Prediction of acute lung injury in severe acute pancreatitis by routine clinical data

AIM: Acute lung injury (ALI) is a common complication of severe acute pancreatitis (SAP) with a high mortality. Early prediction of patients at risk in initial stage can improve the long-term survival. METHODS: A total of 91 patients with SAP out of 1647 acute pancreatitis patients from January 2015 to December 2020 were considered. A predictive model for SAP-associated ALI was constructed based on the valuable risk factors identified from routine clinical characteristics and plasma biomarkers. The value of the model was evaluated and compared with Lung Injury Prediction Score (LIPS). A nomogram was built to visualize the model. RESULTS: Diabetes, oxygen supplementation, neutrophil count and D-dimer were found to be associated with ALI in SAP. The predictive model based on these factors had an area under the receiver operating characteristic curve [AUC: 0.88, 95% confidence interval (CI): 0.81-0.95], which was superior to LIPS (AUC: 0.71, 95% CI: 0.60-0.83), also with the higher sensitivity (65%) and specificity (96%) than LIPS (62%, 74%, respectively). Decision curve analysis of the model showed a higher net benefit than LIPS. Visualization by a nomogram facilitated the application of the model. CONCLUSION: Diabetes, oxygen supplementation, neutrophil count and D-dimer were risk factors for SAP-associated ALI. The combination of these routine clinical data and the model visualization by a nomogram provided a simple and effective way in predicting ALI in the early phase of SAP.