Machine learning techniques in cardiac risk assessment

Kartal, Elif; Balaban, M. Erdal

doi:10.5606/tgkdc.dergisi.2018.15559

Cited by 7 publications

(6 citation statements)

References 11 publications

(11 reference statements)

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“…In a study by Kartal, mortality risk was predicted using the EuroS-CORE and the C4.5 algorithm: both the EuroSCORE and the C4.5 algorithm included age, serum creatinine, LVEF, and mean pulmonary hypertension (mPAP). 28 They used their algorithm to develop a web application for risk prediction after cardiac surgery. Castela Forte et al used machine learning to evaluate 88 perioperative variables in order to predict 5-year mortality after cardiac surgery; they observed that postoperative urea concentration, age and creatinine concentration, achieved the best predictive values across different cardiac surgery types.…”

Section: Discussionmentioning

confidence: 99%

“…[23][24][25] Machine learning algorithms can be used to analyze data and establish risk models more accurately than traditional statistical models. 26,27 Indeed, machine learning has been used to create models that predict mortality after cardiac surgery, [28][29][30][31] as well as estimate the length of hospital stay after CABG. 32 Regardless, these models are still not specific to OPCAB.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning model-based risk prediction of severe complications after off-pump coronary artery bypass grafting

Zhang¹,

Li²,

Li³

et al. 2022

Adv Clin Exp Med

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Background. Compared with coronary artery bypass grafting (CABG) under cardiopulmonary bypass, off-pump coronary artery bypass (OPCAB) is minimally invasive and reduces the risk of intraoperative blood transfusion and acute kidney injury. Nonetheless, OPCAB-related complications still pose a threat. Machine learning technology can analyze a large number of clinical data, establish risk prediction models and help clinicians make early and correct clinical decisions.Objectives. Risk prediction models are available for mortality and morbidity after cardiac surgery, but they are not specific to OPCAB. This study aimed to develop a predictive model of severe complications after OPCAB, based on machine learning. Materials and methods.Anesthesia records of OPCAB from the General Hospital of the Northern Theater Command (Shenyang, China) collected between January 1, 2019, and June 15, 2020, were analyzed. The endpoint of the study was the occurrence of serious complications after OPCAB (postoperative unplanned intra-aortic balloon pump, secondary surgery and death). The features entered into the models were as follows: intraoperative ventricular fibrillation, number of saphenous vein grafts, nerve block (NeB), venous oxygen saturation (SvO 2 ), skin incision-bypass time, and hypertension. A total of 8 machine learning algorithms were tested: logistic regression analysis (LRA), k-nearest neighbor (KNN), naïve Bayes (NB), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical features gradient boosting (CatBoost).Results. Among the 506 patients found in the records, 27 met the endpoint. The highest area under the curve (AUC) value was achieved with the XGBoost model (AUC = 0.94), and the lowest with the SVM model (AUC = 0.75). The highest and lowest accuracy were observed with the XGBoost and NB models, respectively, while the highest and lowest precision were achieved using the SVM and NB models, respectively. Based on the receiver operating characteristic (ROC) curves, the XGBoost model was selected as the most useful in this study.Conclusions. This study suggests using the XGBoost model to predict the risk of complications after OPCAB.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning model-based risk prediction of severe complications after off-pump coronary artery bypass grafting

Zhang¹,

Li²,

Li³

et al. 2022

Adv Clin Exp Med

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“…Open chest surgery leads to large amounts of hard-to-clear sputum. [ 20 - 22 ] Post-cardiothoracic surgery ICU patients are considerably different from general patients with long-term mechanical ventilation on airway resistance. [ 20 , 21 , 23 ] The HMEs are commonly used in patients who need long-term ventilation after cardiothoracic surgery, although there is still a need to study their effects on the airway resistance.…”

Section: Discussionmentioning

confidence: 99%

Heat and moisture exchanger used in a cardiothoracic surgery intensive care unit: Airway resistance and changing interval

Liu

Wang

et al. 2020

Turk Gogus Kalp Dama

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Background: This study aims to investigate the efficacy and safety of heat and moisture exchanger on airway resistance in a cardiothoracic surgery intensive care unit. Methods: A total of 31 patients (18 males, 13 females; mean age 51.5 years; range, 39 to 61 years) who were treated with long-term mechanical ventilation due to low cardiac output syndrome after cardiopulmonary bypass and cardiac surgery were retrospectively analyzed between December 2014 and December 2018. In addition, an in vitro lung model and different doses of hydroxyethyl starch in the heat and moisture exchangers to mimic the airway secretions were used and the proper interval to change heat and moisture exchangers was evaluated. Results: In the in vitro lung model, the mean airway resistance was 19.4±0.2 cmH2O/L/sec in the 5 mL group (p=0.060), 20.3±1.0 cmH2O/L/sec in the 10 mL group (p=0.065), and 30.2±1.7 cmH2O/L/sec in the 15 mL group (p<0.001). The airway resistance of heat and moisture exchangers, and total hospital stay and ventilation duration significantly increased in the seven-day group compared to the one-day and three-day groups. The positive culture of bacteria was also significantly higher in the seven-day group. Conclusion: Our study results suggest that heat and moisture exchangers can be safely used for an efficient and timely removal of airway secretions. Volume of approximately 15 mL of liquid in the airflow can dramatically increase the airway resistance. The three-day interval of changing heat and moisture exchangers is ideal in a cardiothoracic surgery intensive care unit where patients have more airway secretions than patients in the general intensive care unit.

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“…Os tipos de aprendizado usados por máquinas são subdivididos basicamente em duas categorias, sendo elas: aprendizado supervisionado e aprendizado não-supervisionado (DEO, 2015). A principal diferença entre os dois é a presença do atributo alvo no conjunto de dados, segundo Kartal (2018), a aprendizagem supervisionada é uma forma de aprendizado na qual recebem-se diversos dados de treinamento já com respostas e são feitas previsões para pontos diferentes daqueles já treinados anteriormente.…”

Section: Introductionunclassified

“…Aprendizagem não-supervisionada, é uma forma de aprendizado na qual não existem respostas aos dados de treinamento, de acordo com Kartal (2018).…”

Section: Introductionunclassified

Aprendizado de máquina para classificação da desocupação de leitos pós-cirúrgicos: Aprendizado de máquina para classificação da desocupação de leitos pós-cirúrgicos

Teramachi¹

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Congenital heart diseases are among the most common congenital anomalies and if they aren’t discovered and treated properly at na early stage, babies and children can have a poor quality of life and may die over time. In many cases, surgical intervention is necessary before the first year of life and when it occurs, it is importante to estimate the length of stay in post-surgical beds, both for capacity management, planning and optimization of resources by the hospital and to guide patients and their families. The present study aims to propose two models, through the use of Machine Learning algorithms, one to classify the length of stay in post-surgical ICU beds and the other to classify the length of stay in post-surgical ward beds, since research related to the length of stay in postsurgical ward beds is rare. The data used to train the algoritgms are regarding cardiac surgeries performed on congenital heart patients extracted from the ASSIST, private database of the Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (InCor - FMUSP). The trained algorithms were: Random Forest, Extra Trees, Gradient Boosting, Adaboost, Support Vector Machine and the Multilayer Perceptron neural network trained with the Backpropagation algorithm. The model that presented the best performance to classify the length of stay in ICU beds was the Random Forest and to classify the length of stay of ward beds was the Gradient Boosting

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Machine learning techniques in cardiac risk assessment

Cited by 7 publications

References 11 publications

Machine learning model-based risk prediction of severe complications after off-pump coronary artery bypass grafting

Machine learning model-based risk prediction of severe complications after off-pump coronary artery bypass grafting

Heat and moisture exchanger used in a cardiothoracic surgery intensive care unit: Airway resistance and changing interval

Aprendizado de máquina para classificação da desocupação de leitos pós-cirúrgicos: Aprendizado de máquina para classificação da desocupação de leitos pós-cirúrgicos

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