Machine learning models for predicting preeclampsia: a systematic review.

BACKGROUND: This systematic review provides an overview of machine learning (ML) approaches for predicting preeclampsia. METHOD: This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. We searched the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus, and Google Scholar up to February 2023. Search terms were limited to "preeclampsia" AND "artificial intelligence" OR "machine learning" OR "deep learning." All studies that used ML-based analysis for predicting preeclampsia in pregnant women were considered. Non-English articles and those that are unrelated to the topic were excluded. The PROBAST was used to assess the risk of bias and applicability of each included study. RESULTS: The search strategy yielded 128 citations; after duplicates were removed and title and abstract screening was completed, 18 full-text articles were evaluated for eligibility. Four studies were included in this review. Two studies were at low risk of bias, and two had low to moderate risk. All of the study designs included were retrospective cohort studies. Nine distinct models were chosen as ML models from the four studies. Maternal characteristics, medical history, medication intake, obstetrical history, and laboratory and ultrasound findings obtained during prenatal care visits were candidate predictors to train the ML model. Elastic net, stochastic gradient boosting, extreme gradient boosting, and Random forest were among the best models to predict preeclampsia. All four studies used metrics such as the area under the curve, true positive rate, negative positive rate, accuracy, precision, recall, and F1 score. The AUC of ML models varied from 0.860 to 0.973 in four studies. CONCLUSION: The results of studies yielded high prediction performance of ML models for preeclampsia risk from routine early pregnancy information.

Machine learning-based approach for predicting low birth weight.

BACKGROUND: Low birth weight (LBW) has been linked to infant mortality. Predicting LBW is a valuable preventative tool and predictor of newborn health risks. The current study employed a machine learning model to predict LBW. METHODS: This study implemented predictive LBW models based on the data obtained from the "Iranian Maternal and Neonatal Network (IMaN Net)" from January 2020 to January 2022. Women with singleton pregnancies above the gestational age of 24 weeks were included. Exclusion criteria included multiple pregnancies and fetal anomalies. A predictive model was built using eight statistical learning models (logistic regression, decision tree classification, random forest classification, deep learning feedforward, extreme gradient boost model, light gradient boost model, support vector machine, and permutation feature classification with k-nearest neighbors). Expert opinion and prior observational cohorts were used to select candidate LBW predictors for all models. The area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score were measured to evaluate their diagnostic performance. RESULTS: We found 1280 women with a recorded LBW out of 8853 deliveries, for a frequency of 14.5%. Deep learning (AUROC: 0.86), random forest classification (AUROC: 0.79), and extreme gradient boost classification (AUROC: 0.79) all have higher AUROC and perform better than others. When the other performance parameters of the models mentioned above with higher AUROC were compared, the extreme gradient boost model was the best model to predict LBW with an accuracy of 0.79, precision of 0.87, recall of 0.69, and F1 score of 0.77. According to the feature importance rank, gestational age and prior history of LBW were the top critical predictors. CONCLUSIONS: Although this study found that the extreme gradient boost model performed well in predicting LBW, more research is needed to make a better conclusion on the performance of ML models in predicting LBW.

Manifestation of Spontaneous Rupture of the Urinary Bladder in Pregnancy: A Systematic Review of the Literature.

Spontaneous rupture of the urinary bladder (SRUB) during pregnancy is a potentially fatal event that necessitates immediate surgery. The aim of this systematic review is to determine the symptoms, causes, associated factors, and prognosis of SRUB in pregnancy. We searched the literature from inception until December 2022 using the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus, and Google Scholar. Articles not in English and those unrelated to the topic were excluded. The JBI Critical Appraisal Checklist for case reports was employed for the risk of bias assessment. The search strategy yielded 312 citations; 29 full-text articles were evaluated for eligibility after screening. Five case reports were included in this review. The age range of the cases was 27-39 years. Four cases were in their second trimester of pregnancy, and one was in her third. Two cases had undergone previous cesarean sections, and one had an appendectomy and salpingectomy due to an ectopic pregnancy. One case reported a history of alcohol and drug abuse. The most common symptoms were abdominal pain, abdominal distension, oliguria, voiding difficulty, hematuria, fever, and vomiting. The diagnosis of SRUB was primarily made via emergency laparotomy due to the patients' critical conditions. Beyond its diagnostic role, laparotomy was also the treatment method in all cases. Tear repair, antibiotic therapy, and urinary catheterization were all integral parts of the treatment. Four cases resulted in an uneventful pregnancy and a healthy, full-term baby. In one case, a hysterectomy was performed due to a combined uterus and bladder rupture. SRUB often presents with non-specific symptoms, leading to a delayed diagnosis. A high index of suspicion is essential when a pregnant woman exhibits urinary symptoms and severe abdominal pain, especially in those with a history of previous surgery. Early detection and treatment of SRUB are critical for an uneventful recovery.

Maternal and Neonatal Outcomes of Sub-clinical Hypothyroidism Treated With Levothyroxine in Pregnancy: A Retrospective Cohort Study.

INTRODUCTION: The effect of sub-clinical hypothyroidism (SCH) in pregnancy has been controversial. Furthermore, the impact of levothyroxine replacement on improving outcomes in pregnant women with SCH is unknown. This study aimed to assess the maternal and neonatal outcomes of pregnant women with SCH who were treated with levothyroxine replacement. METHODS: This retrospective chart review was conducted at a tertiary hospital in Iran between 2020 and 2022. All pregnant women who had given birth during the study period were recruited. Those who did not have thyroid function test results within 10-12 weeks, as well as those with SCH who did not have levothyroxine replacement, were excluded. The subjects were divided into two groups based on the 2017 American Thyroid Association (ATA) criteria: non-SCH (TSH values 0.27-2.5 mIU/L) and SCH (TSH values more than 4.0 mIU/L). The demographic, obstetric, maternal, and neonatal outcomes of both groups were compared. The Chi-square test was used to compare the categorical variables. Binary logistic regression was used to assess differences in categorical variables. RESULTS: With a frequency of 10.5%, 935 women out of 8888 were diagnosed with SCH. In terms of age, educational level, living residency, medical insurance, access to prenatal care, and smoking status, there were no differences between the two groups. In terms of gestational age, parity, onset of labor, history of infertility, hypertension, cardiovascular disease, anemia, and overt diabetes, there were no differences between the two groups; however, gestational diabetes was more common in those with SCH. Compared with the non-SCH group, the prevalence and risks of gestational diabetes [19.8 vs. 14.2, odds ratio (OR) = 1.14, 95% confidence interval (CI) = 1.72-3.95] were significantly higher in the SCH group after controlling for confounding factors. There were no differences in neonatal outcomes between the two groups. CONCLUSIONS: Except for gestational diabetes, we found no significant adverse events in terms of maternal and neonatal outcomes among women with SCH who were treated with levothyroxine.

Machine learning models for predicting pre-eclampsia: a systematic review protocol.

INTRODUCTION: Pre-eclampsia is one of the most serious clinical problems of pregnancy that contribute significantly to maternal mortality worldwide. This systematic review aims to identify and summarise the predictive factors of pre-eclampsia using machine learning models and evaluate the diagnostic accuracy of machine learning models in predicting pre-eclampsia. METHODS AND ANALYSIS: This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This search strategy includes the search for published studies from inception to January 2023. Databases include the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus and Google Scholar. Search terms include 'preeclampsia' AND 'artificial intelligence' OR 'machine learning' OR 'deep learning'. All studies that used machine learning-based analysis for predicting pre-eclampsia in pregnant women will be considered. Non-English articles and those that are unrelated to the topic will be excluded. PROBAST (Prediction model Risk Of Bias ASsessment Tool) will be used to assess the risk of bias and the applicability of each included study. ETHICS AND DISSEMINATION: Ethical approval is not required, as our review will include published and publicly accessible data. Findings from this review will be disseminated via publication in a peer-review journal. PROSPERO REGISTRATION NUMBER: This review is registered with PROSPERO (ID: CRD42023432415).

Machine Learning-Based Approach to Predict Intrauterine Growth Restriction.

INTRODUCTION: Creating a prediction model incorporating multiple risk factors for intrauterine growth restriction is vital. The current study employed a machine learning model to predict intrauterine growth restriction. METHODS: This cross-sectional study was carried out in a tertiary hospital in Bandar Abbas, Iran, from January 2020 to January 2022. Women with singleton pregnancies above the gestational age of 24 weeks who gave birth during the study period were included. Exclusion criteria included multiple pregnancies and fetal anomalies. Four statistical learning algorithms were used to build a predictive model: (1) Decision Tree Classification, (2) Random Forest Classification, (3) Deep Learning, and (4) the Gradient Boost Algorithm. The candidate predictors of intrauterine growth restriction for all models were chosen based on expert opinion and prior observational cohorts. To investigate the performance of each algorithm, some parameters, including the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and sensitivity, were assessed. RESULTS: Of 8683 women who gave birth during the study period, 712 were recorded as having intrauterine growth restriction, with a frequency of 8.19%. Comparing the performance parameters of different machine learning algorithms showed that among all four machine learning models, Deep Learning had the greatest performance to predict intrauterine growth restriction with an AUROC of 0.91 (95% confidence interval, 0.85-0.97). The importance of the variables revealed that drug addiction, previous history of intrauterine growth restriction, chronic hypertension, preeclampsia, maternal anemia, and COVID-19 were weighted factors in predicting intrauterine growth restriction. CONCLUSIONS: A machine learning model can be used to predict intrauterine growth restriction. The Deep Learning model is an accurate algorithm for predicting intrauterine growth restriction.

Application of machine learning to identify risk factors of birth asphyxia.

BACKGROUND: Developing a prediction model that incorporates several risk factors and accurately calculates the overall risk of birth asphyxia is necessary. The present study used a machine learning model to predict birth asphyxia. METHODS: Women who gave birth at a tertiary Hospital in Bandar Abbas, Iran, were retrospectively evaluated from January 2020 to January 2022. Data were extracted from the Iranian Maternal and Neonatal Network, a valid national system, by trained recorders using electronic medical records. Demographic factors, obstetric factors, and prenatal factors were obtained from patient records. Machine learning was used to identify the risk factors of birth asphyxia. Eight machine learning models were used in the study. To evaluate the diagnostic performance of each model, six metrics, including area under the receiver operating characteristic curve, accuracy, precision, sensitivity, specificity, and F1 score were measured in the test set. RESULTS: Of 8888 deliveries, we identified 380 women with a recorded birth asphyxia, giving a frequency of 4.3%. Random Forest Classification was found to be the best model to predict birth asphyxia with an accuracy of 0.99. The analysis of the importance of the variables showed that maternal chronic hypertension, maternal anemia, diabetes, drug addiction, gestational age, newborn weight, newborn sex, preeclampsia, placenta abruption, parity, intrauterine growth retardation, meconium amniotic fluid, mal-presentation, and delivery method were considered to be the weighted factors. CONCLUSION: Birth asphyxia can be predicted using a machine learning model. Random Forest Classification was found to be an accurate algorithm to predict birth asphyxia. More research should be done to analyze appropriate variables and prepare big data to determine the best model.

Prediction of postpartum hemorrhage using traditional statistical analysis and a machine learning approach.

BACKGROUND: Early detection of postpartum hemorrhage risk factors by healthcare providers during pregnancy and the postpartum period may allow healthcare providers to act to prevent it. Developing a prediction model that incorporates several risk factors and accurately calculates the overall risk for postpartum hemorrhage is necessary. OBJECTIVE: This study used a traditional analytical approach and a machine learning model to predict postpartum hemorrhage. STUDY DESIGN: Women who gave birth at the Khaleej-e-Fars Hospital in Bandar Abbas, Iran, were evaluated retrospectively between January 1, 2020, and January 1, 2022. These pregnant women were divided into 2 groups, namely those who had postpartum hemorrhage and those who did not. We used 2 approaches for the analysis. At the first level, we used the traditional analysis methods. Demographic factors, maternal comorbidities, and obstetrical factors were compared between the 2 groups. A bivariate logistic regression analysis of the risk factors for postpartum hemorrhage was done to estimate the crude odds ratios and their 95% confidence intervals. In the second level, we used machine learning approaches to predict postpartum hemorrhage. RESULTS: Of the 8888 deliveries, we identified 163 women with recorded postpartum hemorrhage, giving a frequency of 1.8%. According to a traditional analysis, factors associated with an increased risk for postpartum hemorrhage in a bivariate logistic regression analysis were living in a rural area (odds ratio, 1.41; 95% confidence interval, 1.08-1.98); primiparity (odds ratio, 3.16; 95% confidence interval, 1.90-4.75); mild to moderate anemia (odds ratio, 5.94; 95% confidence interval 2.81-8.34); severe anemia (odds ratio, 6.01; 95% confidence interval 3.89-11.09); abnormal placentation (odds ratio, 7.66; 95% confidence interval, 2.81-17.34); fetal macrosomia (odds ratio, 8.14; 95% confidence interval, 1.02-14.47); shoulder dystocia (odds ratio, 7.88; 95% confidence interval, 1.07-13.99); vacuum delivery (odds ratio, 2.01; 95% confidence interval, 1.15-5.98); cesarean delivery (odds ratio, 1.86; 95% confidence interval, 1.12-3.79); and general anesthesia during cesarean delivery (odds ratio, 7.66; 95 % confidence interval, 3.11-9.36). According to machine learning analysis, the top 5 algorithms were XGBoost regression (area under the receiver operating characteristic curve of 99%), XGBoost classification (area under the receiver operating characteristic curve of 98%), LightGBM (area under the receiver operating characteristic curve of 94%), random forest regression (area under the receiver operating characteristic curve of 86%), and linear regression (area under the receiver operating characteristic curve of 78%). However, after considering all performance parameters, the XGBoost classification was found to be the best model to predict postpartum hemorrhage. The importance of the variables in the linear regression model, similar to traditional analysis methods, revealed that macrosomia, general anesthesia, anemia, shoulder dystocia, and abnormal placentation were considered to be weighted factors, whereas XGBoost classification considered living residency, parity, cesarean delivery, education, and induced labor to be weighted factors. CONCLUSION: Risk factors for postpartum hemorrhage can be identified using traditional statistical analysis and a machine learning model. Machine learning models were a credible approach for improving postpartum hemorrhage prediction with high accuracy. More research should be conducted to analyze appropriate variables and prepare big data to determine the best model.

The Incidence and Outcomes of Late-Term Pregnancy.

BACKGROUND: Little is known about the outcomes of late-term pregnancy. In this study, we aim to assess the incidence and adverse prenatal outcomes associated with late-term pregnancy. METHODS: We retrospectively assessed all singleton pregnant mothers who gave birth at Khalij-e-Fars Hospital in Bandar Abbas, Iran, between January 2020 and 2022. All preterm and post-term deliveries were excluded. Mothers were divided into two groups: late-term mothers (41 0/7-41 6/7 weeks of gestation) and term mothers (37 0/7-40 6/7 weeks of gestation). Demographic factors, obstetric factors, maternal comorbidities, and prenatal outcomes were extracted from the electronic data of each mother. The incidence of late-term births was calculated. The chi-squared test was used to compare differences between the groups. Logistic regression models were used to assess the association of prenatal outcome with late-term pregnancy. RESULTS: There were 8,888 singleton deliveries during the study period, and 1,269 preterm and post-term pregnancies were ruled out. Of the 7,619 deliveries, 309 (4.1%) were late-term, while 7,310 (95.9%) were term. There were no sociodemographic differences between term and late-term mothers. The late-term group had a higher prevalence of primiparous mothers, and the term group had a higher prevalence of diabetes. Late-term mothers had a higher risk of macrosomia (adjusted odds ratio (aOR): 2.24 (95% confidence interval (CI): 1.34-3.01)), meconium amniotic fluid (aOR: 2.32 (95% CI: 1.59-3.14)), and fetal distress (aOR: 2.38 (95% CI: 1.54-2.79)). When compared to term pregnancy, the risk of low birth weight (LBW) was lower in late-term pregnancy (aOR: 0.69 (95% CI: 0.36-0.81)). CONCLUSIONS: Late-term pregnancy was found to be more likely to be associated with macrosomia, meconium amniotic fluid, and fetal distress, but serious maternal and neonatal adverse events were comparable to term pregnancy.

Machine learning approach to predict postpartum haemorrhage: a systematic review protocol.

INTRODUCTION: Postpartum haemorrhage (PPH) is the most serious clinical problem of childbirth that contributes significantly to maternal mortality worldwide. This systematic review aims to identify predictors of PPH based on a machine learning (ML) approach. METHODS AND ANALYSIS: This review adhered to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol. The review is scheduled to begin on 10 January 2023 and end on 20 March 2023. The main objective is to identify and summarise the predictive factors associated with PPH and propose an ML-based predictive algorithm. From inception to December 2022, a systematic search of the following electronic databases of peer-reviewed journal articles and online search records will be conducted: Cochrane Central Register, PubMed, EMBASE (via Ovid), Scopus, WOS, IEEE Xplore and the Google Scholar search engine. All studies that meet the following criteria will be considered: (1) they include the general population with a clear definition of the diagnosis of PPH; (2) they include ML models for predicting PPH with a clear description of the ML models; and (3) they demonstrate the performance of the ML models with metrics, including area under the receiver operating characteristic curve, accuracy, precision, sensitivity and specificity. Non-English language papers will be excluded. Data extraction will be performed independently by two investigators. The PROBAST, which includes a total of 20 signallings, will be used as a tool to assess the risk of bias and applicability of each included study. ETHICS AND DISSEMINATION: Ethical approval is not required, as our review will include published and publicly accessible data. Findings from this review will be disseminated via publication in a peer-review journal. PROSPERO REGISTRATION NUMBER: The protocol for this review was submitted at PROSPERO with ID number CRD42022354896.

The incidence and risk factors of meconium amniotic fluid in singleton pregnancies: an experience of a tertiary hospital in Iran.

BACKGROUND: Several common maternal or neonatal risk factors have been linked to meconium amniotic fluid (MAF) development; however, the results are contradictory, depending on the study. This study aimed to assess the prevalence and risk factors of MAF in singleton pregnancies. METHODS: This study is a retrospective cohort that assessed singleton pregnant mothers who gave birth at a tertiary hospital in Bandar Abbas, Iran, between January 1st, 2020, and January 1st, 2022. Mothers were divided into two groups: 1) those diagnosed with meconium amniotic fluid (MAF) and 2) those diagnosed with clear amniotic fluid. Mothers with bloody amniotic fluid were excluded. Demographic factors, obstetrical factors, and maternal comorbidities were extracted from the electronic data of each mother. The Chi-square test was used to compare differences between the groups for categorical variables. Logistic regression models were used to assess meconium amniotic fluid risk factors. RESULTS: Of 8888 singleton deliveries during the study period, 1085 (12.2%) were MAF. MAF was more common in adolescents, mothers with postterm pregnancy, and primiparous mothers, and it was less common in mothers with GDM and overt diabetes. The odds of having MAF in adolescents were three times higher than those in mothers 20-34 years old (aOR: 3.07, 95% CI: 1.87-4.98). Likewise, there were significantly increased odds of MAF in mothers with late-term pregnancy (aOR: 5.12, 95% CI: 2.76-8.94), and mothers with post-term pregnancy (aOR: 7.09, 95% CI: 3.92-9.80). Primiparous women were also more likely than multiparous mothers to have MAF (aOR: 3.41, 95% CI: 2.11-4.99). CONCLUSIONS: Adolescents, primiparous mothers, and mothers with post-term pregnancies had a higher risk of MAF. Maternal comorbidities resulting in early termination of pregnancy can reduce the incidence of MAF.

Spontaneous bladder rupture and associated factors during pregnancy:a systematic review and metanalysis protocol.

INTRODUCTION: Spontaneous bladder rupture during pregnancy is a potentially life-threatening event requiring immediate surgery to reduce morbidity and mortality. This systematic review aims to identify associated factors of spontaneous bladder rupture during pregnancy and propose a diagnostic and therapeutic algorithm. METHODS AND ANALYSIS: To improve the reporting of this protocol, the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 statement was used. The primary objective is to identify and summarise the associated factors with spontaneous bladder rupture during pregnancy. The secondary outcome was to determine the diagnostic and treatment approach. From inception to June 2022, a systematic search of the following electronic databases of peer-reviewed journal articles and online search records will be conducted: the Cochrane Central Register, PubMed, Medline (Via PubMed), Embase (Via Ovid), ProQuest, Scopus, WOS and search engine Google Scholar. All types of studies focusing on spontaneous bladder rupture during pregnancy will be included. Two authors will review the studies based on inclusion and exclusion criteria. Three authors will independently extract data using a researcher-created checklist. In the event of a disagreement, an external reviewer will be used. The Newcastle-Ottawa Scale checklist will be used by two authors to assess the quality of the studies independently. Data analysis will be carried out using STATA V.16. ETHICS AND DISSEMINATION: Ethical approval is not required, as our review will include published and publicly accessible data. Findings from this review will be disseminated via publication in a peer-review journal. PROSPERO REGISTRATION NUMBER: The protocol for this review was submitted at PROSPERO on 20 March 2022 with ID number CRD42022319511.

Comparison the efficacy of vaginal progesterone versus 17-alpha-hydroxyprogesterone caproate to prevent preterm birth in high-risk pregnant women undergo cerclage: a randomized clinical trial.

OBJECTIVE: The aim of this study was to compare the effect of vaginal progesterone with 17-alpha-hydroxyprogesterone caproate (17OHP-C) in prevention of preterm birth in high-risk pregnant women undergo cerclage. MATERIALS AND METHODS: This prospective randomized clinical trial registered in the Iranian Registry of Clinical Trials (IRCT20181107041585N4), was performed from May 2017 to August 2018 in Bandar Abbas, Iran. Fifty-eight eligible women who were scheduled for cervical cerclage due to a history of two or more previous preterm birth <28 weeks or a cervical length less than 25 mm with at least one previous preterm birth <34 weeks were randomly divided into two groups. The first group received 200 mg of vaginal progesterone suppository daily and the second one received 250 mg of 17OHP-C intramuscular weekly after cerclage procedure until the end of 36 weeks. Patients were followed up to the end of delivery and the newborn until the first 28 d after delivery. RESULTS: Gestational age at the time of birth in 17OHP-C group was significantly higher than vaginal progesterone group (p=.021). However, the incidence of preterm birth in both groups was not statistically significant (20.7% vs. 24.1%). Apgar scores, newborn birthweight, admission to neonatal intensive care unit (NICU), incidence of respiratory distress syndrome (RDS), sepsis, necrotizing enterocolitis (NEC), and, intraventricular hemorrhage (IVH), was similar in both groups. Adverse events were reported in 48.3% of patients in 17-OHP-C group, and 27.6% of patients in the vaginal progesterone group (p= .014). CONCLUSIONS: Vaginal progesterone and 17OHP-C had similar results in terms of prevention of preterm birth and neonatal outcome. However, the adverse events associated with 17-OHP-C were higher than vaginal progesterone.

Vaginal Laser Therapy for Stress Urinary Incontinence: A Systematic Review of Prospective Randomized Clinical Trials.

The most common type of urinary incontinence in women is stress urinary incontinence (SUI) which negatively impacts several aspects of life. The newly introduced vaginal laser therapy is being considered for treating SUI. This systematic review aimed to evaluate the efficacy of vaginal laser therapy for stress urinary incontinence in menopausal women. We searched the following databases: MEDLINE (via PubMed), EMBASE, Cochrane Library databases, Web of Science, clinical trial registry platforms, and Google Scholar, using the MeSH terms and keywords [Urinary Incontinence, Stress] and [(lasers) OR laser]. In our systematic review, prospective randomized clinical studies on women diagnosed with SUI as per the International Continence Society's diagnostic criteria were included. The Cochrane Risk-of-Bias assessment tool for randomized clinical trials was used to evaluate the quality of studies. A total of 256 relevant records in literature databases and registers and 25 in additional searches were found. Following a review of the titles, abstracts, and full texts, four studies involving 431 patients were included. Three studies used CO2-lasers, and one used Erbium: YAG-laser. The results of all four studies revealed the short-term improvement of SUI following both the Erbium: YAG-laser and CO2-laser therapy. SUI treatment with CO2-laser and Erbium: YAG-laser therapy is a quick, intuitive, well-tolerated procedure that successfully improves incontinence-related symptoms. The long-term impact of such interventions has not been well established as most trials focused on the short-term effects.