Early identification and treatment of sepsis is a highly complex and multifaceted challenge23. It requires highly skilled and well-trained human experts24. However, with the continuous emergence of AI applications in the medical field, some of these decisions will soon be replaced by machines called "intelligence" to improve clinical practice and patient outcomes25. Most of what we call "artificial intelligence" is machine learning, which means learning from data and using this knowledge to acquire new knowledge or skills.
This study used a supervised learning method (a machine learning method) to build a predictive model, which included 20 predictors of sepsis events predicted by the random forest method. The AUC of this newly developed model was 0.91, demonstrating good discriminative power. These prediction results suggest that the ensemble model with 20 key features is feasible and practical.
To our knowledge, most previous studies have developed models to predict the prognosis of sepsis. However, only few researchers have paid attention to the differences in the incidence of sepsis after infection, although it is important for clinical preventive intervention. Thomas et al. developed machine learning models for the early identification of sepsis risk26; however, they did not obtain precise biomarkers that could be applied to clinicians. All calculations are trivial for a computer, which may limit generalization of the results to other hospitals and hospital systems. Other artificial intelligence systems such as random forest models may be a valuable tool to predict sepsis6.
The variables in our model were mainly blood cells, lipids, liver function, hemagglutination, renal function, electrolyte, enzyme, and others. Interestingly, blood-related variables accounted for a large part of our model; the first five variables in Fig. 2 are related to the blood system. Neutrophils were an ideal choice for eliminating pathogenic bacteria because they store a large number of proteolytic enzymes that can rapidly produce reactive oxygen species to degrade internal pathogens. Hence, patients with sepsis often have neutrophil infiltration, and the degree of infiltration is related to tissue damage27,28. Other blood cells, including eosinophils, basophils, lymphocytes, and WBCs, are also associated with the body's defense against infection. For example, some studies have speculated that individuals with basophilic granulocytopenia have a weak resistance to infection and thus are more likely to develop sepsis29. In addition, studies have shown that eosinophilia was a moderate marker for distinguishing SIRS from infection in critically ill patients newly admitted to the hospital, which suggested that eosinophilia may be a useful clinical tool for the prediction of sepsis30. In addition, lymphocyte apoptosis has been recognized as an important step in the pathogenesis of experimental sepsis by inducing a state of “immune paralysis” that renders the host vulnerable to invading pathogens31.
In the past decade, there has been a growing awareness about the role of the coagulation and fibrinolysis systems in the development of inflammation. Patients with sepsis may have common host reactions, such as coagulation, inflammation, and endothelial injury. Abnormal inflammatory and coagulation biomarkers were found to be associated with disease severity and mortality in patients with severe sepsis32. Platelets are the main effector cells involved in blood coagulation and can promote the development of excessive inflammation, DIC, and microthrombosis33. PT can reflect the coagulation function of the body, and D-dimer levels increase under hypercoagulable state34. Therefore, changes in these substances may predict the occurrence of sepsis.
Sepsis is often associated with multiple organ dysfunction such as that involving the heart, liver, and kidney35. Therefore, some indicators reflecting organ function may be used to predict the occurrence of sepsis. Albumin which is the most important protein in human plasma, maintains nutrition and osmotic pressure. When liver synthesis is dysfunctional, its level usually decreases. Lactate dehydrogenase and urea are associated with cardiac and renal function, respectively. Patel et al. revealed an association between serum bilirubin levels and mortality during sepsis, suggesting that serum bilirubin may be a potential predictor of sepsis occurrence and death36.
Previous studies have shown that lipids are also involved in the occurrence and development of sepsis. Yamano S et al. found that low total cholesterol and high total bilirubin levels are associated with prognosis in patients with prolonged sepsis37. Hofer S et al. found that pharmacologic inhibition of cholinesterase improves survival in experimental sepsis, probably by activating the cholinergic anti-inflammatory pathway38. The results of Feng’s study suggest that a decrease in LDL-C levels is significantly associated with an increased risk of sepsis in infected patients, although the association was due to the presence of complications39.
Although the association between electrolytes other than calcium and sepsis appears to be poorly studied, this study found that the decrease of potassium and magnesium is closely related to the occurrence of sepsis. We know that the critical illness itself is associated with a decrease in serum total calcium and free calcium levels, which is related to the severity of underlying diseases as measured by the APACHE II score. In addition, studies have shown that total and ionized hypocalcemia is more significantly associated with increased severity of infection., which suggested the role of calcium in predicting the risk of sepsis in patients with infection340. Regarding magnesium and potassium, a study pointed out that ATP-MgCl2 may be beneficial in sepsis41. An increasing amount of evidence has suggested that potassium channels are involved in cardiovascular dysfunction in sepsis after systemic inflammation, cardiovascular dysfunction, and organ damage, and that potassium channels may affect the emergence of sepsis after infection42. In conclusion, we believe that because sepsis is not a simple disease that can be predicted by a single marker, the biomarkers included in our model can be combined to predict the risk of sepsis in infected patients.
Our study has several limitations. First, this was a retrospective study, which had its own shortcomings, such as information bias. Second, the prediction model may have lacked generality because the 55 variables are still too few, and many other variables were omitted due to the loss of too many values. Generally, the more the variables included, the higher the prediction accuracy. Therefore, we hope to include more patients and variables in future prospective studies.