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Sibanda K, Ndayizigamiye P, Twinomurinzi H. Industry 4.0 Technologies in Maternal Health Care: Bibliometric Analysis and Research Agenda. JMIR Pediatr Parent 2024; 7:e47848. [PMID: 39116433 PMCID: PMC11342010 DOI: 10.2196/47848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 02/29/2024] [Accepted: 03/12/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND Industry 4.0 (I4.0) technologies have improved operations in health care facilities by optimizing processes, leading to efficient systems and tools to assist health care personnel and patients. OBJECTIVE This study investigates the current implementation and impact of I4.0 technologies within maternal health care, explicitly focusing on transforming care processes, treatment methods, and automated pregnancy monitoring. Additionally, it conducts a thematic landscape mapping, offering a nuanced understanding of this emerging field. Building on this analysis, a future research agenda is proposed, highlighting critical areas for future investigations. METHODS A bibliometric analysis of publications retrieved from the Scopus database was conducted to examine how the research into I4.0 technologies in maternal health care evolved from 1985 to 2022. A search strategy was used to screen the eligible publications using the abstract and full-text reading. The most productive and influential journals; authors', institutions', and countries' influence on maternal health care; and current trends and thematic evolution were computed using the Bibliometrix R package (R Core Team). RESULTS A total of 1003 unique papers in English were retrieved using the search string, and 136 papers were retained after the inclusion and exclusion criteria were implemented, covering 37 years from 1985 to 2022. The annual growth rate of publications was 9.53%, with 88.9% (n=121) of the publications observed in 2016-2022. In the thematic analysis, 4 clusters were identified-artificial neural networks, data mining, machine learning, and the Internet of Things. Artificial intelligence, deep learning, risk prediction, digital health, telemedicine, wearable devices, mobile health care, and cloud computing remained the dominant research themes in 2016-2022. CONCLUSIONS This bibliometric analysis reviews the state of the art in the evolution and structure of I4.0 technologies in maternal health care and how they may be used to optimize the operational processes. A conceptual framework with 4 performance factors-risk prediction, hospital care, health record management, and self-care-is suggested for process improvement. a research agenda is also proposed for governance, adoption, infrastructure, privacy, and security.
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Affiliation(s)
- Khulekani Sibanda
- Department of Applied Information Systems, University of Johannesburg, Johannesburg, South Africa
| | - Patrick Ndayizigamiye
- Centre for Applied Data Science, University of Johannesburg, Johannesburg, South Africa
| | - Hossana Twinomurinzi
- Centre for Applied Data Science, University of Johannesburg, Johannesburg, South Africa
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Khan W, Zaki N, Ghenimi N, Ahmad A, Bian J, Masud MM, Ali N, Govender R, Ahmed LA. Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women. PLoS One 2023; 18:e0293925. [PMID: 38150456 PMCID: PMC10752564 DOI: 10.1371/journal.pone.0293925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 10/21/2023] [Indexed: 12/29/2023] Open
Abstract
Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. "While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care. This study analyzed a dataset of 3509 pregnant women in the United Arab Emirates and selected 35 risk factors associated with PTB based on the existing medical and artificial intelligence literature. Six ML algorithms were tested, wherein the XGBoost model exhibited the best performance, with an area under the operator receiving curves of 0.735 and 0.723 for parous and nulliparous women, respectively. The SHAP feature attribution framework was employed to identify the most significant risk factors linked to PTB. Additionally, individual patient analysis was performed using the SHAP and the local interpretable model-agnostic explanation algorithms (LIME). The overall incidence of PTB was 11.23% (11 and 12.1% in parous and nulliparous women, respectively). The main risk factors associated with PTB in parous women are previous PTB, previous cesarean section, preeclampsia during pregnancy, and maternal age. In nulliparous women, body mass index at delivery, maternal age, and the presence of amniotic infection were the most relevant risk factors. The trained ML prediction model developed in this study holds promise as a valuable screening tool for predicting PTB within this specific population. Furthermore, SHAP and LIME analyses can assist clinicians in understanding the individualized impact of each risk factor on their patients and provide appropriate care to reduce morbidity and mortality related to PTB.
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Affiliation(s)
- Wasif Khan
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, UAE
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain, UAE
| | - Nazar Zaki
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, UAE
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain, UAE
| | - Nadirah Ghenimi
- Department Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Amir Ahmad
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, UAE
- Zayed Centre for Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Mohammad M. Masud
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, UAE
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain, UAE
- Zayed Centre for Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Nasloon Ali
- Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Romona Govender
- Department Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
| | - Luai A. Ahmed
- Zayed Centre for Health Sciences, United Arab Emirates University, Al Ain, UAE
- Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, UAE
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Luo B, Luo Z, Zhang X, Xu M, Shi C. Status of cognitive frailty in elderly patients with chronic kidney disease and construction of a risk prediction model: a cross-sectional study. BMJ Open 2022; 12:e060633. [PMID: 36572488 PMCID: PMC9806025 DOI: 10.1136/bmjopen-2021-060633] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 09/02/2022] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVE To investigate the risk factors of cognitive frailty in elderly patients with chronic kidney disease (CKD), and to establish an artificial neural network (ANN) model. DESIGN A cross-sectional design. SETTING Two tertiary hospitals in southern China. PARTICIPANTS 425 elderly patients aged ≥60 years with CKD. METHODS Data were collected via questionnaire investigation, anthropometric measurements, laboratory tests and electronic medical records. The 425 samples were randomly divided into a training set, test set and validation set at a ratio of 5:3:2. Variables were screened by univariate and multivariate logistic regression analyses, then an ANN model was constructed. The accuracy, specificity, sensitivity, receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to evaluate the predictive power of the model. RESULTS Barthel Index (BI) score, albumin, education level, 15-item Geriatric Depression Scale score and Social Support Rating Scale score were the factors influencing the occurrence of cognitive frailty (p<0.05). Among them, BI score was the most important factor determining cognitive frailty, with an importance index of 0.30. The accuracy, specificity and sensitivity of the ANN model were 86.36%, 88.61% and 80.65%, respectively, and the AUC of the constructed ANN model was 0.913. CONCLUSION The ANN model constructed in this study has good predictive ability, and can provide a reference tool for clinical nursing staff in the early prediction of cognitive frailty in a high-risk population.
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Affiliation(s)
- Baolin Luo
- School of Nursing, Shantou University Medical College, Shantou, Guangdong, China
- Nursing Department, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Zebing Luo
- School of Nursing, Shantou University Medical College, Shantou, Guangdong, China
- Cancer Department, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Xiaoyun Zhang
- School of Nursing, Shantou University Medical College, Shantou, Guangdong, China
- Nephrology Department, Shantou Central Hospital, Shantou, Guangdong, China
| | - Meiwan Xu
- Nephrology Department, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Chujun Shi
- School of Nursing, Shantou University Medical College, Shantou, Guangdong, China
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Feleke SF, Anteneh ZA, Wassie GT, Yalew AK, Dessie AM. Developing and validating a risk prediction model for preterm birth at Felege Hiwot Comprehensive Specialized Hospital, North-West Ethiopia: a retrospective follow-up study. BMJ Open 2022; 12:e061061. [PMID: 36167381 PMCID: PMC9516143 DOI: 10.1136/bmjopen-2022-061061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE To develop and validate a risk prediction model for the prediction of preterm birth using maternal characteristics. DESIGN This was a retrospective follow-up study. Data were coded and entered into EpiData, V.3.02, and were analysed using R statistical programming language V.4.0.4 for further processing and analysis. Bivariable logistic regression was used to identify the relationship between each predictor and preterm birth. Variables with p≤0.25 from the bivariable analysis were entered into a backward stepwise multivariable logistic regression model, and significant variables (p<0.05) were retained in the multivariable model. Model accuracy and goodness of fit were assessed by computing the area under the receiver operating characteristic curve (discrimination) and calibration plot (calibration), respectively. SETTING AND PARTICIPANTS This retrospective study was conducted among 1260 pregnant women who did prenatal care and finally delivered at Felege Hiwot Comprehensive Specialised Hospital, Bahir Dar city, north-west Ethiopia, from 30 January 2019 to 30 January 2021. RESULTS Residence, gravidity, haemoglobin <11 mg/dL, early rupture of membranes, antepartum haemorrhage and pregnancy-induced hypertension remained in the final multivariable prediction model. The area under the curve of the model was 0.816 (95% CI 0.779 to 0.856). CONCLUSION This study showed the possibility of predicting preterm birth using maternal characteristics during pregnancy. Thus, use of this model could help identify pregnant women at a higher risk of having a preterm birth to be linked to a centre.
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Affiliation(s)
| | - Zelalem Alamrew Anteneh
- Department of Epidemiology and Biostatistics, Bahir Dar University College of Medical and Health Sciences, Bahir Dar, Ethiopia
| | - Gizachew Tadesse Wassie
- Department of Epidemiology and Biostatistics, Bahir Dar University College of Medical and Health Sciences, Bahir Dar, Ethiopia
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Chan F, Shen S, Huang P, He J, Wei X, Lu J, Zhang L, Xia X, Xia H, Cheng KK, Thangaratinam S, Mol BW, Qiu X. Blood pressure trajectories during pregnancy and preterm delivery: A prospective cohort study in China. J Clin Hypertens (Greenwich) 2022; 24:770-778. [PMID: 35651280 PMCID: PMC9180333 DOI: 10.1111/jch.14494] [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: 01/03/2022] [Revised: 03/24/2022] [Accepted: 04/18/2022] [Indexed: 11/26/2022]
Abstract
Women's blood pressure (BP) changes throughout pregnancy. The effect of BP trajectories on preterm delivery is not clear. The authors aim to evaluate the association between maternal BP trajectories during pregnancy and preterm delivery. The authors studied pregnant women included in the Born in Guangzhou Cohort Study in China between February 2012 and June 2016. Maternal BP was measured at antenatal visits between 13 and 40 gestational weeks, and gestational age of delivery data was collected. The authors used linear mixed models to capture the BP trajectories of women with term, and spontaneous and iatrogenic preterm delivery. BP trajectories of women with various gestational lengths (34, 35, 36, 37, 38, 39, 40 weeks) were compared. Of the 17 426 women included in the analysis, 618 (3.55%) had spontaneous preterm delivery; 158 (.91%) had iatrogenic preterm delivery; and 16 650 (95.55%) women delivered at term. The BP trajectories were all J‐shaped curves for different delivery types. Women with iatrogenic preterm delivery had the highest mean BP from 13 weeks till delivery, followed by those with spontaneous preterm delivery and term delivery (p < .001). Trajectory analysis stratified by maternal parity showed similar results for nulliparous and multiparous women. Excluding women with pre‐eclampsia and gestational hypertension (GH) significantly attenuated the aforementioned association. Also, women with shorter gestational length tend to have higher BP trajectories during pregnancy. In conclusion, Women with spontaneous preterm delivery have a higher BP from 13 weeks till delivery than women with term delivery, while women with iatrogenic preterm delivery have the highest BP.
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Affiliation(s)
- Fanfan Chan
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Provincial Key Clinical Specialty of Woman and Child Health, Guangdong, China
| | - Songying Shen
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Provincial Key Clinical Specialty of Woman and Child Health, Guangdong, China
| | - Peiyuan Huang
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Provincial Key Clinical Specialty of Woman and Child Health, Guangdong, China
| | - Jianrong He
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Provincial Key Clinical Specialty of Woman and Child Health, Guangdong, China
| | - Xueling Wei
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Provincial Key Clinical Specialty of Woman and Child Health, Guangdong, China
| | - Jinhua Lu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Provincial Key Clinical Specialty of Woman and Child Health, Guangdong, China
| | - Lifang Zhang
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Provincial Key Clinical Specialty of Woman and Child Health, Guangdong, China
| | - Xiaoyan Xia
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Provincial Key Clinical Specialty of Woman and Child Health, Guangdong, China
| | - Huimin Xia
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Provincial Clinical Research Center for Child Health, Guangdong, China
| | - Kar Keung Cheng
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Shakila Thangaratinam
- WHO Collaborating Centre for Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Ben Willem Mol
- Department of Obstetrics and Gynecology, School of Medicine, Monash University, Melbourne, Australia
| | - Xiu Qiu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.,Provincial Key Clinical Specialty of Woman and Child Health, Guangdong, China.,Provincial Clinical Research Center for Child Health, Guangdong, China
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6
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Lopes MLB, Barbosa RDM, Fernandes MAC. Unsupervised Learning Applied to the Stratification of Preterm Birth Risk in Brazil with Socioeconomic Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19095596. [PMID: 35564992 PMCID: PMC9102534 DOI: 10.3390/ijerph19095596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/26/2022] [Accepted: 04/27/2022] [Indexed: 11/16/2022]
Abstract
Preterm birth (PTB) is a phenomenon that brings risks and challenges for the survival of the newborn child. Despite many advances in research, not all the causes of PTB are already clear. It is understood that PTB risk is multi-factorial and can also be associated with socioeconomic factors. Thereby, this article seeks to use unsupervised learning techniques to stratify PTB risk in Brazil using only socioeconomic data. Through the use of datasets made publicly available by the Federal Government of Brazil, a new dataset was generated with municipality-level socioeconomic data and a PTB occurrence rate. This dataset was processed using various unsupervised learning techniques, such as k-means, principal component analysis (PCA), and density-based spatial clustering of applications with noise (DBSCAN). After validation, four clusters with high levels of PTB occurrence were discovered, as well as three with low levels. The clusters with high PTB were comprised mostly of municipalities with lower levels of education, worse quality of public services-such as basic sanitation and garbage collection-and a less white population. The regional distribution of the clusters was also observed, with clusters of high PTB located mostly in the North and Northeast regions of Brazil. The results indicate a positive influence of the quality of life and the offer of public services on the reduction in PTB risk.
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Affiliation(s)
- Márcio L B Lopes
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
| | - Raquel de M Barbosa
- Department of Pharmacy and Pharmaceutical Technology, University of Granada, 18071 Granada, Spain
| | - Marcelo A C Fernandes
- Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
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Jiang L, Peng L, Rong M, Liu X, Pang Q, Li H, Wang Y, Liu Z. Nomogram Incorporating Multimodal Transvaginal Ultrasound Assessment at 20 to 24 Weeks' Gestation for Predicting Spontaneous Preterm Delivery in Low-Risk Women. Int J Womens Health 2022; 14:323-331. [PMID: 35264886 PMCID: PMC8901232 DOI: 10.2147/ijwh.s356167] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 02/17/2022] [Indexed: 01/09/2023] Open
Abstract
Background The majority of women who experience spontaneous preterm delivery (SPTD) have low-risk, asymptomatic pregnancies with a cervical length (CL) ≥25mm and no clear risk factors. Despite the fact that cervical elastography is a potential tool for predicting SPTD, there is currently no feasible solution to make a reliable prediction for preventing SPTD. Objective The aim of this study was to construct a nomogram including multimodal transvaginal ultrasound parameters during the second trimester to predict SPTD in low-risk women. Methods This multi-center study enrolled 1260 women with singleton pregnancies between 20 and 24 weeks’ gestation. CL and cervical elastography data were obtained when they were undergoing the second-trimester anomaly scan. Univariate and multivariate Logistic regression were utilized to screen predictors independently related to SPTD from the maternal characteristics and multimodal ultrasound data. Then construct a nomogram to determine the likelihood of SPTD in pregnant women. Results A total of 66 pregnancies in the training cohort (7.8%, 66/842) and 37 pregnancies (8.9%, 37/418) in the validation cohort ended in SPTD. Age, uterine curettage, CL, and strain in the anterior lip of internal os were the independent predictors of SPTD (P < 0.001, < 0.001, = 0.007, and < 0.001, respectively). These predictors constituted a nomogram to predict the probability of SPTD for a pregnant woman in her second trimester. It showed good discrimination (C-index = 0.898 and 0.839), calibration (P = 0.258 and 0.115), and yielded net benefits both in the training and validation cohorts. Conclusion The nomogram including data of multimodal transvaginal ultrasound at 20 to 24 weeks’ gestation is expected to identify women with SPTD in the low-risk, asymptomatic population.
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Affiliation(s)
- Lingli Jiang
- Department of Obstetrics and Gynecology, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, People's Republic of China
| | - Lei Peng
- Department of Obstetrics and Gynecology, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, People's Republic of China
| | - Miaoling Rong
- Department of Obstetrics and Gynecology, First Maternity and Infant Hospital Affiliated to Tongji University, Shanghai, People's Republic of China
| | - Xiaozhi Liu
- Department of Ultrasound, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, People's Republic of China
| | - Qinxia Pang
- Department of Obstetrics and Gynecology, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, People's Republic of China
| | - Huaping Li
- Department of Obstetrics and Gynecology, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, People's Republic of China
| | - Ying Wang
- Department of Obstetrics and Gynecology, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, People's Republic of China
| | - Zhou Liu
- Department of Obstetrics and Gynecology, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, People's Republic of China
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8
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Arabi Belaghi R, Beyene J, McDonald SD. Prediction of preterm birth in nulliparous women using logistic regression and machine learning. PLoS One 2021; 16:e0252025. [PMID: 34191801 PMCID: PMC8244906 DOI: 10.1371/journal.pone.0252025] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 05/10/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE To predict preterm birth in nulliparous women using logistic regression and machine learning. DESIGN Population-based retrospective cohort. PARTICIPANTS Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014. METHODS We used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" sample. RESULTS During the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23-5.42; Type II: AOR: 2.68; 95% CI: 2.05-3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80-2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58-62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21-13.90). During the second trimester, the AUC increased to 65% (95% CI: 63-66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79-81%) with artificial neural networks. All models yielded 94-97% negative predictive values for spontaneous PTB during the first and second trimesters. CONCLUSION Although artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.
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Affiliation(s)
- Reza Arabi Belaghi
- Department of Obstetrics and Gynecology, McMaster University, Hamilton, Ontario, Canada
- Department of Statistics, University of Tabriz, Tabriz, Iran
| | - Joseph Beyene
- Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Ontario, Canada
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
| | - Sarah D. McDonald
- Department of Obstetrics and Gynecology, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Ontario, Canada
- Department of Obstetrics and Gynecology (Division of Maternal-Fetal Medicine), McMaster University, Hamilton, Ontario, Canada
- Department of Radiology, McMaster University, Hamilton, Ontario, Canada
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9
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Espinosa C, Becker M, Marić I, Wong RJ, Shaw GM, Gaudilliere B, Aghaeepour N, Stevenson DK. Data-Driven Modeling of Pregnancy-Related Complications. Trends Mol Med 2021; 27:762-776. [PMID: 33573911 DOI: 10.1016/j.molmed.2021.01.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/01/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022]
Abstract
A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.
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Affiliation(s)
- Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Ivana Marić
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald J Wong
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary M Shaw
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA; Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
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Singh N, Bonney E, McElrath T, Lamont RF. Prevention of preterm birth: Proactive and reactive clinical practice-are we on the right track? Placenta 2020; 98:6-12. [PMID: 32800387 DOI: 10.1016/j.placenta.2020.07.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 07/16/2020] [Accepted: 07/20/2020] [Indexed: 11/27/2022]
Abstract
Preterm birth remains the major cause of death and disability among children under the age of five. In developing countries antenatal preterm birth prevention clinics are set up to provide cervical length surveillance and/or treatment modalities such as cerclage or progesterone for those women with identified risk factors such as previous cervical treatment or preterm birth. However, 85% of women have no risk factors for PTB and currently there is no biomarker to screen women early in pregnancy. Women will present unexpectedly in threatened preterm labour and we have no choice but to adopt a re-active approach to their care by using predication and preparation strategies such as fetal fibronectin, tocolytic therapy and steroids. Despite these strategies approximately 15-20% of these women will give birth preterm before 34 weeks. There is a urgent need to re-design primary, secondary and tertiary prevention strategies for spontaneous preterm labour (sPTL) in singleton pregnancies aimed at identifying and addressing key gaps in clinical practice and research.
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Affiliation(s)
- Natasha Singh
- Department of Obstetrics, Chelsea and Westminster Hospital and Imperial College London, UK.
| | - Elizabeth Bonney
- Department of Obstetrics, Gynaecology, and Reproductive Sciences, University of Vermont, Burlington, VT, USA
| | - Tom McElrath
- Brigham and Women's Hospital, Department of Obstetrics and Gynaecology, Boston, MA, USA
| | - Ronald F Lamont
- Division of Surgery, University College London, Northwick Park Institute of Medical Research Campus, London, UK
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Du L, Zhang LH, Zheng Q, Xie HN, Gu YJ, Lin MF, Wu LH. Evaluation of Cervical Elastography for Prediction of Spontaneous Preterm Birth in Low-Risk Women: A Prospective Study. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2020; 39:705-713. [PMID: 31626344 DOI: 10.1002/jum.15149] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 09/13/2019] [Accepted: 09/21/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVES The aim of this study was to determine whether cervical elastographic parameters in addition to cervical length (CL) during the 3 trimesters of pregnancy would be predictive of spontaneous preterm birth (sPTB) among low-risk women. METHODS This work was a prospective nested case-control study evaluating cervical elastographic parameters and CL in low-risk women during the 3 trimesters of pregnancy. A binary logistic regression analysis was used to calculate significant covariates for prediction of sPTB. The area under the curve of the prediction model was calculated by using a receiver operating characteristic curve. RESULTS There were 286 women (26 cases and 260 controls) included in the analysis. The parameters of cervical elasticity became softened and heterogeneous during the 3 trimesters of pregnancy in both women with and without sPTB. The differences in the mean strain value at the internal os of the cervix (IOS), ratio (strain ratio of the internal os to the external os) during the second trimester and the IOS during the third trimester between the groups had statistical significance (P < .01; P = .01; P < .01, respectively). The CL had no association with sPTB during the 3 trimesters. The IOS during the second trimester was a better predictor of sPTB, with an area under the curve of 0.730, and sensitivity was 72.73%. CONCLUSIONS We observed multiple elastographic parameters and demonstrated the physiologic changes in the cervix during the 3 trimesters of pregnancy. Furthermore, we found that the IOS during the second trimester can be helpful in predicting sPTB. However, the CL had no association with sPTB during the 3 trimesters of pregnancy.
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Affiliation(s)
- Liu Du
- Department of Ultrasonic Medicine, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Li-He Zhang
- Department of Ultrasonic Medicine, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qiao Zheng
- Department of Ultrasonic Medicine, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Hong-Ning Xie
- Department of Ultrasonic Medicine, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yu-Jun Gu
- Department of Ultrasonic Medicine, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Mei-Fang Lin
- Department of Ultrasonic Medicine, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Li-Hong Wu
- Department of Ultrasonic Medicine, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Ramalingam P, Sandhya M, Sankar S. Using an innovative stacked ensemble algorithm for the accurate prediction of preterm birth. J Turk Ger Gynecol Assoc 2019; 20:70-78. [PMID: 30501143 PMCID: PMC6558358 DOI: 10.4274/jtgga.galenos.2018.2018.0105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Objective: A birth before the normal term of 38 weeks of gestation is called a preterm birth (PTB). It is one of the major reasons for neonatal death. The objective of this article was to predict PTB well in advance so that it was converted to a term birth. Material and Methods: This study uses the historical data of expectant mothers and an innovative stacked ensemble (SE) algorithm to predict PTB. The proposed algorithm stacks classifiers in multiple tiers. The accuracy of the classiffication is improved in every tier. Results: The experimental results from this study show that PTB can be predicted with more than 96% accuracy using innovative SE learning. Conclusion: The proposed approach helps physicians in Gynecology and Obstetrics departments to decide whether the expectant mother needs treatment. Treatment can be given to delay the birth only in patients for whom PTB is predicted, or in many cases to convert the PTB to a normal birth. This, in turn, can reduce the mortality of babies due to PTB.
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Affiliation(s)
- Pari Ramalingam
- Department of Computer Science and Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
| | - Maheshwari Sandhya
- Department of Computer Science and Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
| | - Sharmila Sankar
- Department of Computer Science and Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
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Kim YS. Analysis of Spontaneous Preterm Labor and Birth and Its Major Causes Using Artificial Neural Network. J Korean Med Sci 2019; 34:e131. [PMID: 31020818 PMCID: PMC6484176 DOI: 10.3346/jkms.2019.34.e131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 04/19/2019] [Indexed: 11/20/2022] Open
Affiliation(s)
- Yun Sook Kim
- Department of Obstetrics and Gynecology, Soonchunhyang University College of Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Korea.
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He JR, Ramakrishnan R, Lai YM, Li WD, Zhao X, Hu Y, Chen NN, Hu F, Lu JH, Wei XL, Yuan MY, Shen SY, Qiu L, Chen QZ, Hu CY, Cheng KK, Mol BWJ, Xia HM, Qiu X. Predictions of Preterm Birth from Early Pregnancy Characteristics: Born in Guangzhou Cohort Study. J Clin Med 2018; 7:jcm7080185. [PMID: 30060450 PMCID: PMC6111770 DOI: 10.3390/jcm7080185] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 07/25/2018] [Accepted: 07/25/2018] [Indexed: 02/07/2023] Open
Abstract
Preterm birth (PTB, <37 weeks) is the leading cause of death in children <5 years of age. Early risk prediction for PTB would enable early monitoring and intervention. However, such prediction models have been rarely reported, especially in low- and middle-income areas. We used data on a number of easily accessible predictors during early pregnancy from 9044 women in Born in Guangzhou Cohort Study, China to generate prediction models for overall PTB and spontaneous, iatrogenic, late (34–36 weeks), and early (<34 weeks) PTB. Models were constructed using the Cox proportional hazard model, and their performance was evaluated by Harrell’s c and D statistics and calibration plot. We further performed a systematic review to identify published models and validated them in our population. Our new prediction models had moderate discrimination, with Harrell’s c statistics ranging from 0.60–0.66 for overall and subtypes of PTB. Significant predictors included maternal age, height, history of preterm delivery, amount of vaginal bleeding, folic acid intake before pregnancy, and passive smoking during pregnancy. Calibration plots showed good fit for all models except for early PTB. We validated three published models, all of which were from studies conducted in high-income countries; the area under receiver operating characteristic for these models ranged from 0.50 to 0.56. Based on early pregnancy characteristics, our models have moderate predictive ability for PTB. Future studies should consider inclusion of laboratory markers for the prediction of PTB.
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Affiliation(s)
- Jian-Rong He
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Obstetrics and Gynecology, Guangzhou Women and Children Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford OX3 9DU, UK.
| | - Rema Ramakrishnan
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford OX3 9DU, UK.
| | - Yu-Mian Lai
- Department of Obstetrics and Gynecology, Guangzhou Women and Children Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Wei-Dong Li
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Woman and Child Health Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Xuan Zhao
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Woman and Child Health Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Yan Hu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Woman and Child Health Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Nian-Nian Chen
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Woman and Child Health Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Fang Hu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Woman and Child Health Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Jin-Hua Lu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Woman and Child Health Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Xue-Ling Wei
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Woman and Child Health Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Ming-Yang Yuan
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Woman and Child Health Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Song-Ying Shen
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Lan Qiu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Woman and Child Health Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Qiao-Zhu Chen
- Department of Obstetrics and Gynecology, Guangzhou Women and Children Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Cui-Yue Hu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Kar Keung Cheng
- Institute of Applied Health Research, University of Birmingham, Birmingham B15 2TT, UK.
| | - Ben Willem J Mol
- Department of Obstetrics and Gynecology, Monash University, Clayton, Victoria 3204, Australia.
| | - Hui-Min Xia
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Neonatal Surgery, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
| | - Xiu Qiu
- Division of Birth Cohort Study, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Obstetrics and Gynecology, Guangzhou Women and Children Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
- Department of Woman and Child Health Care, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
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lin D, Vasilakos AV, Tang Y, Yao Y. Neural networks for computer-aided diagnosis in medicine: A review. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.08.039] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Kleinrouweler CE, Cheong-See FM, Collins GS, Kwee A, Thangaratinam S, Khan KS, Mol BWJ, Pajkrt E, Moons KG, Schuit E. Prognostic models in obstetrics: available, but far from applicable. Am J Obstet Gynecol 2016; 214:79-90.e36. [PMID: 26070707 DOI: 10.1016/j.ajog.2015.06.013] [Citation(s) in RCA: 117] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Revised: 05/20/2015] [Accepted: 06/01/2015] [Indexed: 12/18/2022]
Abstract
Health care provision is increasingly focused on the prediction of patients' individual risk for developing a particular health outcome in planning further tests and treatments. There has been a steady increase in the development and publication of prognostic models for various maternal and fetal outcomes in obstetrics. We undertook a systematic review to give an overview of the current status of available prognostic models in obstetrics in the context of their potential advantages and the process of developing and validating models. Important aspects to consider when assessing a prognostic model are discussed and recommendations on how to proceed on this within the obstetric domain are given. We searched MEDLINE (up to July 2012) for articles developing prognostic models in obstetrics. We identified 177 papers that reported the development of 263 prognostic models for 40 different outcomes. The most frequently predicted outcomes were preeclampsia (n = 69), preterm delivery (n = 63), mode of delivery (n = 22), gestational hypertension (n = 11), and small-for-gestational-age infants (n = 10). The performance of newer models was generally not better than that of older models predicting the same outcome. The most important measures of predictive accuracy (ie, a model's discrimination and calibration) were often (82.9%, 218/263) not both assessed. Very few developed models were validated in data other than the development data (8.7%, 23/263). Only two-thirds of the papers (62.4%, 164/263) presented the model such that validation in other populations was possible, and the clinical applicability was discussed in only 11.0% (29/263). The impact of developed models on clinical practice was unknown. We identified a large number of prognostic models in obstetrics, but there is relatively little evidence about their performance, impact, and usefulness in clinical practice so that at this point, clinical implementation cannot be recommended. New efforts should be directed toward evaluating the performance and impact of the existing models.
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Morken NH, Källen K, Jacobsson B. Predicting risk of spontaneous preterm delivery in women with a singleton pregnancy. Paediatr Perinat Epidemiol 2014; 28:11-22. [PMID: 24118026 DOI: 10.1111/ppe.12087] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
BACKGROUND Prediction of a woman's risk of a spontaneous preterm delivery (PTD) is a core challenge and an unresolved problem in today's obstetric practice. The objective of this study was to develop prediction models for spontaneous PTD (<37 weeks). METHODS A population-based register study of women born in Sweden with spontaneous onset of delivery was designed using Swedish Medical Birth Register data for 1992-2008. Predictive variables were identified by multiple logistic regression analysis, and outputs were used to calculate adjusted likelihood ratios in primiparous (n = 199 272) and multiparous (n = 249 580) singleton pregnant women. The predictive ability of each model was validated in a separate test sample for primiparous (n = 190 936) and multiparous (n = 239 203) women, respectively. RESULTS For multiparous women, the area under the ROC curve (AUC) of 0.74 [95% confidence interval (CI) 0.73, 0.74] indicated a satisfying performance of the model, while for primiparous women, it was rather poor {AUC: 0.58 [95% CI 0.57, 0.58]}. For both primiparous and multiparous women, the prediction models were quite good for pregnancies with comparatively low risk for spontaneous PTD, whereas more limited to predict pregnancies with ≥30% risk of spontaneous PTD. CONCLUSIONS Spontaneous PTD is difficult to predict in multiparous women and nearly impossible in primiparous, by using this statistical method in a large and unselected sample. However, adding clinical data (like cervical length) may in the future further improve its predictive performance.
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Affiliation(s)
- Nils-Halvdan Morken
- Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway; National Institute of Environmental Health Sciences, Epidemiology Branch, Durham, NC; Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
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Faust O, Acharya UR, Tamura T. Formal Design Methods for Reliable Computer-Aided Diagnosis: A Review. IEEE Rev Biomed Eng 2012; 5:15-28. [DOI: 10.1109/rbme.2012.2184750] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Czabanski R, Jezewski M, Wrobel J, Jezewski J, Horoba K. Predicting the Risk of Low-Fetal Birth Weight From Cardiotocographic Signals Using ANBLIR System With Deterministic Annealing and ${\bm \varepsilon}$ -Insensitive Learning. ACTA ACUST UNITED AC 2010; 14:1062-74. [DOI: 10.1109/titb.2009.2039644] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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