Clinical and experimental rheumatology · 2023
Artificial neural network and deep learning in Sjögren's disease: where we are and where we are going.
Редакционная статьяНазвание и аннотация: Перевод пока не подготовлен
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Chisholm S A, Unemo M, Quaye N, Johansson E, Cole M J, Ison C A, Van de Laar M J
Аннотация
BACKGROUND: Labor epidural analgesia (LEA) is the gold standard for labor pain management, yet its association with intrapartum cesarean delivery (CD) remains controversial. This study aimed to identify risk factors and develop a predictive model for CD conversion in parturients receiving LEA.
METHODS: A retrospective cohort study analyzed 1,896 parturients receiving LEA at Zhengzhou Central Hospital (2022-2023). Participants were categorized into vaginal delivery group (n = 1, 541) and cesarean delivery group (n = 355). The original dataset was randomly split into training/testing sets (7:3 ratio) for analytical purposes. Univariate analysis identified significant variables, followed by Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression to construct a predictive nomogram. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).
RESULTS: The CD conversion rate was 18.7%. Eight independent predictors were identified: height, pre-pregnancy body mass index (BMI), gestational age ≥ 40 weeks, hypertension, meperidine use, cervical dilation at analgesia initiation, intrapartum fever (≥ 38.0 °C), and estimated fetal weight. The nomogram demonstrated good discrimination (training set area under the curve (AUC) = 0.729, 95% confidence interval (CI) [0.695-0.763]; testing set AUC = 0.722, 95% CI [0.670-0.774]) and calibration (Hosmer-Lemeshow P > 0.05). DCA confirmed clinical utility.
CONCLUSIONS: This model provides a tool for individualized labor management that aims to reduce unnecessary cesarean deliveries.
METHODS: A retrospective cohort study analyzed 1,896 parturients receiving LEA at Zhengzhou Central Hospital (2022-2023). Participants were categorized into vaginal delivery group (n = 1, 541) and cesarean delivery group (n = 355). The original dataset was randomly split into training/testing sets (7:3 ratio) for analytical purposes. Univariate analysis identified significant variables, followed by Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression to construct a predictive nomogram. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).
RESULTS: The CD conversion rate was 18.7%. Eight independent predictors were identified: height, pre-pregnancy body mass index (BMI), gestational age ≥ 40 weeks, hypertension, meperidine use, cervical dilation at analgesia initiation, intrapartum fever (≥ 38.0 °C), and estimated fetal weight. The nomogram demonstrated good discrimination (training set area under the curve (AUC) = 0.729, 95% confidence interval (CI) [0.695-0.763]; testing set AUC = 0.722, 95% CI [0.670-0.774]) and calibration (Hosmer-Lemeshow P > 0.05). DCA confirmed clinical utility.
CONCLUSIONS: This model provides a tool for individualized labor management that aims to reduce unnecessary cesarean deliveries.