Short-Term Risk Estimation and Treatment Planning for Cardiovascular Disease Patients after First Diagnostic Catheterizations with Machine Learning Models (original) (raw)
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JACC: Cardiovascular Interventions, 2019
OBJECTIVES This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart failure (CHF) rehospitalization after percutaneous coronary intervention (PCI). BACKGROUND Contemporary risk models for event prediction after PCI have limited predictive ability. Machine learning has the potential to identify complex nonlinear patterns within datasets, improving the predictive power of models. METHODS We evaluated 11,709 distinct patients who underwent 14,349 PCIs between January 2004 and December 2013 in the Mayo Clinic PCI registry. Fifty-two demographic and clinical parameters known at the time of admission were used to predict in-hospital mortality and 358 additional variables available at discharge were examined to identify patients at risk for CHF readmission. For each event, we trained a random forest regression model (i.e., machine learning) to estimate the time-to-event. Eight-fold cross-validation was used to estimate model performance. We used the predicted time-to-event as a score, generated a receiver operating characteristic curve, and calculated the area under the curve (AUC). Model performance was then compared with a logistic regression model using pairwise comparisons of AUCs and calculation of net reclassification indices. RESULTS The predictive algorithm identified a high-risk cohort representing 2% of all patients who had an in-hospital mortality of 45.5% (95% confidence interval: 43.5% to 47.5%) compared with a risk of 2.1% for the general population (AUC: 0.925; 95% confidence interval: 0.92 to 0.93). Advancing age, CHF, and shock on presentation were the leading predictors for the outcome. A high-risk group representing 1% of all patients was identified with 30-day CHF rehospitalization of 8.1% (95% confidence interval: 6.3% to 10.2%). Random forest regression outperformed logistic regression for predicting 30-day CHF readmission (AUC: 0.90 vs. 0.85; p ¼ 0.003; net reclassification improvement: 5.14%) and 180-day cardiovascular death (AUC: 0.88 vs. 0.81; p ¼ 0.02; net reclassification improvement: 0.02%). CONCLUSIONS Random forest regression models (machine learning) were more predictive and discriminative than standard regression methods at identifying patients at risk for 180-day cardiovascular mortality and 30-day CHF rehospitalization, but not in-hospital mortality. Machine learning was effective at identifying subgroups at high risk for postprocedure mortality and readmission.
Machine learning algorithms for predicting mortality after coronary artery bypass grafting
Frontiers in Cardiovascular Medicine, 2022
BackgroundAs the era of big data analytics unfolds, machine learning (ML) might be a promising tool for predicting clinical outcomes. This study aimed to evaluate the predictive ability of ML models for estimating mortality after coronary artery bypass grafting (CABG).Materials and methodsVarious baseline and follow-up features were obtained from the CABG data registry, established in 2005 at Tehran Heart Center. After selecting key variables using the random forest method, prediction models were developed using: Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) algorithms. Area Under the Curve (AUC) and other indices were used to assess the performance.ResultsA total of 16,850 patients with isolated CABG (mean age: 67.34 ± 9.67 years) were included. Among them, 16,620 had one-year follow-up, from which 468 died. Eleven features were chosen to train the models. Total ventil...
Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events
Navigating Healthcare Through Challenging Times
Background: Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented with the right interventions. Objectives: The aim of this study was to develop machine learning-based models for the 5-year risk prediction of MACE. Methods: The data used for modelling included electronic medical records of more than 128,000 patients including 29,262 patients with MACE. A feature selection based on filter and embedded methods resulted in 826 features for modelling. Different machine learning methods were used for modelling on the training data. Results: A random forest model achieved the best calibration and discriminative performance on a separate test data set with an AUROC of 0.88. Conclusion: The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determ...
Trends in telemedicine & E-health, 2024
Background: Cardiovascular diseases, particularly Coronary Artery Disease (CAD), remain the leading cause of death worldwide, imposing significant health and economic burdens. It is crucial to emphasize early diagnosis of CAD to prevent complications and improve patient outcomes. This study aims to predict the likelihood of CAD recurrence within 6 months post-treatment. Methods: The Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective study. Predictive features include demographic data and laboratory test results. A 6-month CAD recurrence was set as the study outcome. We used the Machine Learning (ML) Methods Of Logistic Regression (LR), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to develop a predictive model for CAD recurrence. The prognostic capacity and clinical utility of these three models were compared using the Area Under the Receiver Operating Characteristic Curves (AUROC), precision, sensitivity, specificity, f1 measure and Area Under Precision-Recall (AUPR) curve. Results: Of 7,583 CAD patients in this study population, 2,361 (31%) had CAD recurrence during 6-month follow-up. Out of 38 features selected and extracted from the MIMIC III database, 15 variables were chosen using stepwise regression. The RF model performed best with an AUC of 0.83. The top 6 significant features in our model were platelet, WBC, RBC, INR, chloride, and creatinine. Conclusion: Our study shows that the random forest model outperforms the XGBoost and LR models in predicting CAD recurrence within 6 months post-treatment. The study suggests a connection between certain lab indices (platelet count, WBC, RBC, INR, chloride, calcium, creatinine) and CAD recurrence, bridging knowledge gaps and guiding future research on preventive strategies and treatments for CAD.
2017
Coronary Artery Bypass Graft (CABG) surgery is the most common cardiac operation and its complications are associated with increased long-term mortality rates. Although many factors are known to be linked to this, much remains to be understood about their exact influence on outcome. In this study we used Machine Learning (ML) algorithms to predict long-term mortality in CABG patients using data from routinely measured clinical parameters from a large cohort of CABG patients (n=5868). We compared the accuracy of 5 different ML models with traditional Cox and Logistic Regression, and report the most important variables in the best performing models. In the validation dataset, the Gradient Boosted Machine (GBM) algorithm was the most accurate (AUROC curve [95%CI] of 0.767 [0.739-0.796]), proving to be superior to traditional Cox and logistic regression (p <0.01) for long-term mortality prediction. Measures of variable importance for outcome prediction extracted from the GBM and Rand...
Machine Learning Predictive Models for Coronary Artery Disease
Sn Computer Science, 2021
Coronary artery disease (CAD) is the commonest type of heart disease and over 80% of the deaths resulted from the diseases occurred in developing countries including Nigeria, with majority being in those victims are below 70 years of age. Though, CAD is not a well known disease in Nigeria but however in year 2014, 2.82% of the total of deaths occurred in the country were due to the disease. In this study, a machine leaning predictive models for CAD has been developed with diagnostic CAD dataset obtained in the two General Hospitals in Kano State—Nigeria. The dataset applied on machine learning algorithms which include support vector machine, K nearest neighbor, random tree, Naïve Bayes, gradient boosting and logistic regression algorithms to build the predictive models and the models were evaluated based accuracy, specificity, sensitivity and receiver operating curve (ROC) performance evaluation techniques. In terms of accuracy random forest-based machine learning model emerged to b...
Journal of the American Heart Association
Background The ability to accurately predict the occurrence of in‐hospital death after percutaneous coronary intervention is important for clinical decision‐making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System in order to elucidate the determinants of in‐hospital mortality in patients undergoing percutaneous coronary intervention across New York State. Methods and Results We examined 479 804 patients undergoing percutaneous coronary intervention between 2004 and 2012, utilizing traditional and advanced machine learning algorithms to determine the most significant predictors of in‐hospital mortality. The entire data were randomly split into a training (80%) and a testing set (20%). Tuned hyperparameters were used to generate a trained model while the performance of the model was independently evaluated on the testing set after plotting a receiver‐operator characteristic curve and using the output measure of the area under the curve ( AUC ) and...
Cardio Vascular Disease (CVD) Risk Prediction using Supervised Learning
International Journal For Multidisciplinary Research
Our main goal is to develop a cardiovascular disease (CVD) risk prediction model using supervised learning classifiers that can be used in expert decision with maximum accuracy whether heart disease is present or not. It will prove to be very important to medicine for the diagnosis of heart diseases such as heart attack, heart failure, stroke and other cardiovascular diseases. If such predictions give good results with sufficient accuracy, we can not only avoid inaccurate diagnoses, but also save unnecessary resources. When a patient who does not have heart disease is diagnosed positively, he panics unnecessarily, and when a patient who does have heart disease and is neither diagnosed with heart disease nor has a negative result, he dies will involuntarily miss a chance to cure his illness. Such misdiagnosis is detrimental to both patients and hospitals. With more accurate predictions, we can overcome unnecessary problems.
European heart journal, 2016
Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings. Machine learning (ML) can consider a greater number and complexity of variables. Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics. The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry. All patients underwent CCTA as their standard of care. Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiov...
Supervised Machine Learning-Based Cardiovascular Disease Analysis and Prediction
Mathematical Problems in Engineering
Cardiovascular illness, often commonly known as heart disease, encompasses a variety of diseases that affect the heart and has been the leading cause of mortality globally in recent decades. It is associated with numerous risks for heart disease and a requirement of the moment to get accurate, trustworthy, and reasonable methods to establish an early diagnosis in order to accomplish early disease treatment. In the healthcare sector, data analysis is a widely utilized method for processing massive amounts of data. Researchers use a variety of statistical and machine learning methods to evaluate massive amounts of complicated medical data, assisting healthcare practitioners in predicting cardiac disease. This study covers many aspects of cardiac illness, as well as a model based on supervised learning techniques such as Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). It makes use of an existing dataset from the UCI Cleveland database of heart disease patients. Th...