VALOR PRONÓSTICO DE LA TOMOGRAFÍA COMPUTARIZADA DE ALTA RESOLUCIÓN EN LA EVALUACIÓN DE LA ENFERMEDAD PULMONAR INTERSTICIAL FIBROSANTE: UNA REVISIÓN SISTEMÁTICA
DOI:
https://doi.org/10.56238/levv17n57-047Palabras clave:
Enfermedades Pulmonares Intersticiales, Fibrosis Pulmonar, Tomografía Computarizada por Rayos X, PronósticoResumen
Introducción: Las enfermedades pulmonares intersticiales fibrosantes representan un grupo heterogéneo de trastornos pulmonares crónicos caracterizados por fibrosis progresiva, distorsión arquitectónica irreversible y elevada morbimortalidad. La tomografía computarizada de alta resolución se ha convertido en un elemento central en el abordaje diagnóstico de estas condiciones y es cada vez más reconocida como herramienta de estratificación pronóstica más allá de su papel diagnóstico. Patrones radiológicos específicos y características cuantitativas de imagen se han asociado con la progresión de la enfermedad, el deterioro funcional y los resultados de supervivencia en diferentes subtipos de enfermedad pulmonar intersticial fibrosante.
Objetivo: El objetivo principal de esta revisión sistemática fue evaluar el valor pronóstico de los hallazgos de la tomografía computarizada de alta resolución en pacientes con enfermedad pulmonar intersticial fibrosante. Los objetivos secundarios incluyeron evaluar la asociación entre patrones radiológicos específicos y mortalidad, progresión de la enfermedad y deterioro funcional; examinar el papel pronóstico de métricas cuantitativas de tomografía computarizada de alta resolución; comparar el desempeño pronóstico entre diferentes subtipos de enfermedad pulmonar intersticial fibrosante; y analizar la consistencia de los marcadores pronósticos de tomografía computarizada de alta resolución con las guías clínicas actuales.
Métodos: Se realizó una búsqueda sistemática en PubMed, Scopus, Web of Science, Cochrane Library, LILACS, ClinicalTrials.gov y la International Clinical Trials Registry Platform. Se incluyeron estudios que evaluaron resultados pronósticos asociados con características de la tomografía computarizada de alta resolución en enfermedad pulmonar intersticial fibrosante. La selección de estudios, extracción de datos y evaluación del riesgo de sesgo fueron realizadas de manera independiente por revisores, y los resultados se sintetizaron de forma narrativa con comparación estructurada de los desenlaces.
Resultados y Discusión: Un total de 20 estudios cumplió con los criterios de inclusión y fue incluido en el análisis final. Se observaron asociaciones consistentes entre patrones en la tomografía computarizada de alta resolución, como neumonía intersticial usual, extensión de la fibrosis, bronquiectasias por tracción y puntuaciones cuantitativas de fibrosis, con mayor mortalidad y deterioro funcional acelerado. La evidencia emergente respalda el valor pronóstico incremental de las técnicas cuantitativas de imagen, aunque la heterogeneidad metodológica y en las definiciones de desenlaces sigue siendo una limitación.
Conclusión: La tomografía computarizada de alta resolución proporciona información pronóstica relevante en la enfermedad pulmonar intersticial fibrosante y debe considerarse un componente integral de la evaluación longitudinal de la enfermedad. Los marcadores radiológicos, especialmente la extensión de la fibrosis y características estructurales específicas, ofrecen información clínicamente relevante que puede apoyar la estratificación individualizada del riesgo y la toma de decisiones terapéuticas.
Descargas
Referencias
1. Akman, G., et al. (2023). T-MACS score vs HEART score identification of major adverse cardiac events in the emergency department. American Journal of Emergency Medicine, 64, 21–25. https://doi.org/10.1016/j.ajem.2022.11.015
2. Aktemur, M. R., et al. (2025). Comparative evaluation of HEART, T-MACS, and HE-MACS scores for risk stratification and management of patients with chest pain in the emergency department. Medicine, 104(6), Article e41432. https://doi.org/10.1097/MD.0000000000041432
3. Al-Zaiti, S. S., et al. (2023). Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nature Medicine, 29, 1804–1813. https://doi.org/10.1038/s41591-023-02396-3
4. Ashburn, N. P., et al. (2023). Performance of the European Society of Cardiology 0/1-hour algorithm with high-sensitivity cardiac troponin T among patients with known coronary artery disease. JAMA Cardiology, 8(4), 347–356. https://doi.org/10.1001/jamacardio.2023.0031
5. Ataş, I., et al. (2025). Comparison of pretreatment in European Society of Cardiology acute coronary syndrome guidelines. Western Journal of Emergency Medicine, 26(6), 1679–1687. https://doi.org/10.5811/westjem.43528
6. Barnett, J., Wu, J., Chen, S., et al. (2023). Baseline CT features and response to antifibrotic therapy in fibrotic interstitial lung disease. Respiratory Medicine, 209, Article 107095.
7. Bergmark, B. A., et al. (2022). Acute coronary syndromes. The Lancet, 399(10332), 1347–1358. https://doi.org/10.1016/S0140-6736(21)02391-6
8. Bhatt, D. L., Lopes, R. D., & Harrington, R. A. (2022). Diagnosis and treatment of acute coronary syndromes: A review. JAMA, 327(7), 662–675. https://doi.org/10.1001/jama.2022.0358
9. Biscaglia, S., et al. (2023). Complete or culprit-only PCI in older patients with myocardial infarction. New England Journal of Medicine, 389(10), 889–898. https://doi.org/10.1056/NEJMoa2300468
10. Brown, A. W., Fischer, C. P., Shlobin, O. A., et al. (2015). Outcomes after hospitalization in idiopathic pulmonary fibrosis: A cohort study. Chest, 147(1), 173–179.
11. Byrne, R. A., et al. (2023). 2023 ESC Guidelines for the management of acute coronary syndromes. European Heart Journal, 44(38), 3720–3826. https://doi.org/10.1093/eurheartj/ehad191
12. Cavalier, J. S., et al. (2025). Stress cardiovascular magnetic resonance imaging in intermediate-risk emergency department patients with abnormal high-sensitivity troponin. Journal of Cardiovascular Magnetic Resonance, 27, Article 101851. https://doi.org/10.1016/j.jocmr.2025.101851
13. Cesar, L. A. M., et al. (2025). Diretriz de síndrome coronariana crônica – 2025. Arquivos Brasileiros de Cardiologia, 122(9), Article e20250619. https://doi.org/10.36660/abc.20250619
14. Chung, J. H., Chawla, A., Peljto, A. L., et al. (2022). CT-based deep learning model for predicting mortality in fibrotic lung disease. Radiology, 304(1), 135–143.
15. Cottin, V., Wollin, L., Fischer, A., et al. (2019). Fibrosing interstitial lung diseases: Knowns and unknowns. European Respiratory Review, 28(151), Article 180100.
16. De Barros e Silva, P. G. M., et al. (2025). Diretriz brasileira de atendimento à dor torácica na unidade de emergência – 2025. Arquivos Brasileiros de Cardiologia, 122(9), Article e20250620. https://doi.org/10.36660/abc.20250620
17. Diletti, R., et al. (2023). Immediate versus staged complete revascularization in patients presenting with acute coronary syndrome and multivessel disease (BIOVASC): A prospective, open-label, non-inferiority, randomized trial. The Lancet, 401(10383), 1172–1182. https://doi.org/10.1016/S0140-6736(23)00351-3
18. Doudesis, D., et al. (2023). Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations. Nature Medicine, 29, 1201–1210. https://doi.org/10.1038/s41591-023-02325-4
19. Engström, A., Mokhtari, A., & Ekelund, U. (2024). Direct comparison of the European Society of Cardiology 0/1-hour vs. 0/2-hour algorithms in patients with acute chest pain. Journal of Emergency Medicine, 66(6), e651–e659. https://doi.org/10.1016/j.jemermed.2024.02.004
20. Enomoto, Y., Nakamura, Y., Colby, T. V., et al. (2023). Quantitative assessment of fibrotic changes on HRCT predicts mortality in connective tissue disease–associated interstitial lung disease. European Respiratory Journal, 61(1), Article 2201079.
21. Flaherty, K. R., Wells, A. U., Cottin, V., et al. (2019). Nintedanib in progressive fibrosing interstitial lung diseases. New England Journal of Medicine, 381(18), 1718–1727.
22. Gulati, M., et al. (2021). 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the evaluation and diagnosis of chest pain: A report of the American College of Cardiology/American Heart Association joint committee on clinical practice guidelines. Circulation, 144(22), e368–e454. https://doi.org/10.1161/CIR.0000000000001029
23. Hoffmann-Vold, A. M., Allanore, Y., Alves, M., et al. (2021). Progressive interstitial lung disease in patients with systemic sclerosis-associated interstitial lung disease in the EUSTAR database. Annals of the Rheumatic Diseases, 80(2), 219–227.
24. Jacob, J., Bartholmai, B. J., Rajagopalan, S., et al. (2017a). Functional and prognostic effects of emphysema in idiopathic pulmonary fibrosis. European Respiratory Journal, 50(1), Article 1700379.
25. Jacob, J., Bartholmai, B. J., Rajagopalan, S., et al. (2017b). Mortality prediction in idiopathic pulmonary fibrosis: Evaluation of computer-based CT analysis with conventional severity measures. European Respiratory Journal, 49(1), Article 1601011.
26. Jacob, J., Bartholmai, B. J., Rajagopalan, S., et al. (2018). Evaluation of computer-based quantitative CT analysis for disease staging in idiopathic pulmonary fibrosis. European Respiratory Journal, 52(3), Article 1800442.
27. Jacob, J., Bartholmai, B. J., Rajagopalan, S., et al. (2024). Imaging progression in progressive pulmonary fibrosis and its association with survival. European Respiratory Journal, 63(2), Article 2301456.
28. Kim, H. J., Brown, M. S., Chong, D., et al. (2022). Automated CT analysis of traction bronchiectasis predicts mortality in idiopathic pulmonary fibrosis. Chest, 162(5), 1105–1114.
29. Kim, Y. J., Park, S., Lee, S. M., et al. (2024). Artificial intelligence–based CT biomarkers outperform visual scoring in idiopathic pulmonary fibrosis prognosis. Radiology, 310(2), Article e231245.
30. Kraler, S., et al. (2025). Acute coronary syndromes: Mechanisms, challenges, and new opportunities. European Heart Journal, 46(29), 2866–2889. https://doi.org/10.1093/eurheartj/ehaf289
31. Lambrou, K., et al. (2023). Impacts of high sensitivity troponin T reporting on care and outcomes in clinical practice: Interactions between low troponin concentrations and participant sex within two randomized clinical trials. International Journal of Cardiology, 393, Article 131396. https://doi.org/10.1016/j.ijcard.2023.131396
32. Ley, B., Ryerson, C. J., Vittinghoff, E., et al. (2012). A multidimensional index and staging system for idiopathic pulmonary fibrosis. Annals of Internal Medicine, 156(10), 684–691.
33. Lima Filho, M. O., et al. (2024). Estratégia invasiva na síndrome coronária aguda sem supradesnível do segmento ST. Revista da Sociedade de Cardiologia do Estado de São Paulo, 34(2), 254–261. https://doi.org/10.29381/0103-8559/20243403254-61
34. Lynch, D. A., Sverzellati, N., Travis, W. D., et al. (2018). Diagnostic criteria for idiopathic pulmonary fibrosis: A Fleischner Society White Paper. The Lancet Respiratory Medicine, 6(2), 138–153.
35. Møller, J. E., et al. (2024). Microaxial flow pump or standard care in infarct-related cardiogenic shock. New England Journal of Medicine, 390(15), 1382–1393. https://doi.org/10.1056/NEJMoa2312572
36. Miller, E. R., Putman, R. K., Vivero, M., et al. (2022). Longitudinal changes in interstitial lung abnormalities and mortality. European Respiratory Journal, 59(3), Article 2101083.
37. Rao, S. V., et al. (2025). 2025 ACC/AHA/ACEP/NAEMSP/SCAI Guideline for the management of patients with acute coronary syndromes. Journal of the American College of Cardiology, 85(22), 2135–2237. https://doi.org/10.1016/j.jacc.2024.11.009
38. Raghu, G., Remy-Jardin, M., Myers, J. L., et al. (2020). Diagnosis of idiopathic pulmonary fibrosis: An official ATS/ERS/JRS/ALAT clinical practice guideline. American Journal of Respiratory and Critical Care Medicine, 201(9), e36–e69.
39. Raghu, G., Anstrom, K. J., King, T. E. Jr., et al. (2012). Prednisone, azathioprine, and N-acetylcysteine for pulmonary fibrosis. New England Journal of Medicine, 366(21), 1968–1977.
40. Reynolds, H. R., et al. (2021). Coronary optical coherence tomography and cardiac magnetic resonance imaging to determine underlying causes of myocardial infarction with nonobstructive coronary arteries in women. Circulation, 143(7), 624–640. https://doi.org/10.1161/CIRCULATIONAHA.120.052008
41. Romei, C., Tavanti, L., Sbragia, P., et al. (2021). Idiopathic pulmonary fibrosis: Longitudinal changes of CT findings and functional decline. Radiology, 299(1), 189–197.
42. Salisbury, M. L., Lynch, D. A., van Beek, E. J., et al. (2017). Idiopathic pulmonary fibrosis: The association between extent of fibrosis on CT and lung function decline. Thorax, 72(9), 798–804.
43. Salisbury, M. L., Xia, M., Murray, S., et al. (2020). Predictors of mortality in idiopathic pulmonary fibrosis using automated CT analysis. European Respiratory Journal, 56(4), Article 2001024.
44. Sandoval, Y., et al. (2022). High-sensitivity cardiac troponin and the 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR guidelines for the evaluation and diagnosis of acute chest pain. Circulation, 146(7), 569–581. https://doi.org/10.1161/CIRCULATIONAHA.122.059678
45. Stone, G. W., et al. (2024). Intravascular imaging-guided coronary drug-eluting stent implantation: An updated network meta-analysis. The Lancet, 403(10429), 824–837. https://doi.org/10.1016/S0140-6736(23)02454-6
46. Sverzellati, N., Wells, A. U., Tomassetti, S., et al. (2010). Biopsy-proved idiopathic pulmonary fibrosis: Spectrum of nondiagnostic thin-section CT diagnoses. Radiology, 254(3), 957–964.
47. Sverzellati, N., Ryerson, C. J., Wells, A. U., et al. (2024). Serial high-resolution CT in fibrotic interstitial lung disease: Prognostic implications. European Respiratory Review, 33(171), Article 230176.
48. Thiele, H., et al. (2023). Extracorporeal life support in infarct-related cardiogenic shock. New England Journal of Medicine, 389(14), 1286–1297. https://doi.org/10.1056/NEJMoa2307227
49. Todd, F., Duff, J., & Carlton, E. (2022). Identifying low-risk chest pain in the emergency department without troponin testing: A validation study of the HE-MACS and HEAR risk scores. Emergency Medicine Journal, 39, 515–518. https://doi.org/10.1136/emermed-2021-211669
50. Walsh, S. L. F., Lederer, D. J., Ryerson, C. J., et al. (2019). Diagnostic likelihood thresholds that define a working diagnosis of idiopathic pulmonary fibrosis. American Journal of Respiratory and Critical Care Medicine, 200(9), 1146–1153.
51. Walsh, S. L. F., Devaraj, A., Enghelmayer, J. I., et al. (2018). Role of imaging in progressive-fibrosing interstitial lung diseases. European Respiratory Review, 27(150), Article 180073.
52. Walsh, S. L. F., Maher, T. M., Kolb, M., et al. (2024). Prognostic imaging biomarkers in idiopathic pulmonary fibrosis: International validation study. The Lancet Respiratory Medicine, 12(1), 45–55.
53. Walsh, S. L. F., Wells, A. U., Desai, S. R., et al. (2016). Multicentre evaluation of multidisciplinary team meeting agreement on diagnosis in diffuse parenchymal lung disease. Thorax, 71(6), 587–592.
54. Wells, A. U., Brown, K. K., Flaherty, K. R., et al. (2018). What’s in a name? That which we call IPF, by any other name would act the same. European Respiratory Journal, 51(5), Article 1800692.
55. Wenzl, F. A., et al. (2022). Sex-specific evaluation and redevelopment of the GRACE score in non-ST-segment elevation acute coronary syndromes in populations from the UK and Switzerland: A multinational analysis with external cohort validation. The Lancet, 400(10354), 744–756. https://doi.org/10.1016/S0140-6736(22)01483-0
56. Yoon, S. H., Park, C. M., Goo, J. M., et al. (2021). Quantitative texture-based analysis of CT in idiopathic pulmonary fibrosis: Prognostic implications. European Radiology, 31(3), 1748–1757.
57. Zou, Y., et al. (2021). Sex-differences in the management and clinical outcome among patients with acute coronary syndrome. BMC Cardiovascular Disorders, 21, Article 609. https://doi.org/10.1186/s12872-021-02433-4
