Predicting the likelihood of a live birth for women with endometriosis-related infertility

Clinical prediction models for in vitro fertilization outcomes: a systematic review, meta-analysis, and external validation

STUDY QUESTION

What is the best-performing model currently predicting live birth outcomes for IVF or ICSI?

SUMMARY ANSWER

Among the identified prognostic models, McLernon's post-treatment model outperforms other models in both the meta-analysis and external validation of a Chinese cohort.

WHAT IS KNOWN ALREADY

With numerous similar models available across different time periods and using various predictors in IVF prognostic models, there is a need to summarize and evaluate them, due to a lack of validated evidence distinguishing high-quality from low-quality prediction tools. However, there is a notable dearth of research in the form of meta-analysis or external validation assessing the performance of models in predicting live births in this field.

STUDY DESIGN, SIZE, DURATION

The researchers conducted a comprehensive literature review in PubMed, EMBASE, and Web of Science, using keywords related to prognostic models and IVF/ICSI live birth outcomes. The search included studies published up to 3 April 2024, and was limited to English language studies.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The review included studies that developed or validated prognostic models for IVF live birth outcomes while providing clear reports on model characteristics. Researchers extracted and analysed the data in accordance with the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and other model-related guidelines. For model effects in meta-analysis, the choice would be based on the heterogeneity assessed using the I2 statistic and the Cochrane Q test. Model performance was evaluated by assessing their area under the receiver operating characteristic curves (AUCs) and calibration plots in the studies.

MAIN RESULTS AND THE ROLE OF CHANCE

This review provides a comprehensive summary of data derived from 72 studies with an overall ROB of high or unclear. These studies contained a total of 132 predictors and 86 prognostic models, and then meta-analyses were performed for each of the five selected models. The total random effects of Templeton's, Nelson's, McLernon's pre-treatment and post-treatment model demonstrated AUCs of 0.65 (95% CI: 0.61-0.69), 0.63 (95% CI: 0.63-0.64), 0.67 (95% CI: 0.62-0.71), and 0.73 (95% CI: 0.71-0.75), respectively. The total fixed effects of the intelligent data analysis score (iDAScore) model estimated an AUC of 0.66 (95% CI: 0.63-0.68). The external validation of the initial four models in our cohort produced AUCs ranging from 0.53 to 0.58, and the calibration was confirmed through calibration plots.

LIMITATIONS, REASONS FOR CAUTION

While the focus on English-language studies and live birth outcomes may constrain the generalizability of the findings to diverse populations, this approach equips clinicians, who view live births as the ultimate objective, with more precise and actionable reference guidelines.

WIDER IMPLICATIONS OF THE FINDINGS

This study represents the first meta-analysis in the field of IVF prediction models, definitively confirming the superior performance of McLernon's post-treatment model. The conclusion is reinforced by independent validation from another perspective. Nevertheless, further investigation is warranted to develop new models and to externally validate existing high-performing models for prognostic accuracy in IVF outcomes.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by the National Natural Science Foundation of China (Grant No. 82174517). The authors report no conflict of interest.

REGISTRATION NUMBER

2022 CRD42022312018.

How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-center

OBJECTIVE

To develop a predictive tool in the form of a Nomogram based on the Cox regression model, which incorporates the impact of the length of treatment cycles on the outcome of live birth, to evaluate the probability of infertile couples having a live birth after one or more complete cycles of In Vitro Fertilization (IVF), and to provide patients with a risk assessment that is easy to understand and visualize.

METHODS

A retrospective study for establishing a prediction model was conducted in the reproductive center of Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital). A total of 4413 patients who completed ovarian stimulation treatment and reached the trigger were involved. 70% of the patients were randomly placed into the training set (n = 3089) and the remaining 30% of the patients were placed into the validation set (n = 1324) randomly. Live birth rate (LBR) and cumulative LBR (CLBR) were calculated for one retrieval cycle and the subsequent five frozen embryo transfer (FET) cycles. Proportional Hazards (PH) Assumption test was used for selecting the parameter in the predictive model. A Cox regression model was built based on the basis of training set, and ROC curves were used to test the specificity and sensitivity of the prediction model. Subsequently, the validation set was applied to verify the validity of the model. Finally, for a more intuitive assessment of the CLBR more intuitively for clinicians and patients, a Nomogram model was established based on predictive model. By calculating the scores of the model, the clinicians could more effectively predict the probability for an individual patient to obtain at least one live birth.

RESULTS

In the fresh embryo transfer cycle, the LBR was 38.7%. In the first to fifth FET cycle, the optimal estimate and conservative estimate CLBRs were 59.95%, 65.41%, 66.35%, 66.58%, 66.61% and 56.81%, 60.84%, 61.50%, 61.66%, 61.68%, respectively. Based on PH test results, the potential predictive factors for live birth were insemination method, infertility factors, serum progesterone level (R = 0.043, p = 0.059), and luteinizing hormone level (R = 0.015, p = 0.499) on the day initiated with gonadotropin, basal follicle-stimulating hormone (R = -0.042, p = 0.069) and BMI (R = -0.035, p = 0.123). We used ROC curve to test the predictive power of the model. The AUC was 0.782 (p < 0.01, 95% CI: 0.764-0.801). Then the model was verified using the validation data. The AUC was 0.801 (p < 0.01, 95% CI: 0.774-0.828). A Nomogram model was built based on potential predictive factors that might influence the event of a live birth.

CONCLUSIONS

The Cox regression and Nomogram prediction models effectively predicted the probability of infertile couples having a live birth. Therefore, this model could assist clinicians with making clinical decisions and providing guidance for patients.

TRIAL REGISTRATION

N/A.

Clinical use of artificial intelligence in endometriosis: a scoping review

Endometriosis is a chronic, debilitating, gynecologic condition with a non-specific clinical presentation. Globally, patients can experience diagnostic delays of ~6 to 12 years, which significantly hinders adequate management and places a significant financial burden on patients and the healthcare system. Through artificial intelligence (AI), it is possible to create models that can extract data patterns to act as inputs for developing interventions with predictive and diagnostic accuracies that are superior to conventional methods and current tools used in standards of care. This literature review explored the use of AI methods to address different clinical problems in endometriosis. Approximately 1309 unique records were found across four databases; among those, 36 studies met the inclusion criteria. Studies were eligible if they involved an AI approach or model to explore endometriosis pathology, diagnostics, prediction, or management and if they reported evaluation metrics (sensitivity and specificity) after validating their models. Only articles accessible in English were included in this review. Logistic regression was the most popular machine learning method, followed by decision tree algorithms, random forest, and support vector machines. Approximately 44.4% (n = 16) of the studies analyzed the predictive capabilities of AI approaches in patients with endometriosis, while 47.2% (n = 17) explored diagnostic capabilities, and 8.33% (n = 3) used AI to improve disease understanding. Models were built using different data types, including biomarkers, clinical variables, metabolite spectra, genetic variables, imaging data, mixed methods, and lesion characteristics. Regardless of the AI-based endometriosis application (either diagnostic or predictive), pooled sensitivities ranged from 81.7 to 96.7%, and pooled specificities ranged between 70.7 and 91.6%. Overall, AI models displayed good diagnostic and predictive capacity in detecting endometriosis using simple classification scenarios (i.e., differentiating between cases and controls), showing promising directions for AI in assessing endometriosis in the near future. This timely review highlighted an emerging area of interest in endometriosis and AI. It also provided recommendations for future research in this field to improve the reproducibility of results and comparability between models, and further test the capacity of these models to enhance diagnosis, prediction, and management in endometriosis patients.

[Fertility preservation in cervical cancer, analysis of 30 years of practice and immersion in future developments]

OBJECTIVES

The strategy of fertility preservation (FP) in cervical cancer has been challenged for several years and a therapeutic de-escalation seems to be necessary. In this context, we evaluated the oncological, fertility and obstetric outcomes of surgical techniques performed in our centre for FP.

METHODS

This retrospective uni centric trial included 75 patients, managed at the Gustave Roussy Institute between 1995 and 2020, for cervical cancer (stage IB1 FIGO 2018) and having conducted a fertility preservation project after a complete pre-therapy work-up. The objective of this study was to understand our results on fertility and obstetrical outcomes and to correlate them with oncological data and finally to evaluate the evolution of our surgical practices.

RESULTS

54 patients benefited from an extended trachelectomy and no lymph node involvement was found. 1 patient received a complementary treatment postoperatively which did not allow to preserve her fertility. The recurrence rate was 4.8% (4/75) with one death described. 31 pregnancies were obtained, representing a pregnancy rate of 50%. 74% of pregnancies were obtained spontaneously and 60% of pregnancies were carried to term.

CONCLUSION

Our results are similar to those in the literature. Despite a fertility preservation project, only half of the patients were able to achieve a pregnancy.

Infertility management according to the Endometriosis Fertility Index in patients operated for endometriosis: What is the optimal time frame?

INTRODUCTION

The Endometriosis Fertility Index (EFI) is a validated score for predicting the postoperative spontaneous pregnancy rate in patients undergoing endometriosis surgery. However, the practical use of the EFI to advise patients about postoperative fertility management is unclear.

MATERIALS AND METHODS

All patients participating in the ENDOQUAL study-a prospective observational bi-center cohort study conducted between 01/2012 and 06/2018-who underwent surgery for infertility were asked to complete a questionnaire collecting time and mode of conception. Statistical analysis was performed with the Fine and Gray model of competing risks and analysis of fertility according to the EFI.

RESULTS

Of the 234 patients analyzed, 104 (44.4%) conceived postoperatively including 58 (55.8%) spontaneous pregnancies. An EFI of 0-4 for spontaneous pregnancies was associated with a lower cumulative pregnancy incidence compared to an EFI of 5-10 (52 versus 34 pregnancies respectively, Subdistribution Hazard Ratio (SHR) = 0.47; 95% CI [0.2; 1.1]; p = 0.08). An EFI of 0-4 was associated with a higher cumulative pregnancy rate for pregnancies obtained by artificial reproduction technology (ART), compared to an EFI of 5-10 (12 versus 6 pregnancies respectively, SHR = 1.9; CI95% [0.96; 3.8]; p = 0.06). Fecundability decreased from 12 months for EFI 0-4 and from 24 months for EFI 5-10.

CONCLUSION

Our analysis suggests that patients with an unfavorable EFI (≤4) have more ART pregnancies than patients with a favorable EFI (≥5) and should be referred for ART shortly after surgery. Patients with a favorable EFI may attempt spontaneous pregnancy for 24 months before referral.

Interventions and Practice Implications for the Management of Endometriosis

Endometriosis is a chronic inflammatory disorder in which endometrial tissue grows outside the uterus. Although the disorder is currently estimated to affect approximately 10% of reproductive-age women, there is evidence to suggest that many women remain undiagnosed. Women with endometriosis may experience pain, abnormal menstruation, gastrointestinal symptoms, chronic fatigue, and infertility. Because of the varying symptomatology, the disorder may also foster negative psychosocial outcomes and decrease overall quality of life. Because there is no known cure, an effective patient-clinician relationship is crucial to successful long-term management of the condition. Several interventions exist, including nonsurgical and surgical management. Here, we provide an overview of endometriosis and current treatment options, as well as evidence-based practice implications for nurses who work with women who have endometriosis.

Overweight and high serum total cholesterol were risk factors for the outcome of IVF/ICSI cycles in PCOS patients and a PCOS-specific predictive model of live birth rate was established

PURPOSE

The clinical outcome after in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) is diverse in infertility patients with polycystic ovary syndrome (PCOS). The aim of this study was to develop a nomogram based on an association of patients' characteristics to predict the live birth rate in PCOS patients.

METHODS

All women in a public university hospital who attempted to conceive by IVF/ICSI for PCOS infertility from January 2014 to October 2018 were included. The nomogram was built from a training cohort of 178 consecutive patients and tested on an independent validation cohort of 81 patients. PCOS was confirmed in all participants.

RESULTS

Three variates significantly associated with live birth rate of PCOS patients were BMI, total serum cholesterol (TC) and basal FSH. This predictive model built on the basis of BMI, TC, basal FSH, type of embryo transferred and age showed good calibration and discriminatory abilities, with an area under the curve (AUC) of 0.708 (95% CI 0.632-0.785) for the training cohort. The nomogram showed satisfactory goodness-of-fit and discrimination abilities in the independent validation cohort, with an AUC of 0.686 (95% CI 0.556-0.815).

CONCLUSION

Our simple evidence-based nomogram presents graphically risk factors and prognostic models for IVF/ICSI outcomes in patients with PCOS, which can offer useful guidance to clinicians and patients for individual adjuvant therapy.

Fertility Outcome after CO2 Laser Vaporization versus Cystectomy in Women with Ovarian Endometrioma: A Comparative Study

STUDY OBJECTIVE

To assess the postoperative likelihood of conception in patients with endometriomas managed by either CO₂ laser vaporization or cystectomy.

DESIGN

A retrospective study with prospective recording of data.

SETTING

University hospital.

PATIENTS

One hundred and forty-two patients with symptomatic endometriomas.

INTERVENTIONS

Patients underwent a standardized laparoscopic stripping technique (Group 1) or cyst vaporization with CO₂ fiber laser (Group 2). Patients wishing to become pregnant were allowed to attempt a spontaneous conception after surgery. If spontaneous conception failed, patients were referred for in vitro fertilization (IVF).

MEASUREMENTS AND MAIN RESULTS

The primary objective was to compare pregnancy rates between the 2 groups. The secondary objective was the identification of independent predictors of pregnancy. Thirty-nine women in Group 1 (53.4%) and 39 women in Group 2 (56.5%) desired to conceive after surgery. Three patients (7.7%) in Group 1 became pregnant following donor-IVF and were excluded. Pregnancies were recorded in 72.2% of patients treated with cystectomy and in 74.3% of those managed with CO₂ fiber laser (p = .83). Twenty patients (55.6%) in Group 1 and 14 patients (35.9%) in Group 2 conceived spontaneously (p = .08). Among patients who failed spontaneous conception, 21 patients (28%) achieved pregnancy through IVF (Group 1: n = 6, 16.7%; Group 2: n = 15, 38.5%; p = .08). Twenty patients (26.7%) never became pregnant. Age at the time of surgery (odds ratio (OR) = 0.86; 95% Confidence intervals (CI): 0.78-0.96, p = .002) and duration of infertility (OR=0.80; 95% CI: 0.69-0.92, p = .006) were identified as independent indicators for pregnancy.

CONCLUSION

CO₂ laser-treated endometrioma is associated with pregnancy rates equal to those observed after cystectomy and favorable IVF outcomes. The one step CO₂ fiber laser technique may represent a viable alternative to cystectomy.