Abstract
Despite medical advances, sepsis and septic shock remain some of the leading causes of mortality worldwide, with a high inter-individual variability in prognosis, clinical manifestations and response to treatment. Evidence suggests that pulmonary sepsis is one of the most severe forms of sepsis, while liver dysfunction, left ventricular dysfunction, and coagulopathy impact the prognostic. Sepsis-related hypothermia and a hypoinflammatory state are related to a poor outcome. Given the heterogeneity of sepsis and recent technological progress amongst machine learning analysis techniques, a new, personalized approach to sepsis is being intensively studied. Despite the difficulties when tailoring a targeted approach, with the use of artificial intelligence-based pattern recognition, more and more publications are becoming available, highlighting novel factors that may intervene in the high heterogenicity of sepsis. This has led to the devise of a phenotypical approach in sepsis, further dividing patients based on host and trigger-related factors, clinical manifestations and progression towards organ deficiencies, dynamic prognosis algorithms, and patient trajectory in the Intensive Care Unit (ICU). Host and trigger-related factors refer to patients' comorbidities, body mass index, age, temperature, immune response, type of bacteria and infection site. The progression to organ deficiencies refers to the individual particularities of sepsis-related multi-organ failure. Finally, the patient's trajectory in the ICU points out the need for a better understanding of interindividual responses to various supportive therapies. This review aims to identify the main sources of variability in clustering septic patients in various clinical phenotypes as a useful clinical tool for a precision-based approach in sepsis and septic shock.