ObjectiveTo analyze the prognostic factors of patients with bacterial bloodstream infection sepsis and to identify independent risk factors related to death, so as to potentially develop one predictive model for clinical practice. Method A non-intervention retrospective study was carried out. The relative data of adult sepsis patients with positive bacterial blood culture (including central venous catheter tip culture) within 48 hours after admission were collected from the electronic medical database of the First Affiliated Hospital of Dalian Medical University from January 1, 2018 to December 31, 2019, including demographic characters, vital signs, laboratory data, etc. The patients were divided into a survival group and a death group according to in-hospital outcome. The risk factors were analyzed and the prediction model was established by means of multi-factor logistics regression. The discriminatory ability of the model was shown by area under the receiver operating characteristic curve (AUC). The visualization of the predictive model was drawn by nomogram and the model was also verified by internal validation methods with R language. Results A total of 1189 patients were retrieved, and 563 qualified patients were included in the study, including 398 in the survival group and 165 in the death group. Except gender and pathogen type, other indicators yielded statistical differences in single factor comparison between the survival group and the death group. Independent risk factors included in the logistic regression prediction model were: age [P=0.000, 95% confidence interval (CI) 0.949 - 0.982], heart rate (P=0.000, 95%CI 0.966 - 0.987), platelet count (P=0.009, 95%CI 1.001 - 1.006), fibrinogen (P=0.036, 95%CI 1.010 - 1.325), serum potassium ion (P=0.005, 95%CI 0.426 - 0.861), serum chloride ion (P=0.054, 95%CI 0.939 - 1.001), aspartate aminotransferase (P=0.03, 95%CI 0.996 - 1.000), serum globulin (P=0.025, 95%CI 1.006 - 1.086), and mean arterial pressure (P=0.250, 95%CI 0.995 - 1.021). The AUC of the prediction model was 0.779 (95%CI 0.737 - 0.821). The prediction efficiency of the total score of the model's nomogram was good in the 210 - 320 interval, and mean absolute error was 0.011, mean squared error was 0.00018. Conclusions The basic vital signs within 48 h admitting into hospital, as well those homeostasis disordering index indicated by coagulation, liver and renal dysfunction are highly correlated with the prognosis of septic patients with bacterial bloodstream infection. Early warning should be set in order to achieve early detection and rescue patients’ lives.