Dataset Information

Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.

ABSTRACT:

Aims

To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients.

Method

This was a retrospective study of perioperative medical data from patients undergoing non-cardiac and non-neurology surgery over 65 years old from January 2014 to August 2019. Forty-six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and precision.

Results

In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765-0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%.

Conclusions

The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model.

SUBMITTER: Song YX

PROVIDER: S-EPMC9804041 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.

Song Yu-Xiang YX Yang Xiao-Dong XD Luo Yun-Gen YG Ouyang Chun-Lei CL Yu Yao Y Ma Yu-Long YL Li Hao H Lou Jing-Sheng JS Liu Yan-Hong YH Chen Yi-Qiang YQ Cao Jiang-Bei JB Mi Wei-Dong WD

CNS neuroscience & therapeutics 20221011 1

<h4>Aims</h4>To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients.<h4>Method</h4>This was a retrospective study of perioperative medical data from patients undergoing non-cardiac and non-neurology surgery over 65 years old from January 2014 to August 2019. Forty-six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoo ...[more]

PMID: 36217732

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Dataset Information

Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.

Aims

Method

Results

Conclusions

Publications

Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.

Similar Datasets

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