Assessment of the cortisol awakening response: Real-time analysis and curvilinear effects of sample timing inaccuracy

The cortisol awakening response (CAR) is typically measured in the domestic setting. Moderate sample timing inaccuracy has been shown to result in erroneous CAR estimates and such inaccuracy has been shown partially to explain inconsistency in the CAR literature. The need for more reliable measurement of the CAR has recently been highlighted in expert consensus guidelines where it was pointed out that less than 6% of published studies provided electronic-monitoring of saliva sampling time in the post-awakening period. Analyses of a merged data-set of published studies from our laboratory are presented. To qualify for selection, both time of awakening and collection of the first sample must have been verified by electronic-monitoring and sampling commenced within 15 min of awakening. Participants (n = 128) were young (median age of 20 years) and healthy. Cortisol values were determined in the 45 min post-awakening period on 215 sampling days. On 127 days, delay between verified awakening and collection of the first sample was less than 3 min (‘no delay’ group); on 45 days there was a delay of 4–6 min (‘short delay’ group); on 43 days the delay was 7–15 min (‘moderate delay’ group). Cortisol values for verified sampling times accurately mapped on to the typical post-awakening cortisol growth curve, regardless of whether sampling deviated from desired protocol timings. This provides support for incorporating rather than excluding delayed data (up to 15 min) in CAR analyses. For this population the fitted cortisol growth curve equation predicted a mean cortisol awakening level of 6 nmols/l (±1 for 95% CI) and a mean CAR rise of 6 nmols/l (±2 for 95% CI). We also modelled the relationship between real delay and CAR magnitude, when the CAR is calculated erroneously by incorrectly assuming adherence to protocol time. Findings supported a curvilinear hypothesis in relation to effects of sample delay on the CAR. Short delays of 4–6 min between awakening and commencement of saliva sampling resulted an overestimated CAR. Moderate delays of 7–15 min were associated with an underestimated CAR. Findings emphasize the need to employ electronic-monitoring of sampling accuracy when measuring the CAR in the domestic setting.

Serum estradiol: oocyte ratio as a predictor of reproductive outcome: an analysis of data from >9000 IVF cycles in the Republic of Ireland

Purpose The purpose of this study was to evaluate the serum estradiol (E2) per oocyte ratio (EOR) as a function of selected embryology events and reproductive outcomes with IVF. Methods This retrospective analysis included all IVF cycles where oocyte collection and fresh transfer occurred between January 2001 and November 2012 at a single institution. Patients were divided by three age groups (<35, 35–39, and ≥40 years) and further stratified into nine groups based on EOR (measured in pmol/L/oocyte). Terminal serum E2 (pmol/mL) was recorded on day of hCG trigger administration, and fertilization rate, cleavage rate, number of good quality embryos, and reproductive outcomes were recorded for each IVF cycle. Results During the study interval, 9109 oocyte retrievals were performed for 5499 IVF patients (mean = 1.7 cycles/patient). A total of 63.4 % of transfers were performed on day 3 (n = 4926), while 36.6 % were carried out on day 5 (n = 2843). Clinical pregnancy rates were highest in patients with EOR of 250–750 and declined as this ratio increased, independent of patient age. While the odds ratio (OR) for clinical pregnancy where EOR = 250–750 vs. EOR > 1500 was 3.4 (p < 0.001; 95 % CI 2.67–4.34), no statistically significant correlation was seen in fertilization, cleavage rates or number of good quality embryos as a function of EOR. Conclusions Predicting reproductive outcomes with IVF has great utility both for patients and providers. The former have the opportunity to build realistic expectations, and the latter are better able to counsel according to measured clinical parameters. A better understanding of follicular dynamics and ovarian response to gonadotropin stimulation could optimize IVF treatments going forward.