PurposeOncotype DX (ODX) recurrence score (RS) breast cancer (BC) assay is costly, and performed in only ~1/3 of estrogen receptor (ER)-positive BC patients in the USA. We have now developed a user-friendly nomogram surrogate prediction model for ODX based on a large dataset from the National Cancer Data Base (NCDB) to assist in selecting patients for which further ODX testing may not be necessary and as a surrogate for patients for which ODX testing is not affordable or available.MethodsSix clinicopathologic variables of 27,719 ODX-tested ER+/HER2−/lymph node-negative patients with 6–50 mm tumor size captured by the NCDB from 2010 to 2012 were assessed with logistic regression to predict high-risk or low-risk ODXRS test results with TAILORx-trial and commercial cut-off values; 12,763 ODX-tested patients in 2013 were used for external validation. The predictive accuracy of the regression model was yielded using a Receiver Operator Characteristic analysis. Model fit was analyzed by plotting the predicted probabilities against the actual probabilities. A user-friendly calculator version of nomograms is available online at the University of Tennessee Medical Center website (Knoxville, TN).ResultsGrade and progesterone receptor status were the highest predictors of both low-risk and high-risk ODXRS, followed by age, tumor size, histologic tumor type and lymph-vascular invasion (C-indexes-.0.85 vs. 0.88 for TAILORx-trial vs. commercial cut-off values, respectively).Conclusion This is the first study of this scale showing confidently that clinicopathologic variables can be used for prediction of low-risk or high-risk ODXRS using our nomogram models. These novel nomograms will be useful tools to help physicians and patients decide whether further ODX testing is necessary and are excellent surrogates for patients for which ODX testing is not affordable or available.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-017-4170-3) contains supplementary material, which is available to authorized users.

Rationale: Health outcomes of people with coronavirus disease (COVID-19) range from no symptoms to severe illness and death. Asthma, a common chronic lung disease, has been considered likely to increase the severity of COVID-19, although data addressing this hypothesis have been scarce until very recently.Objectives: To review the epidemiologic literature related to asthma's potential role in COVID-19 severity.Methods: Studies were identified through the PubMed (MEDLINE) and medRxiv (preprint) databases using the search terms "asthma," "SARS-CoV-2" (severe acute respiratory syndrome coronavirus 2), and "COVID-19," and by cross-referencing citations in identified studies that were available in print or online before December 22, 2020.Measurements and Main Results: Asthma prevalence data were obtained from studies of people with COVID-19 and regional health statistics. We identified 150 studies worldwide that allowed us to compare the prevalence of asthma in patients with COVID-19 by region, disease severity, and mortality. The results of our analyses do not provide clear evidence of increased risk of COVID-19 diagnosis, hospitalization, severity, or mortality due to asthma.Conclusions: These findings could provide some reassurance to people with asthma regarding its potential to increase their risk of severe morbidity from COVID-19.

Objectives: Oncotype DX (ODX), 21-gene breast cancer (BC) assay, predicts risk of recurrence and benefits of addition of chemotherapy to hormonal therapy for early-stage BC. We previously published a nomogram/calculator that could predict ODX results without performing the test by using clinicopathologic characteristics of BC available from pathology reports. Patients with intermediate-risk (11e25) ODXRS (RS) were excluded from that nomogram. This update tests the predictive value of clinicopathologic variables for forecasting the ODXRS while including intermediate-risk-ODXRS patients and stratifying ODXRS based on recently published TAILORx clinical trial results (0e25 ¼ low-risk, 26 e100 ¼ high-risk-ODXRS; intermediate-risk-ODXRS belongs to the low-risk category). Material and methods: The nomogram was built on 65,754 ODX-tested ERþ/HER2-/lymph-node-negative patients with 6e50 mm tumor, captured by the National Cancer Data Base (NCDB) from 2010 to 2014.Five clinicopathologic variables (age, tumor size, grade, progesterone-receptor status (PR) and BChistologic type) were assessed with logistic regression to predict for a low-risk (0e25) or a high-risk (26e100) ODXRS. Results were validated on a separate 18,585 ODX-tested cohort from 2015. Results: Grade and PR were the highest significant predictors of both low-risk and high-risk-ODXRS, followed by histologic type, tumor size and age. The Receiver Operator Characteristic (ROC) curve showed strong statistical model for both low-risk and high-risk-ODXRS prediction outcomes (cindex ¼ 0.81). Conclusions: An updated nomogram is now developed/validated on the entire population of ODX-tested patients (84,339) captured by the NCDB. The nomogram/calculator, available on-line at the UTMCK/Shiny website (https://utgsm.shinyapps.io/OncotypeDXCalculator/), will continue serving as a surrogate for BC patients for which ODX testing is not affordable, available or necessary.