Postfertilization telomere marker testing in human embryos: a pilot study of a novel embryo evaluation assay.

M. Brush: None. S. Auh: None. A.L. Ramanathan: None. A. Prakash: None. M. Walter: None. R. Muniyappa: None. R.K. Semple: Consulting Fee; Self; AstraZeneca, Alynylam. Grant Recipient; Self; Wellcome Trust. Speaker; Self; Novartis Pharmaceuticals, Eli Lilly & Company, Amryt. R.J. Brown: None. Severe insulin resistance (IR) can be classified into selective and non-selective IR. In selective IR, glucoregulatory insulin signaling is attenuated in only some insulin-responsive tissues and/or some signal transduction pathways. This causes compensatory hyperinsulinemia, which increases signaling in the relatively unimpaired tissues or pathways. An important example is hepatic lipogenesis, which is overstimulated in “common” obesity-related IR. Selective IR is also seen in the rare condition of lipodystrophy, in which patients lack some or all subcutaneous fat. In contrast, in global, or non-selective IR (usually caused by insulin receptor pathogenic variants), all insulin signal transduction is blocked at the receptor. Impaired receptor signaling affects all pathways and leads to low triglycerides with hyperglycemia. Despite these biochemical differences, these conditions have similar clinical presentations and can require genetic testing to reach a definitive diagnosis. We created accurate and accessible diagnostic tests using IR biomarkers to aid clinical discrimination of selective vs non-selective insulin resistance. We assessed fasting serum insulin (mcU/mL), HDL (mg/dL), triglycerides (mg/dL), adiponectin (ng/mL), and uric acid (mg/dL) from 182 patients with rare and common forms of non-selective (n=59) and selective IR (n=123). Patients with selective IR included those with a diagnosis of partial (n=26) and generalized lipodystrophy (n=14), and obesity (n=83). Patients with non-selective IR included those with pathogenic variants of the insulin receptor gene (n=40) or autoantibodies to the insulin receptor (n=19). The Receiver Operating Characteristic (ROC) area under the curve (AUC), sensitivity, and specificity of each combination of biomarkers were assessed in development and validation cohorts. We identified two combinations of biomarkers with high sensitivity and specificity. Test one, (insulin*HDL)÷(triglycerides*uric acid) had an AUC of 0.988 in the development cohort and 0.997 in the validation cohort. A cut-off of ≥8.3 predicted non-selective IR with a sensitivity of 100% in both the development and validation cohorts and a specificity of 94% and 89% in the development and validation cohorts respectively. Test two, (insulin*HDL*adiponectin)÷ (triglycerides*uric acid), had an AUC of 0.983 in the development cohort and 1.0 in the validation cohort. A cut-off of ≥51682 predicted non-selective IR in the validation cohort with 93% and 100% sensitivity in the development and validation cohorts respectively and 94% specificity in both cohorts. Both tests use easily measured biomarkers to discriminate selective and non-selective IR with high diagnostic accuracy. These accurate and accessible tests could be used in clinical settings to aid diagnostic classification of severe IR. Saturday, June 1, 2024

3543 Background: This study aimed to develop and validate a prognostic score integrating baseline metabolically active tumor volume (MATV) and clinical factors in metastatic colorectal cancer (mCRC) patients. Methods: The development cohort included chemorefractory mCRC patients enrolled in two prospective multicenter non-randomized trials evaluating sorafenib/regorafenib as last line therapy. The validation cohort included mCRC patients from another center, treated with chemotherapy and bevacizumab as first line. Baseline MATV was defined as the sum of metabolically active volumes of all target lesions identified on the baseline 18F-FDG PET/CT. MATV optimal cutoff for OS prediction was determined from the development cohort with Contal and O’Quigley’s method. MATV, age, gender, BMI, ECOG PS, years since diagnosis, and KRAS status were included in a multivariate analysis. A prognostic score to predict OS was developed from the development cohort using Cox proportional hazards model. Results: MATV and clinical factors were evaluable respectively in 155 and 122 patients of the development and validation cohorts. In univariate analysis, MATV with cutoff set at 100 cm³ identified two risk groups with different median OS (mOS) in both the development (4.5 vs 10.9 months, HR: 2.64; p < 0.001) and validation cohorts (20.9 vs 42.9 months, HR: 2.39; p < 0.001). A multivariate analysis identified four independent negative predictors of OS (high MATV, short time since diagnosis, poor PS, BMI < 25). Combining these factors in a prognostic score for OS (best cutoff:-2) allowed to identify two risk groups with different mOS in the development (4.4 vs 13.4 months, HR: 3.67; p < 0.001) and validation cohorts (25 vs 63.8 months, HR: 2.5; p = 0.001). Conclusions: In mCRC patients, the high prognostic value of baseline MATV found in the development cohort was confirmed by external validation, independently of patients’ treatment. In both the development and validation cohorts the prognostic score for OS allowed to identify two risk groups of mCRC patients with significantly different mOS. MATV and our prognostic score for OS should provide a firm basis for risk stratification, in clinical practice and research trials.

ObjectiveThe definition of rectosigmoid junction (RSJ) is still in debate. The treatment and prognosis of patients with rectosigmoid junction cancer (RSJC) and positive lymph nodes (PLN-RSJCs) are mostly based on the American Joint Committee on Cancer (AJCC) staging system. Our study aims to assist clinicians in creating a more intuitive and accurate nomogram model for PLN-RSJCs for the prediction of patient overall survival (OS) after surgery.MethodsBased on the Surveillance, Epidemiology, and End Results (SEER) database, we extracted 3384 patients with PLN-RSJCs and randomly divided them into development (n = 2344) and validation (n = 1004) cohorts at a ratio of 7:3. Using univariate and multivariate COX regression analysis, we identified independent risk factors associated with OS in PLN-RSJCs in the development cohort, which were further used to establish a nomogram model. To verify the accuracy of the model, the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and an internal validation cohort have been employed. Decision curve analysis (DCA) was used to assess the clinical applicability and benefits of the generated model. Survival curves of the low- and high-risk groups were calculated using the Kaplan–Meier method together with the log-rank test.ResultsAge, marital, chemotherapy, AJCC stage, T and N stage of TNM system, tumor size, and regional lymph nodes were selected as independent risk factors and included in the nomogram model. The C-index of this nomogram in the development (0.751;0.737–0.765) and validation cohorts (0.750;0.764–0.736) were more significant than that of the AJCC 7th staging system (0.681; 0.665–0.697). The ROC curve with the calculated area under the curve (AUC) in the development cohort was 0.845,0.808 and 0.800 for 1-year, 3-year and 5-year OS, AUC in the validation cohort was 0.815,0.833 and 0.814 for 1-year, 3-year and 5-year, respectively. The calibration plots of both cohorts for 1-year,3-year and 5-year OS all demonstrated good agreement between actual clinical observations and predicted outcomes. In the development cohort, the DCA showed that the nomogram prediction model is more advantageous for clinical application than the AJCC 7th staging system. Kaplan–Meier curves in the low and high groups showed significant difference in patient OS.ConclusionsWe established an accurate nomogram model for PLN-RSJCs, intended to support clinicians in the treatment and follow-up of patients.

Aneuploidy is one genetic factor leading to the failure of embryo implantation. This study aimed to investigate the relationship between the day of embryo blastulation and the ploidy status of embryo, to aid in selecting embryos with the most likelihood of of beingeuploid in anoninvasive way. This retrospective study recruited women undergoing preimplantation genetic testing (PGT) for aneuploidy (PGT-A) with trophectoderm biopsy from January 2019 to December 2020. The ploidy status of embryos was determined by next-generation sequencing (NGS). Altogether, 2531 blastocysts from 839 PGT-A cycles were evaluated. The euploid rate of day 5 blastocysts was significantly higher than that of day 6 blastocysts, either from the same ovarian stimulation (OS) cycles (49.9% vs 35.7%, P < 0.001) or from different OS cycles (48.2% vs 27.8%, P < 0.001). This effect of increasing time to embryo blastulation significantly reducing the prevalence of euploidy was not seen in women at age 38 or older. However, after single euploid embryo transfer, the clinical outcomes of day 5 blastocysts were comparable to those of day 6 blastocysts. In non-PGT cycles, our data support the selection of day 5 blastocysts for transfer over day 6 blastocysts. Further, women with poor ovarian reserve incline to obtain only day 6 blastocysts, and PGT-A is valuable for identifying the ploidy status of embryos, especially for those with advanced age. However, the stage of blastocysts will not affect the clinical outcome after transfer when they are identified as euploid.

Cancer stage is a key determinant of outcomes; however, stage is not available in claims-based data sources used for real-world evaluations. We compare multiple methods for classifying lung cancer stage from claims data. Our study used the linked SEER-Medicare data. The patient samples included fee-for-service Medicare beneficiaries diagnosed with lung cancer from 2010 to 2011 (development cohort) and 2012 to 2013 (validation cohort) who received chemotherapy. Classification algorithms considered Medicare Part A and B claims for care in the 3 months before and after chemotherapy initiation. We developed a clinical algorithm to predict stage IV (v I to III) cancer on the basis of treatment patterns (surgery, radiotherapy, chemotherapy). We also considered an ensemble of claims-based machine learning algorithms. Classification methods were trained in the development cohort, and performance was measured in both cohorts. The SEER data were the gold standard for cancer stage. Development and validation cohorts included 14,760 and 14,620 patients with lung cancer, respectively. Validation analyses assessed clinical, random forest, and simple logistic regression algorithms. The best performing classifier within the development cohort was the random forests, but this performance was not replicated in validation analysis. Logistic regression had stable performance across cohorts. Compared with the clinical algorithm, the 14-variable logistic regression algorithm demonstrated higher accuracy in both the development (77% v 71%) and validation cohorts (77% v 73%), with improved specificity for stage IV disease. Machine learning algorithms have potential to improve lung cancer stage classification but may be prone to overfitting. Use of ensembles, cross-validation, and external validation can aid generalizability. Degradation of accuracy between development and validation cohorts suggests the need for caution in implementing machine learning in research or care delivery.

Forecasting seizure risk in adults with focal epilepsy: a development and validation study

Lung transplantation is the final treatment option for end-stage lung disease. In this study, we evaluated the individual risk of 1-year mortality at each stage of the lung transplantation process. This was a retrospective analysis of patients undergoing bilateral lung transplantation between January 2014 and December 2019 in three French academic centers. Patients were randomly divided into development and validation cohorts. Three multivariable logistic regression models of 1-year mortality were applied (A) at recipient registration, (B) the graft allocation, and (C) after surgery. The 1-year mortality was predicted for individual patients assigned to three risk groups at time points A-C. The study population consisted of 478 patients with a mean (SD) age of 49.0 (14.3) years. The 1-year mortality rate was 23.0%. There were no significant differences in patient characteristics between the development (n = 319) and validation (n = 159) cohorts. The models analyzed recipient, donor, and intraoperative variables. The discriminatory power (area under the receiver operating characteristic curve) was 0.67 (0.62-0.73), 0.70 (0.63-0.77), and 0.82 (0.77-0.88), respectively, in the development cohort, 0.74 (0.64-0.85), 0.76 (0.66-0.86) and 0.87 (0.79 - 0.95), respectively, in the validation cohort. Survival rates were significantly different among the low- (< 15%), intermediate- (15%-45%), and high-risk (> 45%) groups in both cohorts. Risk prediction models allow estimation of the 1-year mortality risk of individual patients during the lung transplantation process. These models may help caregivers identify high-risk patients at times A-C, and reduce the risk at subsequent time-points.

Infants undergoing congenital heart surgery with cardiopulmonary bypass (CPB) are at high risk for perioperative neurological injury, and early postoperative cerebral desaturation events (CDEs) remain common. Reliable tools for individualized risk stratification are lacking. This study aims to develop and internally validate a nomogram for predicting early postoperative CDEs in infants undergoing congenital heart surgery with CPB. This retrospective cohort study included 397 infants aged >1 month and ≤1 year who underwent elective congenital heart surgery with CPB. Patients were randomly divided into a development cohort (n = 277) and a validation cohort (n = 120). Early postoperative CDEs were assessed using near-infrared spectroscopy monitoring at cardiac intensive care unit admission. Prespecified perioperative variables measured before or during CPB were entered into a multivariable logistic regression model to construct a nomogram. Model performance was evaluated using discrimination, calibration, and decision curve analysis, with internal validation. Early postoperative CDEs occurred in 16.88% of the overall cohort, with comparable incidences in the development (18.1%) and validation (14.2%) cohorts. The final model included body surface area, RACHS-1 category, CPB duration, hematocrit during CPB, and CPB temperature strategy. The nomogram demonstrated good discrimination in the development cohort [C-statistic 0.798, 95% confidence interval (CI) 0.729-0.857] and the validation cohort (0.767, 95% CI 0.629-0.886), with satisfactory calibration. The decision curve analysis suggested potential clinical usefulness across clinically relevant threshold probabilities. An internally validated, infant-specific nomogram based on routinely available perioperative variables was developed to predict early postoperative cerebral desaturation following congenital heart surgery with CPB. External validation is required before clinical implementation.

The Naples Prognostic Score (NPS) is a composite index that combines serum albumin (ALB), total cholesterol (TC), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR). It has proven prognostic value across various cancers. However, its use in oral squamous cell carcinoma (OSCC) has not been sufficiently recognized for disease-specific characteristics. This study aimed to create and validate a modified NPS (mNPS) specifically for OSCC, comparing its prognostic effectiveness to that of the conventional NPS and other established indices. A total of 479 patients with histologically confirmed OSCC who underwent curative-intent surgery between 2012 and 2019 were enrolled. Patients were randomly assigned to development (n=335) and validation (n=144) cohorts. Cohort-specific optimal cut-off values for ALB, TC, NLR, and LMR were determined using X-Tile software to construct the mNPS. Prognostic performance of mNPS was compared with conventional NPS, SII, SIRI, and CONUT using ROC analysis, C-index, and Cox regression. A nomogram incorporating mNPS and other independent risk factors was constructed and validated. Multivariate Cox regression confirmed mNPS as an independent predictor of OS (Group 1: HR 2.18; Group 2: HR 3.10; P<0.01). The mNPS-based nomogram showed superior prognostic accuracy for 1-, 3-, and 5-year OS with AUCs of 0.83, 0.80, and 0.83 in the development cohort, and 0.80, 0.79, and 0.82 in the validation cohort. Corresponding C-index values were 0.73 (OS), 0.72 (DFS), and 0.73 (DSS) in the development cohort, and 0.74, 0.71, and 0.76 in the validation cohort, all outperforming the NPS-based model. Calibration and decision curve analyses confirmed the model's robustness and clinical utility. Through OSCC-specific threshold recalibration, mNPS demonstrated improved prognostic discrimination compared with conventional indices. Incorporating mNPS into a nomogram enhances individualized risk stratification and provides a practical tool for guiding clinical decision-making in OSCC.

MRI has relatively low sensitivity and specificity in detecting lymph node metastases. This study aimed to develop and validate an MRI radiomics-based model for predicting lateral pelvic lymph node (LPLN) metastasis in rectal cancer patients who underwent LPLN dissection, and to compare its performance with that of radiologists. This multicenter retrospective study included 336 rectal cancer patients (199 men; mean age, 58.9 years ± 11.1 [standard deviation]) who underwent LPLN dissection. Patients were divided into development (n = 190) and validation (n = 146) cohorts. Radiomics features were extracted from MR images, and the Least Absolute Shrinkage and Selection Operator regression was used to construct radiomics and clinical-radiomics models. Model performance was compared with radiologists using receiver operating characteristic (ROC) analysis. Malignant LPLN was diagnosed in 32.4% of the development cohort (65/190) and 32.9% of the validation cohort (48/146) (P = 0.798). Seven radiomics features and two clinical features were selected. The radiomics and clinical-radiomics models demonstrated area under the curves (AUCs) of 0.819 and 0.830 in the development cohort and 0.821 and 0.829 in the validation cohort, respectively. The optimal cut-off (− 0.47) yielded sensitivities of 72.3% and 45.8% and specificities of 82.4% and 87.8% in the development and validation cohorts, respectively. Decision curve analysis indicated no additional net benefit from the clinical-radiomics model compared to the radiomics-only model. Radiologists’ AUCs were significantly lower than that of the radiomics model (0.842) and improved with radiomics probability scores (0.734 vs. 0.801; 0.668 vs. 0.791). The MRI-based radiomics model significantly improves the prediction of LPLN metastasis in rectal cancer, outperforming conventional criteria used by radiologists.Trial registration: Retrospectively registered.

Renal cell carcinoma (RCC) is a lethal urological malignancy. Precise risk-stratification is very important for decision-making in postoperative patient management. This study aimed to establish and validate a prognostic nomogram of overall survival (OS) in patients with RCC based on Surveillance, Epidemiology, and End Results (SEER) and TCGA database. The retrospective data of 40,154 patients diagnosed with RCC during 2010 to 2015 from SEER database (development cohort) and 1,188 patients from TCGA database (validation cohort) were downloaded for analysis. Independent prognostic factors were identified by univariate and multivariate Cox regression analyses and adopted to set up a predictive nomogram of OS. The discrimination and calibration of the nomogram were evaluated by ROC curves, C-index values, and calibration plots, and survival analyses were conducted using Kaplan-Meier curves and long-rank tests. The results of multivariate Cox regression analysis demonstrated that age, sex, tumor grade, the American Joint Committee on Cancer (AJCC) stage, tumor size, and pathological types were independent predictors of the OS of RCC patients. These variables were integrated to construct the nomogram, and verification was conducted subsequently. The area under the ROC curve values of 3- and 5-year survival were 0.785 and 0.769 in the development cohort and 0.786 and 0.763 in the validation cohort. The C-index was 0.746 (95% CI: 0.740-0.752) in the development cohort and 0.763 (95% CI: 0.738-0.788) in the validation cohort, indicating good performance of the nomogram. Calibration curve analysis also suggested supreme accuracy on prediction. Finally, patients in the development and validation cohorts were stratified into three risk-level groups (high, intermediate, and low) based on the risk scores calculated by the nomogram, and significant differences in OS were observed among these three groups. In this study, a prognostic nomogram was established to provide tool for clinicians to better advise RCC patients, determine the follow-up strategies and to select suitable patients for clinical trials.

BackgroundRefractory mycoplasma pneumoniae pneumonia (RMPP) can result in severe complications and long-term effects. Early identification of RMPP and appropriate treatments can effectively alleviate complications and restrict the progression of sequelae. There is currently a dearth of a comprehensive and efficient model for predicting and evaluating RMPP.MethodsThe development cohort consisted of patients with mycoplasma pneumoniae pneumonia (MPP) who underwent fiberoptic bronchoscopy between January 2019 and October 2021. Multivariable logistic regression analysis was used to identify independent risk factors for RMPP, and a nomogram model was developed that included initial admission examinations and clinical characteristics. The accuracy of the model was validated using a validation cohort of patients enrolled between November 2021 and April 2023.Result373 patients were enrolled including 229 cases of the development cohort and 144 cases of the validation cohort. Multivariable Logistic regression analysis showed that the six independent risk factors for RMPP were age (OR = 1.151, 95% confidence interval (CI) 1.014–1.306), acute physiology and chronic health evaluation (APACHE) II score(OR = 0.872, 95% CI 0.792–0.961), computed tomography (CT) total score(OR = 1.407, 95% CI 1.258–1.575), secretion color(OR = 2.719, 95% CI 1.562–4.734), mucosal edema(OR = 5.064, 95% CI 2.748-9.300), and procalcitonin (PCT) (OR = 0.871, 95% CI 0.806–0.941). The Area Under Curve (AUC) of the model in the development cohort and validation cohort was 0.913(95%CI 0.875–0.951) and 0.811(95%CI 0.739–0.883), respectively. Hosmer-Lemeshow test showed that the model’s goodness of fit had good consistency in the development cohort and validation cohort (χ2 = 10.546, P = 0.229; χ2 = 7.894, P = 0.342). The DCA of the development and validation cohorts showed clear net benefits using the nomogram to predict RMPP.ConclusionWe developed a nomogram model that integrates clinical, imaging, and bronchoscopic features to enable early and accurate prediction of the risk of RMPP in children. This model provides a quantitative tool for personalized intervention and demonstrates significant clinical application value.

Background: Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer-related death among Hispanic women in the United States. For women of European ancestry, genome-wide association studies (GWAS) have identified common variants, primarily single-nucleotide polymorphisms (SNPs), that individually confer modest risk but together explain a significant proportion of genetic BC predisposition. For Hispanic women, the genetic contribution of SNPs to BC risk is not well understood. In these studies, we aim to develop and validate a polygenic score to improve risk assessment for Hispanic women who test negative for mutations in known BC susceptibility genes. Methods: Genotypes and clinical histories were collected from consecutive development and validation cohorts of patients referred for hereditary cancer testing. Study subjects include women who report strictly Hispanic or Latin American ancestry, and who test negative for mutations in 11 genes associated with breast cancer (BRCA1, BRCA2, TP53, PTEN, STK11, CDH1, PALB2, CHEK2, ATM, NBN, BARD1). Based on a development cohort ascertained through June 2017, we evaluated an 86-SNP Residual Risk Score (RRS) that was previously developed and validated for women of European ancestry. In the same cohort we are developing a Hispanic Residual Risk Score (HRRS) optimized for women of Hispanic ancestry. BC associations of individual SNPs are being established through meta-analysis of the development cohort and published Hispanic studies. Multivariate logistic regression models were used to evaluate the 86-SNP RRS, and were the primary statistical tool for evaluation of individual SNPs and candidate polygenic scores. All models included personal/family cancer history and age as independent variables. P-values are based on likelihood ratio test statistics, and reported as two-sided. The development and validation studies are being conducted according to a protocol approved by the Quorum Institutional Review Board. Results: The development cohort included 5,454 Hispanic women, 24% of whom reported a personal history of BC. The 86-SNP RRS was significantly associated with a personal history of BC after accounting for personal and family cancer history (p&amp;lt;10-19) with odds ratio per unit standard deviation 1.39 (95% CI = 1.30-1.50). To date, more than 5,000 Hispanic women have been ascertained for inclusion in the validation cohort. Results comparing discriminatory accuracy of the RRS and the HRRS will be presented. Conclusions: The implementation of a clinically validated polygenic score may improve risk assessment and medical management of Hispanic women who test negative for monogenic BC mutations. The HRRS will be validated in an independent study population according to a pre-specified statistical analysis plan. Citation Format: Hughes ER, Wagner S, Pruss D, Gallagher SK, Swedlund B, Bulka K, Hoff R, Jammulapati S, Morris B, Perry T, Lanchbury JS, Gutin A. Development and validation of a polygenic score to predict breast cancer risk in unaffected Hispanic women negative for mutations on a multi-gene hereditary cancer panel [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-10-02.

Study question Does age and AMH impact early pregnancy outcomes in euploid single embryo transfers using NGS-based PGT-A for individuals aged 35 and above? Summary answer In this NGS-based PGT-A study, no significant differences in early pregnancy rates were observed based on age or AMH among individuals aged 35 and above. What is known already A recent meta-analysis suggest a decline in pregnancy rates with advancing age in the context of euploid embryo transfer following PGT-A. However, it’s crucial to acknowledge variations in PGT-A techniques across studies. While individuals with low AMH levels may encounter challenges in obtaining euploid embryos, the precise impact of AMH on pregnancy rates post-embryo acquisition remains unclear. Study design, size, duration A retrospective cohort study was conducted on 428 cycles of single euploid embryo transfers in patients aged 35 and above, performed at the single infertility center from January to December 2022. Participants/materials, setting, methods The study examined frozen-thawed embryo transfer cycles in patients aged 35 and above, analyzing early pregnancy outcomes in single euploid embryo transfers. Participants were grouped by age(35-38, 38-42, 42 and above) and AMH levels(&amp;lt;1, 1-3, &amp;gt;3 ng/ml). NGS-based PGT-A conducted embryo biopsy. The analysis aimed to detect differences in initial pregnancy rates among age and AMH subgroups. The differences between groups were compared using Pearson’s chi-squared test. Main results and the role of chance The average age at transfer was 39.05 ± 2.53 years, with a mean AMH of 2.60 ± 1.86(ng/ml). The mean number of prior frozen embryo transfer (FET) cycles was 1.9 ± 2.04, with 119 patients (27.8%) having undergone three or more transfer cycles. Transfer cycles comprised 363 (84.8%) artificial cycles and 65 (15.2%) ovulatory cycles, with an average endometrial thickness of 8.95 ± 1.73 mm at the time of transfer. The β-hCG positive rate was 57.7% (247/428), and the clinical pregnancy rate was 53.5% (229/428). When analyzing positive b-hCG results by age, rates were 59.9% (82/137) for ages 35-38, 55.5% (121/218) for ages 38-42, and 60.3% (44/73) for ages 42 and above (p = 0.641). In terms of AMH, β-hCG positive rates were 52.1% (37/71) for &amp;lt;1, 59.1% (120/203) for 1-3, and 58.0% (83/143) for ≥3, with no statistically significant differences (p = 0.584). Clinical pregnancy rates were 54.0% for ages 35-38, 51.8% for ages 38-42, and 57.5% for ages 42 and above. For AMH, rates were 50.7% for &amp;lt;1, 53.2% for 1-3, and 54.5% for ≥3, with no statistically significant differences (p = 0.869). Limitations, reasons for caution Being a retrospective study analyzing patients undergoing embryo transfer, our study overlooks variables related to oocyte retrieval. Given the challenges of obtaining euploid embryos in advanced age, further investigation is warranted to explore the probability and duration of embryo acquisition in this population. Wider implications of the findings In this study, utilizing NGS-based PGT-A after generating euploid embryos, no significant differences in early pregnancy outcomes were observed for women aged 35 and older, considering age and AMH levels. The study suggests further research is needed to explore if NGS-based PGT-A can address limitations of traditional chromosomal testing methods. Trial registration number not applicable

Does the use of preimplantation genetic testing for aneuploidies (PGT-A), personalized embryo transfer with endometrial receptivity assay (pET-ERA), or the use of donated oocytes modify the incidence of biochemical pregnancy loss (BPL) in frozen single embryo transfer (FSET)? Following FSET, BPL incidence does not differ between own and donated oocytes, and the use of PGT-A with euploid embryo transfer or pET-ERA results in a similar incidence of BPL compared to cycles without embryo or endometrial analysis. BPL occurs frequently after IVF, and many factors have been associated with its incidence. The etiology of BPL is not well known, but the most probable cause seems to be either a low-quality embryo or impaired endometrial maintenance. The impact of techniques diagnosing embryonic ploidy or endometrial receptivity on BPL incidence and the BPL incidence between own and donated oocytes have not been analyzed. This is a retrospective cohort study analyzing the incidence of BPL over 3741 cycles of FSET derived from own (2399 cycles) and donated (1342 cycles) oocytes between January 2013 and January 2022 in 1736 of which PGT-A, pET-ERA, or both were applied. We defined BPL as a pregnancy diagnosed only by serum β-hCG > 10 UI/l followed by a decrease that does not result in a clinical pregnancy. Clinical pregnancy was defined as the presence of gestational sac on transvaginal ultrasound. We compared BPL rates among patients undergoing 2399 FSETs from own oocytes, which comprised 1310 cycles of embryos analyzed by PGT-A, 950 cycles of untested embryos, 30 cycles of untested embryos with pET-ERA, and a subgroup of 109 cycles analyzed by both PGT-A and pET-ERA. We also included a total of 1342 FSET cycles from donated oocytes comprising 132, 1055, 140, and 15 cycles in the same groups, respectively. In FSET from own oocytes, the overall BPL rate per embryo transfer was 8.2% (95% CI [7.09-9.33]). In untested embryo transfers, the BPL rate was 7.5% [5.91-9.37]. In the PGT-A group, the BPL rate was 8.8% [7.32-10.47]. In the pET-ERA group, the rate was 6.7% [0.82-22.07]. In the PGT-A+ERA group, the rate was 6.5% [2.65-12.90]. No significant differences were found (P = 0.626). A multivariate analysis considering clinically meaningful variables that were significantly different among groups, taking the untested embryos/endometrium group as a reference, showed comparable incidences among groups. For PGT-A, the adjusted odds ratio (AdjOR) was 1.154 [0.768-1.735] (P = 0.49) and for PGT-A+ERA 0.885 [0.330-2.375] (P = 0.808). Because of a low number of registered cases in the pET-ERA group, and to prevent statistical errors and convergence issues, this group was excluded from further analysis. In FSET of donated oocytes, the overall BPL rate per embryo transfer was 4.9% [3.76-6.14]. In the PGT-A group, the BPL rate was 6.8% [3.16-12.55]. In the pET-ERA group, the rate was 5.0% [2.03-10.03]. In untested embryo transfers, the rate was 4.7% [3.46-6.10]. No cases occurred in the PGT-A+ERA group, and no significant differences were found (P = 0.578). The multivariate analysis showed comparable incidences among groups. For PGT-A the AdjOR was 1.669 [0.702-3.972] (P = 0.247) and for pET-ERA 1.189 [0.433-3.265] (P = 0.737). The PGT-A+ERA group was eliminated from the model to prevent statistical errors and convergence issues because no BPL cases were registered in this group. In the multivariate analysis, when the sources of oocytes were compared, own versus donated, no significant differences were found in the incidence of BPL. This was a retrospective cohort study with potential biases. In addition, we were unable to control differences among groups due to modifications in medical or laboratory protocols during this long time period, which may modify the relationships being addressed. Factors previously associated with BPL, such as immunological conditions other than thyroid autoimmunity, were not considered in this study. Limited sample sizes of some groups may limit the statistical power for finding differences that can be present in the general population. BPL may be related to a mechanism not associated with the chromosomal constitution of the embryo or the transcriptome of the endometrium. More studies are needed to explore the factors associated with this reproductive issue. No specific funding was available for this study. None of the authors have a conflict of interest to declare with regard to this study. This trial was registered at clinicaltrials.gov (NCT04549909).