Fonder, Amie

BackgroundSarcopenia increases with age and is associated with poor survival outcomes in patients with cancer. By using a deep learning–based segmentation approach, clinical computed tomography (CT) images of the abdomen of patients with newly diagnosed multiple myeloma (NDMM) were reviewed to determine whether the presence of sarcopenia had any prognostic value.MethodsSarcopenia was detected by accurate segmentation and measurement of the skeletal muscle components present at the level of the L3 vertebrae. These skeletal muscle measurements were further normalized by the height of the patient to obtain the skeletal muscle index for each patient to classify them as sarcopenic or not.ResultsThe study cohort consisted of 322 patients of which 67 (28%) were categorized as having high risk (HR) fluorescence in situ hybridization (FISH) cytogenetics. A total of 171 (53%) patients were sarcopenic based on their peri‐diagnosis standard‐dose CT scan. The median overall survival (OS) and 2‐year mortality rate for sarcopenic patients was 44 months and 40% compared to 90 months and 18% for those not sarcopenic, respectively (p < .0001 for both comparisons). In a multivariable model, the adverse prognostic impact of sarcopenia was independent of International Staging System stage, age, and HR FISH cytogenetics.ConclusionsSarcopenia identified by a machine learning–based convolutional neural network algorithm significantly affects OS in patients with NDMM. Future studies using this machine learning–based methodology of assessing sarcopenia in larger prospective clinical trials are required to validate these findings.

AbstractPatients with multiple myeloma (MM) have a lower efficacy from COVID‐19 vaccination and a high rate of mortality from COVID‐19 in hospitalized patients. However, the overall rate and severity of COVID‐19 infection in all settings (including non‐hospitalized patients) and the independent impact of plasma cell‐directed therapies on outcomes needs further study. We reviewed the medical records of 9225 patients with MM or AL amyloidosis (AL) seen at Mayo Clinic Rochester, Arizona, and Florida between 12/01/2019 and 8/31/2021 and identified 187 patients with a COVID‐19 infection (n = 174 MM, n = 13 AL). The infection rate in our cohort was relatively low at 2% but one‐fourth of the COVID‐19 infections were severe. Nineteen (10%) patients required intensive care unit (ICU) admission and 5 (3%) patients required mechanical ventilation. The mortality rate among hospitalized patients with COVID‐19 was 22% (16/72 patients). Among patients that were fully vaccinated at the time of infection (n = 12), two (17%) developed severe COVID‐19 infection, without any COVID‐related death. On multivariable analysis, treatment with CD38 antibody within 6 months of COVID‐19 infection [Risk ratio (RR) 3.6 (95% CI: 1.2, 10.5), p = .02], cardiac [RR 4.1 (95% CI: 1.3, 12.4), p = .014] or pulmonary comorbidities [RR 3.6 (95% CI 1.1, 11.6); p = .029] were independent predictors for ICU admission. Cardiac comorbidity [RR 2.6 (95% CI: 1.1, 6.5), p = .038] was an independent predictor of mortality whereas MM/AL in remission was associated with lower mortality [RR 0.4 (95% CI: 0.2–0.8); p = .008].

Two Letters to Blood address the risks of COVID-19 in populations with precursors of hematological disease. In the first article, Miller and colleagues report on whether clonal hematopoiesis of intermediate potential (CHIP) is associated with adverse outcomes with COVID-19, finding no association between CHIP and 28-day mortality while providing data indirectly linking IL-6 signaling and patient outcomes. In the second article, Ho and colleagues investigate the outcomes of patients with monoclonal gammopathy of undetermined significance (MGUS) with COVID-19, reporting that one-fourth had a severe infection and that on multivariable analysis, adverse outcomes are more likely if immunoparesis is present.