The influence of obesity on incidence of complications in patients hospitalized with ovarian hyperstimulation syndrome

Mandelbaum, Rachel S.; Bainvoll, Liat; Violette, Caroline; Smith, Meghan B.; Matsuzaki, Shinya; Klar, Maximilian; Ho, Jocelyn; Bendikson, Kristin; Paulson, Richard J.; Matsuo, Koji

doi:10.1007/s00404-021-06124-5

Cited by 2 publications

(3 citation statements)

References 49 publications

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“…Irregular menstruation can be associated with conditions that cause hormonal imbalances, such as PCOS, a condition known for its increased risk of OHSS[36–38]. However, the relationship between weight and OHSS remains inconclusive, with some studies finding no significant correlation [39] and others suggesting that obesity may decrease the risk of OHSS-related complications[40]. Interestingly, the model assigned lower importance to secondary infertility as a predictor of OHSS, which contradicts previous findings linking conditions that cause secondary female infertility, such as pituitary adenoma, to an increased incidence of OHSS[41].…”

Section: Discussionmentioning

confidence: 99%

“…Irregular menstruation can be associated with conditions that cause hormonal imbalances, such as PCOS, a condition known for its increased risk of OHSS [36][37][38]. However, the relationship between weight and OHSS remains inconclusive, with some studies finding no significant correlation [39] and others suggesting that obesity may decrease the risk of OHSS-related complications [40].…”

Section: Types Of Women Infertility Irregular Vs Regular Menstrual Cy...mentioning

confidence: 99%

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Prediction of Complicated Ovarian Hyperstimulation Syndrome in Assisted Reproductive Treatment Through Artificial Intelligence

Ziaee,

Khosravi,

Sadeghi

et al. 2024

Preprint

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Background This study explores the utility of machine learning (ML) models in predicting complicated Ovarian Hyperstimulation Syndrome (OHSS) in patients undergoing infertility treatments, addressing the challenge posed by highly imbalanced datasets. Objective This research fills the existing void by introducing a detailed structure for crafting diverse machine learning models and enhancing data augmentation methods to predict complicated OHSS effectively. Importantly, the research also concentrates on pinpointing critical elements that affect OHSS. Method This retrospective study employed a ML framework to predict complicated OHSS in patients undergoing infertility treatment. The dataset included various patient characteristics, treatment details, ovarian response variables, oocyte quality indicators, embryonic development metrics, sperm quality assessments, and treatment specifics. The target variable was OHSS, categorized as painless, mild, moderate, or severe. The ML framework incorporated Ray Tune for hyperparameter tuning and SMOTE-variants for addressing data imbalance. Multiple ML models were applied, including Decision Trees, Logistic Regression, SVM, XGBoost, LightGBM, Ridge Regression, KNN, and SGD. The models were integrated into a voting classifier, and the optimization process was conducted. The SHAP package was used to interpret model outcomes and feature contributions. Results The best model incorporated IPADE-ID augmentation along with an ensemble of classifiers (SGDClassifier, SVC, RidgeClassifier), reaching a recall of 0.9 for predicting OHSS occurrence and an accuracy of 0.76. SHAP analysis identified key factors: GnRH antagonist use, longer stimulation, female infertility factors, irregular menses, higher weight, hCG triggers, and, notably, higher number of embryos . Conclusion This novel study demonstrates ML's potential for predicting complicated OHSS. The optimized model provides insights into contributory factors, challenging certain conventional assumptions. The findings highlight the importance of considering patient-specific factors and treatment details in OHSS risk assessment.

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Section: Discussionmentioning

confidence: 99%

Section: Types Of Women Infertility Irregular Vs Regular Menstrual Cy...mentioning

confidence: 99%

Prediction of Complicated Ovarian Hyperstimulation Syndrome in Assisted Reproductive Treatment Through Artificial Intelligence

Ziaee,

Khosravi,

Sadeghi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…More seriously, by tracking large numbers of obese children and adolescents to adulthood, it has been found that obesity can lead to various complications, such as type 2 diabetes (T2D), hypertension, cardiovascular disease, and so on [ 8 , 9 , 10 , 11 ]. In addition, obesity also increases the incidence of tumors such as prostate, uterine, and breast cancers [ 12 ]. Based on etiology and pathogenesis, obesity can be simply divided into simple obesity and pathological obesity [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Biomaterial-Based Therapeutic Strategies for Obesity and Its Comorbidities

Duan

Liu

et al. 2022

Pharmaceutics

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Obesity is a global public health issue that results in many health complications or comorbidities, including type 2 diabetes mellitus, cardiovascular disease, and fatty liver. Pharmacotherapy alone or combined with either lifestyle alteration or surgery represents the main modality to combat obesity and its complications. However, most anti-obesity drugs are limited by their bioavailability, target specificity, and potential toxic effects. Only a handful of drugs, including orlistat, liraglutide, and semaglutide, are currently approved for clinical obesity treatment. Thus, there is an urgent need for alternative treatment strategies. Based on the new revelation of the pathogenesis of obesity and the efforts toward the multi-disciplinary integration of materials, chemistry, biotechnology, and pharmacy, some emerging obesity treatment strategies are gradually entering the field of preclinical and clinical research. Herein, by analyzing the current situation and challenges of various new obesity treatment strategies such as small-molecule drugs, natural drugs, and biotechnology drugs, the advanced functions and prospects of biomaterials in obesity-targeted delivery, as well as their biological activities and applications in obesity treatment, are systematically summarized. Finally, based on the systematic analysis of biomaterial-based obesity therapeutic strategies, the future prospects and challenges in this field are proposed.

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The influence of obesity on incidence of complications in patients hospitalized with ovarian hyperstimulation syndrome

Cited by 2 publications

References 49 publications

Prediction of Complicated Ovarian Hyperstimulation Syndrome in Assisted Reproductive Treatment Through Artificial Intelligence

Prediction of Complicated Ovarian Hyperstimulation Syndrome in Assisted Reproductive Treatment Through Artificial Intelligence

Biomaterial-Based Therapeutic Strategies for Obesity and Its Comorbidities

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