Establishment and validation of a clinical prediction model for predicting early postpartum pelvic floor muscle weakness among primiparous women after vaginal delivery: a retrospective study.
Huan Dong, Xiaolei Chi, Ye Liu, Wenjuan Liu, Xinliang Chen, Xianjing Wang, Ping Liu
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引用次数: 0
Abstract
Background: Pelvic floor muscle weakness (PFMW) is a significant postpartum complication linked to pelvic floor dysfunction. PFMW impairs quality of life and requires early intervention. This study aimed to develop and validate a clinical prediction model for early postpartum PFMW in primiparous women after vaginal delivery.
Methods: This retrospective cohort study was conducted at a tertiary maternity hospital in Shanghai, China. Primiparous women with vaginal deliveries (July 2021-December 2023) were enrolled. Participants were assessed for PFMW using pelvic floor surface electromyography (sEMG) via the Glazer protocol at 42-90 days postpartum. Maternal and obstetric predictors were analyzed via univariable and multivariable logistic regression to construct a nomogram. Model performance was evaluated using concordance statistics (C-statistics), calibration curves, and decision curve analysis in both the training (n = 2,465) and validation (n = 1,049) cohorts. Internal validation was performed via ten-fold cross-validation.
Results: Among 3,514 enrolled women, PFMW occurred in 25.55% (898/3,514), with comparable baseline characteristics between cohorts (age, pre-pregnancy BMI; P > 0.05). Multivariable analysis revealed five independent predictors: maternal age (OR 1.156, 95% CI 1.116-1.999), gestational weight gain (OR 1.146, 95% CI 1.116-1.178), instrumental delivery (forceps: OR 1.904, 95% CI 1.336-2.714), prolonged second stage of labor (OR 1.026, 95% CI 1.022-1.029), and infant weight (OR 1.003, 95% CI 1.002-1.003). The nomogram demonstrated strong discrimination [C-statistic: 0.866 (95% CI 0.850-0.882) in the training cohort; 0.870 (0.819-0.903) in the validation cohort] and good calibration. Decision curve analysis confirmed the clinical utility across threshold probabilities (0-0.3).
Conclusion: This study established a validated nomogram integrating maternal and obstetric factors to predict early postpartum PFMW in primiparous women after vaginal delivery. This tool may aid in the early identification of high-risk individuals, enabling targeted rehabilitation to mitigate long-term pelvic floor dysfunction.
背景：盆底肌无力（PFMW）是一种与盆底功能障碍相关的重要产后并发症。PFMW损害生活质量，需要早期干预。本研究旨在建立并验证阴道分娩后初产妇产后早期PFMW的临床预测模型。方法：本回顾性队列研究在中国上海的一家三级妇产医院进行。纳入了阴道分娩的初产妇（2021年7月至2023年12月）。在产后42-90天，通过格雷泽方案使用盆底表面肌电图（sEMG）评估参与者的PFMW。通过单变量和多变量logistic回归分析孕产妇和产科预测因子以构建nomogram。在训练队列（n = 2465）和验证队列（n = 1049）中，采用一致性统计（C-statistics）、校准曲线和决策曲线分析来评估模型的性能。通过十倍交叉验证进行内部验证。结果：在3,514名入组妇女中，PFMW发生率为25.55%(898/3,514)，各组间基线特征（年龄、孕前BMI； P < 0.05）具有可比性。多变量分析显示五个独立预测因素：产妇年龄（OR 1.156, 95% CI 1.116-1.999）、妊娠体重增加（OR 1.146, 95% CI 1.116-1.178）、器械分娩（OR 1.904, 95% CI 1.336-2.714）、第二产程延长（OR 1.026, 95% CI 1.022-1.029）和婴儿体重（OR 1.003, 95% CI 1.002-1.003）。nomogram显示出很强的鉴别性[c -统计量：0.866 (95% CI 0.850-0.882)；[0.870(0.819-0.903)]，校准良好。决策曲线分析证实了跨阈值概率（0-0.3）的临床效用。结论：本研究建立了一种有效的结合孕产妇和产科因素的nomogram预测阴道分娩后初产妇早期产后PFMW。该工具可以帮助早期识别高危个体，使有针对性的康复减轻长期盆底功能障碍。
期刊介绍：
Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate
- the use of patient-reported outcomes under real world conditions
- the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines
- the scientific bases for guidelines and decisions from regulatory authorities
- access to medicinal products and medical devices worldwide
- addressing the grand health challenges around the world
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