Predicting perineal trauma during childbirth using data from a general obstetric population

Abstract

Background: Perineal trauma is a common complication of childbirth and can have serious impacts on long-term health. Few studies have examined the combined effect of multiple risk factors. We developed and internally validated a risk prediction model to predict third and fourth degree perineal tears using data from a general obstetric population. Methods: Risk prediction model using data from all singleton vaginal deliveries at Cork University Maternity Hospital (CUMH), Ireland during 2019 and 2020. Third/fourth degree tears were diagnosed by an obstetrician or midwife at time of birth and defined as tears that extended into the anal sphincter complex or involved both the anal sphincter complex and anorectal mucosa. We used univariable and multivariable logistic regression with backward stepwise selection to develop the models. Candidate predictors included infant sex, maternal age, maternal body mass index, parity, mode of delivery, birthweight, post-term delivery, induction of labour and public/private antenatal care. We used the receiver operating characteristic (ROC) curve C-statistic to assess discrimination, and bootstrapping techniques were used to assess internal validation. Results: Of 8,403 singleton vaginal deliveries, 8,367 (99.54%) had complete data on predictors for model development. A total of 128 women (1.53%) had a third/fourth degree tear. Three variables remained in the final model: nulliparity, mode of delivery (specifically forceps delivery or ventouse delivery) and increasing birthweight (per 100 gram increase) (C-statistic: 0.75, 95% CI: 0.71, 0.79). We developed a nomogram to calculate individualised risk of third/fourth degree tears using these predictors. Bootstrapping indicated good internal performance. Conclusions: Use of our nomogram can provide an individualised risk assessment of third/fourth degree tears and potentially aid counselling of women on their potential risk.

Predicting perineal trauma during childbirth using data from a general obstetric population

“…Mother and child characteristics of study participants who did and did not sustain a third/fourth degree tear are outlined in Table 1. Obstetric characteristics of study participants by parity and results of univariable analysis are shown in Tables A1 and A2 as Extended data 8 , with nulliparity, mode of delivery, increasing birthweight and post-term delivery significantly associated with an increased risk of third/fourth degree tears. These variables were used in the multivariable logistic regression with backward stepwise selection to develop the prediction model for third/fourth degree tears.…”

“…There was little difference between the shape of the linear function for birthweight compared to the spline function using 3 and 4 knots, while 5 knots overfit the data (Figure A1, found as Extended data 8 ). The AIC and BIC statistics were lowest for the linear function; therefore, birthweight was analysed as a linear function.…”

“…We obtained ethical approval from the Clinical Research Ethics Committee of the Cork Teaching Hospitals (CREC) (ECM4(v)09/04/2020) in June 2020. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklist was used as a guideline for reporting our study 8 (available here).…”

“…There is some miscalibration at the individual level in the higher risk individuals as suggested by the LOWESS smoother. However, there is very little data at the higher risk probabilities as indicated by the spike plot towards the bottom of the graph (Figure A2, found as Extended data 8 ).…”

Accessing Anonymised Data from Ireland’s Maternal and Newborn Clinical Management System for Research Purposes

“…Furthermore, the MN-CMS provides opportunity for new data-driven discovery to answer a broad range of research questions on maternal and child health 3 . For example, using anonymised MN-CMS data, we previously developed and validated prediction models examining a combination of risk factors to predict postpartum haemorrhage (PPH) and third-and fourth-degree perineal tears in a general obstetric population 4,5 . Additionally, other studies have used MN-CMS data to assess risk factors for antenatal pyelonephritis 6 , to examine the impact of an electronic health record on task time distribution in a Neonatal Intensive Care Unit 7 and to examine institutional rates of unsuccessful operative vaginal delivery in a tertiary level maternity hospital in Ireland 8 .…”