Particulate air pollution at the time of oocyte retrieval is independently associated with reduced odds of live birth in subsequent frozen embryo transfers.
Does exposure to particulate matter (PM) air pollution prior to oocyte retrieval or subsequent frozen embryo transfer (FET) affect the odds of live birth?
Live birth rates are lower when particulate matter (PM2.5 and PM10) levels are higher prior to oocyte retrieval, regardless of the conditions at the time of embryo transfer.
Exposure to air pollution is associated with adverse reproductive outcomes, including reduced fecundity and ovarian reserve, and an increased risk of infertility and pregnancy loss. It is uncertain whether the effect on ART outcomes is due to the effects of pollution on oogenesis or on early pregnancy.
This retrospective cohort study included 3659 FETs in 1835 patients between January 2013 and December 2021, accounting for all FETs performed at a single clinic over the study period. The primary outcome was the live birth rate per FET. Outcome data were missing for two embryo transfers which were excluded. Daily levels of PM2.5, PM10, nitric oxide, nitrogen dioxide, sulphur dioxide, ozone and carbon monoxide were collected during the study period and calculated for the day of oocyte retrieval and the day of embryo transfer, and during the preceding 2-week, 4-week, and 3-month periods.
Clinical and embryological outcomes were analysed for their association with pollution over 24 hours, 2 weeks, 4 weeks, and 3 months, with adjustment for repeated cycles per participant, age at the time of oocyte retrieval, a quadratic age term, meteorological season, year, and co-exposure to air pollutants. Multi-pollutant models were constructed to adjust for co-exposures to other pollutants. Median concentrations in pollutant quartiles were modelled as continuous variables to test for overall linear trends; a Bonferroni correction was applied to maintain an overall alpha of 0.05 across the four exposure periods tested.
Increased PM2.5 exposure in the 3 months prior to oocyte retrieval was associated with decreased odds of live birth (linear trend P = 0.011); the odds of live birth when PM2.5 concentrations were in the highest quartile were reduced by 34% (OR 0.66, 95% CI 0.47-0.92) when compared to the lowest quartile. A consistent direction of effect was seen across other exposure periods prior to oocyte retrieval, with an apparent dose-dependent relationship. Increased exposure to PM10 particulate matter in the 2 weeks prior to oocyte retrieval was associated with decreased odds of live birth (linear trend P = 0.009); the odds of live birth were decreased by 38% (OR 0.62, 95% CI 0.43-0.89, P = 0.010) when PM10 concentrations were in the highest quartile compared with the lowest quartile. Consistent trends were not seen across other exposure periods. None of the gaseous pollutants had consistent effects, prior to either oocyte retrieval or embryo transfer.
This was a retrospective cohort study, however, all FETs during the study period were included and data were missing for only two FETs. The results are based on city-level pollution exposures, and we were not able to adjust for all possible factors that may affect live birth rates. Results were not stratified based on specific patient populations, and it was not possible to calculate the cumulative live birth rate per commenced cycle.
This is the first study to specifically analyse FETs to separate the effects of environmental exposures prior to oocyte retrieval from those around the time of embryo transfer. Our findings suggest that increased PM exposure prior to oocyte retrieval is associated with reduced live birth rate following FET, independent of the conditions at the time of embryo transfer. Importantly, the air quality during the study period was excellent, suggesting that even 'acceptable' levels of air pollution have detrimental reproductive effects during gametogenesis. At the low pollution levels in our study, exposure to gaseous pollutants did not appear to affect live birth rates. This has important implications for our understanding of the effects of pollution on reproduction, and highlights the urgent need for effective policies limiting pollution exposure to protect human health and reproduction.
No funding was provided for this study. S.J.L. is supported by the Jean Murray Jones Scholarship from the Royal Australian and New Zealand College of Obstetricians and Gynaecologists, has received educational sponsorship from Besins, Ferring, Merck, and Organon, honoraria from Hologic and Organon, consulting fees from Merck unrelated to the current study, and is a member of the Reproductive Technology Council of Western Australia. S.J.L. and R.J.H. are board members of Menopause Alliance Australia. C.S.R., M.W., and E.N. have no conflicts of interest to declare. R.J.H. is the Medical Director of Fertility Specialists of Western Australia, the National Medical Director of City Fertility Australia, and a shareholder in CHA SMG. He chairs the Western Australian Minister's Expert Panel on ART and Surrogacy. R.J.H. has made presentations for and received honoraria from Merck, Merck-Serono, Origio, Igenomix, Gideon-Richter, and Ferring, and has received support for attending meetings from Merck, Organon, and Ferring.
N/A.
Leathersich SJ
，Roche CS
，Walls M
，Nathan E
，Hart RJ
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Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.
Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided.
(1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS?
Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses.
Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS.
Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments.
Level III, diagnostic study.
Lee CC
，Chen CW
，Yen HK
，Lin YP
，Lai CY
，Wang JL
，Groot OQ
，Janssen SJ
，Schwab JH
，Hsu FM
，Lin WH
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ResearchGate
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钛学术
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