Clinical validation of an automatic classification algorithm applied on cleavage stage embryos: analysis for blastulation, euploidy, implantation, and live-birth potential.

STUDY QUESTION: Is a commercially available embryo assessment algorithm for early embryo evaluation based on the automatic annotation of morphokinetic timings a useful tool for embryo selection in IVF cycles? SUMMARY ANSWER: The classification provided by the algorithm was shown to be significantly predictive, especially when combined with conventional morphological evaluation, for development to blastocyst, implantation, and live birth, but not for euploidy. WHAT IS KNOWN ALREADY: The gold standard for embryo selection is still morphological evaluation conducted by embryologists. Since the introduction of time-lapse technology to embryo culture, many algorithms for embryo selection have been developed based on embryo morphokinetics, providing complementary information to morphological evaluation. However, manual annotations of developmental events and application of algorithms can be time-consuming and subjective processes. The introduction of automation to morphokinetic annotations is a promising approach that can potentially reduce subjectivity in the embryo selection process and improve the workflow in IVF laboratories. STUDY DESIGN, SIZE, DURATION: This observational, retrospective cohort study was performed in a single IVF clinic between 2018 and 2021 and included 3736 embryos from oocyte donation cycles (423 cycles) and 1291 embryos from autologous cycles with preimplantation genetic testing for aneuploidies (PGT-A, 185 cycles). Embryos were classified on Day 3 with a score from 1 (best) to 5 (worst) by the automatic embryo assessment algorithm. The performance of the embryo classification model for blastocyst development, implantation, live birth, and euploidy prediction was assessed. PARTICIPANTS/MATERIALS, SETTING, METHODS: All embryos were monitored by a time-lapse system with an automatic cell-tracking and embryo assessment software during culture. The embryo assessment algorithm was applied on Day 3, resulting in embryo classification from 1 to 5 (from highest to lowest developmental potential) depending on four parameters: P2 (t3-t2), P3 (t4-t3), oocyte age, and number of cells. There were 959 embryos selected for transfer on Day 5 or 6 based on conventional morphological evaluation. The blastocyst development, implantation, live birth, and euploidy rates (for embryos subjected to PGT-A) were compared between the different scores. The correlation of the algorithm scoring with the occurrence of those outcomes was quantified by generalized estimating equations (GEEs). Finally, the performance of the GEE model using the embryo assessment algorithm as the predictor was compared to that using conventional morphological evaluation, as well as to a model using a combination of both classification systems. MAIN RESULTS AND THE ROLE OF CHANCE: The blastocyst rate was higher with lower the scores generated by the embryo assessment algorithm. A GEE model confirmed the positive association between lower embryo score and higher odds of blastulation (odds ratio (OR) (1 vs 5 score) = 15.849; P < 0.001). This association was consistent in both oocyte donation and autologous embryos subjected to PGT-A. The automatic embryo classification results were also statistically associated with implantation and live birth. The OR of Score 1 vs 5 was 2.920 (95% CI 1.440-5.925; P = 0.003; E = 2.81) for implantation and 3.317 (95% CI 1.615-6.814; P = 0.001; E = 3.04) for live birth. However, this association was not found in embryos subjected to PGT-A. The highest performance was achieved when combining the automatic embryo scoring and traditional morphological classification (AUC for implantation potential = 0.629; AUC for live-birth potential = 0.636). Again, no association was found between the embryo classification and euploidy status in embryos subjected to PGT-A (OR (1 vs 5) = 0.755 (95% CI 0.255-0.981); P = 0.489; E = 1.57). LIMITATIONS, REASONS FOR CAUTION: The retrospective nature of this study may be a reason for caution, although the large sample size reinforced the ability of the model for embryo selection. WIDER IMPLICATIONS OF THE FINDINGS: Time-lapse technology with automated embryo assessment can be used together with conventional morphological evaluation to increase the accuracy of embryo selection process and improve the success rates of assisted reproduction cycles. To our knowledge, this is the largest embryo dataset analysed with this embryo assessment algorithm. STUDY FUNDING/COMPETING INTEREST(S): This research was supported by Agencia Valenciana de Innovació and European Social Fund (ACIF/2019/264 and CIBEFP/2021/13). In the last 5 years, M.M. received speaker fees from Vitrolife, Merck, Ferring, Gideon Richter, Angelini, and Theramex, and B.A.-R. received speaker fees from Merck. The remaining authors have no competing interests to declare. TRIAL REGISTRATION NUMBER: N/A.

Bed 13 is not worse than any other. A retrospective cohort study.

INTRODUCTION: Risk management and patient safety are closely related, following this premise some industries have adopted measures to omit number 13. Healthcare is not left behind, in some hospital the day of surgery's or bed numbering avoid number 13. The objective was to assess whether it is necessary to redesign the safety policies implemented in hospitals based on avoiding 13 in the numbering of rooms/beds. METHODS: A retrospective cohort study was conducted. Mortality and the number of adverse events suffered by patients admitted to rooms/beds numbering 13 (bad chance) or 7 (fair chance) over a two-year period to Intensive Care Unit, Medicine, Gastroenterology, Surgery, and Paediatric service were registered and compared. RESULTS: A total of 8553 admissions were included. They had similar length-of-stay and Charlson Index scores (p-value=0.435). Mortality of bed 13 was 268 (6.2%, 95% CI 5.5-6.9) and 282 in bed 7 (6.7%, 95% CI 5.9-7.5) (p-value=0.3). A total of 422 adverse events from 4342 admissions (9.7%, 95% CI 8.9-10.6) occurred in bed 13, while in bed 7 the count of adverse events was 398 in 4211 admissions (9.4%, 95% CI 8.6-10.4) (p-value=0.6). Odds Ratio for mortality was equal to 0.9 (95% CI 0.8-1.1) and suffering adverse events when admitted to bed 13 versus bed 7 was 1.03 (95% CI 0.9-1.2). CONCLUSIONS: Bed 13 is not a risk factor for patient safety. Hospitals should pay attention to causes and interventions to avoid adverse events based on evidence rather than beliefs or myths.