全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Recent Advances in the Assessment Methods and Indicators for the Severity of Severe Pneumonia

DOI: 10.4236/jbm.2025.135025, PP. 317-330

Keywords: Severe Pneumonia, Severity Assessment, Biomarkers, Imaging Techniques, Artificial Intelligence

Full-Text   Cite this paper   Add to My Lib

Abstract:

As a fatal respiratory disease, accurate assessment of the severity of severe pneumonia is of great significance for clinical treatment and prognosis. In recent years, with the development of biomarkers, imaging technology and artificial intelligence, significant breakthroughs have been made in the assessment of severe pneumonia. This article systematically reviews the clinical scoring system of severe pneumonia, the application of new biomarkers, the innovation of imaging technology, and the role of artificial intelligence and big data, analyzes the advantages and limitations of the existing assessment methods, and looks forward to the future direction of the research, so as to provide a scientific basis for clinical practice and scientific research.

References

[1]  Niederman, M.S. and Torres, A. (2022) Respiratory Infections. European Respiratory Review, 31, Article ID: 220150.
https://doi.org/10.1183/16000617.0150-2022
[2]  Cillóniz, C., Torres, A. and Niederman, M.S. (2021) Management of Pneumonia in Critically Ill Patients. BMJ, 375, e065871.
https://doi.org/10.1136/bmj-2021-065871
[3]  Xie, K., Guan, S., Kong, X., Ji, W., Du, C., Jia, M., et al. (2024) Predictors of Mortality in Severe Pneumonia Patients: A Systematic Review and Meta-Analysis. Systematic Reviews, 13, Article No. 210.
https://doi.org/10.1186/s13643-024-02621-1
[4]  Sun, Z., Xia, L. and Yu, H.M. (2020) Study on the Prognostic Value of PSI Score and CURB-65 Score for Community-Acquired Pneumonia in Elderly with Different Age Segments. Chinese Family Medicine, 18, 392-385, 407.
[5]  Noguchi, S., Katsurada, M., Yatera, K., Nakagawa, N., Xu, D., Fukuda, Y., et al. (2024) Utility of Pneumonia Severity Assessment Tools for Mortality Prediction in Healthcare-Associated Pneumonia: A Systematic Review and Meta-Analysis. Scientific Reports, 14, Article No. 12964.
https://doi.org/10.1038/s41598-024-63618-3
[6]  Yang, Y., Xing, W., Liu, Y., Li, Y., Ta, D., Song, Y., et al. (2025) Medical Imaging-Based Artificial Intelligence in Pneumonia: A Narrative Review. Neurocomputing, 630, Article ID: 129731.
https://doi.org/10.1016/j.neucom.2025.129731
[7]  Khan, M.A., Bajwa, A. and Hussain, S.T. (2025) Pneumonia: Recent Updates on Diagnosis and Treatment. Microorganisms, 13, Article 522.
https://doi.org/10.3390/microorganisms13030522
[8]  Liu, W., Guan, W. and Zhong, N. (2020) Strategies and Progress in Combating Novel Coronaviruses in China. Engineering, 6, 45-64.
[9]  Emergency Physicians Branch of the Chinese Physicians Association (2016) Expert Consensus on Clinical Practice of Emergency Critical Care Pneumonia in China. China Emergency Medicine, 36, 97-107.
[10]  Gelaidan, A., Almaimani, M., Alorfi, Y.A., Alqahtani, A., Alaklabi, N.G., Alshamrani, S.M., et al. (2024) Comparative Effectiveness of CURB-65 and QSOFA Scores in Predicting Pneumonia Outcomes: A Systematic Review. Cureus, 16, e71394.
https://doi.org/10.7759/cureus.71394
[11]  Ehsanpoor, B., Vahidi, E., Seyedhosseini, J. and Jahanshir, A. (2019) Validity of SMART-COP Score in Prognosis and Severity of Community Acquired Pneumonia in the Emergency Department. The American Journal of Emergency Medicine, 37, 1450-1454.
https://doi.org/10.1016/j.ajem.2018.10.044
[12]  Molinnus, D., Beulertz, M., Bickenbach, J., Marx, G. and Benstoem, C. (2024) Observational Study of Missing SOFA Score Data Frequency in RCTs Relative to ICU Length of Stay. Scientific Reports, 14, Article No. 16160.
https://doi.org/10.1038/s41598-024-67089-4
[13]  Zhang, K., Ji, W.S., Kong, X.X., et al. (2023) A Comparative Study of the Predictive Efficacy of Sequential Organ Failure Score and CURB-65 Score and Pneumonia Severity Index Score in Predicting Death at 28 Days in Patients with Severe Pneumonia. Chinese Family Medicine, 26, 2217-22,26.
[14]  Chambliss, A.B., Patel, K., Colón-Franco, J.M., Hayden, J., Katz, S.E., Minejima, E., et al. (2023) AACC Guidance Document on the Clinical Use of Procalcitonin. The Journal of Applied Laboratory Medicine, 8, 598-634.
https://doi.org/10.1093/jalm/jfad007
[15]  Lei, J., Wang, L., Li, Q., Gao, L., Zhang, J. and Tan, Y. (2022) Identification of RAGE and OSM as New Prognosis Biomarkers of Severe Pneumonia. Canadian Respiratory Journal, 2022, Article ID: 3854191.
https://doi.org/10.1155/2022/3854191
[16]  He, M. and He, Z.G. (2023) Progress of Biomarkers Associated with Severe Pneumonia. Systemic Medicine, 8, 190-193.
[17]  Ali, S., Zehra, A., Khalid, M.U., Hassan, M. and Shah, S.I.A. (2023) Role of C-Reactive Protein in Disease Progression, Diagnosis and Management. Discoveries, 11, e179.
https://doi.org/10.15190/d.2023.18
[18]  Cherny, S.S., Brzezinski, R.Y., Wasserman, A., Adler, A., Berliner, S., Nevo, D., et al. (2024) Characterizing CRP Dynamics during Acute Infections. Infection].
https://doi.org/10.1007/s15010-024-02422-7
[19]  Zhuozhuang, L.Y., Yang, H.Y., Li, L., et al. (2025) Expression, Diagnostic Value and Correlation between PCT, WBC and CRP in Respiratory Infections and CPIS Score in ICU. Journal of Kunming Medical University, 46, 136-141.
[20]  Fu, X., Shi, X., Yin, R., Xing, C. and Ma, A. (2025) The Association between Variation of Neutrophil-To-Lymphocyte Ratio and Post-Thrombolysis Early Neurological Outcomes in Patients with Stroke of Different TOAST Classification. Scientific Reports, 15, Article No. 6517.
https://doi.org/10.1038/s41598-025-91334-z
[21]  Sharma, Y., Thompson, C., Zinellu, A., Shahi, R., Horwood, C. and Mangoni, A.A. (2025) The Role of the Neutrophil-To-Lymphocyte Ratio in Predicting Outcomes among Patients with Community-Acquired Pneumonia. Clinical Medicine, 25, Article ID: 100278.
https://doi.org/10.1016/j.clinme.2024.100278
[22]  Zhang, F.R., Zhou, W.F., Li, Y.Q., et al. (2022) Diagnostic Value of Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Severe Mycoplasma Pneumoniae. Chinese Journal of Practical Pediatrics, 37, 260-264.
[23]  Calis, A.G., Karaboga, B., Uzer, F., Kaplan, N., Karaca, M., Gedik, R.B., et al. (2025) Correlation of Pneumonia Severity Index and CURB-65 Score with Neutrophil/Lymphocyte Ratio, Platelet/Lymphocyte Ratio, and Monocyte/Lymphocyte Ratio in Predicting In-Hospital Mortality for Community-Acquired Pneumonia: Observational Study. Journal of Clinical Medicine, 14, Article 728.
https://doi.org/10.3390/jcm14030728
[24]  Abdelaleem, N.A., Makhlouf, H.A., Nagiub, E.M. and Bayoumi, H.A. (2021) Prognostic Biomarkers in Predicting Mortality in Respiratory Patients with Ventilator-Associated Pneumonia. The Egyptian Journal of Bronchology, 15, Article No. 16.
https://doi.org/10.1186/s43168-021-00062-1
[25]  Kiani, M., Shahnouri, H., Mahmoodi, H., Pournasrollah, M., Ahangar, H.G. and Mohammadi, M. (2024) Mean Platelet Volume (MPV) and Red Blood Cell Distribution Width Coefficient of Variation (RDW_CV) as Prognostic Markers in Community-Acquired Pneumonia in Children: A Cross-Sectional Study. Egyptian Pediatric Association Gazette, 72, Article No. 78.
https://doi.org/10.1186/s43054-024-00320-z
[26]  Liang, P., Li, Y., Meng, L., Li, Y., Mai, H., Li, T., et al. (2024) Prognostic Significance of Serum Interleukin-6 in Severe/critical COVID-19 Patients Treated with Tocilizumab: A Detailed Observational Study Analysis. Scientific Reports, 14, Article No. 29634.
https://doi.org/10.1038/s41598-024-81028-3
[27]  Bacci, M.R., Leme, R.C.P., Zing, N.P.C., Murad, N., Adami, F., Hinnig, P.F., et al. (2015) IL-6 and TNF-α Serum Levels Are Associated with Early Death in Community-Acquired Pneumonia Patients. Brazilian Journal of Medical and Biological Research, 48, 427-432.
https://doi.org/10.1590/1414-431x20144402
[28]  Meedeniya, D., Kumarasinghe, H., Kolonne, S., Fernando, C., Díez, I.D.L.T. and Marques, G. (2022) Chest X-Ray Analysis Empowered with Deep Learning: A Systematic Review. Applied Soft Computing, 126, Article ID: 109319.
https://doi.org/10.1016/j.asoc.2022.109319
[29]  Billah, M.M., Al Rakib, A., Ahamed, A.S., Chowdhury, S. and Mitro, S. (2024) A Comparative Study on the Detection of Pneumonia in Chest X-Ray Images Utilizing Deep Learning Models. European Journal of Computer Science and Information Technology, 12, 1-11.
https://doi.org/10.37745/ejcsit.2013/vol12n7111
[30]  Yang, N., Ou, Z., Sun, Q., Pan, J., Wu, J. and Xue, C. (2025) Chlamydia Psittaci Pneumonia-Evolutionary Aspects on Chest CT. BMC Infectious Diseases, 25, Article No. 11.
https://doi.org/10.1186/s12879-024-10374-4
[31]  Yang, T., Zhang, L., Sun, S., Yao, X., Wang, L. and Ge, Y. (2024) Identifying Severe Community-Acquired Pneumonia Using Radiomics and Clinical Data: A Machine Learning Approach. Scientific Reports, 14, Article No. 21884.
https://doi.org/10.1038/s41598-024-72310-5
[32]  Smit, M.R., Mayo, P.H. and Mongodi, S. (2024) Lung Ultrasound for Diagnosis and Management of Ards. Intensive Care Medicine, 50, 1143-1145.
https://doi.org/10.1007/s00134-024-07422-7
[33]  Rocca, E., Zanza, C., Longhitano, Y., Piccolella, F., Romenskaya, T., Racca, F., et al. (2023) Lung Ultrasound in Critical Care and Emergency Medicine: Clinical Review. Advances in Respiratory Medicine, 91, 203-223.
https://doi.org/10.3390/arm91030017
[34]  Guitart, C., Bobillo-Perez, S., Rodríguez-Fanjul, J., Carrasco, J.L., Brotons, P., López-Ramos, M.G., et al. (2024) Lung Ultrasound and Procalcitonin, Improving Antibiotic Management and Avoiding Radiation Exposure in Pediatric Critical Patients with Bacterial Pneumonia: A Randomized Clinical Trial. European Journal of Medical Research, 29, Article No. 222.
https://doi.org/10.1186/s40001-024-01712-y
[35]  Bessat, C., Bingisser, R., Schwendinger, M., Bulaty, T., Fournier, Y., Della Santa, V., et al. (2024) PLUS-IS-LESS Project: Procalcitonin and Lung Ultrasonography-Based Antibiotherapy in Patients with Lower Respiratory Tract Infection in Swiss Emergency Departments: Study Protocol for a Pragmatic Stepped-Wedge Cluster-Randomized Trial. Trials, 25, Article No. 86.
https://doi.org/10.1186/s13063-023-07795-y
[36]  Chassagnon, G., Vakalopoulou, M., Battistella, E., Christodoulidis, S., Hoang-Thi, T., Dangeard, S., et al. (2021) AI-Driven Quantification, Staging and Outcome Prediction of COVID-19 Pneumonia. Medical Image Analysis, 67, Article ID: 101860.
https://doi.org/10.1016/j.media.2020.101860
[37]  Yang, Y., Xing, W., Liu, Y., Li, Y., Ta, D., Song, Y., et al. (2025) Medical Imaging-Based Artificial Intelligence in Pneumonia: A Narrative Review. Neurocomputing, 630, Article ID: 129731.
https://doi.org/10.1016/j.neucom.2025.129731
[38]  Preston, D. (2018) Factors Associated with Pneumonia Severity in Children: A Systematic Review. Journal of the Pediatric Infectious Diseases Society, 7, 323-334.
[39]  Carmo, T.A., Ferreira, I.B., Menezes, R.C., Telles, G.P., Otero, M.L., Arriaga, M.B., et al. (2020) Derivation and Validation of a Novel Severity Scoring System for Pneumonia at Intensive Care Unit Admission. Clinical Infectious Diseases, 72, 942-949.
https://doi.org/10.1093/cid/ciaa183
[40]  Vidal, A. and Santos, L. (2017) Comorbidities Impact on the Prognosis of Severe Acute Community-Acquired Pneumonia. Porto Biomedical Journal, 2, 265-272.
https://doi.org/10.1016/j.pbj.2017.04.009
[41]  Pan, S., Shu, H., Wang, Y., Li, R., Zhou, T., Yu, Y., et al. (2021) The Abnormal Imaging of SARS-CoV-2: A Predictive Measure of Disease Severity. Frontiers in Medicine, 8, Article 694754.
https://doi.org/10.3389/fmed.2021.694754
[42]  Park, H., Eom, J., Song, W., Yoo, H., Jeong, B., Lee, H., et al. (2015) Chronic Obstructive Pulmonary Disease Severity Is Associated with Severe Pneumonia. Annals of Thoracic Medicine, 10, 105-111.
https://doi.org/10.4103/1817-1737.151441
[43]  Lin, Y., Lin, K., Wu, K. and Lien, F. (2024) Enhancing Pneumonia Prognosis in the Emergency Department: A Novel Machine Learning Approach Using Complete Blood Count and Differential Leukocyte Count Combined with CURB-65 Score. BMC Medical Informatics and Decision Making, 24, Article No. 118.
https://doi.org/10.1186/s12911-024-02523-1
[44]  Baik, S.M., Hong, K.S., Lee, J. and Park, D.J. (2024) Integrating Ensemble and Machine Learning Models for Early Prediction of Pneumonia Mortality Using Laboratory Tests. Heliyon, 10, e34525.
https://doi.org/10.1016/j.heliyon.2024.e34525
[45]  Li, J., Wang, Y., Zhao, W., Yang, T., Zhang, Q., Yang, H., et al. (2024) Multi-Omics Analysis Reveals Overactive Inflammation and Dysregulated Metabolism in Severe Community-Acquired Pneumonia Patients. Respiratory Research, 25, Article No. 45.
https://doi.org/10.1186/s12931-024-02669-6
[46]  Wei, X., Guo, L., Cai, H., Gu, S., Tang, L., Leng, Y., et al. (2024) MASS Cohort: Multicenter, Longitudinal, and Prospective Study of the Role of Microbiome in Severe Pneumonia and Host Susceptibility. iMeta, 3, e28.
https://doi.org/10.1002/imt2.218
[47]  Geng, N., Wu, Z., Liu, Z., Pan, W., Zhu, Y., Shi, H., et al. (2024) Strem-1 as a Predictive Biomarker for Disease Severity and Prognosis in COVID-19 Patients. Journal of Inflammation Research, 17, 3879-3891.
https://doi.org/10.2147/jir.s464789
[48]  Qi, J., Wu, Y., Guo, Z., Zhu, S., Xiong, J., Hu, F., et al. (2024) Fibroblast Growth Factor 21 Alleviates Idiopathic Pulmonary Fibrosis by Inhibiting PI3K-AKT-mTOR Signaling and Stimulating Autophagy. International Journal of Biological Macromolecules, 273, Article ID: 132896.
https://doi.org/10.1016/j.ijbiomac.2024.132896
[49]  Kyriazopoulou, E., Leventogiannis, K., Tavoulareas, G., Mainas, E., Toutouzas, K., Mathas, C., et al. (2023) Presepsin as a Diagnostic and Prognostic Biomarker of Severe Bacterial Infections and COVID-19. Scientific Reports, 13, Article No. 3814.
https://doi.org/10.1038/s41598-023-30807-5
[50]  Rabbah, J., Ridouani, M. and Hassouni, L. (2025) Improving Pneumonia Diagnosis with High-Accuracy CNN-Based Chest X-Ray Image Classification and Integrated Gradient. Biomedical Signal Processing and Control, 101, Article ID: 107239.
https://doi.org/10.1016/j.bspc.2024.107239
[51]  Shao, J., Ma, J., Yu, Y., Zhang, S., Wang, W., Li, W., et al. (2024) A Multimodal Integration Pipeline for Accurate Diagnosis, Pathogen Identification, and Prognosis Prediction of Pulmonary Infections. The Innovation, 5, Article ID: 100648.
https://doi.org/10.1016/j.xinn.2024.100648
[52]  Arian, A., Mehrabi Nejad, M., Zoorpaikar, M., Hasanzadeh, N., Sotoudeh-Paima, S., Kolahi, S., et al. (2023) Accuracy of Artificial Intelligence CT Quantification in Predicting COVID-19 Subjects’ Prognosis. PLOS ONE, 18, e0294899.
https://doi.org/10.1371/journal.pone.0294899
[53]  Chamberlin, J.H., Aquino, G., Schoepf, U.J., Nance, S., Godoy, F., Carson, L., et al. (2022) An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality. Academic Radiology, 29, 1178-1188.
https://doi.org/10.1016/j.acra.2022.03.023
[54]  Marco, N. (2024) Comparing Visual and Software-Based Quantitative Assessment Scores of Lungs’ Parenchymal Involvement Quantification in COVID-19 Patients. Diagnostics, 14, Article 985.
[55]  Stoichita, A., Ghita, M., Mahler, B., Vlasceanu, S., Ghinet, A., Mosteanu, M., et al. (2023) Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital. Journal of Clinical Medicine, 12, Article 7115.
https://doi.org/10.3390/jcm12227115
[56]  Shin, H.J., Lee, E.H., Han, K., Ryu, L. and Kim, E. (2024) Development of a New Prognostic Model to Predict Pneumonia Outcome Using Artificial Intelligence-Based Chest Radiograph Results. Scientific Reports, 14, Article No. 14415.
https://doi.org/10.1038/s41598-024-65488-1
[57]  Bhandari, M., Shahi, T.B., Siku, B. and Neupane, A. (2022) Explanatory Classification of CXR Images into COVID-19, Pneumonia and Tuberculosis Using Deep Learning and Xai. Computers in Biology and Medicine, 150, Article ID: 106156.
https://doi.org/10.1016/j.compbiomed.2022.106156
[58]  Shapiro Ben David, S., Romano, R., Rahamim-Cohen, D., Azuri, J., Greenfeld, S., Gedassi, B., et al. (2025) AI Driven Decision Support Reduces Antibiotic Mismatches and Inappropriate Use in Outpatient Urinary Tract Infections. npj Digital Medicine, 8, Article No. 61.
https://doi.org/10.1038/s41746-024-01400-5
[59]  Kraemer, M.U.G., Tsui, J.L., Chang, S.Y., Lytras, S., Khurana, M.P., Vanderslott, S., et al. (2025) Artificial Intelligence for Modelling Infectious Disease Epidemics. Nature, 638, 623-635.
https://doi.org/10.1038/s41586-024-08564-w

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133