Kumar, Shaji

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.

Abstract Overall Survival estimates from diagnosis are valuable for guiding treatment, but do not consider the years already survived. Conditional survival (CS) provides dynamic survival predictions over time. This study was conducted to estimate CS at 1–8 years from diagnosis and the impact of baseline prognostic factors on CS in multiple myeloma (MM) patients. This is a retrospective study including 2556 MM patients diagnosed between 2004–2019. CS (t|s) was defined as the probability of surviving t years given survival of s years. Median age was 64 years. Median follow-up was 6.2 years and median overall survival from diagnosis was 7.5 years. The 5-year CS estimates at s = 0, 1, 2, 3, and 5 years were 0.64, 0.61, 0.61, 0.61, and 0.58, respectively. On multivariate analysis, age ≥ 65 and proteasome inhibitor + immunomodulatory-based induction were associated with decreased survival and increased survival, respectively, retained at 5 years. The adverse impact of 1q gain/amplification, high-risk IgH translocation, and ISS-3 was significant at 1 and 3 years but not 5 years. Chromosome 17 abnormality was associated with decreased survival only at 1 year. Among MM patients, 5-year CS was stable at 1–5 years from diagnosis. The prognostic impact of high-risk cytogenetic factors decreased with additional years survived.

Abstract1q gain (+1q) is the most common high‐risk cytogenetic abnormality (HRCA) in patients with multiple myeloma (MM). However, its prognostic value remains unclear in the era of novel agents. Here, we retrospectively analyzed the impact of +1q on the outcomes of 934 patients newly diagnosed with MM. +1q was identified in 53.1% of patients and verified as an independent variate for inferior overall survival (OS) (hazard ratio, 1.400; 95% confidence interval, 1.097–1.787; p = .007). Concurrence of other HRCAs (particularly t(14;16) and del(17p)) further exacerbated the outcomes of patients with +1q, suggesting prognostic heterogeneity. Thus, a risk‐scoring algorithm based on four risk variates (t(14;16), hypercalcemia, ISS III, and high LDH) was developed to estimate the outcomes of patients with +1q. Of the patients, 376 evaluable patients with +1q were re‐stratified into low (31.6%), intermediate (61.7%), and high risk (6.7%) groups, with significantly different progression‐free survival and OS (p < .0001), in association with early relapse of the disease. The prognostic value of this model was validated in the CoMMpass cohort. While attaining undetectable MRD largely circumvented the adverse impact of +1q, it scarcely ameliorated the outcome of the patients with high risk, who likely represent a subset of patients with extremely poor survival. Hence, patients with +1q are a heterogeneous group of high‐risk patients, therefore underlining the necessity for their re‐stratification. The proposed simple risk‐scoring model can estimate the outcomes of patients with +1q, which may help guide risk‐adapted treatment for such patients.

AbstractInfections are a major cause of morbidity and mortality in multiple myeloma. The increased risk for bacterial and viral infections results mainly from the disease‐inherent and treatment‐induced immunosuppression. Additional risk factors are older age with immune senescence, T cell depletion, polymorbidity, and male gender. Hence, every effort should be taken to reduce the risk for infections by identifying patients at higher risk for these complications and by implementing prophylactic measures, including chemoprophylaxis and immunization against various relevant pathogens. Here, we review the available evidence and provide recommendations for medical prophylaxis and vaccination in clinical practice.