The relationship of Charlson comorbidity index with stent restenosis and extent of coronary artery disease

Karabağ, Turgut; Altuntaş, Emine Elif; Kalaycı, Belma; Şahіn, Bahar; Somuncu, Mustafa Umut; Çakır, Mustafa

doi:10.1556/1646.10.2018.20

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Among the top predictors were features that represent traditional cardiovascular risk factors, such as age, male sex, and hypertension. The Charlson Comorbidity Index had high importance in both models; previously, it has been correlated with recurrence and mortality following acute coronary syndromes 56,57 , as well as anatomic severity of coronary artery disease 58 . Features such as increased serum glucose, which is associated with a higher risk in our models, may better assess IHD risk than the diagnosis of diabetes alone, a feature that was not salient among the SHAP analysis.…”

Section: Discussionmentioning

confidence: 95%

Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach

Zambrano Chaves,

Wentland,

Desai

et al. 2023

Sci Rep

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Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events—the leading cause of global mortality—have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient’s electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD.

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Section: Discussionmentioning

confidence: 95%

Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach

Zambrano Chaves,

Wentland,

Desai

et al. 2023

Sci Rep

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“…CCI has been extensively investigated in various clinical conditions and its significance as a prognostic indicator has been demonstrated. A previous study depicted that CCI was higher in patients with a more diffuse extent of CAD than those with milder disease [ 30 ]. Rashid et al indicated that CCI >2 significantly increased the risk of mortality in acute coronary syndrome [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization

Lin,

Ding,

et al. 2024

PLoS ONE

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Background To construct several prediction models for the risk of stroke in coronary artery disease (CAD) patients receiving coronary revascularization based on machine learning methods. Methods In total, 5757 CAD patients receiving coronary revascularization admitted to ICU in Medical Information Mart for Intensive Care IV (MIMIC-IV) were included in this cohort study. All the data were randomly split into the training set (n = 4029) and testing set (n = 1728) at 7:3. Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression model were applied for feature screening. Variables with Pearson correlation coefficient<9 were included, and the regression coefficients were set to 0. Features more closely related to the outcome were selected from the 10-fold cross-validation, and features with non-0 Coefficent were retained and included in the final model. The predictive values of the models were evaluated by sensitivity, specificity, area under the curve (AUC), accuracy, and 95% confidence interval (CI). Results The Catboost model presented the best predictive performance with the AUC of 0.831 (95%CI: 0.811–0.851) in the training set, and 0.760 (95%CI: 0.722–0.798) in the testing set. The AUC of the logistic regression model was 0.789 (95%CI: 0.764–0.814) in the training set and 0.731 (95%CI: 0.686–0.776) in the testing set. The results of Delong test revealed that the predictive value of the Catboost model was significantly higher than the logistic regression model (P<0.05). Charlson Comorbidity Index (CCI) was the most important variable associated with the risk of stroke in CAD patients receiving coronary revascularization. Conclusion The Catboost model was the optimal model for predicting the risk of stroke in CAD patients receiving coronary revascularization, which might provide a tool to quickly identify CAD patients who were at high risk of postoperative stroke.

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Predictive importance of comorbidity in patients with myocardial infarction and different strategy of treatment

Зыков

Кашталап

Полтаранина

et al. 2019

Medicinskij alfavit

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The aim of the study was to compare the 1-year results of conservative and invasive tactics of treating myocardial infarction (MI) in patients with different severity of comorbid background.Material and methods. The presented results are based on registry in City Hospital No. 4 of Sochi. The present analysis included patients with a diagnosis of myocardial infarction (n = 1 176). Upon discharge from the hospital, all patients underwent analysis of the severity of comorbidity using the Charlson Comorbidity Index (CCI). A year later, 791 patients managed to find out the prognosis after discharge from the hospital. 1-year mortality was 12.6 % (n = 100).Results. The frequency of coronary angiography (CA) and percutaneous coronary intervention (PCI) in patients with CCI = 0 (no or minimal comorbidity, n = 408) was 84.3 % and 63.5 %, with a CCI of 1–2 (moderate comorbidity), n = 438) was 68.0 % and 44.8 %, with CCI ≥ 3 (expressed comorbidity, n = 330) was 50.3 % and 25.8 %, respectively. At the same time, conducting PCI at the hospital observation stage was associated with a decrease mortality during the year after discharge from the hospital from 18.5 % to 5.8 %, p < 0.0001. The greatest positive effect of PCI for optimizing the long-term prognosis of (1 year) was achieved in the group of patients with severe comorbidity (CCI ≥ 3), where the NNT (number needed to treat) was 7. The relative risk of 1-year mortality in patients with severe comorbidity compared to the minimum in the PCI group was 6.75, in the conservative treatment group was 4.63.Conclusion. The results of this study showed that PCI in MI is more often performed by younger, less comorbid patients with a lower risk on the GRACE scale. At the same time, the 1-year survival of patients with MI was significantly higher after PCI compared to the primary conservative treatment strategy, regardless of the severity of comorbidity, and the greatest improvement in the long-term prognosis of PCI was observed in patients with severe comorbidity, as determined by the CCI ≥ 3.

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The relationship of Charlson comorbidity index with stent restenosis and extent of coronary artery disease

Cited by 4 publications

References 21 publications

Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach

Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach

Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization

Predictive importance of comorbidity in patients with myocardial infarction and different strategy of treatment

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