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. 2025 Jul 1;25(1):1082.
doi: 10.1186/s12885-025-14478-1.

A nomogram model to predict postoperative delirium in esophageal cancer patients undergoing esophagectomy

Chen Chen  1 , Jiayu Wang  2 , Yang Li  3
Affiliations

A nomogram model to predict postoperative delirium in esophageal cancer patients undergoing esophagectomy

Chen Chen et al. BMC Cancer. .

Abstract

Background: Postoperative delirium (POD) after esophagectomy is one of the most serious complications for cases with esophageal cancer (EC). This study determined to obtain predictive factors for POD and develop a nomogram model to predict the occurrence of POD among EC patients.

Methods: LASSO and multivariate logistic regression analyses were utilized to identify potential predictive factors. A nomogram model was developed based on the results of multivariate logistic regression analysis.

Results: Totally, 924 EC patients undergoing esophagectomy were included, and 157 (16.99%) patients developed POD. Results of LASSO and multivariate logistic analyses showed that age > 70 years, use of penehyclidine hydrochloride, open surgery, preoperative lymphocyte ≤ 1.45*109/L, preoperative albumin ≤ 43.6 g/L, preoperative prognostic nutritional index (PNI) ≤ 50.9, preoperative neutrophil-to-lymphocyte ratio (NLR) > 2.33, preoperative platelet-to-white cell ratio (PWR) ≤ 34.97, and postoperative PNI ≤ 39.40 were independent risk factors for POD. This nomogram model showed a good predictive ability with a C-index value of 0.832 (95% CI: 0.797-0.867). The calibration curve suggested that the predicted results of this nomogram model were in concordance with the actual results. The decision curve analysis (DCA) of this nomogram indicated that there were net benefits for predicting POD.

Conclusion: This nomogram model helps clinicians to predict the occurrence of POD in patients with EC.

Keywords: Biomarker; Esophageal cancer; Nomogram; Postoperative delirium; Prediction.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study was approved by the Ethics Committee of The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University. Written informed consent was obtained from all participants. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow diagram of patients
Fig. 2
Fig. 2
Parameters selection using the LASSO logistic regression analyses. (A) Identification of the optimal penalization coefficient lambda in the Lasso model using 10-fold cross-validation and the minimum criterion. (B) Lasso coefficient profiles of the 10 clinicopathological features
Fig. 3
Fig. 3
Forest plot of significant predictors for POD after esophagectomy
Fig. 4
Fig. 4
The nomogram for predicting the probability of POD after esophagectomy
Fig. 5
Fig. 5
The receiver operating characteristic curve of the predictive nomogram model for POD
Fig. 6
Fig. 6
The calibration curve of the nomogram-predicted probability of POD
Fig. 7
Fig. 7
Decision curve analysis (DCA) for the nomogram model
See this image and copyright information in PMC

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