursing, Fudan University, Shanghai, China
| | - Jian-Qiong Li
- School of Nursing, Chongqing Three Gorges Medical College, Chongqing, China
| | - Ze-Hong Zheng
- Engineering Training Center, Guizhou Minzu University, Guiyang, China
| | - Sun Guiyu
- Nursing Department, Guizhou Provincial Peoples Hospital, Guiyang, China
| | - Ting Lou
- Nursing Department, Guizhou Provincial Peoples Hospital, Guiyang, China
| | - Jin Pang
- Nursing Department, Guizhou Provincial Peoples Hospital, Guiyang, China
| | - Xiao-Ling Bai
- Nursing Department, Guizhou Provincial Peoples Hospital, Guiyang, China. .,Guizhou Nursing Vocational College, Dazhi Road, Guiyang, 550025, China.
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10
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Lindblad Wollmann C, Hart KD, Liu C, Caughey AB, Stephansson O, Snowden JM. Predicting vaginal birth after previous cesarean: Using machine-learning models and a population-based cohort in Sweden. Acta Obstet Gynecol Scand 2020; 100:513-520. [PMID: 33031579 PMCID: PMC8048592 DOI: 10.1111/aogs.14020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/11/2020] [Accepted: 09/29/2020] [Indexed: 12/21/2022]
Abstract
Introduction Predicting a woman’s probability of vaginal birth after cesarean could facilitate the antenatal decision‐making process. Having a previous vaginal birth strongly predicts vaginal birth after cesarean. Delivery outcome in women with only a cesarean delivery is more unpredictable. Therefore, to better predict vaginal birth in women with only one prior cesarean delivery and no vaginal deliveries would greatly benefit clinical practice and fill a key evidence gap in research. Our aim was to predict vaginal birth in women with one prior cesarean and no vaginal deliveries using machine‐learning methods, and compare with a US prediction model and its further developed model for a Swedish setting. Material and methods A population‐based cohort study with a cohort of 3116 women with only one prior birth, a cesarean, and a subsequent trial of labor during 2008‐2014 in the Stockholm‐Gotland region, Sweden. Three machine‐learning methods (conditional inference tree, conditional random forest and lasso binary regression) were used to predict vaginal birth after cesarean among women with one previous birth. Performance of the new models was compared with two existing models developed by Grobman et al (USA) and Fagerberg et al (Sweden). Our main outcome measures were area under the receiver‐operating curve (AUROC), overall accuracy, sensitivity and specificity of prediction of vaginal birth after previous cesarean delivery. Results The AUROC ranged from 0.61 to 0.69 for all models, sensitivity was above 91% and specificity below 22%. The majority of women with an unplanned repeat cesarean had a predicted probability of vaginal birth after cesarean >60%. Conclusions Both classical regression models and machine‐learning models had a high sensitivity in predicting vaginal birth after cesarean in women without a previous vaginal delivery. The majority of women with an unplanned repeat cesarean delivery were predicted to succeed with a vaginal birth (ie specificity was low). Additional covariates combined with machine‐learning techniques did not outperform classical regression models in this study.
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Affiliation(s)
- Charlotte Lindblad Wollmann
- Clinical Epidemiology Division, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.,Division of Obstetrics and Gynecology, Department of Women's and Children´s Health, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Kyle D Hart
- Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA
| | - Can Liu
- Clinical Epidemiology Division, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Aaron B Caughey
- Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA
| | - Olof Stephansson
- Clinical Epidemiology Division, Department of Medicine, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden.,Division of Obstetrics and Gynecology, Department of Women's and Children´s Health, Karolinska University Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Jonathan M Snowden
- Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, Oregon, USA.,School of Public Health, Oregon Health & Science University and Portland State University, Portland, Oregon, USA
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