A case of Legionnaires’ disease with severe rhabdomyolysis misdiagnosed as COVID-19

Background: COVID-19 case numbers have begun to rise with the recently reported Omicron variant. In the last two years, COVID-19 is the first diagnosis that comes to mind when a patient is admitted with respiratory symptoms and pulmonary ground-glass opacities. However, other causes should be kept in mind as well. Here we present a case of Legionnaires’ disease misdiagnosed as COVID-19. Case presentation: A 48-year-old male was admitted with complaints of dry cough and dyspnea. Chest computed-tomography revealed bilateral ground-glass opacities; therefore, a preliminary diagnosis of COVID-19 was made. However, two consecutive COVID PCR tests were negative and the patient deteriorated rapidly. As severe rhabdomyolysis and acute renal failure were present, Legionnaires’ disease was suspected. Urine antigen test for Legionella and Legionella pneumophila PCR turned out to be positive. The patient responded dramatically to intravenous levofloxacin and was discharged successfully. Discussion: Legionnaires’ disease and COVID-19 may present with similar signs and symptoms. They also share common risk factors and radiological findings. Conclusions: Shared clinical and radiological features between COVID-19 and other causes of acute respiratory failure pose a challenge in diagnosis. Other causes such as Legionnaires’ disease must be kept in mind and appropriate diagnostic tests should be performed accordingly

Diagnostic performance and longitudinal analysis of fungal biomarkers in COVID-19 associated pulmonary aspergillosis

Objectives: Galactomannan lateral flow assay (GM-LFA) is a reliable test for COVID-19 associated pulmonary aspergillosis (CAPA) diagnosis. We aimed to assess the diagnostic performance of GM-LFA with different case definitions, the association between the longitudinal measurements of serum GM-ELISA, GM-LFA, and the risk of death. Methods: Serum and nondirected bronchial lavage (NBL) samples were periodically collected. The sensitivity and specificity analysis for GM-LFA was done in different time periods. Longitudinal analysis was done with the joint model framework. Results: A total of 207 patients were evaluated. On the day of CAPA diagnosis, serum GM-LFA had a sensitivity of 42 % (95 % CI: 23–63) and specificity of 82 % (95 % CI: 78–84), while NBL GM-LFA had a sensitivity of 73 % (95 % CI: 45–92), specificity of 85 % (95 % CI: 76–91) for CAPA. Sensitivity decreased through the following days in both samples. Univariate joint model analysis showed that increasing GM-LFA and GM-ELISA levels were associated with increased mortality, and that effect remained same with serum GM-ELISA in multivariate joint model analysis. Conclusion: GM-LFA, particularly in NBL samples, seems to be a reliable method for CAPA diagnosis. For detecting patients with higher risk of mortality, longitudinal measurement of serum GM-ELISA can be useful

Community-acquired pneumonia - An EFIM guideline critical appraisal adaptation for internists

Background: In real-life settings, guidelines frequently cannot be followed since many patients are multimorbid and/or elderly or have other complicating conditions which carry an increased risk of drug-drug interactions. This document aimed to adapt recommendations from existing clinical practice guidelines (CPGs) to assist physicians' decision-making processes concerning specific and complex scenarios related to acute CAP.Methods: The process for the adaptation procedure started with the identification of unsolved clinical questions (PICOs) in patients with CAP and continued with critically appraising the updated existing CPGs and choosing the recommendations, which are most applicable to these specific scenarios.Results: Seventeen CPGs were appraised to address five PICOs. Twenty-seven recommendations were endorsed based on 7 high, 9 moderate, 10 low, and 1 very low-quality evidence. The most valid recommendations applicable to the clinical practice were the following ones: Respiratory virus testing is strongly recommended during periods of increased respiratory virus activity. Assessing the severity with a validated prediction rule to discriminate where to treat the patient is strongly recommended along with reassessing the patient periodically for improvement as expected. In adults with multiple comorbidities, polypharmacy, or advanced age, it is strongly recommended to check for possible drug interactions before starting treatment. Strong graded recom-mendations exist on antibiotic treatment and its duration. Recommendations on the use of biomarkers such as C -reactive protein or procalcitonin to improve severity assessment are reported.Conclusion: This document provides a simple and reliable updated guide for clinical decision-making in the management of complex patients with multimorbidity and CAP in the real-life setting

Community-acquired pneumonia - An EFIM guideline critical appraisal adaptation for internists.

In real-life settings, guidelines frequently cannot be followed since many patients are multimorbid and/or elderly or have other complicating conditions which carry an increased risk of drug-drug interactions. This document aimed to adapt recommendations from existing clinical practice guidelines (CPGs) to assist physicians' decision-making processes concerning specific and complex scenarios related to acute CAP. The process for the adaptation procedure started with the identification of unsolved clinical questions (PICOs) in patients with CAP and continued with critically appraising the updated existing CPGs and choosing the recommendations, which are most applicable to these specific scenarios. Seventeen CPGs were appraised to address five PICOs. Twenty-seven recommendations were endorsed based on 7 high, 9 moderate, 10 low, and 1 very low-quality evidence. The most valid recommendations applicable to the clinical practice were the following ones: Respiratory virus testing is strongly recommended during periods of increased respiratory virus activity. Assessing the severity with a validated prediction rule to discriminate where to treat the patient is strongly recommended along with reassessing the patient periodically for improvement as expected. In adults with multiple comorbidities, polypharmacy, or advanced age, it is strongly recommended to check for possible drug interactions before starting treatment. Strong graded recommendations exist on antibiotic treatment and its duration. Recommendations on the use of biomarkers such as C-reactive protein or procalcitonin to improve severity assessment are reported. This document provides a simple and reliable updated guide for clinical decision-making in the management of complex patients with multimorbidity and CAP in the real-life setting

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning

Purpose While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. Methods We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16). Results The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 +/- 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface. Conclusion We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19