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Cited by 53Cardiac, Anesthesia and Surgical OutcomesCardiac and Coronary Surgery TechniquesHemodynamic Monitoring and Therapy

Machine learning models with preoperative risk factors and intraoperative hypotension parameters predict mortality after cardiac surgery

Fernandes, Marta Priscila Bento, Armengol de la Hoz, Miguel, Rangasamy, Valluvan, Subramaniam, Balachundhar·2021·Journal of Cardiothoracic and Vascular Anesthesia
DOI 10.1053/j.jvca.2020.07.029
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Abstract

Objectives Machine learning models used to predict postoperative mortality rarely include intraoperative factors. Several intraoperative factors like hypotension (IOH), vasopressor-inotropes, and cardiopulmonary bypass (CPB) time are significantly associated with postoperative outcomes. The authors explored the ability of machine learning models incorporating intraoperative risk factors to predict mortality after cardiac surgery.Design Retrospective study.Setting Tertiary hospital.Participants A total of 5,015 adults who underwent cardiac surgery from 2008 to 2016.Intervention None.Measurements and main results The intraoperative phase was divided into the following: (1) CPB, (2) outside CPB, and (3) total surgery for quantifying IOH only. Phase-specific IOH parameters (area under the curve for mean arterial pressure <65 mmHg), vasopressor-inotropes (norepinephrine equivalents), duration, and cross-clamp time, along with preoperative risk factors ,were incorporated into the models. The primary outcome was mortality. The following 5 models were applied to 3 intraoperative phases separately: (1) logistic regression, (2) random forests, (3) neural networks, (4) support vector machines, and (5) extreme gradient boosting (XGB). Mortality was predicted using area under the receiver operating characteristic curve. Of 5,015 patients included, 112 (2.2%) died. XGB model from the outside-CPB phase predicted mortality better with area under the receiver operating characteristic curve, 95% confidence interval (CI): 0.88(0.83-0.94); positive predictive value, 0.10(0.06-0.15); specificity 0.85 (0.83-0.87) and sensitivity 0.75 (0.57-0.90).Conclusion XGB machine learning model from IOH outside the CPB phase seemed to offer a better discrimination, sensitivity, specificity, and positive predictive value compared with other models. Machine learning models incorporating intraoperative adverse factors might offer better predictive ability for risk stratification and triaging of patients after cardiac surgery.

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  • ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.

    “These ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [22]

  • Healthcare systems have increasingly sought to use ML to assist in risk stratification, and these ML models may outperform statistical models in calibration and discrimination.

    “Healthcare systems have increasingly sought to use ML to assist in risk stratification, and these ML models may outperform statistical models in calibration and discrimination.”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [22]

  • Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated.

    “Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated (16, 20–25).”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [22]

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Authors
Fernandes, M. P. B., Armengol de la Hoz, M., Rangasamy, V., & Subramaniam, B.
Affiliations
  • Harvard University (US)
  • Beth Israel Deaconess Medical Center (US)
Corresponding author
Subramaniam, Balachundhar
ORCID
Venue
Journal of Cardiothoracic and Vascular Anesthesia
Publisher
Elsevier BV
Published
2021-03
Volume
35
Issue
3
Pages
857-865
ISSN
1053-0770 (ISSN-L)
DOI
10.1053/j.jvca.2020.07.029
OpenAlex
W3041324631
Cited by
53
References
34
Citations by year
2026: 3 · 2025: 8 · 2024: 13 · 2023: 7 · 2022: 12 · 2021: 8 · 2020: 1
FWCI
4.38
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Journal of Cardiothoracic and Vascular Anesthesia (publishedVersion)
  • PubMed (publishedVersion)
Funders
Foundation for the National Institutes of Health
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
  • Health Sciences › Medicine › Surgery › Cardiac and Coronary Surgery Techniques
  • Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
Keywords
postoperative mortality prediction, cardiac surgery, intraoperative hypotension, cardiopulmonary bypass, norepinephrine, aortic cross-clamp time, preoperative risk factors, extreme gradient boosting, risk stratification, machine learning prediction models
MeSH terms
Machine Learning, Adult, Cardiac Surgical Procedures, Humans, Hypotension, Postoperative Complications, Retrospective Studies, Risk Factors

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