Sepsis is a critical condition. The key factor affecting the survival of patient is whether standard treatment can be obtained timely. Because of the complexity of its pathogenesis and high heterogeneity, there is no special diagnosis method currently. Early identification is difficult. Delayed diagnosis and treatment is closely related to the mortality of patients. With the continuous updating of the guidelines, sepsis has been included in the “time window” disease, putting forward a great challenge to the early screening and evaluation of sepsis. This article aims to review the application of Sepsis-Related Organ Failure Assessment, sepsis biomarkers and artificial intelligence algorithms in early screening and evaluation of sepsis, so as to provide guidance tools for timely starting standardized treatment of sepsis.
Objective To evaluate the predicting effect of quick Sequential Organ Failure Assessment (qSOFA) on septic shock, and investigate the probability of improving the predicting effect. Methods Patients with sepsis diagnosed in Emergency Department from July 2015 to June 2016 were enrolled. They were divided into shock group and non-shock group based on whether or not they had septic shock during 72 hours after admission. The multivariate logistic regression analysis was used to find out the independent risk factors affecting the incidence of septic shock. Receiver operating characteristic (ROC) curve was used to analyze those risk factors. Modified Early Warning Score (MEWS), Mortality in Emergency Department Sepsis Score (MEDS), Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic HealthEvaluation (APACHE)Ⅱ and qSOFA were also compared with ROC curve analysis. The possibility of improvement of qSOFA predicting effect was discussed. Results A total of 821 patients were enrolled, with 108 in septic shock group and 713 in non-septic shock. The result of multivariate logistic regression analysis indicated that respiratory rate, systolic blood pressure, pH value, oxygenation index, lactate, albumin, Glasgow Coma Score and procalcitonin were the independent risk factors (P<0.05). The result of ROC analysis showed that the area under curve (AUC) of pH value, lactate and procalcitonin was 0.695, 0.678 and 0.694, respectively. Lactate had the highest value of specificity (0.868), positive predictive value (0.356) and positive likelihood ratio (3.644), while the sensitivity (0.889) and negative predictive value (0.961) of procalcitonin were the highest. MEWS, MEDS, SOFA, APACHEⅡ and qSOFA were compared with ROC. SOFA had the best predicting effect with the statistical results of AUC (0.833), sensitivity (0.835), specificity (0.435), positive predictive value (0.971), negative predictive value (0.971), and positive likelihood ratio (5.048); and MEWS had the highest negative likelihood ratio (0.581). qSOFA did not show a best predicting value. Conclusion qSOFA is not the best choice to predict the possibility of septic shock, but its predicting value might be improved when combined with pH value, lactate and procalcitonin.
ObjectiveTo investigate the prognostic value of acute kidney injury (AKI) in patients with severe pneumonia complicated with sepsis.MethodsWe retrospectively analyzed the demographic data, vital signs, laboratory examination and other data of 462 patients with severe pneumonia complicated with sepsis in the Department of Emergency West China hospital, Sichuan University from July 2015 to June 2016, as well as the 7-day and 28-day mortality, 28-day mechanical ventilation rate and 28-day intensive care unit (ICU) hospitalization rate. Multivariate logistic regression analysis was used to determine the correlation between AKI and 28-day mortality in patients with severe pneumonia complicated with sepsis at admission.ResultsA total of 462 patients with severe pneumonia complicated with sepsis were retrospectively enrolled in this study. AKI patients at admission had a higher proportion of 7-day (24.6% vs. 9.7%, P<0.001) and 28-day mortality (44.3% vs. 21.2%, P<0.001), 28-day mechanical ventilation rate (63.9% vs. 45.9%, P=0.009) and 28-day ICU admission rate (65.6% vs. 39.4%, P<0.001) than non-AKI patients. There was a significant difference between the two groups (P<0.05). The scores of systemic infection-related organ failure assessment and acute physiology and chronic health evaluationⅡof AKI patients at admission were significantly higher than those of non-AKI patients at admission (P<0.05). Multivariate logistic regression analysis showed that AKI at admission was an independent risk factor for 28-day mortality in patients with severe pneumonia complicated with sepsis [odds ratio: 2.266, 95% confidence interval (1.058, 4.854), P=0.035].ConclusionAKI at admission is helpful for identifying high-risk pneumonia patients complicated with sepsis, and thus may guide the clinical managements of precise medicine.