Validation and Clinical Utility of the Lung Sliding Index (LSI) for Differentiating Pulmonary Diseases

Overview

This upcoming case-control study aims to confirm the Lung Sliding Index (LSI), a new ultrasound score that measures how well the pleura moves, in various lung diseases. The study will check how well the LSI can tell apart different lung diseases (like pneumothorax, interstitial lung disease, COPD, pneumonia, and pulmonary edema), how it relates to signs of disease severity, and how consistent the results are between different operators who have received the same training. Secondary objectives include assessing patient and operator satisfaction and feasibility using validated Likert scales.

* The study will enroll adults with various pulmonary pathologies and healthy controls. Lung ultrasound will be performed on all subjects using a standardized 12-zone protocol; each zone will be scored for pleural sliding using the LSI (0-3 per zone; total 0-36). Operator training and calibration will precede enrollment to ensure scoring consistency. * A subset of patients will undergo repeat assessments to evaluate intra- and interobserver reliability, using independent, blinded raters. * For correlation with LSI, we will collect clinical data, including spirometry, blood gases, symptom scores, and 6-minute walk tests. Diagnostic utility will be evaluated using ROC curves. Satisfaction and feasibility will be assessed via Likert questionnaires, with validation analyses (internal consistency, test-retest reliability). * All data will be de-identified and securely stored. Written informed consent will be obtained from all participants. * The study groups will include: 1- Pneumothorax 2. Interstitial lung disease (ILD/IPF) 3. COPD/emphysema 4. Bronchiectasis 5. Community-acquired pneumonia 6. Pulmonary edema 7- Pleural effusion 8. Healthy controls * Data Collection Methods: * Standardized data entry forms for clinical, imaging, and outcome data. * Centralized digital storage with access limited to study personnel. Statistical Methods: \- Descriptive statistics for baseline data ANOVA or Kruskal-Wallis for group comparisons; post hoc testing as appropriate ROC analysis for diagnostic cut-offs Pearson/Spearman correlation for clinical associations Intraclass correlation coefficients (ICC) for reliability Cronbach's alpha for Likert scale validation Multivariable regression for confounder adjustment