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Predictors of 30-day mortality using machine learning approach following carotid endarterectomy

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Abstract

Background

Preoperative prognostication of 30-day mortality in patients with carotid endarterectomy (CEA) can optimize surgical risk stratification and guide the decision-making process to improve survival. This study aims to develop and validate a set of predictive variables of 30-day mortality following CEA.

Methods

The patient cohort was identified from the American College of Surgeons National Surgical Quality Improvement Program (2005–2016). We performed logistic regression (enter, stepwise, and forward) and least absolute shrinkage and selection operator (LASSO) method for the selection of variables, which resulted in 28-candidate models. The final model was selected based upon clinical knowledge and numerical results.

Results

Statistical analysis included 65,807 patients with 30-day mortality in 0.7% (n = 466) patients. The median age of our cohort was 71.0 years (range, 16–89 years). The model with 9 predictive factors which included age, body mass index, functional health status, American Society of Anesthesiologist grade, chronic obstructive pulmonary disorder, preoperative serum albumin, preoperative hematocrit, preoperative serum creatinine, and preoperative platelet count—performed best on discrimination, calibration, Brier score, and decision analysis to develop a machine learning algorithm. Logistic regression showed higher AUCs than LASSO across these different models. The predictive probability derived from the best model was converted into an open-accessible scoring system.

Conclusion

Machine learning algorithms show promising results for predicting 30-day mortality following CEA. These algorithms can be useful aids for counseling patients, assessing preoperative medical risks, and predicting survival after surgery.

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Abbreviations

AE:

Adverse events

AUC:

Area under the curve

BMI:

Body mass index

CEA:

Carotid endarterectomy

CI:

Confidence interval

ICD-CM:

International Classification of Disease Clinical Modification

LASSO:

Least absolute shrinkage and selection operator

NSQIP:

National Surgical Quality Improvement Program

ROC:

Receiver operating characteristics

TRIPOD:

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis

USA:

United States of America

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Author information

Authors and Affiliations

  1. Department of Biology (Physiology), McMaster University, Hamilton, ON, Canada

    Ahmed Mohamed

  2. Department of Neurology, University of Alberta, Edmonton, AB, Canada

    Ashfaq Shuaib

  3. Department of Neuroscience, Hamad General Hospital, Doha, Qatar

    Ayman Z. Ahmed

  4. Department of Neuroscience, MSK Trillium Hospital, Institute for Better Health, University of Toronto at Mississauga, Mississauga, ON, Canada

    Maher Saqqur

  5. Division of Neurosurgery, House Institute, Los Angeles, CA, USA

    Nida Fatima

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  1. Ahmed Mohamed
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Corresponding author

Correspondence to Nida Fatima.

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The de-identified NSQIP data is exempt from review by our Institutional Review Board.

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Not applicable as it is a retrospective study from a de-identified data.

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Mohamed, A., Shuaib, A., Ahmed, A.Z. et al. Predictors of 30-day mortality using machine learning approach following carotid endarterectomy. Neurol Sci 44, 253–261 (2023). https://doi.org/10.1007/s10072-022-06392-2

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  • DOI: https://doi.org/10.1007/s10072-022-06392-2

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