In AJOG global reports

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.

Mehrnoush Vahid, Ranjbar Amene, Farashah Mohammadsadegh Vahidi, Darsareh Fatemeh, Shekari Mitra, Jahromi Malihe Shirzadfard

2023-May

analysis, machine learning, postpartum hemorrhage, risk factors

In PeerJ

BACKGROUND : The first women's Australian football (AF) professional competition was established in 2017, resulting in advancement in performance analysis capabilities within the sport. Given the specific constraints of women's AF, it is currently unclear what match-play performance analysis methods and techniques are implemented. Therefore, the aim of this scoping review was to describe and critically appraise physical, technical, and tactical performance analysis methods that have been employed in women's and girls' AF match-play.

METHODOLOGY : A systematic search was conducted on the 27th of June 2022 through five databases. Eligibility criteria were derived from the PCC framework with the population (P) of women and girls AF players, of any level of play; concepts (C) of interest which were measures, data, and methods related to the sport's physical, technical, and tactical performance; and the context (C) of methods that analysed any match-play performance. A narrative synthesis was conducted using extracted study characteristic data such as sample size, population, time period, collection standards, evaluation metrics for results, and application of thematic categorisations of previous sports performance reviews. Critical appraisal of eligible studies' methodologies was conducted to investigate research quality and identify methodological issues.

RESULTS : From 183 studies screened, twelve eligible studies were included, which examined match-play through physical (9/12, 75%), technical (4/12, 33%), and tactical analysis (2/12, 17%). Running demands and game actions analysis were the most researched in senior women's AF. Research into junior girls' AF match-play performance has not been investigated. No research has been conducted on non-running physical demands, contact demands, acceleration, and tactical aspects of women's AF. All studies utilised either inferential statistics or basic predictive models. Critical appraisal deemed most studies as low risk of bias (11/12, 92%), with the remaining study having satisfactory risk.

CONCLUSIONS : Future research utilising increased longitudinal and greater contextual data is needed to combat the prominent issue of data representativeness to better characterise performance within women's and girls' AF. Additionally, research involving junior and sub-elite AF players across the talent pathways is important to conduct, as it provides greater context and insight regarding development to support the evolving elite women's AF competition. Women's AF has been constrained by its resource environment. As such, suggestions are provided for better utilisation of existing data, as well as for the creation of new data for appropriate future research. Greater data generation enables the use of detailed machine learning predictions, neural networks, and network analysis to better represent the intertwined nature of match-play performance from technical, physical, and tactical data.

van der Vegt Braedan R, Gepp Adrian, Keogh Justin W L, Farley Jessica B

2023

AFLW, Australian Football, Data analysis, Female athlete, Game actions, Performance analysis, Running demands

In Journal of sleep research

Gu Yusing, Gagnon Jean-François, Kaminska Marta

2023-Mar-20

K-complex, cyclic alternating pattern, odds ratio product, quantitative electroencephalography, sleep spindles, slow-wave activity

In European urology ; h5-index 128.0

De Backer Pieter, Van Praet Charles, Simoens Jente, Peraire Lores Maria, Creemers Heleen, Mestdagh Kenzo, Allaeys Charlotte, Vermijs Saar, Piazza Pietro, Mottaran Angelo, Bravi Carlo A, Paciotti Marco, Sarchi Luca, Farinha Rui, Puliatti Stefano, Cisternino Francesco, Ferraguti Federica, Debbaut Charlotte, De Naeyer Geert, Decaestecker Karel, Mottrie Alexandre

2023-Mar-18

Augmented reality, Deep learning, Instrument segmentation, Kidney transplantation, Partial nephrectomy, Real time, Renal cell carcinoma, Robotic surgery, Three-dimensional models

In Journal of sleep research

Gu Yusing, Gagnon Jean-François, Kaminska Marta

2023-Mar-20

K-complex, cyclic alternating pattern, odds ratio product, quantitative electroencephalography, sleep spindles, slow-wave activity

In European urology ; h5-index 128.0

De Backer Pieter, Van Praet Charles, Simoens Jente, Peraire Lores Maria, Creemers Heleen, Mestdagh Kenzo, Allaeys Charlotte, Vermijs Saar, Piazza Pietro, Mottaran Angelo, Bravi Carlo A, Paciotti Marco, Sarchi Luca, Farinha Rui, Puliatti Stefano, Cisternino Francesco, Ferraguti Federica, Debbaut Charlotte, De Naeyer Geert, Decaestecker Karel, Mottrie Alexandre

2023-Mar-18

Augmented reality, Deep learning, Instrument segmentation, Kidney transplantation, Partial nephrectomy, Real time, Renal cell carcinoma, Robotic surgery, Three-dimensional models