It is unclear if there is an association between COVID‐19 and cryptococcosis. Therefore, this study aimed to describe the clinical features, risk factors, and outcomes associated with cryptococcosis in hospitalised patients with COVID‐19. The objectives of this study were to determine the incidence of and examine factors associated with cryptococcosis after a diagnosis of COVID‐19. We used TriNetX to identify and sort patients 18 years and older hospitalised with COVID‐19 into two cohorts based on the presence or absence of a diagnosis of cryptococcosis following diagnosis of COVID‐19. Outcomes of interest included the incidence of cryptococcosis following the diagnosis of COVID‐19 as well as the proportion of patients in each group who had underlying comorbidities, received immunomodulatory therapy, required ICU admission or mechanical ventilation (MV), or died. Propensity score matching was used to adjust for confounding. Among 212,479 hospitalised patients with COVID‐19, 65 developed cryptococcosis. The incidence of cryptococcosis following COVID‐19 was 0.022%. Patients with cryptococcosis were more likely to be male and have underlying comorbidities. Among cases, 32% were people with HIV. Patients with cryptococcosis were more likely to have received tocilizumab (p < .0001) or baricitinib (p < .0001), but not dexamethasone (p = .0840). ICU admission (38% vs 29%), MV (23% vs 11%), and mortality (36% vs 14%) were significantly higher among patients with cryptococcosis. Mortality remained elevated after adjusted propensity score matching. Cryptococcosis occurred most often in hospitalised patients with COVID‐19 who had traditional risk factors, comparable to findings in patients without COVID‐19. Cryptococcosis was associated with increased ICU admission, MV, and mortality.

The effect of COVID-19 on the risk and prognosis of cryptococcosis is unclear. We compared the characteristics and outcomes of cryptococcosis in patients with and without COVID-19. Patients 18 years and older with cryptococcosis were identified from TriNetX and separated into two cohorts based on a diagnosis of COVID-19 within 3 months of the index diagnosis of cryptococcosis. Differences examined between groups included comorbidities, immunosuppressive medications, ED visits, hospitalizations, ICU admissions, mechanical ventilation, and deaths. The propensity score matching was performed based on demographics and comorbidities. Of the 6998 patients with cryptococcosis included, 4.4% (n = 306) had COVID-19 prior to cryptococcosis. Mortality was higher in patients with COVID-19 compared to those without COVID-19 (14% vs. 11%, p = 0.032). Additionally, those with COVID-19 were older (55.2 ± 14.4 vs. 51.9 ± 15.2 years, p < 0.001) with higher rates of transplant (29% vs. 13%, p < 0.001), neoplastic disease (37% vs. 21%, p < 0.001), chronic kidney disease (42% vs. 18%, p < 0.001), or diabetes (35% vs. 19%, p < 0.001) but not HIV (30% vs. 31%, p = 0.618). Glucocorticoid use was more common in those with COVID-19 (52% vs. 27%, p < 0.001). More patients with COVID-19 required ED visits (29% vs. 23%, p = 0.025) and ICU admission (18% vs. 11%, p < 0.001). After propensity score matching, patients with COVID-19 had higher rates of neoplastic disease, heart failure, chronic kidney disease, and glucocorticoid use but did not experience worse outcomes compared to those without COVID-19. Patients with COVID-19 who developed cryptococcosis had independently higher rates of comorbidities and glucocorticoid use but similar outcomes, including death, versus those without COVID-19.

While overall survival with multiple myeloma (MM) has improved, patients suffer from overwhelming tumor burden, MM-associated comorbidities, and frequent relapses requiring administration of salvage therapies. As a result, this vicious cycle is often characterized by cumulative immunodeficiency stemming from a combination of disease- and treatment-related factors leading to neutropenia, T-cell deficiency, and hypogammaglobulinemia. Infectious etiologies differ based on the duration of MM and treatment-related factors, such as number of previous treatments and cumulative dose of corticosteroids. Herein, we present the case of a patient who was receiving pomalidomide without concomitant corticosteroids for MM and was later found to have cryptococcosis, as well as findings from a literature review. Most cases of cryptococcosis are reported in patients with late-stage MM, as well as those receiving novel anti-myeloma agents, such as pomalidomide, in combination with corticosteroids or following transplantation. However, it is likely cryptococcosis may be underdiagnosed in this population. Due to the cumulative immunodeficiency present in patients with MM, clinicians must be suspicious of cryptococcosis at any stage of MM.

Background It remains unclear if there is an association between COVID-19 and cryptococcosis. The purpose of this study was to compare demographic characteristics and outcomes of cryptococcosis between patients with COVID-19 to non-COVID-19 controls. Methods Patients 18 years and older with cryptococcosis were identified from TriNetX, a global federated research network, and separated into two cohorts based on a diagnosis of COVID-19 within 3 months prior to the index diagnosis of cryptococcosis. The primary outcome was the percent mortality in each group. The secondary outcomes included the proportion of patients in each group who had underlying comorbidities, received immunosuppressive medications, or required hospitalization or ICU admission. Propensity score matching was performed to control for differences between groups based on demographics and comorbidities. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for outcomes, with p &lt; 0.05 as the cut off for statistical significance. Results A total of 6252 patients with cryptococcosis were included, of which 4.5% (n=283) had COVID-19 prior to diagnosis of cryptococcosis. Mortality was similar between patients with and without COVID-19 (13% vs 10%, p=0.075). Patients with cryptococcosis and previous COVID-19 were older (55.2 ± 14.5 years vs 52 ± 15.2 years, p=0.0005) and more likely to be non-Hispanic (73% vs 65%, p=0.0049). More patients with COVID-19 had a history of transplant (30% vs 13%, p &lt; 0.0001), malignancy (37% vs 21%, p &lt; 0.0001), and diabetes (35% vs 19%, p &lt; 0.0001), but not HIV (29% vs 31%, p=0.5482). Prednisone and dexamethasone use were higher among patients with previous COVID-19 (32% vs 15%, p &lt; 0.0001 and 17% vs 7%, p &lt; 0.0001, respectively). Hospitalization rates were similar (54% vs 57%, p=0.278), but more patients with COVID-19 required ICU admission (19% vs 11%, p &lt; 0.0001). In propensity score-matched analysis, patients with COVID-19 remained at higher odds of ICU admission (OR 1.85, 95% CI 1.15-2.97, p=0.010), but lower odds of hospitalization (OR 0.57, 95% CI 0.41-0.81, p=0.001). Conclusion Patients with COVID-19 who developed cryptococcosis had higher rates of comorbidities, corticosteroid use, and ICU admission but did not experience higher mortality compared to non-COVID-19 controls. Disclosures George R. Thompson, III, MD, Amplyx: Advisor/Consultant|Amplyx: Grant/Research Support|Astellas: Advisor/Consultant|Astellas: Grant/Research Support|Cidara: Advisor/Consultant|Cidara: Grant/Research Support|F2G: Advisor/Consultant|F2G: Grant/Research Support|Merck: Grant/Research Support|Pfizer: DSMB|Scynexis: Advisor/Consultant|Scynexis: Grant/Research Support.

Background Previous studies have observed that multimorbidity, defined as two or more comorbidities, is associated with longer lengths of stay (LOS) and higher mortality in patients with COVID-19. In addition, inequality in social determinants of health (SDOH), dictated by economic stability, education access and quality, healthcare access and quality, neighborhoods and built environment, and social and community context have only added to disparities in morbidity and mortality associated with COVID-19. However, the relationship between SDOH and LOS in COVID-19 patients with multimorbidity is poorly characterized. Analyzing the effect SDOH have on LOS can help identify patients at high risk for prolonged hospitalization and allow prioritization of treatment and supportive measures to promote safe and expeditious discharge. Methods This study was a multicenter, retrospective analysis of adult patients with multimorbidity who were hospitalized with COVID-19. The primary outcome was to determine the LOS in these patients. The secondary outcome was to evaluate the role that SDOH play in LOS. Poisson regression analyses were performed to examine associations between individual SDOH and LOS. Results A total of 370 patients were included with a median age of 65 years (IQR 55-74), of which 57% were female and 77% were African American. Median Charlson Comorbidity Index was 4 (IQR 2-6) with hypertension (77%) and diabetes (51%) being the most common, while in-hospital mortality was 23%. Overall, median length of stay was 7 days (IQR 4-13). White race (-0.16, 95% CI -0.27 to -0.05, p=0.003) and residence in a single-family home (-0.28, 95% CI -0.38 to -0.17, p&lt; 0.001) or nursing home/long term care facility (-0.36, 95% CI -0.51 to -0.21, p&lt; 0.001) were associated with decreased LOS, while Medicare (0.24, 95% CI 0.10 to 0.38, p=0.001) and part-time (0.35, 95% CI 0.13 to 0.57, p=0.002) or full-time (0.25, 95% CI 0.12 to 0.38, p&lt; 0.001) employment were associated with increased LOS. Conclusion Based on our results, differences in SDOH, including economic stability, neighborhood and built environment, social and community context, as well as healthcare access and quality, have observable effects on COVID-19 patient LOS in the hospital. Disclosures All Authors: No reported disclosures