Objective: Development and validation of clinical prediction model (CPM) for serious bacterial infections (SBIs) in children presenting to the emergency department (ED) with febrile illness, based on clinical variables, clinician's "gut feeling," and "sense of reassurance.
Materials And Methods: Febrile children presenting to the ED of Children's Clinical University Hospital (CCUH) between April 1, 2017 and December 31, 2018 were enrolled in a prospective observational study. Data on clinical signs and symptoms at presentation, together with clinician's "gut feeling" of something wrong and "sense of reassurance" were collected as candidate variables for CPM. Variable selection for the CPM was performed using stepwise logistic regression (forward, backward, and bidirectional); Akaike information criterion was used to limit the number of parameters and simplify the model. Bootstrapping was applied for internal validation. For external validation, the model was tested in a separate dataset of patients presenting to six regional hospitals between January 1 and March 31, 2019.
Results: The derivation cohort consisted of 517; 54% ( = 279) were boys, and the median age was 58 months. SBI was diagnosed in 26.7% ( = 138). Validation cohort included 188 patients; the median age was 28 months, and 26.6% ( = 50) developed SBI. Two CPMs were created, namely, CPM1 consisting of six clinical variables and CPM2 with four clinical variables plus "gut feeling" and "sense of reassurance." The area under the curve (AUC) for receiver operating characteristics (ROC) curve of CPM1 was 0.744 (95% CI, 0.683-0.805) in the derivation cohort and 0.692 (95% CI, 0.604-0.780) in the validation cohort. AUC for CPM2 was 0.783 (0.727-0.839) and 0.752 (0.674-0.830) in derivation and validation cohorts, respectively. AUC of CPM2 in validation population was significantly higher than that of CPM1 [ = 0.037, 95% CI (-0.129; -0.004)]. A clinical evaluation score was derived from CPM2 to stratify patients in "low risk," "gray area," and "high risk" for SBI.
Conclusion: Both CPMs had moderate ability to predict SBI and acceptable performance in the validation cohort. Adding variables "gut feeling" and "sense of reassurance" in CPM2 improved its ability to predict SBI. More validation studies are needed for the assessment of applicability to all febrile patients presenting to ED.
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http://dx.doi.org/10.3389/fped.2022.786795 | DOI Listing |
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Olin Business School, Washington University in St. Louis.
How do people predict the outcome of an event from a set of possible outcomes? One might expect people to predict whichever outcome they believe to be most likely to arise. However, we document a robust disconnect between what people predict and what they believe to be most likely. This disconnect arises because people consider not only relative likelihood but also absolute likelihood when predicting.
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NuGut Research Platform, School of Nutrition Sciences, Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada; Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada. Electronic address:
The complex interplay between the gut microbiota, sex hormones, and mental health is emerging as a pivotal factor in understanding and managing psychiatric disorders. Beyond their traditional roles, sex hormones exert profound effects on various physiological systems including the gut microbiota. Fluctuations in sex hormone levels, notably during the menstrual cycle, influence gut physiology and barrier function, shaping gut microbiota composition and immune responses.
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Department of Surgery, Division of General Surgery, University of Toronto, Toronto, Ontario, Canada.
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Cardiovascular Department, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Statins, known as HMG-CoA reductase inhibitors, are widely utilized to reduce blood cholesterol levels and possess pleiotropic effects, including the influence on inflammation and macrophage proliferation. Despite their significant impact in diminishing the incidence of cardiovascular events and mortality, individual responses to statin therapy vary considerably. Understanding this variability is essential for optimizing treatment outcomes and minimizing adverse effects.
View Article and Find Full Text PDFNutrients
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Department of Psychology, University of Milano-Bicocca, 20126 Milan, Italy.
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