Maternal characteristics and induction methodologies as predictors of delivery outcomes: A retrospective analysis of inductions of labor.

IF 2.4 3区 医学 Q2 OBSTETRICS & GYNECOLOGY
Mena Abdalla, Salma Jabak, Aisha Hameed
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引用次数: 0

Abstract

Objective: To analyze maternal characteristics, induction methods, and their association with delivery outcomes in women undergoing induction of labor (IOL).

Methods: We conducted a retrospective analysis of 947 women who underwent IOL at the Princess Royal University Hospital during the period from April 2018 to April 2019. Out of 4316 total births during the study period, 947 women underwent IOL and were included in our analysis. Statistical analyses were performed using STATA version 17.0, with appropriate statistical tests including χ2 tests, t tests, and logistic regression models.

Results: The overall vaginal delivery rate was 71.7% (n = 679) (51.4% (n = 487) spontaneous, 20.3% (n = 192) instrumental), with 28.3% (n = 268) requiring cesarean section. Nulliparity was significantly associated with higher cesarean rates compared with multiparity (39.1% (n = 101/259) versus 24.3% (n = 167/688), P < 0.001; odds ratio [OR] 2.01, 95% confidence interval [CI] 1.52-2.66). Women with a body mass index (BMI; calculated as weight in kilograms divided by the square of height in meters) of 30 or greater had significantly higher cesarean section rates than those with normal BMI (35.4% (n = 91/257) versus 23.2% (n = 94/406), P < 0.001; OR 1.82, 95% CI 1.34-2.47). The mean time from induction to birth was 28.4 h (range 1.05-71.78 h). Logistic regression analysis identified parity (OR 0.61, 95% CI 0.49-0.76), BMI (OR 1.06, 95% CI 1.03-1.09), and maternal age (OR 1.03, 95% CI 1.01-1.05) as independent predictors of cesarean delivery.

Conclusion: Maternal characteristics, particularly parity and BMI, significantly influence delivery outcomes following IOL. Understanding these factors can help optimize patient selection, counseling, and management strategies for IOL. Although statistically significant associations were identified, the clinical significance of some factors requires careful interpretation in individual patient counseling.

Registration number: Clinical Audit Committee Registration Number: 615/23052025.

产妇特征和引产方法作为分娩结果的预测因素:引产的回顾性分析。
目的:分析人工智能(IOL)产妇特征、引产方法及其与分娩结局的关系。方法:回顾性分析2018年4月至2019年4月在皇家公主大学医院接受人工晶状体手术的947例女性患者。在研究期间的4316例分娩中,947名妇女接受了人工晶状体植入,并被纳入我们的分析。采用STATA 17.0版本进行统计分析,采用χ2检验、t检验和logistic回归模型进行相应的统计检验。结果:总阴道分娩率为71.7% (n = 679),其中自然分娩率为51.4% (n = 487),顺产率为20.3% (n = 192),需要剖宫产率为28.3% (n = 268)。与多胎相比,无胎与更高的剖宫产率显著相关(39.1% (n = 101/259)对24.3% (n = 167/688)。结论:母体特征,特别是胎次和BMI显著影响IOL后的分娩结果。了解这些因素有助于优化人工晶状体的患者选择、咨询和管理策略。虽然统计上的显著关联被确定,但一些因素的临床意义需要在个别患者咨询中仔细解释。注册号:临床审核委员会注册号:615/23052025。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.80
自引率
2.60%
发文量
493
审稿时长
3-6 weeks
期刊介绍: The International Journal of Gynecology & Obstetrics publishes articles on all aspects of basic and clinical research in the fields of obstetrics and gynecology and related subjects, with emphasis on matters of worldwide interest.
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