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2.
Hum Reprod ; 35(7): 1505-1514, 2020 07 01.
Article in English | MEDLINE | ID: mdl-32538428

ABSTRACT

STUDY QUESTION: Can a machine-learning-based model trained in clinical and biological variables support the prediction of the presence or absence of sperm in testicular biopsy in non-obstructive azoospermia (NOA) patients? SUMMARY ANSWER: Our machine-learning model was able to accurately predict (AUC of 0.8) the presence or absence of spermatozoa in patients with NOA. WHAT IS KNOWN ALREADY: Patients with NOA can conceive with their own biological gametes using ICSI in combination with successful testicular sperm extraction (TESE). Testicular sperm retrieval is successful in up to 50% of men with NOA. However, to the best of our knowledge, there is no existing model that can accurately predict the success of sperm retrieval in TESE. Moreover, machine-learning has never been used for this purpose. STUDY DESIGN, SIZE, DURATION: A retrospective cohort study of 119 patients who underwent TESE in a single IVF unit between 1995 and 2017 was conducted. All patients with NOA who underwent TESE during their fertility treatments were included. The development of gradient-boosted trees (GBTs) aimed to predict the presence or absence of spermatozoa in patients with NOA. The accuracy of these GBTs was then compared to a similar multivariate logistic regression model (MvLRM). PARTICIPANTS/MATERIALS, SETTING, METHODS: We employed univariate and multivariate binary logistic regression models to predict the probability of successful TESE using a dataset from a retrospective cohort. In addition, we examined various ensemble machine-learning models (GBT and random forest) and evaluated their predictive performance using the leave-one-out cross-validation procedure. A cutoff value for successful/unsuccessful TESE was calculated with receiver operating characteristic (ROC) curve analysis. MAIN RESULTS AND THE ROLE OF CHANCE: ROC analysis resulted in an AUC of 0.807 ± 0.032 (95% CI 0.743-0.871) for the proposed GBTs and 0.75 ± 0.052 (95% CI 0.65-0.85) for the MvLRM for the prediction of presence or absence of spermatozoa in patients with NOA. The GBT approach and the MvLRM yielded a sensitivity of 91% vs. 97%, respectively, but the GBT approach has a specificity of 51% compared with 25% for the MvLRM. A total of 78 (65.3%) men with NOA experienced successful TESE. FSH, LH, testosterone, semen volume, age, BMI, ethnicity and testicular size on clinical evaluation were included in these models. LIMITATIONS, REASONS FOR CAUTION: This study is a retrospective cohort study, with all the associated inherent biases of such studies. This model was used only for TESE, since micro-TESE is not performed at our center. WIDER IMPLICATIONS OF THE FINDINGS: Machine-learning models may lay the foundation for a decision support system for clinicians together with their NOA patients concerning TESE. The findings of this study should be confirmed with further larger and prospective studies. STUDY FUNDING/COMPETING INTEREST(S): The study was funded by the Division of Obstetrics and Gynecology, Soroka University Medical Center, there are no potential conflicts of interest for all authors.


Subject(s)
Azoospermia , Azoospermia/therapy , Female , Humans , Machine Learning , Male , Pregnancy , Prospective Studies , Retrospective Studies , Sperm Retrieval , Spermatozoa , Testis
3.
J Matern Fetal Neonatal Med ; 27(12): 1199-203, 2014 Aug.
Article in English | MEDLINE | ID: mdl-24111654

ABSTRACT

OBJECTIVE: The purpose of this study was to evaluate the association between amniotic fluid index (AFI) and adverse perinatal outcome, and whether a critical cutoff can be defined. METHODS: A retrospective cohort study was conducted. Included were patients who were admitted to the ultrasound unit of the tertiary medical center between the years 1988 and 2010. Parturients were classified into five groups according to their AFI: <20 (n = 9974; comparison group), 20-23 (n = 2771), 24-27 (n = 1315), 28-31 (n = 494) and 32 + (n = 260). Pregnancy and the perinatal outcomes were compared between the groups. Statistical analysis included the chi-square tests for trends, and multivariable models to control for confounders (with AFI as a dummy variable). RESULTS: A significant linear association was found between AFI and adverse perinatal outcomes including hypertensive disorders, diabetes mellitus, preterm labor, macrosomia, placental abruption and low birth weight. Furthermore, using multivariable logistic regression models, controlling for confounders such as maternal and gestational age, hypertension, diabetes mellitus, etc., the significant association between all four subgroups of AFI > 20 and adverse perinatal outcomes remained. CONCLUSION: A significant linear association exists between AFI > 20 and perinatal complications such as perinatal mortality, low Apgar scores and preterm labor. Hence, the critical cutoff for polyhydramnios should be re-evaluated.


Subject(s)
Amniotic Fluid/diagnostic imaging , Polyhydramnios/diagnostic imaging , Pregnancy Outcome , Ultrasonography, Prenatal/standards , Adult , Female , Humans , Infant, Newborn , Infant, Newborn, Diseases/diagnostic imaging , Infant, Newborn, Diseases/epidemiology , Obstetric Labor Complications/diagnostic imaging , Obstetric Labor Complications/epidemiology , Perinatal Mortality , Polyhydramnios/epidemiology , Pregnancy , Pregnancy Outcome/epidemiology , Reference Values , Retrospective Studies , Young Adult
4.
J Matern Fetal Neonatal Med ; 21(5): 331-5, 2008 May.
Article in English | MEDLINE | ID: mdl-18446661

ABSTRACT

OBJECTIVE: To investigate pregnancy outcome in women suffering from idiopathic vaginal bleeding (IVB) during the second half of pregnancy. METHODS: A comparison between patients admitted to the hospital due to bleeding during the second half of pregnancy and patients without bleeding was performed. Patients lacking prenatal care as well as multiple gestations were excluded from the analysis. Stratified analyses using the Mantel-Haenszel technique and a multiple logistic regression model were performed to control for confounders. RESULTS: During the study period, 173,621 singleton deliveries occurred at our institute. Of these, 2077 (1.19%) were complicated with bleeding upon admission during the second half of pregnancy. After excluding cases with bleeding due to placental abruption, placenta previa, cervical problems, etc., 67 patients were classified as having IVB (0.038%). Independent risk factors associated with IVB, using a backward, stepwise multivariable analysis were oligohydramnios (OR=6.2; 95% CI 3.1-12.7; p < 0.001), premature rupture of membranes (OR=3.4; 95% CI 1.8-6.2; p < 0.001), intrauterine growth restriction (IUGR, OR 5.6; 95% CI 2.5-12.2; p < 0.001), and Jewish ethnicity (OR=1.9; 95% CI 1.0-3.5; p=0.036). These patients subsequently were more likely to deliver preterm (<37 weeks, 56.7% vs. 7.3%; mean gestational age of 33.6+/-5.7 weeks vs. 39.2+/-2.1 weeks; p < 0.001) and by cesarean delivery (CD, 35.8% vs. 12.1%, OR=4.0; 95% CI 2.4-6.6; p < 0.001). Higher rates of low Apgar scores (<7) at 1 and 5 minutes were noted in these patients (OR=10.3; 95% CI 5.9-17.8; p < 0.001 and OR=17.8; 95% CI 7.1-44.5; p < 0.001, respectively). Moreover, perinatal mortality rate among patients admitted due to idiopathic bleeding was significantly higher as compared to patients without bleeding (9.6% vs. 1.2%, OR=8.4; 95% CI 3.3-21.2; p < 0.001). However, when controlling for preterm delivery, using the Mantel-Haenszel technique, the association lost its significance. CONCLUSION: Idiopathic vaginal bleeding during the second half of pregnancy is a risk factor for adverse perinatal outcome, mostly due to its significant association with preterm delivery. Careful surveillance, including fetal monitoring, is suggested in these cases in order to reduce the adverse perinatal outcome.


Subject(s)
Infant, Newborn, Diseases/etiology , Obstetric Labor Complications/etiology , Uterine Hemorrhage/complications , Adult , Female , Humans , Infant, Newborn , Perinatal Mortality , Pregnancy , Pregnancy Outcome , Pregnancy Trimester, Second , Pregnancy Trimester, Third , Risk Factors
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