A comparison of morphokinetic models and morphological selection for prioritizing euploid embryos: a multicentre cohort study.

STUDY QUESTION: Are morphokinetic models better at prioritizing a euploid embryo for transfer over morphological selection by an embryologist? SUMMARY ANSWER: Morphokinetic algorithms lead to an improved prioritization of euploid embryos when compared to embryologist selection. WHAT IS KNOWN ALREADY: PREFER (predicting euploidy for embryos in reproductive medicine) is a previously published morphokinetic model associated with live birth and miscarriage. The second model uses live birth as the target outcome (LB model). STUDY DESIGN, SIZE, DURATION: Data for this cohort study were obtained from 1958 biopsied blastocysts at nine IVF clinics across the UK from January 2021 to December 2022. PARTICIPANTS/MATERIALS, SETTING, METHODS: The ability of the PREFER and LB models to prioritize a euploid embryo was compared against arbitrary selection and the prediction of four embryologists using the timelapse video, blinded to the morphokinetic time stamp. The comparisons were made using calculated percentages and normalized discounted cumulative gain (NDCG), whereby an NDCG score of 1 would equate to all euploid embryos being ranked first. In arbitrary selection, the ploidy status was randomly assigned within each cycle and the NDGC calculated, and this was then repeated 100 times and the mean obtained. MAIN RESULTS AND THE ROLE OF CHANCE: Arbitrary embryo selection would rank a euploid embryo first 37% of the time, embryologist selection 39%, and the LB and PREFER ploidy morphokinetic models 46% and 47% of the time, respectively. The AUC for LB and PREFER model was 0.62 and 0.63, respectively. Morphological selection did not significantly improve the performance of both morphokinetic models when used in combination. There was a significant difference between the NDGC metric of the PREFER model versus embryologist selection at 0.96 and 0.87, respectively (t = 14.1, P < 0.001). Similarly, there was a significant difference between the LB model and embryologist selection with an NDGC metric of 0.95 and 0.87, respectively (t = 12.0, P < 0.001). All four embryologists ranked embryos similarly, with an intraclass coefficient of 0.91 (95% CI 0.82-0.95, P < 0.001). LIMITATIONS, REASONS FOR CAUTION: Aside from the retrospective study design, limitations include allowing the embryologist to watch the time lapse video, potentially providing more information than a truly static morphological assessment. Furthermore, the embryologists at the participating centres were familiar with the significant variables in time lapse, which could bias the results. WIDER IMPLICATIONS OF THE FINDINGS: The present study shows that the use of morphokinetic models, namely PREFER and LB, translates into improved euploid embryo selection. STUDY FUNDING/COMPETING INTEREST(S): This study received no specific grant funding from any funding agency in the public, commercial or not-for-profit sectors. Dr Alison Campbell is minor share holder of Care Fertility. All other authors have no conflicts of interest to declare. Time lapse is a technology for which patients are charged extra at participating centres. TRIAL REGISTRATION NUMBER: N/A.

A comparison of 12 machine learning models developed to predict ploidy, using a morphokinetic meta-dataset of 8147 embryos.

STUDY QUESTION: Are machine learning methods superior to traditional statistics in predicting blastocyst ploidy status using morphokinetic and clinical biodata? SUMMARY ANSWER: Mixed effects logistic regression performed better than all machine learning methods for ploidy prediction using our dataset of 8147 embryos. WHAT IS KNOWN ALREADY: Morphokinetic timings have been demonstrated to be delayed in aneuploid embryos. Machine learning and statistical models are increasingly being built, however, until now they have been limited by data insufficiency. STUDY DESIGN, SIZE, DURATION: This is a multicentre cohort study. Data were obtained from 8147 biopsied blastocysts from 1725 patients, treated from 2012 to 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS: All embryos were cultured in a time-lapse system at nine IVF clinics in the UK. A total of 3004 euploid embryos and 5023 aneuploid embryos were included in the final verified dataset. We developed a total of 12 models using four different approaches: mixed effects multivariable logistic regression, random forest classifiers, extreme gradient boosting, and deep learning. For each of the four algorithms, two models were created, the first consisting of 22 covariates using 8027 embryos (Dataset 1) and the second, a dataset of 2373 embryos and 26 covariates (Dataset 2). Four final models were created by switching the target outcome from euploid to aneuploid for each algorithm (Dataset 1). Models were validated using internal-external cross-validation and external validation. MAIN RESULTS AND THE ROLE OF CHANCE: All morphokinetic variables were significantly delayed in aneuploid embryos. The likelihood of euploidy was significantly increased the more expanded the blastocyst (P < 0.001) and the better the trophectoderm grade (P < 0.01). Univariable analysis showed no association with ploidy status for morula or cleavage stage fragmentation, morula grade, fertilization method, sperm concentration, or progressive motility. Male age did not correlate with the percentage of euploid embryos when stratified for female age. Multinucleation at the two-cell or four-cell stage was not associated with ploidy status. The best-performing model was logistic regression built using the larger dataset with 22 predictors (F1 score 0.59 for predicting euploidy; F1 score 0.77 for predicting aneuploidy; AUC 0.71; 95% CI 0.67-0.73). The best-performing models using the algorithms from random forest, extreme gradient boosting, and deep learning achieved an AUC of 0.68, 0.63, and 0.63, respectively. When using only morphokinetic predictors the AUC was 0.61 for predicting ploidy status, whereas a model incorporating only embryo grading was unable to discriminate aneuploid embryos (AUC = 0.52). The ploidy prediction model's performance improved with increasing age of the egg provider. LIMITATIONS, REASONS FOR CAUTION: The models have not been validated in a prospective study design or yet been used to determine whether they improve clinical outcomes. WIDER IMPLICATIONS OF THE FINDINGS: This model may aid decision-making, particularly where pre-implantation genetic testing for aneuploidy is not permitted or for prioritizing embryos for biopsy. STUDY FUNDING/COMPETING INTEREST(S): No specific funding was sought for this study; university funds supported the first author. A.Ca. is a minor shareholder of participating centres. TRIAL REGISTRATION NUMBER: N/A.

Association between a morphokinetic ploidy prediction model risk score and miscarriage and live birth: a multicentre cohort study.

OBJECTIVE: To determine whether the aneuploidy risk score from a morphokinetic ploidy prediction model, Predicting Euploidy for Embryos in Reproductive Medicine (PREFER), is associated with miscarriage and live birth outcomes. DESIGN: Multicentre cohort study. SETTING: Nine in vitro fertilization clinics in the United Kingdom. PATIENTS: Data were obtained from the treatment of patients from 2016-2019. A total of 3587 fresh single embryo transfers were included; preimplantation genetic testing for aneuploidy) cycles were excluded. INTERVENTION: PREFER is a model developed using 8,147 biopsied blastocyst specimens to predict ploidy status using morphokinetic and clinical biodata. A second model using only morphokinetic (MK) predictors was developed, P PREFER-MK. The models will categorize embryos into the following three risk score categories for aneuploidy: "high risk," "medium risk," and "low risk." MAIN OUTCOME MEASURES: The primary outcomes are miscarriage and live birth. Secondary outcomes include biochemical clinical pregnancy per single embryo transfer. RESULTS: When applying PREFER, the miscarriage rates were 12%, 14%, and 22% in the "low risk," "moderate risk," and "high risk" categories, respectively. Those embryos deemed "high risk" had a significantly higher egg provider age compared with "low risk," and there was little variation in risk categories in patients of the same age. The trend in miscarriage rate was not seen when using PREFER-MK; however, there was an association with live birth, increasing from 38%-49% and 50% in the "high risk," "moderate risk," and "low risk" groups, respectively. An adjusted logistic regression analysis demonstrated that PREFER-MK was not associated with miscarriage when comparing "high risk" to "moderate risk" embryos (odds ratio [OR], 0.87; 95% confidence interval [CI], 0.63-1.63) or "high risk" to "low risk" embryos (OR, 1.07; 95% CI, 0.79-1.46). An embryo deemed "low risk" by PREFER-MK was significantly more likely to result in a live birth than those embryos graded "high risk" (OR, 1.95; 95% CI, 1.65-2.25). CONCLUSION: The PREFER model's risk scores were significantly associated with live births and miscarriages. Importantly, this study also found that this model applied too much weight to clinical factors, such that it could no longer rank a patient's embryos effectively. Therefore, a model including only MKs would be preferred; this was similarly associated with live birth but not miscarriage.

Morphological and morphokinetic associations with aneuploidy: a systematic review and meta-analysis.

BACKGROUND: A time lapse system (TLS) is utilized in some fertility clinics with the aim of predicting embryo viability and chance of live birth during IVF. It has been hypothesized that aneuploid embryos display altered morphokinetics as a consequence of their abnormal chromosome complement. Since aneuploidy is one of the fundamental reasons for IVF failure and miscarriage, attention has focused on utilizing morphokinetics to develop models to non-invasively risk stratify embryos for ploidy status. This could avoid or reduce the costs associated with pre-implantation genetic testing for aneuploidy (PGT-A). Furthermore, TLS have provided an understanding of the true prevalence of other dysmorphisms. Hypothetically, the incorporation of morphological features into a model could act synergistically, improving a model's discriminative ability to predict ploidy status. OBJECTIVE AND RATIONALE: The aim of this systematic review and meta-analysis was to investigate associations between ploidy status and morphokinetic or morphological features commonly denoted on a TLS. This will determine the feasibility of a prediction model for euploidy and summarize the most useful prognostic markers to be included in model development. SEARCH METHODS: Five separate searches were conducted in Medline, Embase, PubMed and Cinahl from inception to 1 July 2021. Search terms and word variants included, among others, PGT-A, ploidy, morphokinetics and time lapse, and the latter were successively substituted for the following morphological parameters: fragmentation, multinucleation, abnormal cleavage and contraction. Studies were limited to human studies. OUTCOMES: Overall, 58 studies were included incorporating over 40 000 embryos. All except one study had a moderate risk of bias in at least one domain when assessed by the quality in prognostic studies tool. Ten morphokinetic variables were significantly delayed in aneuploid embryos. When excluding studies using less reliable genetic technologies, the most notable variables were: time to eight cells (t8, 1.13 h, 95% CI: 0.21-2.05; three studies; n = 742; I2 = 0%), t9 (2.27 h, 95% CI: 0.5-4.03; two studies; n = 671; I2 = 33%), time to formation of a full blastocyst (tB, 1.99 h, 95% CI 0.15-3.81; four studies; n = 1640; I2 = 76%) and time to expanded blastocyst (tEB, 2.35 h, 95% CI: 0.06-4.63; four studies; n = 1640; I2 = 83%). There is potentially some prognostic potential in the degree of fragmentation, multinucleation persisting to the four-cell stage and frequency of embryo contractions. Reverse cleavage was associated with euploidy in this meta-analysis; however, this article argues that these are likely spurious results requiring further investigation. There was no association with direct unequal cleavage in an embryo that progressed to a blastocyst, or with multinucleation assessed on Day 2 or at the two-cell stage. However, owing to heterogeneous results and poor-quality evidence, associations between these morphological components needs to be investigated further before conclusions can be reliably drawn. WIDER IMPLICATIONS: This first systematic review and meta-analysis of morphological and morphokinetic associations with ploidy status demonstrates the most useful morphokinetic variables, namely t8, t9 and tEB to be included in future model development. There is considerable variability within aneuploid and euploid embryos making definitively classifying them impossible; however, it is feasible that embryos could be prioritized for biopsy. Furthermore, these results support the mechanism by which algorithms for live birth may have predictive ability, suggesting aneuploidy causes delayed cytokinesis. We highlight significant heterogeneity in our results secondary to local conditions and diverse patient populations, therefore calling for future models to be robustly developed and tested in-house. If successful, such a model would constitute a meaningful breakthrough when accessing PGT-A is unsuitable for couples.

Discovering Innovation at the Intersection of Undergraduate Medical Education, Human Factors, and Collaboration: The Development of a Nasogastric Tube Safety Pack.

PROBLEM: Significant deficiencies exist in the knowledge and skills of medical students and residents around health care quality and safety. The theory and practice of quality and safety should be embedded into undergraduate medical practice so that health care professionals are capable of developing interventions and innovations to effectively anticipate and mitigate errors. APPROACH: Since 2011, Leeds Medical School in the United Kingdom has used case study examples of nasogastric (NG) tube patient safety incidents within the undergraduate patient safety curriculum. In 2012, a medical undergraduate student approached a clinician with an innovative idea after undertaking an NG tubes root cause analysis case study. Simultaneously, a separate local project demonstrated low compliance (11.6%) with the United Kingdom's National Patient Safety Agency NG tubes guideline for use of the correct method to check tube position. These separate endeavors led to interdisciplinary collaboration between a medical student, health care professionals, researchers, and industry to develop the Initial Placement Nasogastric Tube Safety Pack. OUTCOMES: Human factors engineering was used to inform pack design to allow guideline recommendations to be accessible and easy to follow. A timeline of product development, mapped against key human factors and medical device design principles used throughout the process, is presented. The safety pack has since been launched in five UK National Health Service (NHS) hospitals, and the pack has been introduced into health care professional staff training for NG tubes. NEXT STEPS: A mixed-methods evaluation is currently under way in five NHS organizations.