European journal of internal medicine
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Eur. J. Intern. Med. · Mar 2024
ReviewWhipple's disease: A rare disease that can be spotted by many doctors.
Whipple's disease, an extremely rare, chronic infection caused by Tropheryma whipplei, an actinobacterium ubiquitously present in the environment, is a multisystemic condition that can affect several organs. Therefore, Whipple's disease should always be considered by physicians working across various branches of medicine, including internal medicine, rheumatology, infectious diseases, gastroenterology, haematology, and neurology. Initially, Whipple's disease is challenging to diagnose due to both its rarity and non-specific clinical features, almost indistinguishable from rheumatological conditions. ⋯ However, it may also be misleading as false positives can occur. If not promptly recognized and treated, central nervous system involvement may develop, which can be fatal. The therapeutic gold standard has not yet been fully established, particularly in cases of recurrent disease, neurological involvement, and an immune reconstitution inflammatory syndrome that may arise following the initiation of antibiotic therapy.
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Eur. J. Intern. Med. · Mar 2024
ReviewNon-conventional immunomodulation in the management of sepsis.
Sepsis remains a critical global health issue, demanding novel therapeutic strategies. Traditional immunomodulation treatments such as corticosteroids, specific modifiers of cytokines, complement or coagulation, growth factors or immunoglobulins, have so far fallen short. Meanwhile the number of studies investigating non-conventional immunomodulatory strategies is expanding. ⋯ Dexmedetomidine, a sedative, demonstrates anti-inflammatory properties, reducing sepsis mortality rates in some studies. Temperature management, particularly maintaining higher body temperature, has also been associated with improved outcomes in small scale human trials. In conclusion, emerging non-conventional immunomodulatory approaches, including herbal medicine, immunonutrition, and targeted supportive therapies, hold potential for sepsis treatment, but their possible implementation into everyday clinical practice necessitates further research and stringent clinical validation in different settings.
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Eur. J. Intern. Med. · Mar 2024
Meta AnalysisEarly prediction of ventilator-associated pneumonia with machine learning models: A systematic review and meta-analysis of prediction model performance✰.
Machine learning-based prediction models can catalog, classify, and correlate large amounts of multimodal data to aid clinicians at diagnostic, prognostic, and therapeutic levels. Early prediction of ventilator-associated pneumonia (VAP) may accelerate the diagnosis and guide preventive interventions. The performance of a variety of machine learning-based prediction models were analyzed among adults undergoing invasive mechanical ventilation. ⋯ A variety of the prediction models, prediction intervals, and prediction windows were identified to facilitate timely diagnosis. In addition, care-related risk factors susceptible for preventive interventions were identified. In future, there is a need for dynamic machine learning models using time-depended predictors in conjunction with feature importance of the models to predict real-time risk of VAP and related outcomes to optimize bundled care.
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Eur. J. Intern. Med. · Mar 2024
Multicenter Study Observational StudyHow to recognize pulmonary embolism in syncope patients: A simple rule.
Syncope can be the presenting symptom of Pulmonary Embolism (PE). It is not known wether using a standardized algorithm to rule-out PE in all patients with syncope admitted to the Emergency Departments (ED) is of value or can lead to overdiagnosis and overtreatment. ⋯ Most patients with syncope due to PE present with anamnestic and clinical features indicative of PE diagnosis. A clinical decision rule can be used to identify patients who would benefit from further diagnostic tests to exclude PE, while reducing unnecessary exams that could lead to over-testing and over-diagnosis.