To construct and verify the occurrence model of acute respiratory distress syndrome (ARDS) using lung injury prediction score (LIPS) combined with acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score and oxygenation index (PaO₂/FiO₂).

Using a prospective cohort study method, 244 patients with complete medical records who were admitted to the intensive care unit (ICU) of Peking University Third Hospital from December 2020 to July 2022 were selected as research objects according to the inclusion and exclusion criteria. They were divided into training set (173 cases) and validation set (71 cases). Patients' gender, age, body mass index (BMI), various causes (shock, sepsis, craniocerebral injury, pulmonary contusion, multiple trauma, aspiration, pneumonia, acute abdomen, hypoproteinemia, acidosis, major surgery, etc.), underlying diseases (diabetes, malignant tumor, cerebrovascular disease, liver disease, kidney disease) and laboratory test indicators were collected. According to the above data, the LIPS score, APACHE Ⅱ score, sequential organ failure assessment (SOFA) and PaO₂/FiO₂, etc within 24 hours after admission to the ICU were calculated. Univariate analysis was used to screen the influencing factors for the occurrence of ARDS, and the factors with P < 0.2 were included in the multivariate Logistic regression analysis to screen out the independent predictive factors for the occurrence of ARDS. According to the results of multivariate Logistic regression analysis, the risk score of patients with ARDS was obtained to construct the risk prediction model of ARDS, the receiver operator characteristic curve (ROC curve) was drawn, and the area under the ROC curve (AUC) was calculated. The established ARDS prediction model was externally validated, and ROC curves were drawn to evaluate the predictive accuracy of the prediction model for the occurrence of ARDS in critically ill patients, and the AUC of the validation set was calculated to analyze the predictive performance of each risk factor on the occurrence of ARDS.

A total of 173 patients were enrolled in the training set, including 121 patients without ARDS and 52 patients with ARDS; 77 cases of acute abdomen, 64 cases of sepsis, 60 cases of shock, 51 cases of acidosis, 40 cases of hypoproteinemia, 37 cases of diabetes, 34 cases of craniocerebral injury, 34 cases of abnormal liver function, 28 cases of multiple trauma, 23 cases of malignant tumor, 23 cases of spinal orthopedic surgery, 17 cases of obesity, 12 cases of pneumonia, 11 cases of pulmonary contusion, and 7 cases of chronic kidney disease, chemotherapy in 6 cases, and aspiration in 2 cases. The rates of shock, sepsis, acute abdomen, acidosis, abnormal liver function, lung contusion, pneumonia and aspiration, gender, age, LIPS score, APACHE Ⅱ score, and SOFA score in the ARDS group were significantly higher than those in the non-ARDS group (all P < 0.05), moreover, PaO₂/FiO₂ ratio was significantly lower than that of non-ARDS group (P < 0.01). Multivariate Logistic regression analysis showed that LIPS score, APACHE Ⅱ score, and PaO₂/FiO₂ ratio were independent risk factors for ARDS in ICU patients with high risk factors for ARDS, and the odds ratio (OR) was 1.768 [95% confidence interval (95%CI) was 1.380-2.266], 1.242 (95%CI was 1.089-1.417), 0.985 (95%CI was 0.978-0.991), all P < 0.05. ROC curve analysis showed that the AUC of the ARDS prediction model training set was 0.920, the sensitivity was 86.5%, and the specificity was 86.8%; the AUC of the verification set was 0.896, the sensitivity was 96.8%, and the specificity was 76.6%.

LIPS score, APACHE Ⅱ score and PaO₂/FiO₂ are independent risk factors for the occurrence of ARDS in ICU patients with high risk factors for ARDS. The ARDS risk prediction model established based on these three indicators has a good predictive ability for the occurrence of ARDS in critically ill patients, wihich needs to be verified by multicenter cohort studies.

Acute respiratory distress syndrome; Predictive model; Lung injury prediction score; Disease severity score; Predictive value; Intensive care unit