ABSTRACT
This study aimed to explore the independent risk factors for community-acquired pneumonia (CAP) complicated with acute respiratory distress syndrome (ARDS) and to predict and evaluate the risk of ARDS in CAP patients based on artificial neural network models (ANNs). We retrospectively analyzed eligible 989 CAP patients (632 men and 357 women) who met the criteria from the comprehensive intensive care unit (ICU) and the respiratory and critical care medicine department of Changzhou Second People's Hospital, Jiangsu Provincial People's Hospital, Nanjing Military Region General Hospital, and Wuxi Fifth People's Hospital between February 2018 and February 2021. The best predictors to model the ANNs were selected from 51 variables measured within 24 h after admission. By using this model, patients were divided into a training group (n = 701) and a testing group (n = 288 patients). Results showed that in 989 CAP patients, 22 important variables were identified as risk factors. The sensitivity, specificity, and accuracy of the ANNs model training group were 88.9%, 90.1%, and 89.7%, respectively. When ANNs were used in the test group, their sensitivity, specificity, and accuracy were 85.0%, 87.3%, and 86.5%, respectively; when ANNs were used to predict ARDS, the area under the receiver operating characteristic (ROC) curve was 0.943 (95% confidence interval (0.918-0.968)). The nine most important independent variables affecting the ANNs models were lactate dehydrogenase (100%), activated partial thromboplastin time (84.6%), procalcitonin (83.8%), age (77.9%), maximum respiratory rate (76.0%), neutrophil (75.9%), source of admission (68.9%), concentration of total serum kalium (61.3%), and concentration of total serum bilirubin (50.4%) (all important >50%). The ANNs model and the logistic regression models were significantly different in predicting and evaluating ARDS in CAP patients. Thus, the ANNs model has a good predictive value in predicting and evaluating ARDS in CAP patients, and its performance is better than that of the logistic regression model in predicting the incidence of ARDS patients.
ABSTRACT
OBJECTIVE: To investigate the independent risk factors of community-acquired pneumonia (CAP) complicated with acute respiratory distress syndrome (ARDS), and the accuracy and prevention value of ARDS prediction based on artificial neural network model in CAP patients. METHODS: A case-control study was conducted. Clinical data of 414 patients with CAP who met the inclusion criteria and were admitted to the comprehensive intensive care unit and respiratory department of Changzhou Second People's Hospital Affiliated to Nanjing Medical University from February 2020 to February 2021 were analyzed. They were divided into two groups according to whether they had complicated with ARDS. The clinical data of the two groups were collected within 24 hours after admission, the influencing factors of ARDS were screened out by univariate analysis, and the artificial neural network model was constructed. Through the artificial neural network model, the importance of input layer independent variables (that was, the influence factors obtained from univariate analysis) on the output layer dependent variables (whether ARDS occurred) was drawn. The artificial neural network modeling data pairs were randomly divided into training group (n = 290) and verification group (n = 124) in a ratio of 7:3. The overall prediction accuracy of the training group and the verification group was calculated respectively. At the same time, the receiver operator characteristic curve (ROC curve) was drawn, and the area under the ROC curve (AUC) was calculated. RESULTS: All 414 patients were enrolled in the analysis, including 82 patients with ARDS and 332 patients without ARDS. Univariate analysis showed that gender, age, heart rate (HR), maximum systolic blood pressure (MSBP), maximum respiratory rate (MRR), source of admission, C-reactive protein (CRP), procalcitonin (PCT), erythrocyte sedimentation rate (ESR), neutrophil count (NEUT), eosinophil count (EOS), fibrinogen equivalent unit (FEU), activated partial thromboplastin time (APTT), total bilirubin (TBil), albumin (ALB), lactate dehydrogenase (LDH), serum creatinine (SCr), hemoglobin (Hb) and blood glucose (GLU) were significantly different between the two groups, which might be the risk factors of CAP patients complicated with ARDS. Taking the above 19 risk factors as the input layer and whether ARDS occurred as the output layer, the artificial neural network model was constructed. Among the input layer independent variables, the top five indicators with the largest influence weight on the neural network model were LDH (100.0%), PCT (74.4%), FEU (61.5%), MRR (56.9%), and APTT (51.6%), indicating that that these five indicators had a greater impact on the occurrence of ARDS in patients with CAP. The overall prediction accuracy of the artificial neural network model in the training group was 94.1% (273/290), and that of the verification group was 89.5% (111/124). The AUC predicted by the aforementioned artificial neural network model for ARDS in CAP patients was 0.977 (95% confidence interval was 0.956-1.000). CONCLUSIONS: The prediction model of ARDS in CAP patients based on artificial neural network model has good prediction ability, which can be used to calculate the accuracy of ARDS in CAP patients, and specific preventive measures can be given.
