Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated host response to infection. Due to different infection sources, pathogens and basic conditions of patients, there is significant heterogeneity in clinical manifestations, response to treatment and prognosis of patients with sepsis. Accurate classification and individualized treatment of sepsis will help to further improve the prognosis of patients with sepsis. In recent years, the integration of artificial intelligence and bioinformatics has brought new opportunities for the research of sepsis classification. This review systematically introduces a variety of sepsis classification methods and their clinical application value. The clinical data in the electronic medical record, such as the dynamic changes of vital signs such as body temperature, can be used as the basis for sepsis classification. Different subtypes of body temperature trajectories have differences in physiological characteristics and prognosis, which contributes to predict the prognosis of patients and guide fluid management strategies. Biomarker classification can more comprehensively reflect the pathophysiological state of patients. Immune index classification is helpful to identify immunocompromised patients so as to carry out targeted immunotherapy. Transcriptome data and genotyping reveal the heterogeneity of sepsis at the molecular level and provide a new perspective for precision medicine. In addition, a detailed systematic review of sepsis-related organ function damage, such as acute respiratory distress syndrome (ARDS), acute kidney injury (AKI), and acute liver injury, has also been conducted, which is helpful to develop targeted organ protection and treatment strategies. These typing methods have shown good application prospects in clinical practice. However, there are still limitations in the current research, such as typing stability and biomarker selection, which need to be further explored. Future research should focus on the development of stable and efficient typing tools to achieve precise treatment of sepsis and improve the prognosis of patients.
Humans ; Sepsis/classification* ; Multiple Organ Failure/classification* ; Prognosis ; Artificial Intelligence ; Biomarkers ; Computational Biology ; Respiratory Distress Syndrome

PURPOSE: The mortality rate for severely injured patients with the injury severity score (ISS) ≥16 has decreased in Germany. There is robust evidence that mortality is influenced not only by the acute trauma itself but also by physical health, age and sex. The aim of this study was to identify other possible influences on the mortality of severely injured patients. METHODS: In a matched-pair analysis of data from Trauma Register DGU®, non-surviving patients from Germany between 2009 and 2014 with an ISS≥16 were compared with surviving matching partners. Matching was performed on the basis of age, sex, physical health, injury pattern, trauma mechanism, conscious state at the scene of the accident based on the Glasgow coma scale, and the presence of shock on arrival at the emergency room. RESULTS: We matched two homogeneous groups, each of which consisted of 657 patients (535 male, average age 37 years). There was no significant difference in the vital parameters at the scene of the accident, the length of the pre-hospital phase, the type of transport (ground or air), pre-hospital fluid management and amounts, ISS, initial care level, the length of the emergency room stay, the care received at night or from on-call personnel during the weekend, the use of abdominal sonographic imaging, the type of X-ray imaging used, and the percentage of patients who developed sepsis. We found a significant difference in the new injury severity score, the frequency of multi-organ failure, hemoglobine at admission, base excess and international normalized ratio in the emergency room, the type of accident (fall or road traffic accident), the pre-hospital intubation rate, reanimation, in-hospital fluid management, the frequency of transfusion, tomography (whole-body computed tomography), and the necessity of emergency intervention. CONCLUSION Previously postulated factors such as the level of care and the length of the emergency room stay did not appear to have a significant influence in this study. Further studies should be conducted to analyse the identified factors with a view to optimising the treatment of severely injured patients. Our study shows that there are significant factors that can predict or influence the mortality of severely injured patients.
Accidents ; classification ; Adult ; Age Factors ; Blood Transfusion ; Data Analysis ; Emergency Medical Services ; Female ; Fluid Therapy ; Germany ; epidemiology ; Hemoglobins ; Humans ; International Normalized Ratio ; Intubation ; statistics & numerical data ; Male ; Matched-Pair Analysis ; Multiple Organ Failure ; Registries ; Sex Factors ; Survival Rate ; Trauma Severity Indices ; Wounds and Injuries ; mortality