A Clinical Risk Model to Predict Obstetric Anal Sphincter Injuries in Laboring Patients

• Published in: Female pelvic medicine & reconstructive surgery Vol. 26; no. 8; pp. 520 - 525
• Main Authors: Woo, Victoria G., Hung, Yun-Yi, Ritterman-Weintraub, Miranda L., Painter, Caitlyn E., Ramm, Olga
• Format: Journal Article
• Language: English
• Published: United States Lippincott Williams & Wilkins 01-08-2020; Copyright American Urogynecologic Society. All rights reserved
• ISSN: 2151-8378; 2154-4212
• DOI: 10.1097/SPV.0000000000000777

OBJECTIVEOur objective was to develop a risk stratification tool for predicting obstetric anal sphincter injury risk in women during labor.
METHODSIn this retrospective cohort study of singleton deliveries within Kaiser Permanente Northern California, a predictive model for anal sphincter injury was developed within the 2013 birth cohort and validated in the 2014 birth cohort of 22,741 births. Predictors of obstetric anal sphincter injury were identified using multiple regression analysis and used to create a risk calculator tool based on effect size and clinical judgment.
RESULTSDuration of second stage of labor, vacuum delivery, history of anal sphincter injury, maternal and gestational ages, and maternal race and ethnicity were associated with elevated risk of anal sphincter injury. Using these risk factors, we developed a validated parity-stratified scoring system. Among nulliparous women, 116 (1.3%) had a score of zero, corresponding to a 0.9% risk, and 1024 (11.7%) had a score higher than 6, corresponding to a 27.5% risk of anal sphincter injury. Among the multiparous women, 1181 (8.4%) had a score of zero, corresponding to a 0.1% risk, and the 260 (1.9%) with a score higher than 10 had a 7.7% risk. The predictive ability of the score derived based on the 2013 data was applied to the 2014 data, and results showed that the predictive abilities were statistically similar, except for one subgroupmultiparous women with a total score of ≥10.
CONCLUSIONSOur anal sphincter injury risk stratification tool effectively predicts individual risk and can inform clinician and patient decision making to minimize maternal birth trauma.