BackgroundMultiple organ dysfunction syndrome (MODS) is the one of common complications,and the leading cause of late mortality in multiple trauma patients.The present study aims to develop and validate a nomogram based on clinical characteristics in order to identify the patients with multiple trauma who were at risk of developing MODS.MethodsAn retrospective cohort study was performed with data from January 2011 to December 2019,totally 770 patients with multiple trauma were enrolled in our

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... udy.They were randomly categorized into training set (n=514) and validation set (n=256).The univariate and multivariate logistic regression analyses were used to screen the predictors for multiple trauma patients who were at risk of developing MODS from training set data.Then we established a nomogram based on these above predictors.The discriminative capacity was assessed by receiver operating characteristic (ROC) curve area under the curve (AUC), and the predictive precision was depicted by calibration plot.The Hosmer-Lemeshow test was used to evaluate the the model's goodness of fit.ResultsOur study showed that age,ISS,hemorrhagic shock,heart rate,blood glucose,D-dimer and APTT were independent risk factors for MODS in patients with multiple trauma by multivariate logistic regression analysis.A nomogram was established on basis of these above risk factors.The area under the curve (AUC) was 0.868 (95% confidence interval [CI]:0.829-0.908) in the training set and 0.884 (95% confidence interval [CI]:0.833-0.935) in the validation set.The Hosmer-Lemeshow test has a p value of 0.227 in training set and 0.554 in validation set respectively,which confirm the model's goodness of fit.Calibration plot showed that the predicted and actual incidence of MODS probability were fitted well on both internal and external validations.ConclusionsThe present nomogram had a well predictive precision and discrimination capacity,which can facilitate improved screening and early identification of multiple trauma patients who were at high risk of developing MODS.