A Clinical Nomogram Based on the Triglyceride-Glucose Index to Predict Contrast-Induced Acute Kidney Injury after Percutaneous Intervention in Patients with Acute Coronary Syndrome with Diabetes Mellitus

Hu, Yue; Wang, Xiaotong; Xiao, Shengjue; Sun, Na; Huan, Chunyan; Wu, Huimin; Guo, Minjia; Xu, Tao; Pan, Defeng

doi:10.1155/2022/5443880

Cited by 6 publications

(15 citation statements)

References 46 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Additionally, our results revealed that hyperuricemia constituted a significant risk factor for CIAKI in diabetes. A recent study from China proved that hyperuricemia was associated with CIAKI (OR = 2.363, 95% CI 1.653–3.377, P < 0.001) [ 40 ]. What’s more, it was also shown that patients with uric acid levels above 8.0 mg/dL not only had a greater risk of CIAKI but also an increased risk of hemodialysis [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study

Ma,

Wan,

Chen

et al. 2023

J Transl Med

View full text Add to dashboard Cite

Background In patients undergoing percutaneous coronary intervention (PCI), contrast-induced acute kidney injury (CIAKI) is a frequent complication, especially in diabetics, and is connected with severe mortality and morbidity in the short and long term. Therefore, we aimed to develop a CIAKI predictive model for diabetic patients. Methods 3514 patients with diabetes from four hospitals were separated into three cohorts: training, internal validation, and external validation. We developed six machine learning (ML) algorithms models: random forest (RF), gradient-boosted decision trees (GBDT), logistic regression (LR), least absolute shrinkage and selection operator with LR, extreme gradient boosting trees (XGBT), and support vector machine (SVM). The area under the receiver operating characteristic curve (AUC) of ML models was compared to the prior score model, and developed a brief CIAKI prediction model for diabetes (BCPMD). We also validated BCPMD model on the prospective cohort of 172 patients from one of the hospitals. To explain the prediction model, the shapley additive explanations (SHAP) approach was used. Results In the six ML models, XGBT performed best in the cohort of internal (AUC: 0.816 (95% CI 0.777–0.853)) and external validation (AUC: 0.816 (95% CI 0.770–0.861)), and we determined the top 15 important predictors in XGBT model as BCPMD model variables. The features of BCPMD included acute coronary syndromes (ACS), urine protein level, diuretics, left ventricular ejection fraction (LVEF) (%), hemoglobin (g/L), congestive heart failure (CHF), stable Angina, uric acid (umol/L), preoperative diastolic blood pressure (DBP) (mmHg), contrast volumes (mL), albumin (g/L), baseline creatinine (umol/L), vessels of coronary artery disease, glucose (mmol/L) and diabetes history (yrs). Then, we validated BCPMD in the cohort of internal validation (AUC: 0.819 (95% CI 0.783–0.855)), the cohort of external validation (AUC: 0.805 (95% CI 0.755–0.850)) and the cohort of prospective validation (AUC: 0.801 (95% CI 0.688–0.887)). SHAP was constructed to provide personalized interpretation for each patient. Our model also has been developed into an online web risk calculator. MissForest was used to handle the missing values of the calculator. Conclusion We developed a novel risk calculator for CIAKI in diabetes based on the ML model, which can help clinicians achieve real-time prediction and explainable clinical decisions.

show abstract

Section: Discussionmentioning

confidence: 99%

A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study

Ma,

Wan,

Chen

et al. 2023

J Transl Med

View full text Add to dashboard Cite

show abstract

“…Studies have suggested that the TyG index could serve as a predictive marker for cardiovascular risk such as coronary artery disease, heart attack, and stroke (9,10,12). In addition, some studies have explored the potential connection between the TyG index and nephropathy, particularly in diabetic nephropathy (13)(14)(15)(16)(17). A higher TyG index, indicative of insulin resistance, can contribute to inflammation, oxidative stress, and vascular dysfunction (9,15,18).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, some studies have explored the potential connection between the TyG index and nephropathy, particularly in diabetic nephropathy (13)(14)(15)(16)(17). A higher TyG index, indicative of insulin resistance, can contribute to inflammation, oxidative stress, and vascular dysfunction (9,15,18). In addition, the TyG index exhibits a positive correlation with a high risk of developing diabetic nephropathy and other renalrelated complications (15,17).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnostic efficacy of the triglyceride–glucose index in the prediction of contrast-induced nephropathy following percutaneous coronary intervention

Chang,

Liu,

Huang

et al. 2023

Front. Endocrinol.

View full text Add to dashboard Cite

IntroductionContrast-induced nephropathy (CIN) is a common complication of percutaneous coronary intervention (PCI). Identifying patients at high CIN risk remains challenging. The triglyceride-glucose (TyG) index may help predict CIN but evidence is limited. We conducted a meta-analysis to evaluate the diagnostic value of TyG index for CIN after PCI.MethodsA systematic literature search was performed in MEDLINE, Cochrane, and EMBASE until August 2023 (PROSPERO registration: CRD42023452257). Observational studies examining TyG index for predicting CIN risk in PCI patients were included. This diagnostic meta-analysis aimed to evaluate the accuracy of the TyG index in predicting the likelihood of CIN. Secondary outcomes aimed to assess the pooled incidence of CIN and the association between an elevated TyG index and the risk of CIN.ResultsFive studies (Turkey, n=2; China, n=3) with 3518 patients (age range: 57.6 to 68.22 years) were included. The pooled incidence of CIN was 15.3% [95% confidence interval (CI) 11-20.8%]. A high TyG index associated with increased CIN risk (odds ratio: 2.25, 95% CI 1.82-2.77). Pooled sensitivity and specificity were 0.77 (95% CI 0.59-0.88) and 0.55 (95% CI 0.43-0.68) respectively. Analysis of the summary receiver operating characteristic (sROC) curve revealed an area under the curve of 0.69 (95% CI 0.65-0.73). There was a low risk of publication bias (p = 0.81).ConclusionThe TyG index displayed a noteworthy correlation with the risk of CIN subsequent to PCI. However, its overall diagnostic accuracy was found to be moderate in nature. While promising, the TyG index should not be used in isolation for CIN screening given the heterogeneity between studies. In addition, the findings cannot be considered conclusive given the scarcity of data. Further large-scale studies are warranted to validate TyG cutoffs and determine how to optimally incorporate it into current risk prediction models.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023452257, identifier CRD42023452257.

show abstract

“…The score established by Mehran et al was widely used in clinics[8,9]. In recent years, due to the advent of big data, many models have been built based on machine learning methods [10][11][12]. However, there are still some challenges in the application of these models.…”

mentioning

confidence: 99%

Development and validation of Machine Learning Predictive Model for Contrast-associated Acute Kidney Injury in Patients with Acute Coronary Syndrom

Ma,

Chen,

Guo

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Patients with acute coronary syndrome (ACS) often need coronary angiography (CAG). After CAG, patients with ACS may develop contrast-associated acute kidney injury (CA-AKI). However, effective preventive measures are lacking in treatment for CA-AKI. Therefore, an efficient interpretable predictive model of CA-AKI is crucial. Methods We enrolled 1013 ACS patients who received percutaneous coronary intervention or coronary angiography in Sir Run Run Hospital, Nanjing Medical University, and Nanjing First Hospital from September 2020 to December 2021. To screen features, the sliding windows sequential forward feature selection technique (SWSFS) was used. The model was built using five machine learning (ML) algorithms: logical regression (LR), random forest (RF), support vector machines (SVM), extreme gradient boosting (XGBT), and ensemble model (ENS). We evaluated predictive performance by comparing the model with Mehran score. The model features were explained through shapley additive explanations (SHAP) and a web-based calculator was built. Results CA-AKI occurred in 215 patients (21.27%). In the training set, SWSFS identified 15 variables. The top 5 variables included diuretics, creatine phosphokinase MB isoenzyme, unstable angina, lactate dehydrogenase, and Triglycerides × Total Cholesterol × Body Weight Index (TCBI). Overall, ML models outperformed Mehran score. In the internal and external validation sets, the ENS model obtained the highest AUC of 0.828 (95%CI: 0.779 ~ 0.876) and 0.811 (95%CI: 0.750 ~ 0.867). SHAP explained the 15 selected features' importance and contribution. We also built a web-based calculator for clinical use. Conclusions Based on machine learning, 15 clinically accessible features were screened. The established model and the web-based calculator had the potential for real-time risk assessment of CA-AKI in clinical practice.

show abstract

A Clinical Nomogram Based on the Triglyceride-Glucose Index to Predict Contrast-Induced Acute Kidney Injury after Percutaneous Intervention in Patients with Acute Coronary Syndrome with Diabetes Mellitus

Cited by 6 publications

References 46 publications

A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study

A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study

Diagnostic efficacy of the triglyceride–glucose index in the prediction of contrast-induced nephropathy following percutaneous coronary intervention

Development and validation of Machine Learning Predictive Model for Contrast-associated Acute Kidney Injury in Patients with Acute Coronary Syndrom

Contact Info

Product

Resources

About