Risk Stratification in Patients with Coronary Artery Disease: a Practical Walkthrough in the Landscape of Prognostic Risk Models

Buccheri, Sergio; D’Arrigo, Paolo; Franchina, Gabriele; Capodanno, Davide

doi:10.15420/icr.2018.16.2

Cited by 23 publications

(15 citation statements)

References 67 publications

(60 reference statements)

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“…A recent deep-learning machine analysis was used to create a nomogram for in-hospital mortality, in which age and ejection fraction were the major predictors [ 16 ]. A recent review presented the main models used for the prognosis of STEMI [ 44 ]. Taken together, those models and the present one globally used the same variables but with different weighted values.…”

Section: Discussionmentioning

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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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%

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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“…Impaired HRQoL leads to poor health outcomes in the form of morbidity or mortality. 4 Cardiac rehabilitation has been adopted in the cardiac institute to deal with post-ACS patients’ physical and mental well-being. Many clinical studies 5 , 6 has proven the effectiveness of cardiac rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

Mobile Health Augmented Cardiac Rehabilitation (MCard) in Post-Acute Coronary Syndrome Patients: A randomised controlled trial protocol

Hisam¹,

Haq

Khan

et al. 2021

Pak J Med Sci

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Objectives: To determine the effectiveness of mobile health augmented cardiac rehabilitation (MCard) on health-related quality of life (HRQoL), clinical and behavioural outcomes in post-ACS. Methods: A single-centre, single-blinded, two-arm randomised controlled trial is planned at Armed Forces Institute of Cardiology (AFIC), Pakistan. The duration was two years, that is from January 2019 till December 2020. A total of 160 participants were recruited and randomly allocated to the control group or the intervention group. Intervention is a mobile health augmented cardiac rehabilitation (MCard), a medically supervised cardiac rehabilitation program for 23-24 weeks. The phase one includes individual counselling during the hospital stay and in phase two includes communication of standardised messages related to healthy lifestyle modification through a specifically designed software. Results: This clinical trial results will give insight into the impact of MCard in improving the health outcomes (HRQoL, clinical and behavioural) of participants. If proven to be effective, this technology can be scaled up and implemented in other cardiac centres in the country. It utilises fewer human resources and can be delivered at a lower cost. Conclusion: The study protocol will be giving evidence either MCard can contribute to improving the HRQoL, clinical and behavioural outcomes of post-ACS patients following hospital discharge. Considering the COVID-9 situation, this is the perfect time to implement and evaluate the effectiveness of MCard on health outcomes among post-ACS patients. doi: https://doi.org/10.12669/pjms.37.3.3664 How to cite this:Hisam A, Zia-Ul-Haq, Khan Z, Doherty P, Pell J. Mobile Health Augmented Cardiac Rehabilitation (MCard) In Post-Acute Coronary Syndrome Patients: A randomised controlled trial protocol. Pak J Med Sci. 2021;37(3):---------. doi: https://doi.org/10.12669/pjms.37.3.3664 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…Current risk stratification models for patients with ischemic heart disease are mainly based on patients with documented obstructive CAD, ranging from stable CAD to acute coronary syndrome [13]; however, a predictive model focused on patients with angina, including both obstructive and non-obstructive CAD, is still lacking. In addition, glucose perturbation represents a well-documented risk factor for atherosclerosis and is commonly seen in patients with ischemic heart disease [14].…”

Section: Introductionmentioning

confidence: 99%

Predicting Long-Term Mortality in Patients with Angina across the Spectrum of Dysglycemia: A Machine Learning Approach

Sheu

Yeh

et al. 2021

Diagnostics

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We aimed to develop and validate a model for predicting mortality in patients with angina across the spectrum of dysglycemia. A total of 1479 patients admitted for coronary angiography due to angina were enrolled. All-cause mortality served as the primary endpoint. The models were validated with five-fold cross validation to predict long-term mortality. The features selected by least absolute shrinkage and selection operator (LASSO) were age, heart rate, plasma glucose levels at 30 min and 120 min during an oral glucose tolerance test (OGTT), the use of angiotensin II receptor blockers, the use of diuretics, and smoking history. This best performing model was built using a random survival forest with selected features. It had a good discriminative ability (Harrell’s C-index: 0.829) and acceptable calibration (Brier score: 0.08) for predicting long-term mortality. Among patients with obstructive coronary artery disease confirmed by angiography, our model outperformed the Global Registry of Acute Coronary Events discharge score for mortality prediction (Harrell’s C-index: 0.829 vs. 0.739, p < 0.001). In conclusion, we developed a machine learning model to predict long-term mortality among patients with angina. With the integration of OGTT, the model could help to identify a high risk of mortality across the spectrum of dysglycemia.

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Risk Stratification in Patients with Coronary Artery Disease: a Practical Walkthrough in the Landscape of Prognostic Risk Models

Cited by 23 publications

References 67 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

Mobile Health Augmented Cardiac Rehabilitation (MCard) in Post-Acute Coronary Syndrome Patients: A randomised controlled trial protocol

Predicting Long-Term Mortality in Patients with Angina across the Spectrum of Dysglycemia: A Machine Learning Approach

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