Background The aim of this study was to construct a nomogram model for discriminating the risk of delirium in patients undergoing cardiovascular surgery. Methods From January 2017 to June 2020, we collected data from 838 patients who underwent cardiovascular surgery at the Affiliated Hospital of Nantong University. Patients were randomly divided into a training set and a validation set at a 5:5 ratio. A nomogram model was established based on logistic regression. Discrimination and calibration were used to evaluate the predictive performance of the model. Results The incidence of delirium was 48.3%. A total of 389 patients were in the modelling group, and 449 patients were in the verification group. Logistic regression analysis showed that CPB duration (OR $$=$$ = 1.004, 95% CI: 1.001–1.008, $$P=$$ P = 0.018), postoperative serum sodium (OR $$=$$ = 1.112, 95% CI: 1.049–1.178, $$P<$$ P < 0.001), age (OR $$=$$ = 1.027, 95% CI: 1.006–1.048, $$P=$$ P = 0.011), and postoperative MV (OR $$=$$ = 1.019, 95% CI: 1.008–1.030, $$P<$$ P < 0.001) were independent risk factors. The results showed that AUC$$^\text {ROC}$$ ROC was 0.712 and that the 95% CI was 0.661–0.762. The Hosmer-Lemeshow goodness of fit test showed that the predicted results of the model were in good agreement with the actual situation ($$\chi ^{2}=$$ χ 2 = 6.200, $$P=$$ P = 0.625). The results of verification showed that the AUC$$^\text {ROC}$$ ROC was 0.705, and the 95% CI was 0.657–0.752. The Hosmer-Lemeshow goodness of fit test results were $$\chi ^{2}=$$ χ 2 = 8.653 and $$P=$$ P = 0.372, indicating that the predictive effect of the model is good. Conclusions The establishment of the model provides accurate and objective assessment tools for medical staff to start preventing postoperative delirium in a purposeful and focused manner when a patient enters the CSICU after surgery.

Objective The aim of this study was to construct a Nomogram model for discriminating the risk of delirium in patients with cardiovascular surgery.Methods From January 2017 to June 2020, we collected 838 patients with cardiovascular surgery in Affiliated Hospital of Nantong University. Patients were randomly divided into a training set and a validation set in a 5:5 ratio. Nomogram model was established based on the Logistic regression. The discrimination and calibration were used to evaluate the prediction performance of the model.ResultsThe incidence of delirium was 48.3%. 389 cases were in the modeling group and 449 cases were in the verification group. Logistic regression analysis showed that CPB duration (OR=1.004, 95%CI: 1.001-1.008, P=0.018), Postoperative Serum Sodium(OR=1.112, 95%CI: 1.049-1.178, P＜0.001), age (OR=1.027, 95%CI: 1.006-1.048, P=0.011), postoperative MV(OR=1.019, 95%CI: 1.008-1.030, P＜0.001) were independent risk factors. The results showed that AUCROC was 0.712 and the 95%CI was 0.661-0.762. Hosmer-Lemeshow goodness of fit test showed that the predicted results of the model were in good agreement with the actual situation (χ2=6.200, P=0.625). The results of verification showed that AUCROC was 0.705 and the 95%CI was 0.657-0.752. Hosmer-Lemeshow goodness of fit test results showed that χ2=8.653 and P=0.372, indicating the prediction effect of the model is good.ConclusionsThe establishment of the model provides medical staff with accurate and objective assessment tools, so that medical staff can start to prevent postoperative delirium with a purpose and focus when the patient enters the CSICU after surgery.

Aims The study aimed to develop a nomogram model for predicting prolonged mechanical ventilation (PMV) in patients undergoing cardiovascular surgery. Methods and results In total, 693 patients undergoing cardiovascular surgery at an Affiliated Hospital of Nantong University between January 2018 and June 2020 were studied. Postoperative PMV was required in 147 patients (21.2%). Logistic regression analysis showed that delirium (Odds ratio [OR], 3.063; 95% confidence interval [CI], 1.991–4.713; P &lt; 0.001), intraoperative blood transfusion (OR, 2.489; 95% CI, 1.565–3.960; P &lt; 0.001), obesity (OR, 2.789; 95% CI, 1.543–5.040; P = 0.001), postoperative serum creatinine level (mmol/L; OR, 1.012; 95% CI, 1.007–1.017; P &lt; 0.001), postoperative serum albumin level (g/L; OR, 0.937; 95% CI, 0.902–0.973; P = 0.001), and postoperative total bilirubin level (μmol/L; OR, 1.020; 95% CI, 1.005–1.034; P = 0.008) were independent risk factors for PMV. The area under the receiver operating characteristic curve for our nomogram was found to be 0.770 (95% CI, 0.727–0.813). The goodness-of-fit test indicated that the model fitted the data well (χ2 = 12.480, P = 0.131). After the model was internally validated, the calibration plot demonstrated good performance of the nomogram, as supported by the Harrell concordance index of 0.760. DCA demonstrated that the nomogram was clinically useful in identifying patients at risk for PMV. Conclusions We established a new nomogram model that may provide an individual prediction of PMV. This model may provide nurses, social workers, physicians, and administrators with an accurate and objective assessment tool to identify patients at high risk for PMV after cardiovascular surgery.

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