[Out-of-hours General Practice: a case-report about Neisseria meningitidis in the health district of Grado (Gorizia), Italy.] / Continuità assistenziale: un case-report su Neisseria meningitidis nel distretto sanitario di Grado (Gorizia).

Neisseria meningitidis causes life-threatening invasive diseases, such as sepsis, pneumonia, and meningitis. In Italy, as in many other countries, despite vaccination programs, the misdiagnosis of meningococcal infections and the persistence of vaccination hesitancy and of unvaccinated people can lead to possible epidemics. Following the CARE Guidelines, this case-report describes a young woman presented with fever and a strange rash to the General Practice out-of-hours in the island of Grado (Gorizia - Italy), lately recognized as Neisseria meningitidis infection. After a prompt evaluation, she was referred to a central hospital for correct diagnosis and treatment. Study and management of the case and its close-contacts was also carried out by the Prevention Department to provide prophylaxis. The Italian Ooh service is active every night, weekend and on holidays, representing an aid to limit improper access to the Emergency Department, but especially in rural areas, patients with urgent conditions also mainly go to the Ooh, first or alternatively to reach a hospital equipped with ER. The presented case underlines the fundamental role played by Ooh in the timely identification of a meningococcal infection in a patient presenting with non-neurological symptoms, making the correct referral to the specialist department of the nearest central hospital. It is essential to conduct a detailed medical history, even by telephone, and carry out a visit to evaluate the opportunity for hospital admission, especially to achieve early identification of life-threatening communicable diseases. It is also important that medical training, even after graduation, emphasizes the importance of maintaining awareness and making rapid recognition of the key symptoms of these conditions, although rare.

Machine learning algorithms in sepsis.

Sepsis remains a significant global health challenge due to its high mortality and morbidity, compounded by the difficulty of early detection given its variable clinical manifestations. The integration of machine learning (ML) into laboratory medicine for timely sepsis identification and outcome forecasting is an emerging field of interest. This comprehensive review assesses the current body of research on ML applications for sepsis within the realm of laboratory diagnostics, detailing both their strengths and shortcomings. An extensive literature search was performed by two independent investigators across PubMed and Scopus databases, employing the keywords "Sepsis," "Machine Learning," and "Laboratory" without publication date limitations, culminating in January 2023. Each selected study was meticulously evaluated for various aspects, including its design, intent (diagnostic or prognostic), clinical environment, demographics, sepsis criteria, data gathering period, and the scope and nature of features, in addition to the ML methodologies and their validation procedures. Out of 135 articles reviewed, 39 fulfilled the criteria for inclusion. Among these, the majority (30 studies) were focused on devising ML algorithms for diagnosis, fewer (8 studies) on prognosis, and one study addressed both aspects. The dissemination of these studies across an array of journals reflects the interdisciplinary engagement in the development of ML algorithms for sepsis. This analysis highlights the promising role of ML in the early diagnosis of sepsis while drawing attention to the need for uniformity in validating models and defining features, crucial steps for ensuring the reliability and practicality of ML in clinical setting.