Development, assessment and validation of a novel nomogram model for predicting stroke mimics in stroke center：A single-center observational study

Abstract

BackgroundEarly recognition and prediction of stroke mimics (SM) can avoid inappropriate recanalization therapy and delay in the management of SM etiology. The purpose of this study is to screen the predictors for SM and develop a novel predictive nomogram model for predicting SM. Meanwhile, the diagnostic performance of the nomogram model was evaluated and validated. The diagnostic efficacy of the nomogram model was also compared with four other SM structured scales. MethodsThe clinical data of eligible patients were retrospectively enrolled as training datasets from January 2020 to December 2021; and the clinical data of eligible patients were prospectively enrolled as validation datasets from February to December 2022 in stroke center, Shengjing hospital, respectively. Univariate analysis and Lasso regression were used to select the optimal predictors for the training set, and a nomogram model was constructed by multivariate logistics regression, predictive scoring based on nomogram model is performed for each subject suffering from suspected acute ischemic stroke. Area under the curve (AUC), Hosmer-Lemeshow goodness-of-fit test, Calibration curve, decision curve analysis (DCA), clinical impact curve (CIC) analysis and bootstrap sampling were performed to assess and validate the predictive performance and clinical utility of the nomogram model, and the DeLong test was used to compare the overall diagnostic performance of the nomogram model with the other four structured SM scales. The Delong test was also conducted to assess the external reliability of the SM nomogram model by comparing the predictive diagnostic performance of the validation set with the training set. Additionally, the Calibration curve was utilized to evaluate the diagnostic calibration capability of the SM nomogram model in the validation set. Results703 eligible patients (68 with SM, accounting for 9.7 %) were assigned to the training set, while 301 patients (26 with SM, accounting for 8.6 %) were assigned to the validation set. A nomogram model was then developed using these six parameters (SBP, history of epilepsy, isolated dizziness, isolated sensory impairment, headache, and absence of speech impairment symptoms), a dynamic web-based version of the nomogram was subsequently created. Comparing with four other scales, the nomogram model showed the highest overall diagnostic performance (AUC = 0.929, 95%CI = 0.908–0.947). The Hosmer-Lemeshow goodness-of-fit test was conducted to assess the agreement between the predicted SM values from the model and the observed SM values. The results of the test indicated a favorable consistency (χ2 = 9.299, P = 0.3177) between the predicted and observed SM. The results obtained from the analysis of the Calibration curve, DCA curve, and CIC analysis suggested that the nomogram possesses a favorable predictive capacity and superior clinical usefulness. Furthermore, the external validation demonstrated that there is no significant difference in the overall predictive diagnostic performance between the validation set and training set (0.929 vs 0.910, P > 0.05), thereby confirming the favorable stability of the nomogram model. ConclusionOur study firstly proposed a nomogram prediction approach based on the clinical features of SM, which could effectively predict the occurrence of SM. The utilization of the nomogram in stroke center proves advantageous for the identification and evaluation of SM, thereby enhancing diagnostic decision-making and strategies employed for suspected acute stroke patients.