Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2026 Jan;38(1):131-137. doi: 10.3760/cma.j.cn121430-20250102-00004.
ABSTRACT
OBJECTIVE: To identify the risk factors for the progression of severe pneumonia to acute respiratory distress syndrome (ARDS) and to construct a prediction model based on the random forest algorithm, providing a basis for disease assessment, early intervention, and prognosis improvement in severe pneumonia.
METHODS: A retrospective observational study was conducted. Patients with severe pneumonia admitted to the intensive care unit (ICU) of the Second Affiliated Hospital of Zunyi Medical University from January 2020 to May 2024 were enrolled. Data including general patient information, vital signs, blood test results, disease assessment indicators within 24 hours of ICU admission, and outcome measures were collected. Patients were divided into ARDS group and non-ARDS group according to whether they progressed to ARDS. Univariate Logistic regression analysis was used to screen the risk factors for the progression of severe pneumonia to ARDS, and a random forest based prediction model was constructed. Model performance and stability were validated using 1 000 resampling iterations.
RESULTS: A total of 181 severe pneumonia patients were included, of whom 73 progressed to ARDS, with an incidence rate of 40.3%. Compared to the non-ARDS group, the ARDS group had significantly lower lowest systolic blood pressure, lowest diastolic blood pressure, lowest oxygenation index, pH value, and albumin level, while showing significantly higher maximum activated partial thromboplastin time (APTT), Acute Physiology and Chronic Health Evaluation (APACHE), and Lung Injury Prediction Score (LIPS; all P<0.05). There were no statistically significant differences in other baseline data comparisons (all P>0.05). Univariate Logistic regression analysis showed that pH, lowest systolic blood pressure, albumin, APACHE score, and LIPS score were risk factors for the progression to ARDS in severe pneumonia patients [pH: odds ratio (OR)=0.04, 95% confidence interval (95%CI) was 0.00-0.96, P=0.047, cut-off was 7.34; lowest systolic blood pressure: OR=0.98, 95%CI was 0.97-1.00, P=0.044, cut-off was 90 mmHg (1 mmHg=0.133 kPa); albumin: OR=0.94, 95%CI was 0.89-0.99, P=0.032, cut-off was 28.05 g/L; APACHE score: OR=1.08, 95%CI was 1.02-1.14, P=0.008, cut-off was 23; LIPS: OR=1.37, 95%CI was 1.09-1.72, P=0.007, cut-off was 5]. A random forest model constructed with these risk factors ranked the importance of the indicators from high to low as follows: pH, lowest systolic blood pressure, albumin, APACHE score, and LIPS (with Gini Index of 31.08, 30.74, 29.35, 28.01, and 24.92, respectively). Validation with 1 000 bootstrap resamplings showed that the model had an area under the receiver operator characteristic curve (AUC) of 0.909 (95%CI was 0.870-0.943), a sensitivity of 0.823 (95%CI was 0.699-0.932), and a specificity of 0.869 (95%CI was 0.741-0.963).
CONCLUSIONS: pH<7.34, lowest systolic blood pressure<90 mmHg, albumin<28.05 g/L, APACHE>23, and LIPS>5 are risk factors for the progression of severe pneumonia to ARDS. The model constructed based on these factors using the random forest algorithm can effectively predict whether severe pneumonia patients will progress to ARDS.
PMID:41876238 | DOI:10.3760/cma.j.cn121430-20250102-00004
