Zhongguo quanke yixue (Jul 2022)
Development and Improvement of Nomograms Predicting the Prognosis in Patients with Severe Multiple Trauma
Abstract
Background Severe multiple trauma prevalence has been increasing recently, which has become the leading cause of labor force loss. Early and rapid assessment of patients' conditions will greatly affect their prognosis, which could be significantly supported by a concise and effective visual scoring system. Objective To identify and Screen the prognostic factors of severe multiple trauma, and use them to develop two nomograms, then improve them, and verify their clinical application values. Methods Patients with severe multiple trauma were recruited from the general ICU and EICU, Suzhou Ninth People's Hospital, including 321 treated during December 2015 to December 2020 (model group) , and 136 treated during January to August 2021 (validation group) . General data at admission and clinical data within 24 hours of admission were retrospectively collected. Prognosis (successful or unsuccessful treatment result) was assessed at discharge. Prognostic factors of severe multiple trauma were Screened using univariate and LASSO regression, and used to develop models using multivariate Logistic regression with restricted cubic splines, then based on this, two nomograms were developed, and their calibration accuracies were estimated using the bootstrap approach and decision curve analysis (DCA) . The receiver operating characteristic (ROC) analysis with associated AUC values was used to estimate the prognostic value of two nomograms in severe multiple trauma. External verification of the nomograms was carried out in the validation group to evaluate their clinical application values. Results (1) In the model group, successful and unsuccessful treatment results occurred in 244 and 77 cases, respectively. LASSO regression with multivariate Logistic regression analyses showed that age (OR=1.028) , Glasgow Coma Score (GCS) (OR=0.616) , arterial lactate (OR=1.202) , platelet count (OR=3.888) and Injury Severity Score (ISS) (OR=1.104) were associated with the prognosis of severe multiple trauma (P<0.05) . Hosmer-Lemeshow test indicated that this model fitted the data well (χ2=2.717, P=0.951) , and was appropriate for developing a static and network-based dynamic nomogram (nomogram 1) . LASSO plus multivariate regression analyses with restricted cubic splines revealed that age and GCS had nonlinear correlation with treatment results (P=0.027, 0.001) , and the fit of this model was satisfactory assessed using Hosmer-Lemeshow test (χ2=2.468, P=0.932) , and was appropriate for developing a static and network-based dynamic nomogram (nomogram 2) . Calibration charts showed that the standard curve fitted well with the probability calibration curves of nomograms 1 and 2 (absolute error=0.010, and 0.019) , indicating that the calibration accuracies of both models were good. The AUC of nomogram 1 in predicting the prognosis of severe multiple trauma was 0.963〔95%CI (0.936, 0.981) 〕with 0.414 was the optimal cut-off value, and that of nomogram 2 was 0.974〔95%CI (0.949, 0.988) 〕 with 0.261 as the optimal cut-off value. Nomogram 2 had a larger AUC value than nomogram 1 (Z=-2.400, P=0.016) . The DCA results showed that under any threshold probability (0-100%) , the net benefit rate of nomogram 2 was higher than that of nomogram 1. (2) In the validation group, successful and unsuccessful treatment results occurred in 104 and 32 cases, respectively. The AUC of nomogram 2 predicting the prognosis of severe multiple trauma was 0.949〔95%CI (0.898, 0.979) 〕. And the model fitted well (χ2=5.813, P=0.668) revealed by Hosmer-Lemeshow test. The AUC of nomogram 2 in predicting the prognosis of severe multiple trauma in model and validation groups had insignificant changes (Z=1.124, P=0.263) . Conclusion Age, GCS, arterial lactate, platelet count and ISS were prognostic factors of severe multiple trauma, and the two nomograms in this study based on these five factors had good prognosis predictive value. In particular, the optimized nomogram 2 had higher accuracy (the network-based dynamic version is available at https://yinfxyz.shinyapps.io/dynnomapp2/) , which was rapid, and easy-to-use, and it can help clinicians to identify patients early and improve the prognosis of patients.
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