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International Journal of World Medicine, 2022, 3(4); doi: 10.38007/IJWM.2022.030401.

Chinese Medicine for Inflammatory Coagulation in Sepsis


Zhenxing Wang and Xiangnong Wang

Corresponding Author:
Zhenxing Wang

Xi'an Hospital of Chinese Medicine and Brain Diseases, Xi'an, Shaanxi 710032, China


With the improvement of medical technology and level, more and more research on sepsis malefactors, the diagnosis and treatment of the disease are now more accurate and perfect with the gradual deepening of people's understanding of the disease, Western medicine has its own standards and norms of treatment, but also has its limitations and shortcomings, therefore, the search for new treatment methods is imperative, Chinese medicine as the traditional medicine of the motherland, in the process of inheritance of continuous innovation As a traditional medicine of the motherland, TCM is constantly innovating in the process of transmission, giving TCM an understanding of new diseases and proposing treatment options. The main objective of this paper is to analyse research related to the treatment of inflammatory coagulation caused by sepsis in Chinese medicine. It is found that the early stage of sepsis is characterised by more evidence of solid heat, which should be treated from the perspective of clearing heat and detoxifying the blood, cooling the blood and nourishing the yin, so as to interrupt the disease in a timely manner and avoid the emergence of severe symptoms of sepsis such as deficiency and decompensation. The study of TCM theory has provided new ideas and methods to be tried for the clinical adjunctive treatment of sepsis in TCM. From time to time, clinicians, especially TCM clinicians, use herbs, herbal injections or TCM external treatments as adjuncts in the treatment of sepsis. With the gradual development of basic and clinical applications of TCM in the treatment of sepsis, TCM has become more and more effective in improving coagulation disorders and coping with inflammatory reactions.


Chinese Medicine, Chinese Medical Evidence, Sepsis, Inflammatory Coagulation

Cite This Paper

Zhenxing Wang and Xiangnong Wang. Chinese Medicine for Inflammatory Coagulation in Sepsis. International Journal of World Medicine (2022), Vol. 3, Issue 4: 1-10. https://doi.org/10.38007/IJWM.2022.030401.


[1] Solitano V , Jairath V . The future of drug development for inflammatory bowel disease: the need to ACT (advanced combination treatment). Gut, 2022, 359(N):1541-9.

[2] Fatemeh Amrollahi, Supreeth Prajwal Shashikumar, Angela Meier, Lucila Ohno-Machado, Shamim Nemati, Gabriel Wardi: Inclusion of social determinants of health improves sepsis readmission prediction models. J. Am. Medical Informatics Assoc. 29(7): 1263-1270 (2022). https://doi.org/10.1093/jamia/ocac060

[3] Priti Shaw, Kaustubh Pachpor, Suresh Sankaranarayanan: Explainable AI Enabled Infant Mortality Prediction Based on Neonatal Sepsis. Comput. Syst. Sci. Eng. 44(1): 311-325 (2023). https://doi.org/10.32604/csse.2023.025281

[4] Redwan Hasif Alvi, Md. Habibur Rahman, Adib Al Shaeed Khan, Rashedur M. Rahman: Deep learning approach on tabular data to predict early-onset neonatal sepsis. J. Inf. Telecommun. 5(2): 226-246 (2021).

[5] Melissa Y. Yan, Lise Tuset Gustad, Øystein Nytrø: Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review. J. Am. Medical Informatics Assoc. 29(3): 559-575 (2022). https://doi.org/10.1093/jamia/ocab236

[6] Nora El-Rashidy, Tamer AbuHmed, Louai Alarabi, Hazem M. El-Bakry, Samir Abdelrazek, Farman Ali, Shaker H. Ali El-Sappagh: Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning. Neural Comput. Appl. 34(5): 3603-3632 (2022). https://doi.org/10.1007/s00521-021-06631-1

[7] Karen Divalerio Gibbs, Yan Shi, Nicole Sanders, Anthony Bodnar, Terri Brown, Mona D. Shah, Lauren M. Hess: Evaluation of a Sepsis Alert in the Pediatric Acute Care Setting. Appl. Clin. Inform. 12(03): 469-478 (2021).https://doi.org/10.1055/s-0041-1730027

[8] Samit Baral, Abeer Alsadoon, P. W. C. Prasad, Sarmad Al Aloussi, Omar Hisham Alsadoon: A novel solution of using deep learning for early prediction cardiac arrest in Sepsis patient: enhanced bidirectional long short-term memory (LSTM). Multim. Tools Appl. 80(21): 32639-32664 (2021). https://doi.org/10.1007/s11042-021-11176-5

 9] Ujjwol Shrestha, Abeer Alsadoon, P. W. C. Prasad, Sarmad Al Aloussi, Omar Hisham Alsadoon: Supervised machine learning for early predicting the sepsis patient: modified mean imputation and modified chi-square feature selection. Multim. Tools Appl. 80(13): 20477-20500 (2021). https://doi.org/10.1007/s11042-021-10725-2

[10] Joseph Agor, Ni Luh Putu S. P. Paramita, Osman Y. Özaltin: Prediction of Sepsis Related Mortality: An Optimization Approach. IEEE J. Biomed. Health Informatics 25(11): 4207-4216 (2021). https://doi.org/10.1109/JBHI.2021.3096470 

[11] Ali Jazayeri, Muge Capan, Julie S. Ivy, Ryan Arnold, Christopher C. Yang: Proximity of Cellular and Physiological Response Failures in Sepsis. IEEE J. Biomed. Health Informatics 25(11): 4089-4097 (2021). https://doi.org/10.1109/JBHI.2021.3098428

[12] Cristhyne León, Guy Carrault, Patrick Pladys, Alain Beuchée: Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability. IEEE J. Biomed. Health Informatics 25(4): 1006-1017 (2021). https://doi.org/10.1109/JBHI.2020.3021662 

[13] Maya Dewan, Rhea Vidrine, Matthew Zackoff, Zachary Paff, Brandy Seger, Stephen Pfeiffer, Philip Hagedorn, Erika L. Stalets: Design, Implementation, and Validation of a Pediatric ICU Sepsis Prediction Tool as Clinical Decision Support. Appl. Clin. Inform. 11(02): 218-225 (2020). https://doi.org/10.1055/s-0040-1705107

[14] Andrew K. Teng, Adam B. Wilcox: A Review of Predictive Analytics Solutions for Sepsis Patients. Appl. Clin. Inform. 11(03): 387-398 (2020).https://doi.org/10.1055/s-0040-1710525