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Abstract: Bone marrow microenvironment plays an important role in promoting growth and survival of multiple myeloma (MM) cells. The tumor-promoting immune microenvironment is augmented while antitumor immune responses are inhibited. Although clinical and genomic markers of high-risk MM have been described, the immune status is just being recognized as a potential mediator of disease behavior. This is even more important with the development of a number of immune-based therapies. Based on these considerations, we evaluated peripheral blood absolute lymphocyte count (ALC) as an easily accessible marker representing immune microenvironment at diagnosis and after treatment of MM. We retrospectively evaluated 11 427 patients diagnosed with MM between 2000 and 2019 at Veterans Administration hospitals using ALC obtained closest to diagnosis and up to 2.5 years thereafter. Patients were stratified into 3 ALC categories: severely low, low, and normal (1.5 × 103/μL, respectively). Lymphopenia (including severely low and low ALC) was present in 53% of patients at MM diagnosis and was associated with inferior overall survival (OS). The median OS for patients with severely low, low, and normal ALC at diagnosis was 2.7, 3.3, and 4.2 years (P < .001), respectively. Moreover, persistent or new development of lymphopenia during treatment and follow-up was also associated with inferior OS. Our findings support the use of ALC as a biomarker for risk stratification in MM.

Abstract Background: COVID-associated pulmonary aspergillosis (CAPA) was described early in the pandemic as a complication of SARS-CoV-2. Data about incidence of aspergillosis and characteristics of affected patients after mid-2021 are limited. Methods: A retrospective, nationwide cohort of US Veterans with SARS-CoV-2 from 1/1/2020 to 2/7/2024 was created. Potential cases of aspergillosis ≤12 weeks of a SARS-CoV-2 test were flagged electronically (based on testing results indicative of invasive fungal infection, antifungal therapy, and/or ICD-10 codes), followed by manual review to establish the clinical diagnosis of pulmonary aspergillosis. Incidence rates were calculated per 10,000 SARS-CoV-2 cases. Selected clinical characteristics included age >70, receipt of immune-compromising drugs, hematologic malignancy, chronic respiratory disease, vaccination status, and vaccine era. Multivariate logistic regression was used to estimate the independent effects of these variables via adjusted odds ratios (aOR). Results: Among 674,343 Veterans with SARS-CoV-2, 165 were electronically flagged for review. Of these, 66 were judged to be cases of aspergillosis. Incidence proportions ranged from 0.30/10,000 among patients with zero risk factors to 34/10,000 among those with ≥3 risk factors; rates were similar in the pre- and post-vaccination eras. The 90-day mortality among aspergillosis cases was 50%. In the multivariate analysis, immune suppression (aOR 6.47, CI 3.84–10.92), chronic respiratory disease (aOR 3.57, CI 2.10–6.14), and age >70 (aOR 2.78, CI 1.64–4.80) were associated with aspergillosis. Conclusions: Patients with underlying risk factors for invasive aspergillosis continue to be at some risk despite SARS-CoV-2 immunization. Risk in patients without immune suppression or preexisting lung disease is very low.

Background: Cancer-associated thrombosis (CAT) is a leading cause of death in patients diagnosed with cancer. However, pharmacologic thromboprophylaxis use in cancer patients must be carefully evaluated due to a 2-fold increased risk of experiencing a major bleeding event within this population. The electronic health record CAT (EHR-CAT) risk assessment model (RAM) was recently developed, and reports improved performance over the widely used Khorana score. Extensive RAM external validation is crucial to determine accuracy across diverse patient populations prior to clinical utilization. Objectives: To externally validate EHR-CAT using data from 2103 patients with cancer at the Boston Medical Center (BMC), New England’s largest safety-net hospital, and to compare this RAM with the Khorana score. Methods: We conducted a retrospective study of BMC cancer patients diagnosed between January 2014 and December 2022 using data from the BMC tumor registry and EHR system. We validated the RAM using measures of discrimination and calibration. Results: The EHR-CAT score exhibited a strong ability to discriminate the risk of CAT (C statistic, 0.67), which was substantially higher than the classic Khorana score (C statistic, 0.58). This increased discrimination power reflects the 20% of patients that were reclassified into high or low risk by the expanded score. Model calibration was also strong in this dataset. Conclusion: In our external validation, the recently published EHR-CAT score showed clear and improved separation of patients at high and low risk for CAT. The utilization of this expanded CAT score could facilitate improved targeting of at-risk cancer patients for prophylactic therapy.