Development and validation of a clinical prediction model of fertilization failure during routine IVF cycles
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PROVIDER: S-EPMC10834765 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundsDespite the great advances in assisted reproductive technology (ART), poor ovarian response (POR) is still one of the most challenging tasks in reproductive medicine. This predictive model we developed aims to predict the individual probability of clinical pregnancy failure for poor ovarian responders (PORs) under in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI).MethodsThe nomogram was developed in 281 patients with POR according to the Bologna criteria from January 2016 to December 2019, with 179 in the training group and 102 in the validation group. Univariate and multivariate logistic regression analyses were used to identify characteristics that were associated with clinical pregnancy failure. The nomogram was constructed based on regression coefficients. Performance was evaluated using both calibration and discrimination.ResultsAge >35 years, body mass index (BMI) >24 kg/m2, basic follicle-stimulating hormone (FSH) >10 mIU/ml, basic E2 >60 pg/ml, type B or C of endometrium on human chorionic gonadotropin (hCG) day, and the number of high-quality embryos <2 were associated with pregnancy failure of POR patients. The area under the receiver operating characteristic curve (AUC) of the training set is 0.786 (95% confidence interval (CI): 0.710-0.861), and AUC in the validation set is 0.748 (95% CI: 0.668-0.827), showing a satisfactory goodness of fit and discrimination ability in this nomogram.ConclusionOur nomogram can predict the probability of clinical pregnancy failure in PORs before embryo transfer in IVF/ICSI procedure, to help practitioners make appropriate clinical decisions and to help infertile couples manage their expectations.
| S-EPMC8419272 | biostudies-literature
Project description:This study aimed to compare the clinical outcomes between in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) in sibling oocytes. Additionally, we evaluated whether the implementation of split insemination contributed to an increase in the number of ICSI procedures.A total of 571 cycles in 555 couples undergoing split insemination cycles were included in this study. Among them, 512 cycles (89.7%) were a couple's first IVF cycle. The patients were under 40 years of age and at least 10 oocytes were retrieved in all cycles. Sibling oocytes were randomly allocated to IVF or ICSI.Total fertilization failure was significantly more common in IVF cycles than in ICSI cycles (4.0% vs. 1.4%, p<0.05), but the low fertilization rate among retrieved oocytes (as defined by fertilization rates greater than 0% but <30%) was significantly higher in ICSI cycles than in IVF cycles (17.2% vs. 11.4%, p<0.05). The fertilization rate of ICSI among injected oocytes was significantly higher than for IVF (72.3%±24.3% vs. 59.2%±25.9%, p<0.001), but the fertilization rate among retrieved oocytes was significantly higher in IVF than in ICSI (59.2%±25.9% vs. 52.1%±22.5%, p<0.001). Embryo quality before embryo transfer was not different between IVF and ICSI. Although the sperm parameters were not different between the first cycle and the second cycle, split insemination or ICSI was performed in 18 of the 95 cycles in which a second IVF cycle was performed.The clinical outcomes did not differ between IVF and ICSI in split insemination cycles. Split insemination can decrease the risk of total fertilization failure. However, unnecessary ICSI is carried out in most split insemination cycles and the use of split insemination might make ICSI more common.
| S-EPMC5545226 | biostudies-other
Project description:The morphological assessment of oocytes is important for embryologists to identify and select MII oocytes in IVF/ICSI cycles. Dysmorphism of oocytes decreases viability and the developmental potential of oocytes as well as the clinical pregnancy rate. Several reports have suggested that oocytes with a dark zona pellucida (DZP) correlate with the outcome of IVF treatment. However, the effect of DZP on oocyte quality, fertilization, implantation, and pregnancy outcome were not investigated in detail. In this study, a retrospective analysis was performed in 268 infertile patients with fallopian tube obstruction and/or male factor infertility. In 204 of these patients, all oocytes were surrounded by a normal zona pellucida (NZP, control group), whereas 46 patients were found to have part of their retrieved oocytes enclosed by NZP and the other by DZP (Group A). In addition, all oocytes enclosed by DZP were retrieved from 18 patients (Group B). No differences were detected between the control and group A. Compared to the control group, the rates of fertilization, good quality embryos, implantation and clinical pregnancy were significantly decreased in group B. Furthermore, mitochondria in oocytes with a DZP in both of the two study groups (A and B) were severely damaged with several ultrastructural alterations, which were associated with an increased density of the zona pellucida and vacuolization. Briefly, oocytes with a DZP affected the clinical outcome in IVF/ICSI cycles and appeared to contain more ultrastructural alterations. Thus, DZP could be used as a potential selective marker for embryologists during daily laboratory work.
| S-EPMC3933533 | biostudies-literature
Project description:BackgroundGestational trophoblastic disease (GTD) usually affects young women of childbearing age. After treatment for GTD, 86% of women wish to achieve pregnancy. On account of the impacts of GTD and treatments as well as patient anxiety, large numbers of couples turn to assisted reproductive technology (ART), especially in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI). But few studies have investigated whether a history of GTD affects the outcomes of IVF/ICSI in secondary infertile patients and how it occurs. We investigate whether a history of GTD affects the IVF/ICSI outcomes and the live birth rates in women with secondary infertility.MethodsThis retrospective cohort study enrolled 176 women with secondary infertility who underwent IVF/ICSI treatment at the reproductive medical center of Nanjing Drum Tower Hospital from January 1, 2016, to December 31, 2020. Participants were divided into the GTD group (44 women with GTD history) and control group (132 women without GTD history matched from 8318 secondary infertile women). The control group and the study group were matched at a ratio of 3:1 according to patient age, infertility duration, number of cycles and body mass index (BMI). We assessed retrieved oocytes and high-grade embryos, biochemical pregnancy, miscarriage, ectopic pregnancy, gestational age at delivery, delivery mode and live birth rates.Result(s)We found a significantly reduced live-birth rate (34.1% vs 66.7%) associated with IVF/ICSI cycles in patients with a GTD history compared to those without a GTD history. The biochemical pregnancy and miscarriage rates of the GTD group were slightly higher than those of the control group. In addition, there was a difference in gestational age at delivery between the GTD and control groups (p < 0.001) but no differences in the mode of delivery (p = 0.267). Furthermore, the number of abandoned embryos in the GTD group was greater than that in the control group (p = 0.018), and the number of good-quality embryos was less than that in the control group (p = 0.019). The endometrial thickness was thinner (p < 0.001) in the GTD group. Immunohistochemistry (IHC) showed abnormal endometrial receptivity in the GTD group.Conclusion(s)The GTD history of patients undergoing IVF/ICSI cycles had an impact on the live-birth rate and gestational age at delivery, which might result from the thinner endometrium and abnormal endometrial receptivity before embryo transfer.
| S-EPMC8815202 | biostudies-literature
Project description:BackgroundWomen who conceived with in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) are more likely to experience adverse pregnancy outcomes than women who conceived naturally. Cervical insufficiency (CI) is one of the important causes of miscarriage and premature birth, however there is no published data available focusing on the potential risk factors predicting CI occurrence in women who received IVF/ICSI treatment. This study aimed to identify the risk factors that could be integrated into a predictive model for CI, which could provide further personalized and clinically specific information related to the incidence of CI after IVF/ICSI treatment.Patients and methodsThis retrospective study included 4710 patients who conceived after IVF/ICSI treatment from Jan 2011 to Dec 2018 at a public university hospital. The patients were randomly divided into development (n = 3108) and validation (n = 1602) samples for the building and testing of the nomogram, respectively. Multivariate logistic regression was developed on the basis of pre-pregnancy clinical covariates assessed for their association with CI occurrence.ResultsA total of 109 patients (2.31%) experienced CI among all the enrolled patients. Body mass index (BMI), basal serum testosterone (T), gravidity and uterine length were associated with CI occurrence. The statistical nomogram was built based on BMI, serum T, gravidity and uterine length, with an area under the curve (AUC) of 0.84 (95% confidence interval: 0.76-0.90) for the developing cohort. The AUC for the validation cohort was 0.71 (95% confidence interval: 0.69-0.83), showing a satisfactory goodness-of-fit and discrimination ability in this nomogram.ConclusionThe user-friendly nomogram which graphically represents the risk factors and a pre-pregnancy predicted tool for the incidence of CI in patients undergoing IVF/ICSI treatment, provides a useful guide for medical staff on individualized decisions making, where preventive measures could be carried out during the IVF/ICSI procedure and subsequent pregnancy.
| S-EPMC7789534 | biostudies-literature
Project description:PurposePoor fertilization during conventional IVF is difficult to predict in the absence of abnormal semen parameters; large-scale studies are lacking. The purpose of this study is to evaluate factors associated with low fertilization rates in conventional insemination IVF cycles.MethodsA retrospective cohort study evaluating demographic, reproductive evaluation, and IVF cycle characteristics to identify predictors of low fertilization (defined as 2PN/MII ≤ 30% per cycle). Participants were included if they were undergoing their first IVF cycle utilizing fresh autologous oocytes and conventional insemination with male partner's sperm (with normal pretreatment semen analysis). They were randomly divided into a training set and a validation set; validation modeling with logistic regression and binary distribution was utilized to identify covariates associated with low fertilization.ResultsPostprocessing sperm concentration of less than 40 million/ml and postprocessing sperm motility < 50% on the day of retrieval were the strongest predictors of low fertilization in the training dataset. Next, in the validation set, cycles with either low postprocessing concentration (≤ 40 million/ml) or low postprocessing progressive motility (≤ 50%) were 2.9-times (95% CI 1.4, 6.2) more likely to have low fertilization than cycles without either risk factor. Furthermore, cycles with low postprocessing concentration and progressive motility were 13.4 times (95% CI 4.01, 45.06) more likely to have low fertilization than cycles without either risk factor.ConclusionsPostprocessing concentration and progressive motility on the day of oocyte retrieval are predictive of low fertilization in conventional IVF cycles with normal pretreatment diagnostic semen analysis parameters.
| S-EPMC6420587 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has affected millions of individuals and caused hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease. We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome.MethodsIn this prediction model development and validation study, we applied machine learning techniques to clinical data from a large cohort of patients with COVID-19 treated at the Mount Sinai Health System in New York City, NY, USA, to predict mortality. We analysed patient-level data captured in the Mount Sinai Data Warehouse database for individuals with a confirmed diagnosis of COVID-19 who had a health system encounter between March 9 and April 6, 2020. For initial analyses, we used patient data from March 9 to April 5, and randomly assigned (80:20) the patients to the development dataset or test dataset 1 (retrospective). Patient data for those with encounters on April 6, 2020, were used in test dataset 2 (prospective). We designed prediction models based on clinical features and patient characteristics during health system encounters to predict mortality using the development dataset. We assessed the resultant models in terms of the area under the receiver operating characteristic curve (AUC) score in the test datasets.FindingsUsing the development dataset (n=3841) and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient's age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits).InterpretationAn accurate and parsimonious COVID-19 mortality prediction model based on three features might have utility in clinical settings to guide the management and prognostication of patients affected by this disease. External validation of this prediction model in other populations is needed.FundingNational Institutes of Health.
| S-EPMC7508513 | biostudies-literature
Project description:Preimplantation genetic testing for aneuploidies (PGT-A) is arguably the most effective embryo selection strategy. Nevertheless, it requires greater workload, costs, and expertise. Therefore, a quest towards user-friendly, non-invasive strategies is ongoing. Although insufficient to replace PGT-A, embryo morphological evaluation is significantly associated with embryonic competence, but scarcely reproducible. Recently, artificial intelligence-powered analyses have been proposed to objectify and automate image evaluations. iDAScore v1.0 is a deep-learning model based on a 3D convolutional neural network trained on time-lapse videos from implanted and non-implanted blastocysts. It is a decision support system for ranking blastocysts without manual input. This retrospective, pre-clinical, external validation included 3604 blastocysts and 808 euploid transfers from 1232 cycles. All blastocysts were retrospectively assessed through the iDAScore v1.0; therefore, it did not influence embryologists' decision-making process. iDAScore v1.0 was significantly associated with embryo morphology and competence, although AUCs for euploidy and live-birth prediction were 0.60 and 0.66, respectively, which is rather comparable to embryologists' performance. Nevertheless, iDAScore v1.0 is objective and reproducible, while embryologists' evaluations are not. In a retrospective simulation, iDAScore v1.0 would have ranked euploid blastocysts as top quality in 63% of cases with one or more euploid and aneuploid blastocysts, and it would have questioned embryologists' ranking in 48% of cases with two or more euploid blastocysts and one or more live birth. Therefore, iDAScore v1.0 may objectify embryologists' evaluations, but randomized controlled trials are required to assess its clinical value.
| S-EPMC10002983 | biostudies-literature
Project description:BackgroundNoninvasive ventilation (NIV) has been widely used in critically ill patients after extubation. However, NIV failure is associated with poor outcomes. This study aimed to determine early predictors of NIV failure and to construct an accurate machine-learning model to identify patients at risks of NIV failure after extubation in intensive care units (ICUs).MethodsPatients who underwent NIV after extubation in the eICU Collaborative Research Database (eICU-CRD) were included. NIV failure was defined as need for invasive ventilatory support (reintubation or tracheotomy) or death after NIV initiation. A total of 93 clinical and laboratory variables were assessed, and the recursive feature elimination algorithm was used to select key features. Hyperparameter optimization was conducted with an automated machine-learning toolkit called Neural Network Intelligence. A machine-learning model called Categorical Boosting (CatBoost) was developed and compared with nine other models. The model was then prospectively validated among patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University.ResultsOf 929 patients included in the eICU-CRD cohort, 248 (26.7%) had NIV failure. The time from extubation to NIV, age, Glasgow Coma Scale (GCS) score, heart rate, respiratory rate, mean blood pressure (MBP), saturation of pulse oxygen (SpO2), temperature, glucose, pH, pressure of oxygen in blood (PaO2), urine output, input volume, ventilation duration, and mean airway pressure were selected. After hyperparameter optimization, our model showed the greatest accuracy in predicting NIV failure (AUROC: 0.872 [95% CI 0.82-0.92]) among all predictive methods in an internal validation. In the prospective validation cohort, our model was also superior (AUROC: 0.846 [95% CI 0.80-0.89]). The sensitivity and specificity in the prediction group is 89% and 75%, while in the validation group they are 90% and 70%. MV duration and respiratory rate were the most important features. Additionally, we developed a web-based tool to help clinicians use our model.ConclusionsThis study developed and prospectively validated the CatBoost model, which can be used to identify patients who are at risk of NIV failure. Thus, those patients might benefit from early triage and more intensive monitoring.Trial registrationNCT03704324. Registered 1 September 2018, https://register.Clinicaltrialsgov .
| S-EPMC9358918 | biostudies-literature
Project description:AimsPatients with heart valvular regurgitation is increasing; early screening of potential patients developing heart failure (HF) is crucial.MethodsFrom 1 November 2019 to 31 October 2023, a total of 509 patients with heart valvular regurgitation hospitalized in the Department of Cardiovascular Disease of the First Affiliated Hospital of Guangzhou University of Traditional Medicine were enrolled. Three hundred fifty-six cases were selected as the training set for modelling, and 153 cases were selected as the validation set for the internal validation of the model.ResultsA predictive model of heart failure with the following nine risk factors was developed: atrial fibrillation (AF), pulmonary infection (PI), coronary artery disease (CAD), creatinine (CREA), low-density lipoprotein cholesterol (LDL-C), d-dimer (DDi), left ventricular end-diastolic diameter (LVEDd), mitral regurgitation (MR) and aortic regurgitation (AR). The model was evaluated by the C-index [the training set: area under curve (AUC) 0.937, 95% confidence interval (CI) 0.911-0.963; the validation set: AUC 0.928, 95% CI 0.890-0.967]. Hosmer-Lemeshow test (the training set: χ2 10.908, P = 0.207; the validation set: χ2 4.896, P = 0.769) revealed that both the training and validation sets performed well in terms of model differentiation and calibration. Decision curve analysis showed that both the training and validation sets have higher net benefits, indicating that the model has good utility. Ten-fold cross-validation showed that the training set has high similarities with the validation set, which means that the model has good stability.ConclusionsThe occurrence of heart failure in patients with valvular regurgitation has a significant correlation with AF, PI, CAD, CREA, LDL-C, DDi, LVEDd, MR and AR. Based on these risk factors, a prediction model for heart failure was developed and validated, which showed good differentiation and utility, high accuracy and stability, providing a method for predicting heart failure.
| S-EPMC11424342 | biostudies-literature