A Quantitative and Semi-Quantitative Analysis of Computed Tomography Indices in Patients With Pneumonia and Acute Respiratory Failure in the Intensive Care Unit

Acute respiratory failure is a primary cause of intensive care unit admissions. It occurs when the lungs fail to adequately oxygenate arterial blood and/or prevent carbon dioxide retention. While the definition does not include absolute values, arterial PaO2 below 60 mmHg and arterial PaCO2 above 50 mmHg are generally accepted as indicators. However, these values should be interpreted in the context of individual patient characteristics. Pneumonia remains the most common etiology of acute respiratory failure. Typically of infectious origin, pneumonia alters respiratory mechanics, disrupting the lung's gas exchange function, ventilation-perfusion balance, and volumetric spirometric parameters.

Mortality and morbidity rates increase significantly when pneumonia patients require invasive mechanical ventilation. Recent advancements in quantitative CT technology enable clinicians to assess the volumetric state of the lungs without performing spirometric tests. Volumetric lung measurements aid in diagnosing lung diseases, assessing severity, planning treatment strategies, and predicting prognosis. Numerous studies have demonstrated promising correlations between quantitative CT data and physiological measurements in monitoring various pulmonary conditions, including Interstitial Lung Disease, Chronic Obstructive Pulmonary Disease, Small Airway Diseases, and COVID-19 Pneumonia.

CT scans are routinely performed on patients presenting with acute respiratory failure due to pneumonia. While imaging primarily evaluates lung parenchyma, additional tests such as spirometry are typically required to assess functional volumetric changes in the lungs. However, performing spirometric tests on critically ill patients is extremely challenging and often impractical, though theoretically possible.

Previous research has successfully demonstrated correlations between quantitative CT measurements and disease prognosis, particularly in chronic lung diseases. These measurements have also been utilized in acute conditions such as COVID-19. In critically ill patients with acute respiratory failure, additional lung information can assist clinicians in prognostic prediction and facilitate earlier intervention.

This study employs quantitative CT (qCT) measurements derived from CT attenuation histograms to examine the relationship between these parameters and disease prognosis in pneumonia patients with acute respiratory failure on invasive mechanical ventilation. These measurements include mean lung attenuation (MLA) and threshold-based volumetric measurements [low-density volume (LDV), medium-density volume (MDV), high-density volume (HDV), the ratio of MDV to total lung volume (MDV/TLV), and the ratio of HDV to total lung volume (HDV/TLV)].

Statistical analyses were planned to be conducted using IBM SPSS for Windows version 29.0 (IBM Corp., Armonk, NY, USA). The Kolmogorov-Smirnov test was intended to be employed to assess the normality assumption. Continuous variables were planned to be presented as median and interquartile range (IQR) in cases where the normality assumption was not met. Categorical variables were to be summarized as frequencies and percentages. Inter-group comparisons were planned to be performed using the Mann-Whitney U test. The relationships between categorical variables were intended to be examined using the Chi-square test.