ABSTRACT
Background: To evaluate whether the ongoing coronavirus disease 2019 (COVID-19) pandemic has had an impact on assisted reproductive technology (ART) outcomes and assess the possible role of geographic differences in the pandemic's trajectory on these outcomes. Methods: Multi-center retrospective cohort study involving patients who underwent oocyte cryopreservation, in vitro fertilization (IVF), embryo cryopreservation, or frozen euploid embryo transfer in 2019 and 2020 at two academic fertility centers located in regionally distinct areas of the US with high coronavirus infection rates. Patients were screened for infectious symptoms, exposure to sick contacts, and fevers, and tested with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction testing within 5 days of oocyte retrieval. The primary outcomes were the number of oocytes retrieved, embryos fertilized, blastocyst or euploid embryos produced in oocyte retrieval and IVF cycles, and rates of embryo implantation, biochemical pregnancy or no pregnancy following frozen embryo transfer (FET). Results: We found no consistent significant differences in the number of oocytes retrieved, embryos fertilized, blastocysts or euploid embryos produced at either institution over the study period. Furthermore, we did not detect any differences in FET outcomes, including rates of embryo implantation, biochemical pregnancy, or no pregnancy, at either institution during the study time period. Conclusions: There were no significant differences in ART outcomes in patients who received fertility treatment during the pandemic at our centers. Patients and providers can be reassured that with proper testing, sanitizing, and distancing measures, treatments can continue safely during the pandemic without compromising outcomes. © 2022 S.O.G. CANADA Inc.. All rights reserved.
ABSTRACT
Purpose: To train a deep learning classification algorithm to predict chest radiograph severity scores and clinical outcomes in patients with coronavirus disease 2019 (COVID-19). Materials and Methods: In this retrospective cohort study, patients aged 21-50 years who presented to the emergency department (ED) of a multicenter urban health system from March 10 to 26, 2020, with COVID-19 confirmation at real-time reverse-transcription polymerase chain reaction screening were identified. The initial chest radiographs, clinical variables, and outcomes, including admission, intubation, and survival, were collected within 30 days (n = 338;median age, 39 years;210 men). Two fellowship-trained cardiothoracic radiologists examined chest radiographs for opacities and assigned a clinically validated severity score. A deep learning algorithm was trained to predict outcomes on a holdout test set composed of patients with confirmed COVID-19 who presented between March 27 and 29, 2020 (n = 161;median age, 60 years;98 men) for both younger (age range, 21-50 years;n = 51) and older (age >50 years, n = 110) populations. Bootstrapping was used to compute CIs. Results: The model trained on the chest radiograph severity score produced the following areas under the receiver operating characteristic curves (AUCs): 0.80 (95% CI: 0.73, 0.88) for the chest radiograph severity score, 0.76 (95% CI: 0.68, 0.84) for admission, 0.66 (95% CI: 0.56, 0.75) for intubation, and 0.59 (95% CI: 0.49, 0.69) for death. The model trained on clinical variables produced an AUC of 0.64 (95% CI: 0.55, 0.73) for intubation and an AUC of 0.59 (95% CI: 0.50, 0.68) for death. Combining chest radiography and clinical variables increased the AUC of intubation and death to 0.88 (95% CI: 0.79, 0.96) and 0.82 (95% CI: 0.72, 0.91), respectively. Conclusion: The combination of imaging and clinical information improves outcome predictions. Supplemental material is available for this article.© RSNA, 2020.