Diagnostic value of lung tissue density maps based on computed tomography data in patients in the intensive care unit of a multidisciplinary hospital

Aim: To assess the diagnostic value of lung tissue density maps based on computed tomography (CT) in patients in the intensive care unit of a multidisciplinary hospital.

Material and Methods. 78 patients in the intensive care unit were examined. Patients underwent CT scans of the chest with an assessment of lung tissue density maps. Age: 47 ± 5,8 years, 45 (57.7%) men, 33 (42.3%) women. CT scan was performed on GE EVOLUTION EVO device, 64 sections, with a voltage from 80 to 120 kV (depending on the patient’s physique), with an assessment of the lung tissue density maps. Data processing was carried out using descriptive statistics and sample comparison methods using nonparametric criteria.

Results. For analyzing lung tissue density map data, a summary quantitative criterion was calculated: interstitial changes (%) + consolidation process (%) + lack of aeration (%). Despite the fact that 53 patients had no changes in the lung tissue according to CT scan of the chest, in 25 (47.2%) of them, according to the lung tissue density map, quantitative criterion ranged from 14% to 25%, the qualitative density image was characterized by inhomogeneity pattern of the lung parenchyma in the posterior-basal, central segments. In 25 (32.1%) patients out of 78, according to CT of the chest, stages II (n = 19) and III (n = 6) of acute respiratory distress syndrome (ARDS) were established. According to the data of lung tissue density maps, in 14 (73.6%) patients out of 19 people, it was noted that the qualitative image was characterized by pronounced diffuse inhomogeneity of the lung parenchyma. Quantitative indicators of lung tissue density maps in acute lung injury (ALI) were more than 26%, which correlated with negative clinical laboratory dynamics.

Conclusions. 1. To obtain the results of lung tissue density maps on CT scans in patients with ALI, it is necessary to evaluate the total amount criterion: interstitial changes (%) + consolidation process (%) + lack of aeration.

2. Summation quantitative indicators from 14% to 25% corresponded to negative clinical symptoms (shortness of breath, cyanosis, tachypnea), a decrease in partial oxygen pressure in arterial blood (p < 0.05).
3. An increase in the percentage of the summation quantitative indicators of lung tissue density maps was accompanied by negative laboratory dynamics (increased pCO₂, decreased pO₂, increased blood lactate).
4. With a cumulative quantitative indicator of lung tissue density maps of more than 26%, the frequency of CT signs damage of the lung tissue increases, indicating a previously unfavorable course of ALI.

1. Preiser J.S., Herridge M., Azoulay E. Syndrome of the consequences of intensive care. Moscow: GEOTAR-Media; 2022:37–55.

2. Shanin V.Yu. Pathophysiology of critical conditions. St. Petersburg: IP Makov M.Yu; 2021:346–337. (In Russ.).

3. Fuzhenko E.E., Pogoreltsev V.O., Dzhanelidze T.D., Krainyukov P.E. MSCT – visualization of lung tissue damage in acute respiratory distress syndrome. Chief physician. 2017;54(2):59–64. (In Russ.).

4. Bernard G. R., Frtigas A., Brighamk L. Definitions, mechanisms, relevant outcomes, and clinical trial coordination. The American – European Consensus on ARDS.1994;3(149):818–824 DOI: 10.1164/ajrccm.149.3.7509706.

5. Speranskaya A.A. Conclusions in thoracic computed tomography. Symptom, syndrome, diagnosis. St. Petersburg: IP Makov M.Yu.; 2023:91–103. (In Russ.).

6. Risoli C., Nicolò M., Colombi D., Moia M., Rapacioli F., Anselmi P. et al. Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients. Diagnostics. 2022;6(12). DOI: 10.3390/diagnostics12061501.

7. Kalabukha I., Maietnyi E., Vysotsky А.G. Clinical use of densitometric analysis of lung pathology and digital data processing programs for de termining surgical tactics in phthisiosurgical patients with HIV status. Tuberculosis Lung Diseases HIV Infection. 2023;53(2). DOI: 10.30978/TB2023-2-36.

8. Noll E., Soler L., Ohana M., Ludes P.-O., Pottecher J., Bennett-Guerrero E.t al. A novel, automated, quantification of abnormal lung parenchyma in patients with COVID-19 infection: Initial description of feasibility and association with clinical outcome. Anaesthesia Critical Care & Pain Medicine. 2021;1(40). DOI: 10.1016/j.accpm.2020.10.014.

9. Malbouisson L.M., Muller J.-C., Constantin J.-M., Lu Q., Puybasset L., Rouby J.-J. Computed tomography assessment of positive end-expiratory pressure-induced alveolar recruitment in patients with acute respiratory distress syndrome. American Journal of Respiratory and Critical Care Medicine. 2021;163(6) DOI: 10.1164/ajrccm.163.6.2005001.

10. Klapsing P., Herrmann P., Moerer O. Automatic quantitative computed tomography (QCT) segmentation and analysis of aerated lung volumes in ARDS – a comparative diagnostic study. Journal of Critical Care. 2017; 42:184–191. DOI: 10.1016/j.jcrc.2016.11.001.

11. Mirkhaidarov A.R., Farkhutdinov R.R., Yuldashev M.T., Mironov P.I., Mardanov A.Z. Method for evaluating acute lung injury syndrome severity degree. Patent RU 2 157 996 C1. Registration date: 02.24.1999. (In Russ.). URL: https://yandex.ru/patents/doc/RU2168945C1_20010620

12. Agafonova N.V., Rodionov E.P., Kreines V.M. Method for diagnosing early signs of acute respiratory distress syndrome. Patent RU 2 168 945 C1. Date of registration: 24.03.2000. (In Russ.). URL: https://yandex.ru/patents/doc/RU2168945C1_20010620