Does intravesical Bacillus Calmette-Guérin for bladder cancer protect from COVID-19?

INTRODUCTION: The study aimed to correlate the history of intravesical BCG as well as infantile BCG immunization with the incidence and severity of COVID-19 infection. METHODS: Retrospective data collection of patients with high-risk non muscle invasive bladder cancer (NMIBC) from two Canadian centers. Data collection included a history of BCG instillation, infantile immunization, and the development of COVID-19 infection. Admission and/ or mortality because of COVID-19 was reported. RESULTS: We could include data from 348 patients: including 188 and 160 patients from Ontario and British Columbia respectively. COVID-19 affected 15% of these patients. Intravesical BCG was used in 44% of these patients. Intravesical BCG and/or infantile BCG immunization did not correlate with the incidence of COVID-19 infection. CONCLUSIONS: Previous intravesical BCG and/ or a history of infantile BCG vaccination were not more/ less frequent in patients who had COVID-19 infection.

Prevalence of stress urinary incontinence and urge urinary incontinence in multiples sclerosis patients: a systematic review and meta-analysis.

Urinary incontinence (UI), encompassing stress urinary incontinence (SUI) and urge urinary incontinence (UUI), is a prevalent and debilitating condition in patients with multiple sclerosis (MS), profoundly impacting their quality of life. This systematic review and meta-analysis aimed to elucidate the worldwide prevalence rates of SUI and UUI among MS patients. This study was conducted by examining observational studies published between 2000 and 2023. An exhaustive literature search was conducted across databases such as PubMed, MEDLINE, Web of Science, Scopus, ProQuest, and Google Scholar. The Meta-prop method facilitated pooled prevalence estimation of UUI and SUI, while Egger tests assessed publication bias. In total, 27 studies with 15,052 participants were included in the meta-analysis. The findings revealed a high random effect pooled prevalence of UUI at 41.02% (95% Confidence Interval [CI]: 30.57-51.89; I2 = 99%, p < 0.001) and SUI at 25.67% (95% CI: 19.30-32.58%; I2 = 94.9%, P < 0.001). Additionally, the pooled prevalence of mixed urinary incontinence (MUI) was reported at 18.81% (95% CI: 7.55-33.48; I2:95.44%, p < 0.001). The high heterogeneity observed suggests variable prevalence across populations and highlights the intricate nature of UI in MS. These findings underscore the critical need for dedicated supportive, therapeutic, and rehabilitative interventions to manage this common complaint in MS patients effectively.

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.

Coping Strategies with Genito-Pelvic Pain/Penetration Disorder: A Qualitative Study.

Background: Genital/pelvic pain penetration disorder (GPPPD) decreased mental and physical functioning, reduced quality of life, and reduced feelings of inadequacy and worthlessness, all of which impair the ability of women with GPPPD to enjoy sex. This qualitative study was conducted to identify which factors can reduce sexual stress and help Iranian women cope with GPPPD. Methods: This qualitative study was conducted through the participation of 18 women with GPPPD diagnosed by a sexologist and using DSM-IV diagnostic criteria from March to July 2022, Iran. The samples were selected using the purposive sampling method and considering the maximum variation. The semistructured question guide was used as a data collection tool and data collection continued until data saturation was reached. The collected data were analyzed using conventional content analysis approach. Results: Data analysis led to the emergence of three main themes: "problem-focused coping" which included the three categories of received social support, problem self-control, and penetration replacement; "emotion-focused coping" which included three categories: a couple's negative reaction to the problem, attachment disorder, and surrendering the problem; and "treatment-seeking" which consisted of searching and choosing a therapist to solve the problem, ineffective medical approaches, and ineffective nonmedical approaches. Conclusion: Coping strategies in women with GPPPD were classified as "problem-focused coping," "emotion-focused coping," and "treatment-seeking." These findings indicate a need for GPPPD information and education, as well as a need for healthcare professionals to actively inquire about sexual problems and commit to serious treatment efforts. Cultural interventions that promote sexual pleasure can aid in the management of GPPPD.

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.

Endophytic to total tumour volume ratio: An added variable to patients with T1b/T2 renal tumours undergoing partial nephrectomy.

INTRODUCTION: Partial nephrectomy is the standard of care to patients with small renal masses. It is still encouraged to larger tumours whenever feasible. The aim of this study is to look for the endophytic to total tumour volume ratio as an added variable to study the complexity of partial nephrectomy to patients with T1b/ T2 renal tumours. METHODS: Retrospective data collection of patients that had partial nephrectomy for T1b/T2 renal tumours by a single surgeon was done. Radiological re-assessment for the CT images to measure the endophytic to total tumour volume ratio was done. RESULTS: The mean age of the patients was 63 years. The study included 25 males and 11 females. All cases were managed by open surgery using retroperitoneal transverse lateral lumbotomy and warm ischemia was used in all patients. The mean tumour volume was 74 cc, the mean endophytic tumour volume was 29 cc. The mean percentage of endophytic to total tumour volume was 42%. CONCLUSIONS: Partial nephrectomy is safe for most of the patients with good performance status, having large renal masses. More complex surgery can be predicted in patients with endophytic to total tumour volume greater than 42%.

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.

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).

Analysis of Cesarean Section Rates in a Public Tertiary Hospital During Teaching and Non-teaching Periods Using the Robson Ten Group Classification System.

INTRODUCTION: The rising cesarean section (CS) rate is a global concern. One of the hospital characteristics that may explain the variation in CS among hospitals is hospital teaching status. This study aims to assess the rate of CS in a tertiary hospital during the teaching and non-teaching periods and to conduct an analysis using the Robson ten-group classification system. METHODS: This study is a retrospective cohort that assessed pregnant mothers who gave birth at a tertiary hospital in Bandar Abbas. The study population was divided into two groups: those who gave birth during the hospital's teaching period (November 1st, 2019 to October 30th, 2020) and those who gave birth after that (November 1st, 2020 to October 30th, 2021). The primary outcome was the rate of CS according to Robson's classification system. The secondary outcome was the contributions of each group of Robson to the overall CS rate. Data were extracted by trained collectors from the "Iranian Maternal and Neonatal Network (IMaN Net)," a valid national system, using electronic patient records. RESULTS: Of the total number of births (8382), 62.9 % occurred during the teaching period and 37.1 % during the non-teaching period. A 7% increase in CS was observed during the teaching period of the hospital compared to the non-teaching period (p<0.01). CS rate in Robson groups 1,2,4,7, and 10 differs significantly between teaching and non-teaching periods. According to the findings, Groups 5, 10, and 2 were the three most significant contributors to overall CS in our hospital during the study period. CONCLUSION: The efforts to reduce the overall CS rate should be focused on groups 2,5, and 10 of Robson.

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.

History of infantile BCG immunization did not predict lamina propria invasion and/or high-grade in patients with non-muscle invasive bladder cancer.

OBJECTIVE: To evaluate the utility of infantile BCG vaccination history in predicting stage and grade of tumours in non-muscle invasive bladder cancer (NMIBC). MATERIALS AND METHODS: We retrospectively analyzed data from patients from a single center who were diagnosed with new NMIBC and underwent transurethral resection of bladder tumour (TURBT) between 2017 and 2022. We assessed BCG immunization status with various demographics and comorbidities, as well as tumour recurrence, progression, stage, and grade. RESULTS: A total of 188 patients met the inclusion criteria for our study. The mean age of patients at the time of diagnosis was significantly lower in those that had been immunized with BCG (71 ± 9) than those who had not (77 ± 10) (p < 0.0001). History of BCG immunization did not correlate with sex, history of diabetes mellitus (DM), prior history of intravesical BCG treatment, and tumour recurrence, progression, stage, and grade. CONCLUSIONS: History of infantile BCG vaccination did not correlate with the depth of invasion and/or the grade in patients with non-muscle invasive bladder cancer. Patients that received infantile BCG vaccination were significantly younger at the time of diagnosis of NMIBC.

Rapid metachronous bladder metastasis of type 2 papillary renal cell carcinoma.

Renal cell carcinoma (RCC) frequently spreads to distant organs like the lung, lymph nodes, bone, and liver. However, there have been some reports of RCC bladder metastasis. We present a case of a 61-year-old man presented with total painless gross hematuria. The patient had a history of right radical nephrectomy for papillary (type 2) RCC, high-grade, pT3a with negative surgical margins. There was no evidence of metastases on 6-month surveillance CT. After one-year post-operation, at this current admission, the cystoscopy discovered a solid bladder mass away from the trigone in the right lateral bladder wall. The resected bladder mass was metastatic papillary RCC with PAX-8 positive but GATA-3 negative on immunostaining. A positron emission tomography scan confirmed multiple lung, liver, and osseous metastases. This case report can highlight the importance of having bladder metastasis in RCC mind, although rare, and may necessitate the surveillance measures like urine analysis at more frequent interval and CT Urography instead of regular CT to detect the RCC metastatic bladder cancer at early stage.

Timing of Urinary Catheter Removal After Urethroplasty: A Systematic Review.

OBJECTIVE: To synthesize existing evidence to evaluate the outcomes of different urinary catheter removal timing (early vs late) after urethroplasty. METHODS: We performed a comprehensive search of PubMed, Embase, the Cochrane Library, and Web of Science from inception to August 7, 2022. Articles were initially screened by title, abstract, and subsequently by a full paper review before being included in the final analysis. All comparative studies that assessed the association between urethral catheterization duration and frequency of extravasation and recurrence rate in patients who underwent urethroplasty were included in the analysis. Exclusion criteria were case reports, case series, letters to editors, and non-English studies. The risk of bias was assessed using the Newcastle-Ottawa Scale. RESULTS: Of the 439 relevant records in the literature databases, 5 studies involving 634 patients were included. In all 5 studies, the extravasation rate was not significantly different between the early and late catheter removal groups. Among the 3 studies that reported recurrence rates, the recurrence rate was low, with no statistically significant difference between the early and late catheter removal groups. Wound and urinary tract infections were among the most common complications, with a higher rate in patients with late catheter removal. CONCLUSION: Early catheter removal following urethroplasty does not increase the rate of extravasation or recurrence during long-term follow-up. The existing evidence can serve as the foundation for additional research with a larger sample size.

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.

Same-day trial of void and discharge following standard vs. MOSESTM holmium laser enucleation of the prostate: A single-center experience.

INTRODUCTION: We aimed to compare perioperative and postoperative outcomes and to assess the safety and feasibility of same-day trial of void (TOV) in patients who underwent standard holmium laser enucleation of the prostate (HoLEP) vs. MOSESTM HoLEP (MoLEP). METHODS: We conducted a retrospective review of prospectively collected data of patients that underwent HoLEP (100 W) or MoLEP (120 W) with same-day catheter removal three hours postoperatively at our institution from August 2018 to September 2021. Patient demographics, intraoperative parameters, and postoperative outcomes were analyzed. Data were compared as means with standard deviation and medians with interquartile range (IQR) or numbers and percentages. Continuous and categorical variables were assessed using the Mann-Whitney U test and Chi-squared test, respectively. Predictors of shorter enucleation time and failed same-day TOV were investigated. RESULTS: Of the 90 patients included, 28 underwent HoLEP while 62 had MoLEP. There was no significant difference between the groups in terms of the successful TOV (23 [82%] vs. 58 [93.5%], p=0.1) and readmission rate (3 [10.7%] vs. 1 [1.6%], p=0.08); however, the MoLEP group had a significantly shorter mean enucleation time (p<0.001), mean hemostasis time (p<0.001), mean morcellation time (p=0.003), and lower mean energy used (p<0.001). On the logistic regression model, MoLEP (odds ratio [OR] 0.03, 95% confidence interval [CI] 0.007-0.19, p<0.001), lower preoperative prostate-specific antigen (PSA) test (OR 1.25, 95% CI 1.01-1.55, p=0.03), and smaller prostate size (OR 1.06, 95% CI 1.02-1.09, p<0.001) were independent predictors of shorter enucleation time. History of preoperative retention was the only significant factor associated with a failed same-day TOV (p=0.04). There was no difference in intraoperative or postoperative complication rates or postoperative functional outcomes between the two technologies. CONCLUSIONS: Same-day TOV and discharge are feasible following standard HoLEP and MoLEP, with comparable outcomes; however, the use of MOSESTM technology offered better enucleation efficiency with excellent hemostatic potential. Preoperative retention was the only predictor of failed same-day TOV.

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.