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Predicting postoperative delirium after hip arthroplasty for elderly patients using machine learning

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Abstract

Background

Postoperative delirium (POD) is a common and severe complication in elderly hip-arthroplasty patients.

Aim

This study aims to develop and validate a machine learning (ML) model that determines essential features related to POD and predicts POD for elderly hip-arthroplasty patients.

Methods

The electronic record data of elderly patients who received hip-arthroplasty surgery between January 2017 and April 2021 were enrolled as the dataset. The Confusion Assessment Method (CAM) was administered to the patients during their perioperative period. The feature section method was employed as a filter to determine leading features. The classical machine learning algorithms were trained in cross-validation processing, and the model with the best performance was built in predicting the POD. Metrics of the area under the curve (AUC), accuracy (ACC), sensitivity, specificity, and F1-score were calculated to evaluate the predictive performance.

Results

476 Arthroplasty elderly patients with general anesthesia were included in this study, and the final model combined feature selection method mutual information (MI) and linear binary classifier using logistic regression (LR) achieved an encouraging performance (AUC = 0.94, ACC = 0.88, sensitivity = 0.85, specificity = 0.90, F1-score = 0.87) on a balanced test dataset.

Conclusion

The model could predict POD with satisfying accuracy and reveal important features of suffering POD such as age, Cystatin C, GFR, CHE, CRP, LDH, monocyte count, history of mental illness or psychotropic drug use and intraoperative blood loss. Proper preoperative interventions for these factors could reduce the incidence of POD among elderly patients.

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Data availability

The datasets can be available from the corresponding author on reasonable request.

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Funding

This study was supported by the Brand Discipline Construction Funds of the First Affiliated Hospital of Chongqing Medical University, Scientific research project of Chongqing Municipal Health Commission (2022WSJK117), General Program of Chongqing Natural Science Foundation (CSTB2022NSCQ-MSX0854), the Key Research and Development Project of the Ministry of Science and Technology of the People’s Republic of China (2020YFF0305104), and the Key Research and Development Project of Science and Technology Department of Sichuan Province (2020YFS0324).

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Author notes

  1. Daiyu Chen and Weijia Wang have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Anesthesiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Daiyu Chen, Siqi Wang, Minghe Tan, Jun Yang & Jun Cao

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Weijia Wang & Yong Tang

  3. Center for Artificial Intelligence in Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China

    Song Su & Jiali Wu

  4. Department of General Surgery (Hepatobiliary Surgery), The Affiliated Hospital of Southwest Medical University, Luzhou, China

    Song Su

  5. Department of Anesthesiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China

    Jiali Wu

  6. Department of Pathology, School of Basic Medicine, Chongqing Medical University, Chongqing, China

    Qingshu Li

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  1. Daiyu Chen
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Correspondence to Yong Tang or Jun Cao.

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This study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Ethics code: 2021-201) and conducted according to the principles of the Declaration of Helsinki.

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In this retrospective study, we utilized routine clinical data from the records, and the requirement for patients’ informed consent was waived by the Ethics Committee.

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Chen, D., Wang, W., Wang, S. et al. Predicting postoperative delirium after hip arthroplasty for elderly patients using machine learning. Aging Clin Exp Res 35, 1241–1251 (2023). https://doi.org/10.1007/s40520-023-02399-7

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