OBJECTIVE: To explore the independent risk factors of acute respiratory distress syndrome (ARDS) in patients with sepsis, establish an early warning model, and verify the predictive value of the model based on synthetic minority oversampling technique (SMOTE) algorithm.
METHODS: A retrospective case-control study was conducted. 566 patients with sepsis who were admitted to Jinan People's Hospital from October 2016 to October 2022 were enrolled. General information, underlying diseases, infection sites, initial cause, severity scores, blood and arterial blood gas analysis indicators at admission, treatment measures, complications, and prognosis indicators of patients were collected. The patients were grouped according to whether ARDS occurred during hospitalization, and the clinical data between the two groups were observed and compared. Univariate and binary multivariate Logistic regression analysis were used to select the independent risk factors of ARDS during hospitalization in septic patients, and regression equation was established to construct the early warning model. Simultaneously, the dataset was improved using the SMOTE algorithm to build an enhanced warning model. Receiver operator characteristic curve (ROC curve) was drawn to validate the prediction efficiency of the model.
RESULTS: 566 patients with sepsis were included in the final analysis, of which 163 developed ARDS during hospitalization and 403 did not. Univariate analysis showed that there were statistically significant differences in age, body mass index (BMI), malignant tumor, blood transfusion history, pancreas and peripancreatic infection, gastrointestinal tract infection, pulmonary infection as the initial etiology, acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, albumin (Alb), blood urea nitrogen (BUN), mechanical ventilation therapy, septic shock and length of intensive care unit (ICU) stay between the two groups. Binary multivariate Logistic regression analysis showed that age [odds ratio (OR) = 3.449, 95% confidence interval (95%CI) was 2.197-5.414, P = 0.000], pulmonary infection as the initial etiology (OR = 2.309, 95%CI was 1.427-3.737, P = 0.001), pancreas and peripancreatic infection (OR = 1.937, 95%CI was 1.236-3.035, P = 0.004), septic shock (OR = 3.381, 95%CI was 1.890-6.047, P = 0.000), SOFA score (OR = 9.311, 95%CI was 5.831-14.867, P = 0.000) were independent influencing factors of ARDS during hospitalization in septic patients. An early warning model was established based on the above risk factors: P1 = -4.558+1.238×age+0.837×pulmonary infection as the initial etiology+0.661×pancreas and peripancreatic infection+1.218×septic shock+2.231×SOFA score. ROC curve analysis showed that the area under the ROC curve (AUC) of the model for ARDS during hospitalization in septic patients was 0.882 (95%CI was 0.851-0.914) with sensitivity of 79.8% and specificity of 83.4%. The dataset was improved based on the SMOTE algorithm, and the early warning model was rebuilt: P2 = -3.279+1.288×age+0.763×pulmonary infection as the initial etiology+0.635×pancreas and peripancreatic infection+1.068×septic shock+2.201×SOFA score. ROC curve analysis showed that the AUC of the model for ARDS during hospitalization in septic patients was 0.890 (95%CI was 0.867-0.913) with sensitivity of 85.3% and specificity of 79.1%. This result further confirmed that the early warning model constructed by the independent risk factors mentioned above had high predictive performance.
CONCLUSIONS: Risk factors for the occurrence of ARDS during hospitalization in patients with sepsis include age, pulmonary infection as the initial etiology, pancreatic and peripancreatic infection, septic shock, and SOFA score. Clinically, the probability of ARDS in patients with sepsis can be assessed based on the warning model established using these risk factors, allowing for early intervention and improvement of prognosis.