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Abstract: Background: Cold urticaria (ColdU), i.e. the occurrence of wheals or angioedema in response to cold exposure, is classified into typical and atypical forms. The diagnosis of typical ColdU relies on whealing in response to local cold stimulation testing (CST). It can also manifest with cold-induced anaphylaxis (ColdA). We aimed to determine risk factors for ColdA in typical ColdU. Methods: An international, cross-sectional study COLD-CE was carried out at 32 urticaria centers of reference and excellence (UCAREs). Detailed history was taken and CST with an ice cube and/or TempTest® performed. ColdA was defined as an acute cold-induced involvement of the skin and/or visible mucosal tissue and at least one of: cardiovascular manifestations, difficulty breathing, or gastrointestinal symptoms. Results: Of 551 ColdU patients, 75% (n=412) had a positive CST and ColdA occurred in 37% (n=151) of the latter. Cold-induced generalized wheals, angioedema, acral swelling, oropharyngeal/laryngeal symptoms, and itch of earlobes were identified as signs/symptoms of severe disease. ColdA was most commonly provoked by complete cold water immersion and ColdA caused by cold air was more common in countries with a warmer climate. Ten percent (n=40) of typical ColdU patients had a concomitant chronic spontaneous urticaria (CSU). They had a lower frequency of ColdA than those without CSU (4% vs 39%, p=0.003). We identified the following risk factors for cardiovascular manifestations: previous systemic reaction to a Hymenoptera sting, angioedema, oropharyngeal/laryngeal symptoms, and itchy earlobes. Conclusion: ColdA is common in typical ColdU. High-risk patients require education about their condition and how to use an adrenaline autoinjector.
Keywords: adrenaline autoinjector, cold urticaria, COLD-CE, risk factors, systemic reactions, epinephrine, therapeutic use, self administration, injections, intramuscular, methods
Published in DKUM: 06.08.2024; Views: 139; Downloads: 8
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Abstract: Critical illness may disrupt nutritional, protective, immune, and endocrine functions of the gastrointestinal tract, leading to a state of gastrointestinal dysmotility. We aimed to identify factors associated with the occurrence of gastrointestinal dysmotility in critically ill patients. A cross-sectional retrospective study was conducted, using patient files as a source of data. The study included 185 critically ill patients treated in the intensive care unit of the University Clinical Center, Kragujevac, Serbia, from January 1, 2016, to January 1, 2022. Significant risk factors associated with some form of gastrointestinal dysmotility were acute kidney injury (with paralytic ileus, nausea, vomiting, and constipation), recent abdominal surgery (with ileus, nausea, vomiting, and constipation), mechanical ventilation (with ileus, and nausea), age (with ileus and constipation), and use of certain medication such as opioids (with ileus, gastro-esophageal reflux, nausea, vomiting, and constipation), antidepressants (with ileus, nausea, and vomiting), and antidiabetics (with ileus). On the other hand, Charlson comorbidity index had divergent effects, depending on the form of gastrointestinal dysmotility: it increased the risk of gastro-esophageal reflux but protected against ileus, nausea, and vomiting. In clonclusion, recognition of factors associated with gastrointestinal dysmotility should initiate preventative measures and, thus, accelerate the recovery of critically ill.
Keywords: gastrointestinal dysmotility, critically ill patients, risk factors, intensive care unit
Published in DKUM: 04.04.2024; Views: 188; Downloads: 6
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Abstract: The increasing availability of electronic health care records has provided remarkable progress in the field of population health. In particular the identification of disease risk factors has flourished under the surge of available data. Researchers can now access patient data across a broad range of demographics and geographic locations. Utilizing this Big healthcare data researchers have been able to empirically identify specific high-risk conditions found within differing populations. However to date the majority of studies approached the issue from the top down, focusing on the prevalence of specific diseases within a population. Through our work we demonstrate the power of addressing this issue bottom-up by identifying specifically which diseases are higher-risk for a specific population. In this work we demonstrate that network-based analysis can present a foundation to identify pairs of diagnoses that differentiate across population segments. We provide a case study highlighting differences between high and low income individuals in the United States. This work is particularly valuable when addressing population health management within resource-constrained environments such as community health programs where it can be used to provide insight and resource planning into targeted care for the population served.
Keywords: population screening, risk factors, network analysis
Published in DKUM: 23.06.2017; Views: 1537; Downloads: 392
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