During the COVID-19 pandemic, ibrutinib with or without rituximab was approved in England for initial treatment of mantle cell lymphoma (MCL) instead of immunochemotherapy. Because limited data are available in this setting, we conducted an observational cohort study evaluating safety and efficacy. Adults receiving ibrutinib with or without rituximab for untreated MCL were evaluated for treatment toxicity, response, and survival, including outcomes in high-risk MCL (TP53 mutation/deletion/p53 overexpression, blastoid/pleomorphic, or Ki67 ≥ 30%). A total of 149 patients from 43 participating centers were enrolled: 74.1% male, median age 75 years, 75.2% Eastern Cooperative Oncology Group status of 0 to 1, 36.2% high-risk, and 8.9% autologous transplant candidates. All patients received ≥1 cycle ibrutinib (median, 8 cycles), 39.0% with rituximab. Grade ≥3 toxicity occurred in 20.3%, and 33.8% required dose reductions/delays. At 15.6-month median follow-up, 41.6% discontinued ibrutinib, 8.1% due to toxicity. Of 104 response-assessed patients, overall (ORR) and complete response (CR) rates were 71.2% and 20.2%, respectively. ORR was 77.3% (low risk) vs 59.0% (high risk) (P = .05) and 78.7% (ibrutinib-rituximab) vs 64.9% (ibrutinib; P = .13). Median progression-free survival (PFS) was 26.0 months (all patients); 13.7 months (high risk) vs not reached (NR) (low risk; hazard ratio [HR], 2.19; P = .004). Median overall survival was NR (all); 14.8 months (high risk) vs NR (low risk; HR, 2.36; P = .005). Median post-ibrutinib survival was 1.4 months, longer in 41.9% patients receiving subsequent treatment (median, 8.6 vs 0.6 months; HR, 0.36; P = .002). Ibrutinib with or without rituximab was effective and well tolerated as first-line treatment of MCL, including older and transplant-ineligible patients. PFS and OS were significantly inferior in one-third of patients with high-risk disease and those unsuitable for post-ibrutinib treatment, highlighting the need for novel approaches in these groups.

Bendamustine is among the most effective chemotherapeutics for indolent B-cell non-Hodgkin lymphomas (iNHL), but trial reports of significant toxicity, including opportunistic infections and excess deaths, led to prescriber warnings. We conducted a multicenter observational study evaluating bendamustine toxicity in real-world practice. Patients receiving at least 1 dose of bendamustine with/without rituximab (R) for iNHL were included. Demographics, lymphoma and treatment details, and grade 3 to 5 adverse events (AEs) were analyzed and correlated. In total, 323 patients were enrolled from 9 National Health Service hospitals. Most patients (96%) received bendamustine-R, and 46%, R maintenance. Overall, 21.7% experienced serious AEs (SAE) related to treatment, including infections in 12%, with absolute risk highest during induction (63%), maintenance (20%), and follow-up (17%) and the relative risk highest during maintenance (54%), induction (34%), and follow-up (28%). Toxicity led to permanent treatment discontinuation for 13% of patients, and 2.8% died of bendamustine-related infections (n = 5), myelodysplastic syndrome (n = 3), and cardiac disease (n = 1). More SAEs per patient were reported in patients with mantle cell lymphoma, poor preinduction performance status (PS), poor premaintenance PS, and abnormal preinduction total globulins and in those receiving growth factors. Use of antimicrobial prophylaxis was variable, and 3 of 10 opportunistic infections occurred despite prophylaxis. In this real-world analysis, bendamustine-related deaths and treatment discontinuation were similar to those of trial populations of younger, fitter patients. Poor PS, mantle cell histology, and maintenance R were potential risk factors. Infections, including late onset events, were the most common treatment-related SAE and cause of death, warranting extended antimicrobial prophylaxis and infectious surveillance, especially for maintenance-treated patients.

PURPOSE: Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET). METHODS: Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O₂) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort. RESULTS: The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation. CONCLUSION: CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer.

Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants.

Background Cancer patients are at increased risk of severe COVID-19. As COVID-19 presentation and outcomes are heterogeneous in cancer patients, decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical. Objective To identify features of COVID-19 in cancer patients predicting severe disease and build a decision-support online tool; COVID-19 Risk in Oncology Evaluation Tool (CORONET) Method Data was obtained for consecutive patients with active cancer with laboratory confirmed COVID-19 presenting in 12 hospitals throughout the United Kingdom (UK). Univariable logistic regression was performed on pre-specified features to assess their association with admission (≥24 hours inpatient), oxygen requirement and death. Multivariable logistic regression and random forest models (RFM) were compared with patients randomly split into training and validation sets. Cost function determined cut-offs were defined for admission/death using RFM. Performance was assessed by sensitivity, specificity and Brier scores (BS). The CORONET model was then assessed in the entire cohort to build the online CORONET tool. Results Training and validation sets comprised 234 and 66 patients respectively with median age 69 (range 19-93), 54% males, 46% females, 71% vs 29% had solid and haematological cancers. The RFM, selected for further development, demonstrated superior performance over logistic regression with AUROC predicting admission (0.85 vs. 0.78) and death (0.76 vs. 0.72). C-reactive protein was the most important feature predicting COVID-19 severity. CORONET cut-offs for admission and mortality of 1.05 and 1.8 were established. In the training set, admission prediction sensitivity and specificity were 94.5% and 44.3% with BS 0.118; mortality sensitivity and specificity were 78.5% and 57.2% with BS 0.364. In the validation set, admission sensitivity and specificity were 90.7% and 42.9% with BS 0.148; mortality sensitivity and specificity were 92.3% and 45.8% with BS 0.442. In the entire cohort, the CORONET decision support tool recommended admission of 99% of patients requiring oxygen and of 99% of patients who died. Conclusions and Relevance CORONET, a decision support tool validated in hospitals throughout the UK showed promise in aiding decisions regarding admission and predicting COVID-19 severity in patients with cancer presenting to hospital. Future work will validate and refine the tool in further datasets.