Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study

Baskaran, Lohendran; Ying, Xiaohan; Xu, Zhuoran; Al’Aref, Subhi J.; Lee, Benjamin C.; Lee, Sang Eun; Danad, Ibrahim; Park, Hyung Bok; Bathina, Ravi; Baggiano, Andrea; Beltrama, Virginia; Cerci, Rodrigo; Choi, Eui Young; Choi, Jung Hyun; Choi, So Yeon; Cole, Jason; Doh, Joon Hyung; Ha, Sang Jin; Her, Ae Young; Kępka, Cezary; Kim, Jang Young; Kim, Jin Won; Kim, Sang Wook; Kim, Woong; Lu, Yao; Kumar, Amit; Heo, Ran; Lee, Ji Hyun; Sung, Ji Min; Valeti, Uma; Andreini, Daniele; Han, Donghee; Villines, Todd C.; Lin, Fay Y.; Chang, Hyuk Jae; Min, James K.; Shaw, Leslee J.

doi:10.1371/journal.pone.0233791

Cited by 18 publications

(13 citation statements)

References 39 publications

(50 reference statements)

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“…For example, Rong et al ( 22 ) observed that T-wave repolarization synchronization was an important factor for determining the presence of ischemic heart disease using the non-invasive XGBoost machine learning algorithm, and found the correlation between magnetic pole characteristics and cardiac ischemia. Localized ischemia provided an opportunity, and Baskaran et al ( 23 ) used machine learning to obtain insight into the role of images and clinical variables in predicting obstructive coronary artery disease and revascularization, while Tseng et al ( 24 ) determined the risks after cardiac surgery, which could optimize postoperative treatment strategies and minimize postoperative complications.…”

Section: Discussionmentioning

confidence: 99%

Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression

Han

Zhang

et al. 2022

Front. Cardiovasc. Med.

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BackgroundRheumatic heart disease (RHD) accounts for a large proportion of Intensive Care Unit (ICU) deaths. Early prediction of RHD can help with timely and appropriate treatment to improve survival outcomes, and the XGBoost machine learning technology can be used to identify predictive factors; however, its use has been limited in the past. We compared the performance of logistic regression and XGBoost in predicting hospital mortality among patients with RHD from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.MethodsThe patients with RHD in the MIMIC-IV database were divided into two groups retrospectively according to the availability of data and its clinical significance based on whether they survived or died. Backward stepwise regression was used to analyze the independent factors influencing patients with RHD, and to compare the differences between the two groups. The XGBoost algorithm and logistic regression were used to establish two prediction models, and the areas under the receiver operating characteristic curves (AUCs) and decision-curve analysis (DCA) were used to test and compare the models. Finally, DCA and the clinical impact curve (CIC) were used to validate the model.ResultsData on 1,634 patients with RHD were analyzed, comprising 207 who died during hospitalization and 1,427 survived. According to estimated results for the two models using AUCs [0.838 (95% confidence interval = 0.786–0.891) and 0.815 (95% confidence interval = 0.765–0.865)] and DCA, the logistic regression model performed better. DCA and CIC verified that the logistic regression model had convincing predictive value.ConclusionsWe used logistic regression analysis to establish a more meaningful prediction model for the final outcome of patients with RHD. This model might be clinically useful for patients with RHD and help clinicians to provide detailed treatments and precise management.

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Section: Discussionmentioning

confidence: 99%

Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression

Han

Zhang

et al. 2022

Front. Cardiovasc. Med.

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“…Also, no ML model is better than the others in all situations. Baskaran and colleagues, for instance, were successful in predicting obstructive coronary artery disease (AUC: 0.779) and revascularization (AUC: 0.958) from clinical and imaging data ( 44 ). Thus, it is usually interesting to analyze more than one model.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Filho

Fernandes

Wiefels

et al. 2021

Front. Cardiovasc. Med.

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Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.

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“…To date, the application of artificial intelligence has allowed satisfactory results to be achieved in the world of medicine, and a growing body of data is emerging [ 20 , 21 , 22 , 23 ], including COVID-19 research. [ 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer

Imbalzano

Orlando

Sciacqua

et al. 2021

JCM

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To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. Methods: We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 variables was analyzed. Data were randomly split into training (70%) and testing (30%) sets. Model performance was assessed by confusion matrix metrics on the testing data for each model as positive predictive value, sensitivity and F1-score. Results: We showed that the five selected models outperformed classical statistical methods of predictive validity and logistic regression was the most effective, being able to classify with an accuracy of 81%. The most relevant result was finding a patient-proof where python function was able to obtain the exact dose of low weight molecular heparin to be administered and thereby to prevent the occurrence of VTE. Conclusions: The world of machine learning and artificial intelligence is constantly developing. The identification of a specific LMWH dose for preventing VTE in very high-risk populations, such as the COVID-19 and active cancer population, might improve with the use of new training ML-based algorithms. Larger studies are needed to confirm our exploratory results.

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Machine learning insight into the role of imaging and clinical variables for the prediction of obstructive coronary artery disease and revascularization: An exploratory analysis of the CONSERVE study

Cited by 18 publications

References 39 publications

Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression

Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression

Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer

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