Last data update: Jan 27, 2025. (Total: 48650 publications since 2009)
Records 1-2 (of 2 Records) |
Query Trace: Cato SG[original query] |
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CDC guidelines for the prevention and treatment of anthrax, 2023
Bower WA , Yu Y , Person MK , Parker CM , Kennedy JL , Sue D , Hesse EM , Cook R , Bradley J , Bulitta JB , Karchmer AW , Ward RM , Cato SG , Stephens KC , Hendricks KA . MMWR Recomm Rep 2023 72 (6) 1-47 THIS REPORT UPDATES PREVIOUS CDC GUIDELINES AND RECOMMENDATIONS ON PREFERRED PREVENTION AND TREATMENT REGIMENS REGARDING NATURALLY OCCURRING ANTHRAX. ALSO PROVIDED ARE A WIDE RANGE OF ALTERNATIVE REGIMENS TO FIRST-LINE ANTIMICROBIAL DRUGS FOR USE IF PATIENTS HAVE CONTRAINDICATIONS OR INTOLERANCES OR AFTER A WIDE-AREA AEROSOL RELEASE OF: Bacillus anthracis spores if resources become limited or a multidrug-resistant B. anthracis strain is used (Hendricks KA, Wright ME, Shadomy SV, et al.; Workgroup on Anthrax Clinical Guidelines. Centers for Disease Control and Prevention expert panel meetings on prevention and treatment of anthrax in adults. Emerg Infect Dis 2014;20:e130687; Meaney-Delman D, Rasmussen SA, Beigi RH, et al. Prophylaxis and treatment of anthrax in pregnant women. Obstet Gynecol 2013;122:885-900; Bradley JS, Peacock G, Krug SE, et al. Pediatric anthrax clinical management. Pediatrics 2014;133:e1411-36). Specifically, this report updates antimicrobial drug and antitoxin use for both postexposure prophylaxis (PEP) and treatment from these previous guidelines best practices and is based on systematic reviews of the literature regarding 1) in vitro antimicrobial drug activity against B. anthracis; 2) in vivo antimicrobial drug efficacy for PEP and treatment; 3) in vivo and human antitoxin efficacy for PEP, treatment, or both; and 4) human survival after antimicrobial drug PEP and treatment of localized anthrax, systemic anthrax, and anthrax meningitis. CHANGES FROM PREVIOUS CDC GUIDELINES AND RECOMMENDATIONS INCLUDE AN EXPANDED LIST OF ALTERNATIVE ANTIMICROBIAL DRUGS TO USE WHEN FIRST-LINE ANTIMICROBIAL DRUGS ARE CONTRAINDICATED OR NOT TOLERATED OR AFTER A BIOTERRORISM EVENT WHEN FIRST-LINE ANTIMICROBIAL DRUGS ARE DEPLETED OR INEFFECTIVE AGAINST A GENETICALLY ENGINEERED RESISTANT: B. anthracis strain. In addition, these updated guidelines include new recommendations regarding special considerations for the diagnosis and treatment of anthrax meningitis, including comorbid, social, and clinical predictors of anthrax meningitis. The previously published CDC guidelines and recommendations described potentially beneficial critical care measures and clinical assessment tools and procedures for persons with anthrax, which have not changed and are not addressed in this update. In addition, no changes were made to the Advisory Committee on Immunization Practices recommendations for use of anthrax vaccine (Bower WA, Schiffer J, Atmar RL, et al. Use of anthrax vaccine in the United States: recommendations of the Advisory Committee on Immunization Practices, 2019. MMWR Recomm Rep 2019;68[No. RR-4]:1-14). The updated guidelines in this report can be used by health care providers to prevent and treat anthrax and guide emergency preparedness officials and planners as they develop and update plans for a wide-area aerosol release of B. anthracis. |
Evaluation of machine learning for predicting COVID-19 outcomes from a national electronic medical records database (preprint)
Browning S , Lee SH , Belay E , DeCuir J , Cato SG , Patel P , Schwartz N , Wong KK . medRxiv 2022 14 ![]() Objective: When novel diseases such as COVID-19 emerge, predictors of clinical outcomes might be unknown. Using data from electronic medical records (EMR) allows evaluation of potential predictors without selecting specific features a priori for a model. We evaluated different machine learning models for predicting outcomes among COVID-19 inpatients using raw EMR data. Material(s) and Method(s): In Premier Healthcare Data Special Release: COVID-19 Edition (PHD-SR COVID-19, release date March, 24 2021), we included patients admitted with COVID-19 during February 2020 through April 2021 and built time-ordered medical histories. Setting the prediction horizon at 24 hours into the first COVID-19 inpatient visit, we aimed to predict intensive care unit (ICU) admission, hyperinflammatory syndrome (HS), and death. We evaluated the following models: L2-penalized logistic regression, random forest, gradient boosting classifier, deep averaging network, and recurrent neural network with a long short-term memory cell. Result(s): There were 57,355 COVID-19 patients identified in PHD-SR COVID-19. ICU admission was the easiest outcome to predict (best AUC=79%), and HS was the hardest to predict (best AUC=70%). Models performed similarly within each outcome. Discussion(s): Although the models learned to attend to meaningful clinical information, they performed similarly, suggesting performance limitations are inherent to the data. Conclusion(s): Predictive models using raw EMR data are promising because they can use many observations and encompass a large feature space; however, traditional and deep learning models may perform similarly when few features are available at the individual patient level. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. |
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