Literature DB >> 34275553

Development of an early prediction model for postoperative delirium in neurosurgical patients admitted to the ICU after elective craniotomy (E-PREPOD-NS): A secondary analysis of a prospective cohort study.

Hua-Wei Huang1, Guo-Bin Zhang2, Hao-Yi Li2, Chun-Mei Wang1, Yu-Mei Wang1, Xiu-Mei Sun1, Jing-Ran Chen1, Guang-Qiang Chen1, Ming Xu1, Jian-Xin Zhou3.   

Abstract

Postoperative delirium (POD) is a significant clinical problem in neurosurgical patients after intracranial surgery. Identification of high-risk patients may optimize perioperative management, but an adequate risk model for use at early phase after operation has not been developed. In the secondary analysis of a prospective cohort study, 800 adult patients admitted to the ICU after elective intracranial surgeries were included. The POD was diagnosed as Confusion Assessment Method for the ICU positive on postoperative day 1 to 3. Multivariate logistic regression analysis was used to develop early prediction model (E-PREPOD-NS) and the final model was validated with 200 bootstrap samples. The incidence of POD in this cohort was19.6%. We identified nine variables independently associated with POD in the final model: advanced age (OR 3.336, CI 1.765-6.305, 1 point), low education level (OR 2.528, 1.446-4.419, 1), smoking history (OR 2.582, 1.611-4.140, 1), diabetes (OR 2.541, 1.201-5.377, 1), supra-tentorial lesions (OR 3.424, 2.021-5.802, 1), anesthesia duration > 360 min (OR 1.686, 1.062-2.674, 0.5), GCS < 9 at ICU admission (OR 6.059, 3.789-9.690, 1.5), metabolic acidosis (OR 13.903, 6.248-30.938, 2.5), and neurosurgical drainage tube (OR 1.924, 1.132-3.269, 0.5). The area under the receiver operator curve (AUROC) of the risk score for prediction of POD was 0.865 (95% CI 0.835-0.895). The AUROC was 0.851 after internal validation (95% CI 0.791-0.912). The model showed good calibration. The E-PREPOD-NS model can predict POD in patients admitted to the ICU after elective intracranial surgery with good accuracy. External validation is needed in the future.
Copyright © 2021. Published by Elsevier Ltd.

Entities:  

Keywords:  Intensive care unit; Intracranial surgery; Neurosurgical patients; Postoperative delirium; Risk prediction model

Mesh:

Year:  2021        PMID: 34275553     DOI: 10.1016/j.jocn.2021.06.004

Source DB:  PubMed          Journal:  J Clin Neurosci        ISSN: 0967-5868            Impact factor:   1.961


  2 in total

1.  Automated machine learning-based model predicts postoperative delirium using readily extractable perioperative collected electronic data.

Authors:  Xiao-Yi Hu; He Liu; Xue Zhao; Xun Sun; Jian Zhou; Xing Gao; Hui-Lian Guan; Yang Zhou; Qiu Zhao; Yuan Han; Jun-Li Cao
Journal:  CNS Neurosci Ther       Date:  2021-11-18       Impact factor: 5.243

2.  Post-Operative Cognitive Impairment: A Cognitive Epidemiology Perspective.

Authors:  Insa Feinkohl
Journal:  J Intell       Date:  2022-03-11
  2 in total

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