Revisiting the Marrow Metabolic Changes after Chemotherapy in Lymphoma: A Step towards Personalized Care

Purpose. The aims were to correlate individual marrow metabolic changes after chemotherapy with bone marrow biopsy (BMBx) for its potential value of personalized care in lymphoma. Methods. 26 patients (mean age, 58 ± 15 y; 13 female, 13 male) with follicular lymphoma or diffuse large B-cell lymphoma, referred to FDG-PET/CT imaging, who had BMBx from unilateral or bilateral iliac crest(s) before chemotherapy, were studied retrospectively. The maximal standardized uptake value (SUV) was measured from BMBx site over the same area on both initial staging and first available restaging FDG-PET/CT scan. Results. 35 BMBx sites in 26 patients were evaluated. 12 of 35 sites were BMBx positive with interval decrease in SUV in 11 of 12 sites (92%). The remaining 23 of 35 sites were BMBx negative with interval increase in SUV in 21 of 23 sites (91%). The correlation between SUV change over the BMBx site before and after chemotherapy and BMBx result was significant (P < 0.0001). Conclusions. This preliminary result demonstrates a strong correlation between marrow metabolic changes (as determined by FDG PET) after chemotherapy and bone marrow involvement proven by biopsy. This may provide a retrospective means of personalized management of marrow involvement in deciding whether to deliver more extended therapy or closer followup of lymphoma patients

Geriatric Syndromes in People Living with HIV Associated with Ageing and Increasing Comorbidities: Implications for Neurocognitive Complications of HIV Infection

Long-term survival of treated people living with HIV (PLWH) currently approaches that of the general population. The average age of PLWH is currently in the mid-50s in resource-rich countries and is predicted that over 40% of PLWH will be older than 60 within a decade. Similar trends have been confirmed in all communities of PLWH with access to antiretroviral therapies. However, the positive impact on survival has been challenged by several developments. Ageing PLWH have clinical features similar to the general population about 5-10&nbsp;years older. In addition to the earlier occurrence of common age-related conditions common geriatric syndromes have also impacted this population prematurely. These are often difficult to evaluate and manage conditions usually of multifactorial aetiology. They include polypharmacy, frailty, impaired mobility and falls, sarcopenia, sensory impairment, and increasingly, non-dementing cognitive decline. Cognitive decline is of particular concern to PLWH and their care providers. In the general geriatric population cognitive impairment increases with age and occurs in all populations with a prevalence of over 25% in people over 80. Effective treatments are lacking and therefore minimizing risk factors plays an important role in maintaining healthspan. In the general population geriatric syndromes may increase the risk of cognitive decline. The corollary is that decreasing the risk of their development may limit cognitive impairment. Whether a similar status holds in PLWH is uncertain. This chapter will address the question of whether common geriatric syndromes in PLWH contribute to cognitive impairment. Common risk factors may provide clues to limit or delay cognitive decline

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc

Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19

Introduction: Acute kidney injury (AKI) has been identified as one of the most common and significant problems in hospitalized patients with COVID-19. However, studies examining the relationship between COVID-19 and AKI in low- and low-middle income countries (LLMIC) are lacking. Given that AKI is known to carry a higher mortality rate in these countries, it is important to understand differences in this population. Methods: This prospective, observational study examines the AKI incidence and characteristics of 32,210 patients with COVID-19 from 49 countries across all income levels who were admitted to an intensive care unit during their hospital stay. Results: Among patients with COVID-19 admitted to the intensive care unit, AKI incidence was highest in patients in LLMIC, followed by patients in upper-middle income countries (UMIC) and high-income countries (HIC) (53%, 38%, and 30%, respectively), whereas dialysis rates were lowest among patients with AKI from LLMIC and highest among those from HIC (27% vs. 45%). Patients with AKI in LLMIC had the largest proportion of community-acquired AKI (CA-AKI) and highest rate of in-hospital death (79% vs. 54% in HIC and 66% in UMIC). The association between AKI, being from LLMIC and in-hospital death persisted even after adjusting for disease severity. Conclusions: AKI is a particularly devastating complication of COVID-19 among patients from poorer nations where the gaps in accessibility and quality of healthcare delivery have a major impact on patient outcomes

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use