Risk of obstetric anal sphincter injuries at the time of admission for delivery: A clinical prediction model

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.

Automated summary

Obstetric anal sphincter injuries (OASIS) can be accurately predicted based on information available at the time of admission for labour, and these predictions could be useful for selectively implementing prevention strategies.