Journal of Clinical Medicine

Background: Prolonged mechanical ventilation and tracheostomy in patients with COVID-19 is associated with longer hospital stays. Guidance on which patients are at risk for tracheostomy due to the progression of COVID-19 is limited. Objectives: This study aimed to identify risk factors associated with the need for tracheostomy in patients intubated for COVID-19 between 1 March and 31 December 2020. Methods: The methodology for this study involved a single-center retrospective analysis of 120 patients who were intubated due to COVID-19 infection between 1 March 2020 and 31 December 2020. A comparison of variables was performed using the Wilcoxon test, Chi-squared test, and Fisher’s exact test alongside univariate analysis. Results: Several risk factors were found to be significantly associated with the need for tracheostomy, including age, P/F ratio, creatinine level, and history of arrhythmia. Conclusions: Initial exploration indicates the presence of certain factors that can help us understand future need for tracheostomy earlier in the patient’s clinical course. Further analysis should be performed with a larger sample size to validate these findings and increase the generalizability of the present study.

Background: Pulmonary embolism (PE) is a significant cause of cardiovascular mortality, and emergency department (ED) management requires early risk assessment to guide monitoring and disposition. Because key decisions are often needed while diagnostic evaluation is ongoing, the simplified Pulmonary Embolism Severity Index (sPESI) may provide limited discrimination for in-hospital outcomes. We evaluated whether explainable machine-learning (ML) models integrating routine ED variables with validated risk scores can predict in-hospital mortality in adults evaluated for suspected acute PE. Methods: A retrospective single-center cohort study was performed, including 220 consecutive adults evaluated for suspected acute PE in the ED between January 2021 and March 2025, comprising both PE-confirmed and PE-excluded cases. Predictors included demographics, vital signs, arterial blood gas indices, available imaging/echocardiographic findings, and Wells, Revised Geneva, and sPESI scores. Seven ML algorithms were trained and internally evaluated using the area under the receiver operating characteristic curve (AUC) and complementary metrics. Model interpretability was assessed using SHAP (SHAPley Additive exPlanations), and a sensitivity analysis was conducted in the PE-confirmed subgroup. Results: Tree-based ensemble models demonstrated higher discrimination for in-hospital all-cause mortality than simpler classifiers. SHAP analyses consistently highlighted sPESI, oxygenation/arterial blood gas indices, and malignancy as key contributors to mortality risk. Findings were similar in the PE-confirmed sensitivity analysis. Conclusions: Explainable ML models combining established risk scores with routinely collected ED variables may complement risk stratification along the suspected-PE pathway. External multicenter validation and prospective impact studies are warranted before clinical implementation.

Background: One of the main tools for investigating pulmonary disorders is computed tomography. Starting with a CT, analyses can be qualitative (e.g., direct interpretation of 2D slices, virtual bronchoscopy) or quantitative (e.g., fibrosis score). Qualitative analyses can be performed without segmentation, but quantitative analyses require lung segmentation. Methods: We present the concepts for a class of lung segmentation methods that use region-growing algorithms, the implementation and testing details, and the results obtained in our software platform. Accurate segmentation of lung regions from medical images is a crucial step in computer-aided diagnosis (CAD) systems for pulmonary diseases such as chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer. Manual segmentation is time-consuming and subjective, while fully automated methods may fail under challenging imaging conditions. Results: This article presents a semi-automated lung segmentation approach, based on region-growing methods, that balances automation with user control. Conclusions: The proposed technique effectively delineates lung boundaries in computed tomography (CT), minimizing computational complexity and manual effort.