The COVID-19 pandemic poses global healthcare challenges due to its unpredictable clinical course. The aim of this study is to identify inflammatory biomarkers and other routine laboratory parameters associated with in-hospital mortality in critical COVID-19 patients. We performed a retrospective observational study on 117 critical COVID-19 patients. Following descriptive statistical analysis of the survivor and non-survivor groups, optimal cut-off levels for the statistically significant parameters were determined using the ROC method, and the corresponding Kaplan-Meier survival curves were calculated. The inflammatory parameters that present statistically significant differences between survivors and non-survivors are IL-6 (p = 0.0004, cut-off = 27.68 pg/mL), CRP (p = 0.027, cut-off = 68.15 mg/L) and IL-6/Ly ratio (p = 0.0003, cut-off = 50.39). Additionally, other statistically significant markers are creatinine (p = 0.031, cut-off = 0.83 mg/dL), urea (p = 0.0002, cut-off = 55.85 mg/dL), AST (p = 0.0209, cut-off = 44.15 U/L), INR (p = 0.0055, cut-off = 1.075), WBC (p = 0.0223, cut-off = 11.68 × 109/L) and pH (p = 0.0055, cut-off = 7.455). A survival analysis demonstrated significantly higher in-hospital mortality rates of patients with values of IL-6, IL-6/Ly, AST, INR, and pH exceeding previously mentioned thresholds. In our study, IL-6 and IL-6/Ly have a predictive value for the mortality of critically-ill patients diagnosed with COVID-19. The integration of these parameters with AST, INR and pH could contribute to a prognostic score for the risk stratification of critical patients, reducing healthcare costs and facilitating clinical decision-making.

Introduction: Enchondromas are benign tumors originating in the cartilaginous tissue of the hyaline gristle, rarely located in the chest wall. They sometimes undergo a sarcomatous transformation, becoming secondary chondrosarcomas. Case presentation: We present the case of a 53-year-old patient who, following a chest computed tomography scan performed after a thoracic trauma, was diagnosed with an osteolytic tumor at the chondrocostal junction of rib 4. Surgery was performed, with partial straight resection of ribs 3–5. Histopathological examination of the resection piece identified the existence of a chest wall chondrosarcoma on the background of malignant degeneration of an enchondromatosis lesion. The postoperative evolution was favorable, and the patient was discharged on the eighth postoperative day. Conclusion: In patients with even asymptomatic chest wall enchondromas, periodic clinical evaluation of these lesions is required, given their risk of malignant degeneration.

Identification of predictive biomarkers for the evolution of critically ill COVID-19 patients would represent a milestone in the management of patients and in human and financial resources prioritization and allocation. This retrospective analysis performed for 396 critically ill COVID-19 patients admitted to the intensive care unit aims to find the best predictors for fatal outcomes in this category of patients. The inflammatory and metabolic parameters were analyzed and Machine Learning methods were performed with the following results: (1) decision tree with Chi-Square Automatic Interaction Detector (CHAID) algorithm, based on the cut-off values using ROC Curve analysis, indicated NLR, IL-6, comorbidities, and AST as the main in-hospital mortality predictors; (2) decision tree with Classification and Regression Tree (CRT) algorithm confirmed NLR alongside CRP, ferritin, IL-6, and SII (Systemic Inflammatory Index) as mortality predictors; (3) neural networks with Multilayer Perceptron (MLP) found NLR, age, and CRP to be the best mortality predictors. Structural Equation Modeling (SEM) analysis was complementarily applied to statistically validate the resulting predictors and to emphasize the inferred causal relationship among factors. Our findings highlight that for a deeper understanding of the results, the combination of Machine Learning and statistical methods ensures identifying the most accurate predictors of in-hospital mortality to determine classification rules for future events.