2.3. Model Construction and Validation of Queues
aSAH, DCI
REGRESSION, HOSPITAL INFECTION, DCI, EVENT
The training group was used for univariate analysis, multifactor logistic regression analysis, and construction of nomograms. Factors affecting the occurrence of DCI in patients with aSAH were screened by univariate analysis, and the relationship between each factor and the occurrence of hospital infection in patients was clarified by multifactor logistic regression analysis, and the odds ratio (OR) was used to determine the effect of each factor on the occurrence of hospital infection. The results of the multifactor analysis were visualized and processed through the rms package in the R Studio software to obtain the nomogram. The line segment of the risk value was assigned to the level of value taken by each influencing factor in the model according to the degree of contribution of each influencing factor to the outcome variable. The individual scores were then summed to obtain the total score, and the risk value of the occurrence of the outcome event for that individual was calculated using the functional transformation relationship between the total score and the probability of the occurrence of the outcome event.The constructed nomograms were examined in the training and the verification groups, where the receiver operating characteristic (ROC) curve was used to verify the model discrimination; the area under the curve (AUC) values were between 0.5 and 1.0, and at AUC > 0.5, the closer the AUC was to 1, the better the diagnosis. When AUC = 0.5, the diagnostic method does not work and has no diagnostic value.Bootstrap repeated sampling was performed 1000 times to obtain the calibration curve. The Brier value ranged from 0 to 1 and 0 to <0.25, indicating that the model had a predictive value, where the closer the Brier score was to 0, the more accurate the model. The mean absolute error was used to assess consistency between the actual prediction of the model and the corrected prediction, and the closer the mean absolute error was to 0, the better the consistency. Applying the calibration curve with the Hosmer-Lemeshow test (DCA was applied to verify the clinical validity and benefit rate of the model, and the threshold probability indicated the critical probability of patients having DCI after surgery. The benefit rate of the model on the decision curve was higher than that of all patients with DCI who received treatment or of all patients who did not receive DCI and did not receive treatment, indicating the validity and usefulness of the model.