Multiple myeloma with t(11;14): impact of novel agents on outcome.

Multiple myeloma (MM) patients with t(11;14) present unique biological features and their prognosis is not well established. We report a retrospective study of 591 MM patients, 17.3% of whom had t(11;14). It was designed to determine the prognostic impact of this abnormality and the effect of novel agents on the response and outcomes. Three groups were established based on their cytogenetics: (1) t(11;14); (2) high-risk chromosomal abnormalities; and (3) standard risk (SR). After 80.1 months (1.2-273.8 months) of follow-up, no differences were observed in overall survival (OS) between the t(11;14) and SR groups (75.8 vs. 87.2 months; P = 0.438). Treatment of MM t(11;14) with novel agents did not improve their overall response rate (ORR) or complete response (CR) compared with those who received conventional therapy (ORR: 87.2 vs. 79.5%, P = 0.336; CR: 23.4 vs. 12.8%, P = 0.215). This effect translated into a similar PFS (39.6 vs. 30.0 months; P = 0.450) and OS (107.6 vs. 75.7 months; P = 0.175). In summary, MM t(11;14) patients did not benefit from the introduction of novel agents as much as SR patients did, indicating that other therapies are needed to improve their outcomes.

Novel Agents as Main Drivers for Continued Improvement in Survival in Multiple Myeloma.

(1) Background: New therapeutic strategies have improved the prognosis of multiple myeloma (MM), changing the accepted view of this disease from being incurable to treatable. (2) Methods: We studied 1001 patients with MM between 1980 and 2020, grouping patients into ten-year periods by diagnosis 1980-1990, 1991-2000, 2001-2010 and 2011-2020. (3) Results: After 65.1 months of follow-up, the median OS of the cohort was 60.3 months, and OS increased significantly over time: 22.4 months in 1980-1990, 37.4 months in 1991-2000, 61.8 months in 2001-2010 and 103.6 months in 2011-2020 (p < 0.001). Using novel agents in the front-line setting for myeloma patients yielded a significantly better OS than in those treated with conventional therapies, especially when combinations of at least two novel agents were used. The median OS of patients treated with the combination of at least two novel agents in induction was significantly prolonged compared to those treated with a single novel agent or conventional therapy in induction: 143.3 vs. 61.0 vs. 42.2 months (p < 0.001). The improvement was apparent in all patients regardless of age at diagnosis. In addition, 132 (13.2%) patients were long-term survivors (median OS ≥ 10 years). Some independent clinical predictors of long-term survival were identified: ECOG < 1, age at diagnosis ≤ 65 years, non-IgA subtype, ISS-1 and standard-risk cytogenetic. Achieving CR and undergoing ASCT were positively associated with >10 years of survival. (4) Conclusions: The combination of novel agents appears to be the main factor for the improvement in survival in MM, which is becoming a chronic and even curable disease in a subtype of patients without high-risk features.

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.