Onset time and prognostic value of acute kidney injury in patients with acute myocardial infarction.
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ABSTRACT: Background
The mechanisms and clinical impact of acute kidney injury (AKI) after acute myocardial infarction (AMI) may differ depending on whether AKI develops during the early or late phase after AMI. The present study assessed the timing of AKI onset and the prognostic impact on long-term outcomes in patients hospitalized with AMI.Methods
The present study enrolled consecutive AMI survivors who had undergone successful percutaneous coronary interventions at admission. AKI was defined as an increase in the serum creatinine level of ≥0.3 mg/dL above the admission value within 7 days of hospitalization. AKI patients were further divided into two subgroups (early-phase AKI: within 3 days vs. late-phase AKI: 4 to 7 days after AMI onset). The primary endpoint was all-cause death.Results
In total, 506 patients were included in this study, with 385 men and a mean age of 69.5 ± 13.5 years old. The mean follow-up duration was 1289.5 ± 902.8 days. AKI developed in 127 patients (25.1%). Long-term mortality was significantly higher in the AKI group than in the non-AKI group (log-rank p < 0.001). Early-phase AKI developed in 98 patients (19.3%), and late-phase AKI developed in 28 patients (5.5%). In the multivariable analysis, early-phase AKI was significantly associated with all-cause mortality (HR 2.83, 95% CI [1.51-5.29], p = 0.0012), while late-phase AKI was not.Conclusion
Early-phase AKI but not late-phase AKI was associated with poor long-term mortality. Careful clinical attention and intensive care are needed when AKI is observed within 3 days of AMI onset.
SUBMITTER: Kosaki R
PROVIDER: S-EPMC8233135 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:(1) Background: Patients with acute myocardial infarction (AMI) still experience many major adverse cardiovascular events (MACEs), including myocardial infarction, heart failure, kidney failure, coronary events, cerebrovascular events, and death. This retrospective study aims to assess the prognostic value of machine learning (ML) for the prediction of MACEs. (2) Methods: Five-hundred patients diagnosed with AMI and who had undergone successful percutaneous coronary intervention were included in the study. Logistic regression (LR) analysis was used to assess the relevance of MACEs and 24 selected clinical variables. Six ML models were developed with five-fold cross-validation in the training dataset and their ability to predict MACEs was compared to LR with the testing dataset. (3) Results: The MACE rate was calculated as 30.6% after a mean follow-up of 1.42 years. Killip classification (Killip IV vs. I class, odds ratio 4.386, 95% confidence interval 1.943-9.904), drug compliance (irregular vs. regular compliance, 3.06, 1.721-5.438), age (per year, 1.025, 1.006-1.044), and creatinine (1 µmol/L, 1.007, 1.002-1.012) and cholesterol levels (1 mmol/L, 0.708, 0.556-0.903) were independent predictors of MACEs. In the training dataset, the best performing model was the random forest (RDF) model with an area under the curve of (0.749, 0.644-0.853) and accuracy of (0.734, 0.647-0.820). In the testing dataset, the RDF showed the most significant survival difference (log-rank p = 0.017) in distinguishing patients with and without MACEs. (4) Conclusions: The RDF model has been identified as superior to other models for MACE prediction in this study. ML methods can be promising for improving optimal predictor selection and clinical outcomes in patients with AMI.
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Project description:Objective: To develop predictive models for contrast induced acute kidney injury (CI-AKI) among acute myocardial infarction (AMI) patients treated invasively. Methods: Patients with AMI who underwent angiography therapy were enrolled and randomly divided into training cohort (75%) and validation cohort (25%). Machine learning algorithms were used to construct predictive models for CI-AKI. The predictive models were tested in a validation cohort. Results: A total of 1,495 patients with AMI were included. Of all the patients, 226 (15.1%) cases developed CI-AKI. In the validation cohort, Random Forest (RF) model with top 15 variables reached an area under the curve (AUC) of 0.82 (95% CI: 0.76-0.87), while the best logistic model had an AUC of 0.69 (95% CI: 0.62-0.76). ACEF (age, creatinine, and ejection fraction) model reached an AUC of 0.62 (95% CI: 0.53-0.71). RF model with top 15 variables achieved a high recall rate of 71.9% and an accuracy of 73.5% in the validation group. Random Forest model significantly outperformed logistic regression in every comparison. Conclusions: Machine learning algorithms especially Random Forest algorithm improves the accuracy of risk stratifying patients with AMI and should be used to accurately identify the risk of CI-AKI in AMI patients.
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Project description:Acute kidney injury (AKI) predicts poor prognosis in patients with acute myocardial infarction (MI) and diabetes mellitus (DM) is an independent risk factor of AKI. Recent clinical studies have shown the beneficial effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors on cardiovascular and renal outcomes in patients with DM. We recently reported that canagliflozin normalized susceptibility of diabetic rats to AKI after acute MI via ?-hydroxybutyrate-mediated suppression of NOX expression. Here we examined whether the same renoprotective effect is shared by empagliflozin. Serum creatinine levels were not changed by MI induced by coronary artery occlusion in LETO, non-diabetic control rats, and OLETF, obese type 2 diabetic rats. However, immunohistochemistry revealed that MI increased renal expression of NGAL and KIM-1, early markers of tubular injury, by 3.2-fold and 2.6-fold, respectively, in OLETF. These increases in injury markers were not observed in LETO. Pretreatment with empagliflozin of OLETF for 2 weeks improved hyperglycemia, increased blood ?-hydroxybutyrate level, and suppressed MI-induced expression of NGAL and KIM-1. Empagliflozin suppressed upregulation of NOX2 and NOX4 in the kidney of OLETF. Taken together with the results of our previous study, it was concluded that treatment with the SGLT2 inhibitor protects the diabetic kidney from MI-induced AKI.
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