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May 28, 2026World Journal of PsychiatryOpen Access

The predictive model demonstrated an AUC of 0.924 (95% CI: 0.897-0.951) in the modeling cohort and 0.931 (95% CI: 0.866-0.997) in the external validation cohort.

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Why the study?

Emergence delirium is a common postoperative complication in older adults, but research focused on identifying predictive factors and constructing risk models in the post-anesthesia care unit is lacking.

Population

705 older surgical patients for modeling and 115 for validation

Design

Prediction model development and validation study

Authors

YXYi XinBHBin HeXWXiao-Hui Wei

Discussion

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Overview

Dynamic nomogram may aid ED risk stratification in older adults; leaves open clinical adoption pending prospective validation.

Key Points

  • To develop and validate a dynamic risk-prediction nomogram for postoperative emergence delirium in older surgical patients to guide early clinical interventions.
  • Enrolled 705 older surgical patients (January–October 2024) for model development and 115 patients (November–December 2024) for external validation.
  • Used least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression to construct an online dynamic nomogram, validated via 10-fold cross-validation and external testing.
  • Postoperative emergence delirium occurred in 17.16% of patients, with preoperative Mini-Mental State Examination score, preoperative albumin, surgical duration, surgical risk score, indwelling catheter count, and extubation time identified as independent risk factors (all P < 0.05).
  • The predictive model achieved an AUC of 0.924 (95% CI: 0.897–0.951; Hosmer-Lemeshow χ² = 7.934, P = 0.541) in the development cohort.
  • Model performance remained robust during internal validation (AUC = 0.920, 95% CI: 0.571–0.959) and external validation (AUC = 0.931, 95% CI: 0.866–0.997; Hosmer-Lemeshow χ² = 5.772, P = 0.763).

Structured PICO

P
Population
820 older surgical patients (705 enrolled for modeling, 115 for validation)
I
Intervention
Risk prediction model for emergence delirium using an online dynamic nomogram
O
Outcome
Predictive efficacy of the model for emergence delirium (measured by Area Under the Curve)

A newly developed dynamic nomogram accurately predicts the risk of emergence delirium in older surgical patients, which may aid in early identification and clinical intervention.

Cite This Study

Xin et al. (2026) studied this question.

synapsesocial.com/papers/6a73a755ef8a0d4f3a4c9e7bhttps://doi.org/10.5498/wjp.v16.i6.115839
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