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Management Strategies in Septic Coagulopathy: A Review of the Current Literature.

Piotr F CzempikAgnieszka Wiórek
Published in: Healthcare (Basel, Switzerland) (2023)
One of the 'organs' that can be affected by sepsis is the coagulation system. Coagulopathy in sepsis may take the form of sepsis-induced coagulopathy (SIC) or sepsis-associated disseminated intravascular coagulation (DIC). It is important to identify SIC early, as at this stage of coagulopathy anticoagulants may be of the greatest benefit. The most recent diagnostic scoring systems for septic coagulopathy come from the International Society on Thrombosis and Hemostasis and the Japanese Association for Acute Medicine. Recommendations regarding the management of septic coagulopathy differ between organizations. Moreover, septic coagulopathy is an area of intense research in recent years. Therefore we searched three databases to review the most recent management strategies in septic coagulopathy. The mainstream management strategies in septic coagulopathy include the causal treatment of sepsis, unfractionated heparin, low-molecular-weight heparin, antithrombin, and recombinant human thrombomodulin. The last two have been associated with the highest survival benefit. Nevertheless, the indiscriminate use of these anticoagulants should be avoided due to the lack of mortality benefit and increased risk of bleeding. The early diagnosis of SIC and monitoring of coagulation status during sepsis is crucial for the timely management and selection of the most suitable treatment at a time. New directions in septic coagulopathy include new diagnostic biomarkers, dynamic diagnostic models, genetic markers for SIC management, and new therapeutic agents. These new research avenues may potentially result in timelier SIC diagnosis and improved management of all stages of septic coagulopathy by making it more effective, safe, and personalized.
Keyphrases
  • acute kidney injury
  • intensive care unit
  • septic shock
  • systematic review
  • gene expression
  • oxidative stress
  • type diabetes
  • liver failure
  • machine learning
  • respiratory failure
  • mechanical ventilation