Developing a nomogram for postoperative delirium in elderly patients with hip fractures

Figure 1 Feature screening and Venn diagram analysis based on Lasso regression, support vector machine, and random forest. A: Important features and their coefficients screened out by Lasso regression; B: Important features and their importance scores screened out by support vector machine; C: Important features and their impact on model accuracy screened out by random forest; D: The Venn diagram shows the features commonly screened out by the three methods, including age, previous history of cognitive impairment, preoperative red blood cell count, preoperative albumin, preoperative hemoglobin, preoperative platelet count, and intraoperative blood loss. ASA: American Society of Anesthesiologists; SVM: support vector machine; COPD: Chronic obstructive pulmonary disease; BMI: Body mass index.

Figure 2 Univariate logistic regression for screening risk factors of postoperative delirium. HR: Hazard ratio; CI: Confidence interval; OR: Odds ratio; RBC: Red blood cell; PLT: Preoperative platelet count.

Figure 3 The value of 7 characteristic factors in predicting postoperative delirium in patients. A: Age; B: History of cognitive impairment; C: Preoperative red blood cells; D: Preoperative albumin; E: Preoperative hemoglobin; F: Preoperative platelet; G: Intraoperative blood loss. AUC: Area under the curve; RBC: Red blood cell; PLT: Preoperative platelet count.

Figure 4 Multivariate logistic regression for screening risk factors of postoperative delirium. HR: Hazard ratio; CI: Confidence interval; OR: Odds ratio; NA: Not available; RBC: Red blood cell; PLT: Preoperative platelet count.

Figure 5 Construction process of the risk prediction model for delirium based on six characteristic factors. RBC: Red blood cell; PLT: Preoperative platelet count.

Figure 6 Risk model validation. A: The receiver operating characteristic curve shows the discriminatory capacity of the model in the training group, with an area under the curve of 0.833; B: The precision-recall curve presents the model’s precision at different precision-recall rates in the training group; C: The calibration curve presents the relationship between the predicted probability of the model and the actual incidence rate in the training group; D: The decision curve analysis curve indicates that the model has clinical benefits within the risk threshold range of 0%-30% in the training group; E: The receiver operating characteristic curve shows the discriminative ability of the model in the validation group, with an area under the curve of 0.850; F: The precision-recall curve displays the accuracy of the model at different precision-recall rates in the validation group; G: The calibration curve displays the relationship between the predicted probability of the model and the actual occurrence rate in the validation group; H: The decision curve analysis curve indicates that the model has clinical benefits within the 0%-36% risk threshold range in the validation group. TPR: True positive rate; FPR: False positive rate; AUC: Area under the curve; CI: Confidence interval.