Background: Stress urinary incontinence (SUI), the prevalent form of urinary incontinence, significantly impairs women's quality of life. This study aims to create a visual nomogram to estimate the risk of SUI within one year postpartum for early intervention in high-risk Chinese women.

Methods: We recruited 1,531 postpartum women who gave birth at two hospitals in Kunshan City from 2021 to 2022. Delivery details were meticulously extracted from the hospitals' medical records system, while one-year postpartum follow-ups were conducted via phone surveys specifically designed to ascertain SUI status. Utilizing data from one hospital as the training set, logistic regression analysis was performed to pinpoint significant factors and subsequently construct the nomogram. To ensure robustness, an independent dataset sourced from the second hospital served as the external validation cohort. The model's performance was rigorously evaluated using calibration plots, ROC curves, AUC values, and DCA curves.

Results: The study population was 1,125 women. The SUI incidence within one year postpartum was 26% (293/1125). According to the regression analysis, height, pre-pregnancy BMI, method of induction, mode of delivery, perineal condition, neonatal weight, SUI during pregnancy, and SUI during the first pregnancy were incorporated into the nomogram. The AUC of the nomogram was 0.829 (95% CI 0.790-0.867), and the external validation set was 0.746 (95% CI 0.689-0.804). Subgroup analysis based on parity showed good discrimination. The calibration curve indicated concordance. The DCA curve showed a significant net benefit.

Conclusion: Drawing from real-world data, we have successfully developed an SUI predictive model tailored for postpartum Chinese women. Upon successful external validation, this model holds immense potential as an effective screening tool for SUI, enabling timely interventions and ultimately may improve women's quality of life.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11430263PMC
http://dx.doi.org/10.1186/s12905-024-03363-xDOI Listing

Publication Analysis

Top Keywords

external validation
16
urinary incontinence
12
predictive model
8
stress urinary
8
chinese women
8
sui
8
women's quality
8
quality life
8
year postpartum
8
regression analysis
8

Similar Publications

A Simple Machine Learning-Based Quantitative Structure-Activity Relationship Model for Predicting pIC Inhibition Values of FLT3 Tyrosine Kinase.

Pharmaceuticals (Basel)

January 2025

Centro de Química Médica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago 7780272, Chile.

Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure-activity relationship (QSAR) model to predict the inhibitory potency (pIC values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting.

View Article and Find Full Text PDF

: New-onset postoperative atrial fibrillation (POAF) is the most common complication after cardiac surgery, occurring approximately in one-third of the patients. This study considered all-comer patients who underwent cardiac surgery to build a predictive model for POAF. : A total of 3467 (Center 1) consecutive patients were used as a derivation cohort to build the model.

View Article and Find Full Text PDF

: A prediction model for anatomical cystocele recurrence after native tissue repair was developed and internally validated in 2016. This model estimates a patients' individual risk of recurrence and can be used for counseling. Before implementation in urogynecological clinical practice, external validation is needed.

View Article and Find Full Text PDF

Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is a frequently occurring mutation in non-small-cell lung cancer (NSCLC) and influences cancer treatment and disease progression. In this study, a machine learning (ML) pipeline was applied to radiomic features extracted from public and internal CT images to identify KRAS mutations in NSCLC patients. Both datasets were analyzed using parametric ( test) and non-parametric statistical tests (Mann-Whitney U test) and dimensionality reduction techniques.

View Article and Find Full Text PDF

: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the risk of occult cancer between 30 days and 24 months after a venous thrombotic event using machine learning (ML). : We designed a case-control study nested in a cohort of patients with VTE included in a prospective registry from two Spanish hospitals between 2005 and 2021.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!