A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid Endarterectomy

Tan, Jinyun; Wang, Qi; Shi, Weihao; Liang, Kun; Yu, Bo; Mao, Qingqing

doi:10.1016/j.avsg.2020.07.001

Cited by 7 publications

(10 citation statements)

References 27 publications

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“…Intraoperative monitoring: Other than one study focusing on prediction of bradycardia, the remainder utilized time-series analysis of intraoperative data to enable real-time trending of vital signs. Intraoperative depth of anesthesia using real-time EEG data, 88 acute events such as intraoperative hypotension, 90–94,132,133 postoperative hypertension, 51 bradycardia, 95 hypoxemia 97 and blood product use during caesarean section 81 were modeled. As an example, Cartailler et al 88 analyzed continuous EEG readings using a model that recognized abnormal wave patterns to identify suppression bursts.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Arina,

Kaczorek,

Hofmaenner

et al. 2023

Anesthesiology

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Background The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. Methods A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. Results A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. Conclusions The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

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Section: Resultsmentioning

confidence: 99%

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Arina,

Kaczorek,

Hofmaenner

et al. 2023

Anesthesiology

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“…In this cross‐sectional, cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES), we observed that a machine‐learning model leveraging both nutritional covariates and demographic covariates could accurately predict hypertension risk (AUROC = 0.84). These match commonly applied risk scores within diet related diseases such as heart disease, with AUROC ranging from 0.6 to 0.88 15,19,35–37 …”

Section: Discussionmentioning

confidence: 99%

“…However, due to the complexity of the non‐parametric algorithms that are common in machine‐learning methods, it is impossible for a human to analyze each tree and execute an explanation of how the machine‐learning method works 7,28,30,35,42–45 . Without methods that explain how machine learning algorithms reach their predictions, clinicians will not be able to identify if models are reliable and generalizable or just replicating the biases within the training datasets 11,35–37,46 . This study is one of the first in the literature that predicts risk for hypertension from nutritional covariates using machine‐learning methods and executes model explanation algorithms to add transparency to the methods.…”

Section: Discussionmentioning

confidence: 99%

“…These match commonly applied risk scores within diet related diseases such as heart disease, with AUROC ranging from 0.6 to 0.88. 15,19,[35][36][37] To provide context over the antihypertensive use amongst the Prior studies for risk prediction have utilized traditional parametric methods such as linear regression, cox proportional hazards analysis, and logistic regression. 28,31,32,39,40 In traditional parametric methods such as linear regression, each covariate can be interpreted clearly (e.g., for each one increase in x, we observe two increases in y).…”

Section: Discussionmentioning

confidence: 99%

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Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Huang,

Huang

2023

J of Clinical Hypertension

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Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long‐term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine‐learning techniques could accurately predict hypertension risk using nutritional information. A cross‐sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine‐learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine‐learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.

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“…All CEA procedures were performed under general anesthesia, and the specific surgical methods can be referred to in our previous studies (14). In our center, shunts were routinely used for patients combined with contralateral carotid stenosis or lower back pressure (<40 mmHg).…”

Section: Carotid Revascularizationmentioning

confidence: 99%

Analysis of the anatomic eligibility for transcarotid artery revascularization in Chinese patients who underwent carotid endarterectomy and transfemoral carotid artery stenting

Fan

Shi

Lü

et al. 2023

Front. Cardiovasc. Med.

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ObjectiveTranscarotid artery revascularization (TCAR) is thought to be a promising technique and instrument for treating carotid stenosis with favorable outcomes. Since there remain several differences in anatomic characteristics among races, this study was conducted to investigate the anatomic eligibility of TCAR in Chinese patients who underwent carotid revascularization.MethodsA retrospective review of patients with carotid stenosis from 2019 to 2021 was conducted. The anatomic eligibility of TCAR was based on the instruction of the ENROUTE Transcarotid Neuroprotection System. The carotid artery characteristics and configuration of the circle of Willis (CoW) were evaluated by CT angiography. The demographic and clinical characteristics and procedure-related complications were recorded. Logistic regression was used to analyze the independent factors for TCAR eligibility.ResultsOf 289 consecutive patients [222 for carotid endarterectomy (CEA) and 67 for transfemoral carotid artery stenting (TF-CAS)] identified, a total of 215 patients (74.4%) met TCAR anatomic eligibility. Specifically, 83.7% had mild common carotid artery (CCA) puncture site plaque, 95.2% had 4–9 mm internal carotid artery diameters, 95.8% had >6 mm CCA diameter, and 98.3% had >5 cm clavicle to carotid bifurcation distance. Those who were female (OR, 5.967; 95% CI: 2.545–13.987; P < 0.001), were of an older age (OR, 1.226; 95% CI: 1.157–1.299; P < 0.001), and higher body mass index (OR, 1.462; 95% CI: 1.260–1.697; P < 0.001) were prone to be associated with TCAR ineligibility. In addition, 71 patients with TCAR eligibility (33.0%) were found to combine with incomplete CoW. A high risk for CEA was found in 29 patients (17.3%) with TCAR eligibility, and a high risk for TF-CAS was noted in nine patients (19.1%) with TCAR eligibility. Overall, cranial nerve injury (CNI) was found in 22 patients after CEA, while 19 of them (11.3%) met TCAR eligibility.ConclusionA significant proportion of Chinese patients meet the anatomic criteria of TCAR, making TCAR a feasible treatment option in China. Anatomic and some demographic factors play key roles in TCAR eligibility. Further analysis indicates a potential reduction of procedure-related complications in patients with high-risk carotid stenosis under the TCAR procedure.

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A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid Endarterectomy

Cited by 7 publications

References 27 publications

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Analysis of the anatomic eligibility for transcarotid artery revascularization in Chinese patients who underwent carotid endarterectomy and transfemoral carotid artery stenting

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