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Objective <p>This study aimed to develop and internally validate a dynamic a nomogram model by analysing the risk factors for postoperative delirium (POD) in elderly patients with oral cancer.</p> Methods <p>This was a single-centre, retrospective study. We used the convenience sampling method to select 359 elderly oral cancer patients from January 2020-August 2023 in Nanjing Stomatological Hospital. The original dataset was randomly divided into a training group (<i>n</i> = 252) and a validation group (<i>n</i> = 107) by a computer-generated random number sequence in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator Regression (LASSO regression) were used to screen the best predictor variables. Logistic regression was used to build the model and visualized by nomogram. The performance of the model was evaluated by area under the curve (AUC), calibration curve and decision curve analysis (DCA).</p> Results <p>Our predictive model showed that seven variables, age, sex, alcohol consumption history, marriage, preoperative anxiety, preoperative sleep disorder, and ICU length of stay, were associated with POD. The nomogram showed high predictive accuracy with an AUC of 0.82 (95% CI: 0.76–0.87) for the training group and 0.84 (95% CI: 0.76–0.92) for the internal validation group. In two groups, there was good agreement between the predicted results and the true observations. DCA showed that the predictive model had a good net clinical benefit.</p> Conclusion <p>We developed a new predictive model to predict risk factors for POD in elderly oral cancer patients. The nomogram can help physicians assess POD quickly and effectively.</p>

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Development and validation of a nomogram model for predicting postoperative delirium in elderly patients with oral cancer: a retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Chen Ying,
  • Liu Xiaona,
  • Zhang Aili,
  • Wang Zengxiang,
  • Wu Ying,
  • Pu Yu,
  • Zhang Hongbo,
  • Wang Danni,
  • Jiang Meiping,
  • Dai Hongyuan

摘要

Objective

This study aimed to develop and internally validate a dynamic a nomogram model by analysing the risk factors for postoperative delirium (POD) in elderly patients with oral cancer.

Methods

This was a single-centre, retrospective study. We used the convenience sampling method to select 359 elderly oral cancer patients from January 2020-August 2023 in Nanjing Stomatological Hospital. The original dataset was randomly divided into a training group (n = 252) and a validation group (n = 107) by a computer-generated random number sequence in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator Regression (LASSO regression) were used to screen the best predictor variables. Logistic regression was used to build the model and visualized by nomogram. The performance of the model was evaluated by area under the curve (AUC), calibration curve and decision curve analysis (DCA).

Results

Our predictive model showed that seven variables, age, sex, alcohol consumption history, marriage, preoperative anxiety, preoperative sleep disorder, and ICU length of stay, were associated with POD. The nomogram showed high predictive accuracy with an AUC of 0.82 (95% CI: 0.76–0.87) for the training group and 0.84 (95% CI: 0.76–0.92) for the internal validation group. In two groups, there was good agreement between the predicted results and the true observations. DCA showed that the predictive model had a good net clinical benefit.

Conclusion

We developed a new predictive model to predict risk factors for POD in elderly oral cancer patients. The nomogram can help physicians assess POD quickly and effectively.