Current guidelines for heparin-induced thrombocytopenia (HIT) management recommend heparin cessation and switching to a nonheparin anticoagulant (ie, argatroban, danaparoid) upon clinical suspicion. Fondaparinux may be effective but information supporting its use is limited. We retrospectively evaluated 239 patients who received a nonheparin anticoagulant (fondaparinux = 133, danaparoid = 59, and argatroban = 47) for suspected or confirmed HIT. A propensity score was constructed based on age, gender, creatinine, 4T scores, and comorbidity index, and used to match 133 patients to 60 controls. Outcomes were thrombosis or thrombosis-related death and major bleeding. In the matched population there were 22 (16.5%) episodes of thromboses in the fondaparinux group and 13 (21.4%) in the control group (χ²P = .424). Bleeding was observed in 28 (21.1%) patients in the fondaparinux group compared with 12 (20%) in the control group (χ²P = .867). Survival analysis, and subgroup and unmatched analyses showed similar results. In the fondaparinux group, 60% of patients received prophylactic doses. Fondaparinux has similar effectiveness and safety as argatroban and danaparoid in patients with suspected HIT. Prophylactic fondaparinux doses seem to be effective if no indication for full anticoagulation exists. Key Points Fondaparinux seems to be an effective and safe alternative for the management of suspected HIT.

Introduction Venous thromboembolism (VTE) has been observed as a frequent complication in patients with severe novel coronavirus disease 2019 (COVID-19) infection requiring hospital admission. Aim This study was aimed to evaluate the epidemiology of VTE in hospitalized intensive care unit (ICU) and non-ICU patients. Materials and Methods PubMed was searched up to November 13, 2020, and updated in December 12, 2020. We included studies that evaluated the epidemiology of VTE, including pulmonary embolism (PE) and/or deep vein thrombosis (DVT), in patients with COVID-19. Results A total of 91 studies reporting on 35,017 patients with COVID-19 was included. The overall frequency of VTE in all patients, ICU and non-ICU, was 12.8% (95% confidence interval [CI]: 11.103–14.605), 24.1% (95% CI: 20.070–28.280), and 7.7% (95% CI: 5.956–9.700), respectively. PE occurred in 8.5% (95% CI: 6.911–10.208), and proximal DVT occurred in 8.2% (95% CI: 6.675–9.874) of all hospitalized patients. The relative risk for VTE associated with ICU admission was 2.99 (95% CI: 2.301–3.887, p <0.001). DVT and PE estimated in studies that adopted some form of systematic screening were higher compared with studies with symptom-triggered screening. Analysis restricted to studies in the 5th quintile of sample size reported significantly lower VTE estimates. Conclusion This study confirmed a high risk of VTE in hospitalized COVID-19 patients, especially those admitted to the ICU. Nevertheless, sensitivity analysis suggests that previously reported frequencies of VTE in COVID-19 might have been overestimated.

Background: The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity.Objective: We aimed to conduct a scoping review of the literature to address the question, “What are the current and potential impacts of AI technologies on health equity in oncology?”Methods: Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology.Results: Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes.Conclusions: Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI’s ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.