Background: Several easy-to-use risk scoring systems have been built to identify patients at risk of developing complications associated with COVID-19. However, information about the ability of each score to early predict major adverse outcomes during hospitalization of severe COVID-19 patients is still scarce.
Methods: Eight risk scoring systems were rated upon arrival at the Emergency Department, and the occurrence of thrombosis, need for mechanical ventilation, death, and a composite that included all major adverse outcomes were assessed during the hospital stay. The clinical performance of each risk scoring system was evaluated to predict each major outcome. Finally, the diagnostic characteristics of the risk scoring system that showed the best performance for each major outcome were obtained.
Results: One hundred and fifty-seven adult patients (55 ± 12 years, 66% men) were assessed at admission to the Emergency Department and included in the study. A total of 96 patients (61%) had at least one major outcome during hospitalization; 32 had thrombosis (20%), 80 required mechanical ventilation (50%), and 52 eventually died (33%). Of all the scores, Obesity and Diabetes (based on a history of comorbid conditions) showed the best performance for predicting mechanical ventilation (area under the ROC curve (AUC), 0.96; positive likelihood ratio (LR+), 23.7), death (AUC, 0.86; LR+, 4.6), and the composite outcome (AUC, 0.89; LR+, 15.6). Meanwhile, the inflammation-based risk scoring system (including leukocyte count, albumin, and C-reactive protein levels) was the best at predicting thrombosis (AUC, 0.63; LR+, 2.0).
Conclusions: Both the Obesity and Diabetes score and the inflammation-based risk scoring system appeared to be efficient enough to be integrated into the evaluation of COVID-19 patients upon arrival at the Emergency Department.
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http://dx.doi.org/10.3390/jcm10163657 | DOI Listing |
Clinics (Sao Paulo)
January 2025
Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Electronic address:
Objectives: It is estimated that up to 65 % of pwMS (people with multiple sclerosis) experience varying degrees of cognitive impairment, the most commonly affected domain being Information Processing Speed (IPS). As sleep disturbance is a predictor of detriments in IPS, the authors aimed to study the association between the severity of Restless Legs Syndrome (RLS) and Obstructive Sleep Apnea (OSA) symptoms with IPS in pwMS.
Methods: In a cross-sectional study, the authors enrolled people with relapsing-remitting and secondary progressive MS referred to the comprehensive MS center of Kashani Hospital in Isfahan, Iran.
Am J Manag Care
January 2025
McGovern Medical School at UTHealth Houston, 4513 Teas St, Bellaire, TX 77401.
Objective: To examine the effect of physiologic insulin resensitization (PIR) on the cost of treating patients with diabetes and chronic kidney disease (CKD).
Study Design: The mean 1-year cost of treating 66 Medicare Advantage patients with diabetes and CKD who were receiving PIR was compared with that of treating 1301 Medicare Advantage patients with diabetes and CKD not receiving PIR. Differences in disease severity were compared using mean risk adjustment factor scores.
JMIR Form Res
January 2025
Department of Medical Informatics, Amsterdam UMC - University of Amsterdam, Amsterdam, Netherlands.
Background: The prognosis for patients with several types of cancer has substantially improved following the introduction of immune checkpoint inhibitors, a novel type of immunotherapy. However, patients may experience symptoms both from the cancer itself and from the medication. A prototype of the eHealth tool Cancer Patients Better Life Experience (CAPABLE) was developed to facilitate symptom management, aimed at patients with melanoma and renal cell carcinoma treated with immunotherapy.
View Article and Find Full Text PDFN Z Med J
January 2025
Associate Professor, University of Otago, Christchurch.
Aim: Electronic cigarette use (vaping) has increased rapidly among adolescents globally. Most electronic cigarettes (e-cigarettes) contain nicotine, which is addictive and can cause behaviour problems and mood dysregulation. We sought to assess whether an educational intervention increased knowledge about vaping-related health risks and desire to quit among high school students.
View Article and Find Full Text PDFEnviron Health Perspect
January 2025
Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK.
Background: Environmental change in coastal areas can drive marine bacteria and resulting infections, such as those caused by , with both foodborne and nonfoodborne exposure routes and high mortality. Although ecological drivers of in the environment have been well-characterized, fewer models have been able to apply this to human infection risk due to limited surveillance.
Objectives: The Cholera and Other Illness Surveillance (COVIS) system database has reported infections in the United States since 1988, offering a unique opportunity to both explore the forecasting capabilities machine learning could provide and to characterize complex environmental drivers of infections.
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