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Lasso-Based Machine Learning Algorithm for Predicting Postoperative Lung Complications in Elderly: A Single-Center

Jie Liu¹, Yilei Ma², Wanli Xie¹

¹Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.

Clinical Interventions in Aging

|April 21, 2023

Summary

A new machine learning model effectively predicts postoperative pulmonary complications (PPCs) in elderly patients. This model, using factors like age and albumin/NLR score, outperforms the ARISCAT score for better patient interventions.

Keywords:

ANS older adult postoperative complications risk factors the albumin/NLR score

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Area of Science:

Anesthesiology
Geriatric Medicine
Data Science in Healthcare

Background:

The predictive value of systemic inflammatory markers for postoperative pulmonary complications (PPCs) in elderly patients is not well-established.
Machine learning models are underutilized for predicting surgical risks in geriatric populations.

Purpose of the Study:

To develop and validate a machine learning-based prediction model for PPCs in elderly patients undergoing general anesthesia.
To compare the performance of the developed model against the established ARISCAT risk score.

Main Methods:

Retrospective analysis of 9775 elderly patients undergoing general anesthesia.
Development of a prediction model using the Least Absolute Shrinkage and Selection Operator (LASSO) regression.
Validation of the model's discrimination, calibration, and reclassification improvement against the ARISCAT model.

Main Results:

The final model incorporated age, preoperative SpO2, Albumin/NLR Score (ANS), operation time, and red blood cell transfusion.
The model demonstrated strong predictive performance with concordance index (C-index) values of 0.740 (development) and 0.748 (validation).
The developed model significantly outperformed the ARISCAT model in both C-index and Net Reclassification Improvement (NRI).

Conclusions:

A LASSO machine learning model provides superior prediction of PPCs in elderly patients compared to the ARISCAT model.
The identified predictors can guide clinicians in implementing targeted preventive strategies for high-risk elderly surgical patients.