Risk of obstetric anal sphincter injuries at the time of admission for delivery: A clinical prediction model.
Date
2022-11
Journal Title
Journal ISSN
Volume Title
Repository Usage Stats
views
downloads
Citation Stats
Abstract
Objective
To develop and validate a model to predict obstetric anal sphincter injuries (OASIS) using only information available at the time of admission for labour.Design
A clinical predictive model using a retrospective cohort.Setting
A US health system containing one community and one tertiary hospital.Sample
A total of 22 873 pregnancy episodes with in-hospital delivery at or beyond 21 weeks of gestation.Methods
Thirty antepartum risk factors were identified as candidate variables, and a prediction model was built using logistic regression predicting OASIS versus no OASIS. Models were fit using the overall study population and separately using hospital-specific cohorts. Bootstrapping was used for internal validation and external cross-validation was performed between the two hospital cohorts.Main outcome measures
Model performance was estimated using the bias-corrected concordance index (c-index), calibration plots and decision curves.Results
Fifteen risk factors were retained in the final model. Decreasing parity, previous caesarean birth and cardiovascular disease increased risk of OASIS, whereas tobacco use and black race decreased risk. The final model from the total study population had good discrimination (c-index 0.77, 95% confidence interval [CI] 0.75-0.78) and was able to accurately predict risks between 0 and 35%, where average risk for OASIS was 3%. The site-specific model fit using patients only from the tertiary hospital had c-stat 0.74 (95% CI 0.72-0.77) on community hospital patients, and the community hospital model was 0.77 (95%CI 0.76-0.80) on the tertiary hospital patients.Conclusions
OASIS can be accurately predicted based on variables known at the time of admission for labour. These predictions could be useful for selectively implementing OASIS prevention strategies.Type
Department
Description
Provenance
Citation
Permalink
Published Version (Please cite this version)
Publication Info
Luchristt, Douglas, Ana Rebecca Meekins, Congwen Zhao, Chad Grotegut, Nazema Y Siddiqui, Brooke Alhanti and John Eric Jelovsek (2022). Risk of obstetric anal sphincter injuries at the time of admission for delivery: A clinical prediction model. BJOG : an international journal of obstetrics and gynaecology, 129(12). pp. 2062–2069. 10.1111/1471-0528.17239 Retrieved from https://hdl.handle.net/10161/27474.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
Collections
Scholars@Duke
Nazema Yusuf Siddiqui
Research on pelvic floor disorders. Specific interests include: 1) studying the urinary microbiome in aging, recurrent urinary tract infections, and overactive bladder; 2) pathophysiology of overactive bladder with particular emphasis on translational biology.
Brooke Alhanti
John E Jelovsek
Dr. Jelovsek is the F. Bayard Carter Distinguished Professor of OBGYN at Duke University and serves as Director of Data Science for Women’s Health. He is Board Certified in OBGYN by the American Board of OBGYN and in Female Pelvic Medicine & Reconstructive Surgery by the American Board of OBGYN and American Board of Urology. He has an active surgical practice in urogynecology based out of Duke Raleigh. He has expertise as a clinician-scientist in developing and evaluating clinical prediction models using traditional biostatistics and machine learning approaches. These “individualized” patient-centered prediction tools aim to improve decision-making regarding the prevention of lower urinary tract symptoms (LUTS) and other pelvic floor disorders after childbirth (PMID:29056536), de novo stress urinary incontinence and other patient-perceived outcomes after pelvic organ prolapse surgery, risk of transfusion during gynecologic surgery, and urinary outcomes after mid-urethral sling surgery (PMID: 26942362). He also has significant expertise in leading trans-disciplinary teams through NIH-funded multi-center research networks and international settings. As alternate-PI for the Cleveland Clinic site in the NICHD Pelvic Floor Disorders Network, he was principal investigator on the CAPABLe trial (PMID: 31320277), one of the largest multi-center trials for fecal incontinence studying anal exercises with biofeedback and loperamide for the treatment of fecal incontinence. He was the principal investigator of the E-OPTIMAL study (PMID: 29677302), describing the long-term follow up sacrospinous ligament fixation compared to uterosacral ligament suspension for apical vaginal prolapse. He was also primary author on research establishing the minimum important clinical difference for commonly used measures of fecal incontinence. Currently, he serves as co-PI in the NIDDK Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) (U01DK097780-05) where he has been involved in studies in the development of Symptoms of Lower Urinary Tract Dysfunction Research Network Symptom Index-29 (LURN SI-29) and LURN SI-10 questionnaires for men and women with LUTS. He is also the site-PI for the PREMIER trial (1R01HD105892): Patient-Centered Outcomes of Sacrocolpopexy versus Uterosacral Ligament Suspension for the Treatment of Uterovaginal Prolapse.
Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.