Performance of a Machine Learning Algorithm in Predicting Outcomes of Aortic Valve Replacement

Kilic, Arman; Goyal, Anshul; Miller, Judy Z.; Gleason, Thomas G.; Dubrawksi, Artur

doi:10.1016/j.athoracsur.2020.05.107

Cited by 33 publications

(28 citation statements)

References 15 publications

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“…The XGboost algorithm is a widely used machine learning method that can build complex models and make accurate decisions when given adequate data [23]. The accuracy and high efficiency of XGBoost make it show excellent performance in clinical research, especially in the field of vascular diseases [23][24][25]. The new prediction model can accurately predict the AIS risk in Chinese patients and all variables involved in the model can be simple and easily obtained on admission.…”

Section: Discussionmentioning

confidence: 99%

Estimation of the LDL subclasses in ischemic stroke as a risk factor in a Chinese population

Duan

Wang

et al. 2020

BMC Neurol

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Background Acute ischemic stroke (AIS) is one of the leading causes of mortality and long-term disability worldwide. Our study aims to clarify the role of low-density lipoproteins (LDL) subclasses in the occurrence of AIS and develop a risk xprediction model based on these characteristics to identify high-risk people. Methods Five hundred and sixty-six patients with AIS and 197 non-AIS controls were included in this study. Serum lipids and other baseline characteristics including fasting blood glucose (GLU), serum creatinine (Scr), and blood pressure were investigated in relation to occurrence of AIS. The LDL subfractions were classified and measured with the Lipoprint System by a polyacrylamide gel electrophoresis technique. Results Levels of LDL-3, LDL-4 and LDL-5 subclasses were significantly higher in the AIS group compared to the non-AIS group and lower level of LDL-1 was prevalent in the AIS patients. Consistently, Spearman correlation coefficient demonstrated that sd-demonevels, especially LDL-3 and LDL-4 levels, were significantly positively correlated with AIS. Furthermore, there is a significant positive correlation between small dense LDL (sd-LDL, that is LDL-3 to 7) levels and serum lipids including total cholesterol (TC), Low density lipoprotein cholesterol (LDL-C), and Triglyceride (TG). Increased LDL-3 and LDL-4 as well as decreased LDL-1 and LDL-2 were correlated to the occurrence of AIS, even in the people with normal LDL-C levels. A new prediction model including 12 variables can accurately predict the AIS risk in Chinese patients (AUC = 0.82 ± 0.04). Conclusions Levels of LDL subclasses should be considered in addition to serum LDL-C in assessment and management of AIS. A new prediction model based on clinical variables including LDL subtractions can help clinicians identify high of AIS, even in the people with norm.

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Section: Discussionmentioning

confidence: 99%

Estimation of the LDL subclasses in ischemic stroke as a risk factor in a Chinese population

Duan

Wang

et al. 2020

BMC Neurol

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“…Based on 2010 patients in the database of Seoul National University Hospital, Lee et al 12 found that among machine learning algorithms including decision tree, support vector machine, and random forest, XGBoost (Test accuracy: 0.74; AUC: 0.78) has the best performance to predict AKI after cardiac surgery and a website was created to process patients' data in real-time. Kilic et al 13 also applied XGBoost to predict multiple complications, including operative mortality (AUC: 0.771), renal failure (AUC: 0.776), prolonged ventilation (AUC: 0.739), reoperation (AUC: 0.637), stroke (AUC: 0.684), and deep sternal wound infection (AUC: 0.599), for adult patients after surgical aortic valve replacement in the Society of Thoracic Surgeons National Database. In addition, other researchers usually paid attention to common complications such as AKI, sepsis, and hospital mortality.…”

Section: Discussionmentioning

confidence: 99%

Machine learning prediction models for prognosis of critically ill patients after open-heart surgery

Zhong

Yuan

Liu

et al. 2021

Sci Rep

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We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, aortic valve replacement, or other heart-related surgeries between 2001 and 2012 were extracted from MIMIC-III databases. Extreme gradient boosting, random forest, artificial neural network, and logistic regression were employed to build models by utilizing fivefold cross-validation and grid search. Receiver operating characteristic curve, area under curve (AUC), decision curve analysis, test accuracy, F1 score, precision, and recall were applied to access the performance. Among 6844 patients enrolled in this study, 215 patients (3.1%) died within 30 days after surgery, part of patients appeared liver dysfunction (248; 3.6%), septic shock (32; 0.5%), and thrombocytopenia (202; 2.9%). XGBoost, selected to be our final model, achieved the best performance with highest AUC and F1 score. AUC and F1 score of XGBoost for 4 outcomes: 0.88 and 0.58 for 30-days mortality, 0.98 and 0.70 for septic shock, 0.88 and 0.55 for thrombocytopenia, 0.89 and 0.40 for liver dysfunction. We developed a promising model, presented as software, to realize monitoring for patients in ICU and to improve prognosis.

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“…The XGboost algorithm is a widely used machine learning method that can build complex models and make accurate decisions when given adequate data [23]. The accuracy and high e ciency of XGBoost make it show excellent performance in clinical research, especially in the eld of vascular diseases [23][24][25]. The new prediction model can accurately predict the AIS risk in Chinese patients and all variables involved in the model can be simple and easily obtained on admission.…”

Section: Discussionmentioning

confidence: 99%

“…Tables Table 1. Clinical and (13,25) 18 (13,24) 0.318 LDL-3 (mg/dL) 7(2, 13) 3(1, 6) < 0.001 * LDL-4 (mg/dL) 0(0, 3) 0(0, 0) < 0.001 * LDL-5 (mg/dL) 0(0, 0) 0(0, 0) 0.032 * LDL-6 (mg/dL) 0(0, 0) 0(0, 0) 0.117 LDL-7 (mg/dL) 0(0, 0) 0(0, 0) 0.206 LDL, low density lipoprotein *p< 0.05 Table 6. Comparison of laboratory characteristics between the AIS group and control group with normal LDL-C levels (<2.59 mmol/ L)…”

Section: Discussionmentioning

confidence: 99%

Estimation of the LDL Subclasses in Ischemic Stroke as a Risk Factor in a Chinese population

Duan

Xue

Wang

et al. 2020

Preprint

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Background: Acute ischemic stroke (AIS) is one of the leading causes of mortality and long-term disability worldwide. Our study aims to clarify the role of low-density lipoproteins (LDL) subclasses in the occurrence of AIS and develop a risk prediction model based on these characteristics to identify high-risk people.Methods: Five hundred and sixty-six patients with AIS and 197 non-AIS controls were included in this study. Serum lipids and other baseline characteristics including fasting blood glucose (GLU), serum creatinine (Scr), and blood pressure were investigated in relation to occurrence of AIS. The LDL subfractions were classified and measured with the Lipoprint System by a polyacrylamide gel electrophoresis technique.Results: Levels of LDL-3, LDL-4 and LDL-5 subclasses were significantly higher in the AIS group compared to the non-AIS group and lower level of LDL-1 was prevalent in the AIS patients. Consistently, Spearman correlation coefficient demonstrated that sd‐demonevels, especially LDL-3 and LDL-4 levels, were significantly positively correlated with AIS. Furthermore, there is a significant positive correlation between small dense LDL (sd-LDL, that is LDL-3 to 7) levels and serum lipids including total cholesterol (TC), Low density lipoprotein cholesterol (LDL‐C), and Triglyceride (TG). Increased LDL-3 and LDL-4 as well as decreased LDL-1 and LDL-2 were correlated to the occurrence of AIS, even in the people with normal LDL-C levels. A new prediction model including 12 variables can accurately predict the AIS risk in Chinese patients (AUC=0.82±0.04).Conclusions: Levels of LDL subclasses should be considered in addition to serum LDL-C in assessment and management of AIS. A new prediction model based on clinical variables including LDL subtractions can help clinicians identify high of AIS, even in the people with norm.

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Performance of a Machine Learning Algorithm in Predicting Outcomes of Aortic Valve Replacement

Cited by 33 publications

References 15 publications

Estimation of the LDL subclasses in ischemic stroke as a risk factor in a Chinese population

Estimation of the LDL subclasses in ischemic stroke as a risk factor in a Chinese population

Machine learning prediction models for prognosis of critically ill patients after open-heart surgery

Estimation of the LDL Subclasses in Ischemic Stroke as a Risk Factor in a Chinese population

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