Determinants of In‐Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach

Al’Aref, Subhi J.; Singh, Gurpreet; Rosendael, Alexander R. van; Kolli, Kranthi K.; Ma, Xiaoyue; Maliakal, Gabriel; Pandey, Mohit; Lee, Bejamin C.; Wang, Jing; Xu, Zhuoran; Zhang, Yiye; Min, James K.; Wong, S. Chiu; Minutello, Robert M.

doi:10.1161/jaha.118.011160

Cited by 65 publications

(49 citation statements)

References 38 publications

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“…Vertical lines were drawn at the value selected using tenfold cross-validation, where optimal λ resulted in 14 non-zero coefficients additional risks, but this time from the primary condition that can recur or complicate because of insufficient treatment. The previous models have several disadvantages and do not allow for a proper personalization of treatments [16][17][18][19][20][21][22][23][24]. Nevertheless, how the model determined in the present study can be used to personalize the treatments remains to be explored.…”

Section: Discussionmentioning

confidence: 99%

“…Primary PCI is one of the first-line therapeutic strategies for acute STEMI, and increasing evidence suggests that primary PCI can improve the prognosis of AMI [ 10 – 13 ]. Nevertheless, the mortality risk is still high, especially in patients with AMI complicated by cardiogenic shock and malignant arrhythmia, despite the use of other management modalities such as intra-aortic balloon pump (IABP), percutaneous cardiopulmonary support (PCPs), and other mechanical auxiliary devices [ 14 – 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Gao

Dang

et al. 2020

BMC Cardiovasc Disord

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Background Currently, how to accurately determine the patient prognosis after a percutaneous coronary intervention (PCI) remains unclear and may vary among populations, hospitals, and datasets. The aim of this study was to establish a prediction model of in-hospital mortality risk after primary PCI in patients with acute ST-elevated myocardial infarction (STEMI). Methods This was a multicenter, observational study of patients with acute STEMI who underwent primary PCI. The outcome was in-hospital mortality. The least absolute shrinkage and selection operator (LASSO) method was used to select the features that were the most significantly associated with the outcome. A regression model was built using the selected variables to select the significant predictors of mortality. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results Totally, 1169 and 316 patients were enrolled in the training and validation sets, respectively. Fourteen predictors were identified by the LASSO analysis: sex, Killip classification, left main coronary artery disease (LMCAD), grading of thrombus, TIMI classification, slow flow, application of IABP, administration of β-blocker, ACEI/ARB, symptom-to-door time (SDT), symptom-to-balloon time (SBT), syntax score, left ventricular ejection fraction (LVEF), and CK-MB peak. The mortality risk prediction nomogram achieved good discrimination for in-hospital mortality (training set: C-statistic = 0.987; model calibration: P = 0.722; validation set: C-statistic = 0.984, model calibration: P = 0.669). Area under the curve (AUC) values for the training and validation sets are 0.987 (95% CI: 0.981–0.994, P = 0.003) and 0.990 (95% CI: 0.987–0.998, P = 0.007), respectively. DCA shows that the nomogram can achieve good net benefit. Conclusions A novel nomogram was developed and is a simple and accurate tool for predicting the risk of in-hospital mortality in patients with acute STEMI who underwent primary PCI.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Gao

Dang

et al. 2020

BMC Cardiovasc Disord

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“…Machine learning applied to medical records, in particular, can be an effective tool both to predict the survival of each patient having heart failure symptoms [18,19], and to detect the most important clinical features (or risk factors) that may lead to heart failure [20,21]. Scientists can take advantage of machine learning not only for clinical prediction [22,23], but also for feature ranking [24].…”

Section: Introductionmentioning

confidence: 99%

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Chicco

Jurman

2020

BMC Med Inform Decis Mak

381

171

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Background: Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body. Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. Methods: In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. Results: Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. Conclusions: This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.

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“…Importantly, supervised learning is a method that learns from labeled data, i.e., data with outcome information to develop a prediction model, while unsupervised learning aims to find patterns and association rules in data that do not have labels ( Figure 4). Regardless of the choice of the algorithms, it is imperative to perform hyperparameter tuning and model regularization to produce the optimal performance (65,66). These processes may be more important than selecting the types of algorithms that could impact the interpretability, simplicity, and accuracy.…”

Section: Selection Of Machine Learning Modelsmentioning

confidence: 99%

Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist

Sengupta

Shrestha

Berthon

et al. 2020

JACC: Cardiovascular Imaging

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155

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Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for general cardiologists. The current document builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. The document thus aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying seven sets of requirements that may reduce algorithmic errors and biases. Finally, the document summarizes a list of reporting items as an itemized checklist that highlight steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.

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Determinants of In‐Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach

Cited by 65 publications

References 38 publications

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Establishment and validation of a risk model for prediction of in-hospital mortality in patients with acute ST-elevation myocardial infarction after primary PCI

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist

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