Introduction A high incidence of mortality and severe COVID‐19 infection was reported in hematopoietic stem cell transplant (HSCT) recipients during the early phases of the COVID‐19 pandemic; however, outcomes with subsequent severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) variants, such as the omicron variant, have yet to be reported. Additionally, rollout of COVID‐19 vaccinations in subsequent pandemic waves may modify COVID‐19 disease severity and mortality in this immunocompromised population. We describe COVID‐19 outcomes among a highly vaccinated population of HSCT recipients at a single center during successive waves of community transmission arising from the SARS‐CoV‐2 delta and omicron variants. Methods We retrospectively reviewed medical records of all HSCT recipients at our institution who tested positive for SARS‐CoV‐2 from May 2021 to May 2022. Descriptive statistics were reported; the chi‐square test was utilized to identify factors associated with 90‐day all‐cause mortality and severity of COVID‐19 infection. Results Over the 1‐year study period, 77 HSCT recipients at our center contracted COVID‐19 (43 allogenic; 34 autologous). Twenty‐six (33.8%) patients were infected with the SARS‐CoV‐2 delta variant, while 51 (66.2%) had the SARS‐CoV‐2 omicron variant. Thirty‐nine (50.6%) patients required hospitalization. More than 80% had received prior COVID‐19 vaccination (57.1% with two doses, 27.3% with three doses). The majority (90.9%) had mild disease; only one (1.3%) patient required mechanical ventilation. Active hematological disease at time of COVID‐19 infection was associated with increased odds of mortality [odds ratio (OR) = 6.90, 95% confidence interval (CI) = 1.20–40]. The 90‐day all‐cause mortality was 7.8% (six patients). Infection with the omicron variant (vs. delta) was associated with less severe illness (OR = 0.05, 95% CI = 0.01–0.47) and decreased odds of mortality (OR = 0.08, 95% CI = 0.01–0.76). Being on immunosuppression (OR = 5.10, 95% CI = 1.10–23.60) and being unvaccinated at disease onset (OR = 14.76, 95% CI = 2.89–75.4) were associated with greater severity of COVID‐19 infection. Conclusion We observed favorable outcomes with COVID‐19 infection in a cohort of vaccinated HSCT patients. The SARS‐CoV‐2 omicron variant was associated with both less severe illness and decreased odds of mortality. As COVID‐19 moves toward endemicity, early access to treatment and encouraging vaccination uptake is crucial in mitigating the challenge of COVID‐19 management among HSCT recipients. Surveillance and assessment of clinical outcomes with new SARS‐CoV‐2 variants also remains important in this immunocompromised population.
<b><i>Background:</i></b> The ILD-GAP model was developed and validated in a Western cohort to predict 1-, 2- and 3-year mortality in chronic interstitial lung disease (ILD). <b><i>Objectives:</i></b> We aimed to validate the ILD-GAP model and identify predictors of mortality to derive a nomogram to predict mortality in our local Asian population. <b><i>Methods:</i></b> Characteristics of patients on follow-up in a tertiary ILD referral center were retrospectively reviewed. <b><i>Results:</i></b> There were 181 patients and 48 mortalities. 29.8% had idiopathic pulmonary fibrosis, 2.8% unclassifiable ILD, 33.1% connective tissue disease-associated interstitial lung disease (CTD-ILD), 28.7% idiopathic nonspecific interstitial pneumonia and 5.5% chronic hypersensitivity pneumonitis. Univariable analysis showed that a higher ILD-GAP index, unclassified ILD, males, older age, higher pulmonary artery systolic pressure, lower forced vital capacity percent predicted and carbon monoxide diffusion capacity (DLCO) correlated with increased mortality, and CTD had lower mortality. Multivariable analysis utilizing Akaike’s information criterion stopping rule showed males and a lower DLCO predicted increased mortality, while CTD predicted lower mortality. These were used to generate a nomogram which predicted overall mortality better (C index 0.817, adequacy index 99.5%) than ILD-GAP (C index 0.777, adequacy index 60.7%) and provided superior estimates based on likelihood ratio testing. Calibration plots showed the nomogram predicted 1-year mortality better, whilst the ILD-GAP model predicted 2- and 3-year mortality closer to actual mortality rates but underpredicted 1-year mortality. <b><i>Conclusion:</i></b> The nomogram performed better than ILD-GAP in predicting overall mortality and 1-year mortality. Both demonstrated good performance in predicting mortality risk.
The coronavirus disease 2019 (COVID-19) pandemic has caused a catastrophic global health crisis. There is a lack of mitigation and clinical management strategies for COVID-19 in specific patient cohorts such as hemodialysis (HD) patients. We report our experience in treating the first case of COVID-19 in a HD patient in Singapore who had a severe clinical course including acute respiratory distress syndrome and propose a clinical management strategy. We propose a clinical workflow in managing such patients based on available evidence from literature review. We also highlight the importance of early recognition and intervention for disease control, dialysis support in an acute hospital isolation facility, deisolation protocol, and discharge planning due to prolonged viral shedding. The case highlights important points specific to a HD patient with a COVID-19 diagnosis, tailored interventions for each stage of the disease, and deisolation considerations in the recovery phase.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.