A Simple-to-Use Web-Based Calculator for Survival Prediction in Acute Respiratory Distress Syndrome

• Main Authors: Yong Liu, Jian Liu, Liang Huang
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-02-01
• Series: Frontiers in Medicine
• Subjects: acute respiratory distress syndrome; LASSO regression; nomogram; model; survival
• Online Access: https://www.frontiersin.org/articles/10.3389/fmed.2021.604694/full

Background: The aim of this study was to construct and validate a simple-to-use model to predict the survival of patients with acute respiratory distress syndrome.Methods: A total of 197 patients with acute respiratory distress syndrome were selected from the Dryad Digital Repository. All eligible individuals were randomly stratified into the training set (n=133) and the validation set (n=64) as 2: 1 ratio. LASSO regression analysis was used to select the optimal predictors, and receiver operating characteristic and calibration curves were used to evaluate accuracy and discrimination of the model. Clinical usefulness of the model was also assessed using decision curve analysis and Kaplan-Meier analysis.Results: Age, albumin, platelet count, PaO2/FiO2, lactate dehydrogenase, high-resolution computed tomography score, and etiology were identified as independent prognostic factors based on LASSO regression analysis; these factors were integrated for the construction of the nomogram. Results of calibration plots, decision curve analysis, and receiver operating characteristic analysis showed that this model has good predictive ability of patient survival in acute respiratory distress syndrome. Moreover, a significant difference in the 28-day survival was shown between the patients stratified into different risk groups (P < 0.001). For convenient application, we also established a web-based calculator (https://huangl.shinyapps.io/ARDSprognosis/).Conclusions: We satisfactorily constructed a simple-to-use model based on seven relevant factors to predict survival and prognosis of patients with acute respiratory distress syndrome. This model can aid personalized treatment and clinical decision-making.