mTOR inhibitors, mycophenolates, and other immunosuppression regimens on antibody response to SARS-CoV-2 mRNA vaccines in solid organ transplant recipients.

A recent study concluded that SARS-CoV-2 mRNA vaccine responses were improved among transplant patients taking mTOR inhibitors (mTORi). This could have profound implications for vaccine strategies in transplant patients; however, limitations in the study design raise concerns about the conclusions. To address this issue more robustly, in a large cohort with appropriate adjustment for confounders, we conducted various regression- and machine learning-based analyses to compare antibody responses by immunosuppressive agents in a national cohort (n = 1037). MMF was associated with significantly lower odds of positive antibody response (aOR = 0.09 0.130.18 ). Consistent with the recent mTORi study, the odds tended to be higher with mTORi (aOR = 1.00 1.452.13 ); however, importantly, this seemingly protective tendency disappeared (aOR = 0.47 0.731.12 ) after adjusting for MMF. We repeated this comparison by combinations of immunosuppression agents. Compared to MMF + tacrolimus, MMF-free regimens were associated with higher odds of positive antibody response (aOR = 2.39 4.267.92 for mTORi+tacrolimus; 2.34 5.5415.32 for mTORi-only; and 6.78 10.2515.93 for tacrolimus-only), whereas MMF-including regimens were not, regardless of mTORi use (aOR = 0.81 1.542.98 for MMF + mTORi; and 0.81 1.512.87 for MMF-only). We repeated these analyses in an independent cohort (n = 512) and found similar results. Our study demonstrates that the recently reported findings were confounded by MMF, and that mTORi is not independently associated with improved vaccine responses.

Predicting a Positive Antibody Response After 2 SARS-CoV-2 mRNA Vaccines in Transplant Recipients: A Machine Learning Approach With External Validation.

BACKGROUND: Solid organ transplant recipients (SOTRs) are less likely to mount an antibody response to SARS-CoV-2 mRNA vaccines. Understanding risk factors for impaired vaccine response can guide strategies for antibody testing and additional vaccine dose recommendations. METHODS: Using a nationwide observational cohort of 1031 SOTRs, we created a machine learning model to explore, identify, rank, and quantify the association of 19 clinical factors with antibody responses to 2 doses of SARS-CoV-2 mRNA vaccines. External validation of the model was performed using a cohort of 512 SOTRs at Houston Methodist Hospital. RESULTS: Mycophenolate mofetil use, a shorter time since transplant, and older age were the strongest predictors of a negative antibody response, collectively contributing to 76% of the model's prediction performance. Other clinical factors, including transplanted organ, vaccine type (mRNA-1273 versus BNT162b2), sex, race, and other immunosuppressants, showed comparatively weaker associations with an antibody response. This model showed moderate prediction performance, with an area under the receiver operating characteristic curve of 0.79 in our cohort and 0.67 in the external validation cohort. An online calculator based on our prediction model is available at http://transplantmodels.com/covidvaccine/ . CONCLUSIONS: Our machine learning model helps understand which transplant patients need closer follow-up and additional doses of vaccine to achieve protective immunity. The online calculator based on this model can be incorporated into transplant providers' practice to facilitate patient-centric, precision risk stratification and inform vaccination strategies among SOTRs.

The GUIDE-HF trial of pulmonary artery pressure monitoring in heart failure: impact of the COVID-19 pandemic.

AIMS: During the coronavirus disease 2019 (COVID-19) pandemic, important changes in heart failure (HF) event rates have been widely reported, but few data address potential causes for these changes; several possibilities were examined in the GUIDE-HF study. METHODS AND RESULTS: From 15 March 2018 to 20 December 2019, patients were randomized to haemodynamic-guided management (treatment) vs. control for 12 months, with a primary endpoint of all-cause mortality plus HF events. Pre-COVID-19, the primary endpoint rate was 0.553 vs. 0.682 events/patient-year in the treatment vs. control group [hazard ratio (HR) 0.81, P = 0.049]. Treatment difference was no longer evident during COVID-19 (HR 1.11, P = 0.526), with a 21% decrease in the control group (0.536 events/patient-year) and no change in the treatment group (0.597 events/patient-year). Data reflecting provider-, disease-, and patient-dependent factors that might change the primary endpoint rate during COVID-19 were examined. Subject contact frequency was similar in the treatment vs. control group before and during COVID-19. During COVID-19, the monthly rate of medication changes fell 19.2% in the treatment vs. 10.7% in the control group to levels not different between groups (P = 0.362). COVID-19 was infrequent and not different between groups. Pulmonary artery pressure area under the curve decreased -98 mmHg-days in the treatment group vs. -100 mmHg-days in the controls (P = 0.867). Patient compliance with the study protocol was maintained during COVID-19 in both groups. CONCLUSION: During COVID-19, the primary event rate decreased in the controls and remained low in the treatment group, resulting in an effacement of group differences that were present pre-COVID-19. These outcomes did not result from changes in provider- or disease-dependent factors; pulmonary artery pressure decreased despite fewer medication changes, suggesting that patient-dependent factors played an important role in these outcomes. Clinical Trials.gov: NCT03387813.