scholarly journals Adding Pre-procedural Glycemia to the Mehran Score Enhances Its Ability to Predict Contrast-induced Acute Kidney Injury in Patients With and Without Diabetes Undergoing Percutaneous Coronary Intervention

2020 ◽  
Author(s):  
Annunziata Nusca ◽  
Fabio Mangiacapra ◽  
Alessandro Sticchi ◽  
Giovanni Polizzi ◽  
Giulia D’Acunto ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Percutaneous Coronary Intervention ◽  
Risk Model ◽  
Validation Cohort ◽  
Kidney Injury ◽  
Predictive Ability ◽  
Coronary Intervention ◽  
Derivation Cohort ◽  
Diabetic Status ◽  
Percutaneous Coronary

Abstract Background: The Mehran score is the most widely accepted tool for predicting contrast-induced acute kidney injury (CI-AKI), a major complication of percutaneous coronary intervention (PCI). Similarly, abnormal fasting pre-procedural glycemia (FPG) represents a modifiable risk factor for CI-AKI, but it is not included in current risk models for CI-AKI prediction. We sought to analyze whether adding FPG to the Mehran score improves its ability to predict CI-AKI following PCI.Methods: We analyzed 671 consecutive patients undergoing PCI (age 69 [63,75] years, 23% females), regardless of their diabetic status, to derive a revised Mehran score obtained by including FPG in the original Mehran score (Derivation Cohort). The new risk model (GlyMehr) was externally validated in 673 consecutive patients (Validation Cohort) (age 69 [62,76] years, 21% females). Results: In the Derivation Cohort, both FPG and the original Mehran score predicted CI-AKI (AUC 0.703 and 0.673, respectively). The GlyMehr score showed a better predictive ability when compared with the Mehran score both in the Derivation Cohort (AUC 0.749, 95%CI 0.662-0.836; p=0.0016) and the Validation Cohort (AUC 0.848, 95%CI, 0.792–0.903; p=0.0008). In the overall population (n=1344), the GlyMehr score confirmed its independent and incremental predictive ability regardless of diabetic status (p≤0.0034) or unstable/stable coronary syndromes (p≤0.0272). Conclusions: Adding FPG to the Mehran score significantly enhances our ability to predict CI-AKI. The GlyMehr score may contribute to improve the clinical management of patients undergoing PCI by identifying those at high risk of CI-AKI and potentially detecting modifiable risk factors.

scholarly journals Machine learning prediction model of acute kidney injury after percutaneous coronary intervention

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Toshiki Kuno ◽  
Takahisa Mikami ◽  
Yuki Sahashi ◽  
Yohei Numasawa ◽  
Masahiro Suzuki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Kidney Injury ◽  
Percutaneous Coronary Intervention ◽  
Logistic Regression ◽  
Risk Model ◽  
Kidney Injury ◽  
Coronary Intervention ◽  
Percutaneous Coronary ◽  
St Elevation

AbstractAcute kidney injury (AKI) after percutaneous coronary intervention (PCI) is associated with a significant risk of morbidity and mortality. The traditional risk model provided by the National Cardiovascular Data Registry (NCDR) is useful for predicting the preprocedural risk of AKI, although the scoring system requires a number of clinical contents. We sought to examine whether machine learning (ML) techniques could predict AKI with fewer NCDR-AKI risk model variables within a comparable PCI database in Japan. We evaluated 19,222 consecutive patients undergoing PCI between 2008 and 2019 in a Japanese multicenter registry. AKI was defined as an absolute or a relative increase in serum creatinine of 0.3 mg/dL or 50%. The data were split into training (N = 16,644; 2008–2017) and testing datasets (N = 2578; 2017–2019). The area under the curve (AUC) was calculated using the light gradient boosting model (GBM) with selected variables by Lasso and SHapley Additive exPlanations (SHAP) methods among 12 traditional variables, excluding the use of an intra-aortic balloon pump, since its use was considered operator-dependent. The incidence of AKI was 9.4% in the cohort. Lasso and SHAP methods demonstrated that seven variables (age, eGFR, preprocedural hemoglobin, ST-elevation myocardial infarction, non-ST-elevation myocardial infarction/unstable angina, heart failure symptoms, and cardiogenic shock) were pertinent. AUC calculated by the light GBM with seven variables had a performance similar to that of the conventional logistic regression prediction model that included 12 variables (light GBM, AUC [training/testing datasets]: 0.779/0.772; logistic regression, AUC [training/testing datasets]: 0.797/0.755). The AKI risk model after PCI using ML enabled adequate risk quantification with fewer variables. ML techniques may aid in enhancing the international use of validated risk models.

Abstract 110: A Novel Method for Estimating the Optimal Contrast Amount Needed to Minimize Acute Kidney Injury After Percutaneous Coronary Intervention

2016 ◽  
Vol 9 (suppl_2) ◽  
Author(s):  
Ali Shafiq ◽  
Yashashwi Pokarel ◽  
Mohammed Qintar ◽  
Kevin Kennedy ◽  
John A Spertus ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Percutaneous Coronary Intervention ◽  
Risk Reduction ◽  
Risk Model ◽  
Kidney Injury ◽  
Coronary Intervention ◽  
Baseline Risk ◽  
Number Needed To Treat ◽  
Average Risk ◽  
Percutaneous Coronary

Introduction: Risk models are the foundation of personalized medicine. Converting risk into clinically actionable strategies to improve care, however, can be difficult. Acute kidney injury (AKI) after percutaneous coronary intervention (PCI) occurs in 7% of cases and contrast use has a linear relationship with risk. As the amount of contrast use is readily modifiable, we developed a novel approach to convert the National Cardiovascular Data Registry (NCDR) AKI risk model into an actionable guide for defining ‘safe contrast limits’. Methods: Because some patients are at low risk for AKI and the amount of contrast does not markedly increase their absolute risk, we reasoned that only patients with an above average pre-PCI risk (>7%) for AKI would be targets for contrast minimization. Providers could then define the magnitude of risk reduction they wanted to achieve (e.g. 5%, 10% or 15%). Given this target, we were able to back-calculate the safe contrast limits to achieve the respective magnitude of risk reductions. Results: 25% of patients were estimated to have above average risk (>7%) for developing AKI after PCI. The safe contrast limits for alternative magnitudes of relative risk reduction (RRR), across the range of patients’ pre-procedural risk, and the percentage of patients are shown (Figure-A). The number needed to treat (NNT) to prevent 1 AKI episode was <50 for patients with baseline risk of ≥14% for a 15% RRR and for patients with baseline risk of ≥21% for a 10% RRR, however it was never <50 for a 5% RRR (Figure-B). Conclusion: We were able to convert a complex prediction model into a clinically-actionable guide for prospectively improving treatment. Testing whether prospective targets of safe contrast limits (achieved through avoiding left ventriculograms, staging multi-vessel PCI or using bare wire lesions/injection minimization) could improve safety and costs should be prospectively tested.

scholarly journals Nutritional status and risk of contrast-associated acute kidney injury in elderly patients undergoing percutaneous coronary intervention

2021 ◽  
Author(s):  
Xiaoqi Wei ◽  
Hanchuan Chen ◽  
Zhebin You ◽  
Jie Yang ◽  
Haoming He ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Percutaneous Coronary Intervention ◽  
Nutritional Status ◽  
Elderly Patients ◽  
Kidney Injury ◽  
Coronary Intervention ◽  
Severe Malnutrition ◽  
Percutaneous Coronary ◽  
Conut Score

Abstract Background This study aimed to investigate the connection between malnutrition evaluated by the Controlling Nutritional Status (CONUT) score and the risk of contrast-associated acute kidney injury (CA-AKI) in elderly patients who underwent percutaneous coronary intervention (PCI). Methods A total of 1308 patients aged over 75 years undergoing PCI was included. Based on the CONUT score, patients were assigned to normal (0–1), mild malnutrition (2–4), moderate-severe malnutrition group (≥ 5). The primary outcome was CA-AKI (an absolute increase in ≥ 0.3 mg/dL or ≥ 50% relative serum creatinine increase 48 h after contrast medium exposure). Results Overall, the incidence of CA-AKI in normal, mild, moderate-severe malnutrition group was 10.8%, 11.0%, and 27.2%, respectively (p < 0.01). Compared with moderate-severe malnutrition group, the normal group and the mild malnutrition group showed significant lower risk of CA-AKI in models adjusting for risk factors for CA-AKI and variables in univariate analysis (odds ratio [OR] = 0.48, 95% confidence interval [CI]: 0.26–0.89, p = 0.02; OR = 0.46, 95%CI: 0.26–0.82, p = 0.009, respectively). Furthermore, the relationship were consistent across the subgroups classified by risk factors for CA-AKI except anemia. The risk of CA-AKI related with CONUT score was stronger in patients with anemia. (overall interaction p by CONUT score = 0.012). Conclusion Moderate-severe malnutrition is associated with higher risk of CA-AKI in elderly patients undergoing PCI.

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