Browsing by Subject "Parity"

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    Prediction models for postpartum urinary and fecal incontinence in primiparous women.
    (Female pelvic medicine & reconstructive surgery, 2013-03) Jelovsek, J Eric; Piccorelli, Annalisa; Barber, Matthew D; Tunitsky-Bitton, Elena; Kattan, Michael W
    OBJECTIVES: This study aimed to develop and internally validate a nomogram that facilitates decision making between patient and physician by predicting a woman's individual probability of developing urinary (UI) or fecal incontinence (FI) after her first delivery. METHODS: This study used Childbirth and Pelvic Symptoms Study data, which estimated the prevalence of postpartum UI and FI in primiparous women after vaginal or cesarean delivery. Two models were developed using antepartum variables, and 2 models were developed using antepartum plus labor and delivery variables. Urinary incontinence was defined by a response of leaking urine "sometimes" or "often" using the Medical, Epidemiological, and Social Aspects of Aging Questionnaire. Fecal incontinence was defined as any involuntary leakage of mucus, liquid, or solid stool using the Fecal Incontinence Severity Index. Logistic regression models allowing nonlinear effects were used and displayed as nomograms. Overall performance was assessed using the Brier score (zero equals perfect model) and concordance index (c-statistic). RESULTS: A total of 921 women enrolled in the Childbirth and Pelvic Symptoms Study, and 759 (82%) were interviewed by telephone 6 months postpartum. Two antepartum models were generated, which discriminated between women who will and will not develop UI (Brier score = 0.19, c-statistic = 0.69) and FI (Brier score = 0.10, c-statistic = 0.67) at 6 months and 2 models were generated (Brier score = 0.18, c-statistic= 0.68 and Brier score = 0.09, c-statistic = 0.68) for predicting UI and FI, respectively, for use after labor and delivery. CONCLUSIONS: These models yielded 4 nomograms that are accurate for generating individualized prognostic estimates of postpartum UI and FI and may facilitate decision making in the prevention of incontinence.
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    ItemOpen Access
    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, 2022-11) Luchristt, Douglas; Meekins, Ana Rebecca; Zhao, Congwen; Grotegut, Chad; Siddiqui, Nazema Y; Alhanti, Brooke; Jelovsek, John Eric

    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.