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.

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.