Background Mortality in coronavirus disease of 2019 (COVID-19) is associated with increases in prothrombotic parameters, particularly D-dimer levels. Anticoagulation has been proposed as therapy to decrease mortality, often adjusted for illness severity. Objective We wanted to investigate whether anticoagulation improves survival in COVID-19 and if this improvement in survival is associated with disease severity. Methods This is a cohort study simulating an intention-to-treat clinical trial, by analyzing the effect on mortality of anticoagulation therapy chosen in the first 48 hours of hospitalization. We analyzed 3,625 COVID-19+ inpatients, controlling for age, gender, glomerular filtration rate, oxygen saturation, ventilation requirement, intensive care unit admission, and time period, all determined during the first 48 hours. Results Adjusted logistic regression analyses demonstrated a significant decrease in mortality with prophylactic use of apixaban (odds ratio [OR] 0.46, p = 0.001) and enoxaparin (OR = 0.49, p = 0.001). Therapeutic apixaban was also associated with decreased mortality (OR 0.57, p = 0.006) but was not more beneficial than prophylactic use when analyzed over the entire cohort or within D-dimer stratified categories. Higher D-dimer levels were associated with increased mortality (p < 0.0001). When adjusted for these same comorbidities within D-dimer strata, patients with D-dimer levels < 1 µg/mL did not appear to benefit from anticoagulation while patients with D-dimer levels > 10 µg/mL derived the most benefit. There was no increase in transfusion requirement with any of the anticoagulants used. Conclusion We conclude that COVID-19+ patients with moderate or severe illness benefit from anticoagulation and that apixaban has similar efficacy to enoxaparin in decreasing mortality in this disease. For original data, please contact the corresponding author. H.H.B. originated the concept, helped in the analysis, and wrote the manuscript. M.R.G. helped originate the concept, helped in the analysis, and reviewed and edited the manuscript. J.S. helped in the statistical analysis and reviewed and edited the manuscript. K.I. helped in the analysis and reviewed and edited the manuscript. L.S. developed the cohorts, helped in the statistical analysis, and reviewed and edited the manuscript. Y.L. helped in the statistical analysis and reviewed and edited the manuscript. S.R. reviewed patient data and reviewed and edited the manuscript. J.D.G.L. reviewed patient data and reviewed and edited the manuscript. M.K. reviewed the data and edited the manuscript. M.B. reviewed patient data and reviewed and edited the manuscript. L.G. reviewed the data and edited the manuscript. E.B. developed the CLG program, developed the cohorts, performed the statistical analyses, and reviewed and edited the manuscript. ^* Equal authorship. Received: 25 August 2020 Accepted: 24 September 2020 Publication Date: 13 November 2020 (online) © 2020. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

Morbid obesity is a health problem that has been shown to be refractory to diet, exercise, and medical treatment. Surgeries designed to promote weight loss, termed bariatric surgery and typically involving a gastric bypass procedure, have recently been implemented to treat obesity with high success rates. However, long‐term sequelae can result in micronutrient deficiencies. This review will focus on iron deficiency and its association with obesity and bariatric surgery. Iron deficiency develops after gastric bypass for several reasons including intolerance for red meat, diminished gastric acid secretion, and exclusion of the duodenum from the alimentary tract. Menstruating women, pregnant women, and adolescents may be particularly predisposed toward developing iron deficiency and microcytic anemias after bypass surgery. Preoperative assessment of patients should include a complete hematological work‐up, including measurement of iron stores. Postoperatively, oral iron prophylaxis and vitamin C in addition to a multivitamin should be prescribed for bypass patients, especially for vulnerable populations. Once iron deficiency has developed, it may prove refractory to oral treatment, and require parenteral iron, blood transfusions, or surgical interventions. Bariatric surgery patients require lifelong follow‐up of hematological and iron parameters since iron deficiency and anemia may develop years after surgery. Am. J. Hematol., 2008.

Background: During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 disease severity. Previous studies have typically tested only one machine learning algorithm and limited performance evaluation to area under the curve analysis. To obtain the best results possible, it may be important to test different machine learning algorithms to find the best prediction model. Objective: In this study, we aimed to use automated machine learning (autoML) to train various machine learning algorithms. We selected the model that best predicted patients’ chances of surviving a SARS-CoV-2 infection. In addition, we identified which variables (ie, vital signs, biomarkers, comorbidities, etc) were the most influential in generating an accurate model. Methods: Data were retrospectively collected from all patients who tested positive for COVID-19 at our institution between March 1 and July 3, 2020. We collected 48 variables from each patient within 36 hours before or after the index time (ie, real-time polymerase chain reaction positivity). Patients were followed for 30 days or until death. Patients’ data were used to build 20 machine learning models with various algorithms via autoML. The performance of machine learning models was measured by analyzing the area under the precision-recall curve (AUPCR). Subsequently, we established model interpretability via Shapley additive explanation and partial dependence plots to identify and rank variables that drove model predictions. Afterward, we conducted dimensionality reduction to extract the 10 most influential variables. AutoML models were retrained by only using these 10 variables, and the output models were evaluated against the model that used 48 variables. Results: Data from 4313 patients were used to develop the models. The best model that was generated by using autoML and 48 variables was the stacked ensemble model (AUPRC=0.807). The two best independent models were the gradient boost machine and extreme gradient boost models, which had an AUPRC of 0.803 and 0.793, respectively. The deep learning model (AUPRC=0.73) was substantially inferior to the other models. The 10 most influential variables for generating high-performing models were systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score. After the autoML models were retrained with these 10 variables, the stacked ensemble model still had the best performance (AUPRC=0.791). Conclusions: We used autoML to develop high-performing models that predicted the survival of patients with COVID-19. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method for generating machine learning–based clinical decision support tools.

The lifespan of sickle cell patients has increased substantially. We chose to compare the attributes of older sickle cell patients with younger ones within the confines of a single institutional study serving the local Bronx population. Laboratory and clinical assessments from 40 patients with sickle cell disease over 40 years of age were compared to 40 patients under 30 years of age. When the older group was compared to the younger group, hemoglobin, indirect bilirubin, and platelet counts were significantly lower. Creatinine clearances were lower and BUN levels were higher in the older group. Although the prevalence of radiographically measured cardiomegaly was significantly higher in the older group, systolic and diastolic blood pressures remained unchanged. The number of crises and number of admissions were higher in the younger group than in the older, but there was wide variation, and no significant differences could be detected; days spent in hospital over the previous 2 years did not differ. No statistically significant differences were observed for absolute reticulocyte count, MCV, % HbF, white cell count, liver function tests, or oxygen saturation. Comparing genders within age groups, younger males had higher hemoglobin levels and absolute reticulocyte counts than younger females, while only the older females had higher HbF levels than the corresponding males. Lower levels of hemoglobin associated with older age may reflect decreased hematopoietic potential that may in part be due to decreased renal function. Lower platelet counts in older sickle cell patients may be a consequence of this decreased hematopoietic potential but may also represent a secondary survival benefit effect in sickle cell anemia. Am. J. Hematol. 76:101–106, 2004.

Indian J Med Res, Official publication of Indian Council of Medical Research,India

Summary. Background and objectives: The benefit of an inferior vena cava (IVC) filter in addition to standard anticoagulation regimens is unknown. Methods: We examined data for patients who received IVC filters with anticoagulation (AC‐Filter) after an episode of venous thromboembolism (VTE) and compared them with data for those who received anticoagulation only (AC‐Only). Outcome measures were new pulmonary embolism (PE), recurrent deep vein thrombosis (DVT), and mortality at 90 days and at 5 years. Demographic data included age, gender, and ethnicity/race, prior thromboembolic history, cancer, serum albumin, and time in therapeutic range. In addition, subsets matched for age, gender and race/ethnicity were examined in detail. Results: AC‐Filter patients (n = 251), when compared to AC‐Only patients (n = 1377), did not differ significantly with regard to gender or cancer status, but white males in general had better outcomes. AC‐Filter patients were more likely to have had a previous history of PE or VTE (P < 0.001). In comparison to AC‐Only patients, AC‐Filter patients had lower mean serum albumin levels (3.1 ± 0.8 vs. 3.6 ±0.8 mg dL^–1, P < 0.001) and were older (65 ± 16.1 years vs. 60 ± 17.5 years, P < 0.001). After stratification according to previous history of PE or VTE prior to the index VTE event, no differences in the outcome measures of new PE, recurrent DVT or mortality were identified between groups, but patients with a prior history of PE from either group were more likely to have a new PE (hazard ratio 1.9, P < 0.001). Conclusions: These data suggest that IVC filters may not provide any substantial additional benefit for patients who can tolerate anticoagulant therapy.