Low free triiodothyronine is associated with contrast-induced acute kidney injury and long-term outcome in elderly patients who underwent percutaneous coronary intervention

Urinary neutrophil gelatinase-associated lipocalin predicted to contrast-associated acute kidney injury after planned percutaneous coronary intervention in elderly patients

AIM

To determine the proportion of contrast-associated acute kidney injury (CA-AKI) after percutaneous coronary intervention (PCI) and the predictive value of urine neutrophil gelatinase-associated lipocalin (uNGAL) for CA-AKI in elderly patients with chronic coronary artery disease.

METHODS

A total of 509 patients who had planned percutaneous coronary intervention (mean age was 63.58 ± 11.63 years and 63.3% of males) were divided into two groups: group 1 (n = 153; elderly patients) with ≥70 years old and group 2 (n = 356) with <70 years old. Urine NGAL was measured by the ELISA method. Clinical and laboratory data were collected on the day before intervention. CA-AKI was defined based on Kidney Disease: Improving Global Outcomes criteria.

RESULTS

The ratio of CA-AKI in group 1 was 23.5% which was higher than that of group 2 (8.7%) with a p-value < 0.001. Urine NGAL level in group 1 was significantly higher than that of group 2 [31.3 (19.16-55.13) ng/ml vs. 19.86 (13.21-29.04) ng/ml, p < 0.001]. At a cut-off value of 44.43 ng/ml, uNGAL had a predictive value for CA-AKI in all patients (AUC = 0.977, p < 0.001). Especially at a cut-off value of 44.14 ng/ml, uNGAL had a predictive value for CA-AKI in elderly patients (AUC = 0.979, p < 0.001).

CONCLUSIONS

The rate of CA-AKI after PCI in elderly patients was 23.5%. Urine NGAL before PCI had a good predictive value for CA-AKI in elderly patients with chronic coronary artery disease.

Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction

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.

Negative association between free triiodothyronine level and contrast-induced acute kidney injury in patients undergoing primary percutaneous coronary intervention

BACKGROUND

A low FT3 level is significantly associated with a variety of kidney disease and acute myocardial infarction (AMI). However, it remains unclear whether low FT3 is associated with CI-AKI in patients who underwent pPCI.

METHODS

Single-center retrospective study evaluated 363 STEMI patients undergoing pPCI. Patients were classfied into 2 groups, low FT3 group (FT3 < 3.1 pmol/L) and normal FT3 group (FT3 ≥ 3.1 pmol/L);CI-AKI was defined as an increase in the serum creatinine levels of ≥50% or 0.3 mg/dL above the baseline level within 48 h after contrast medium exposure.

RESULTS

Overall, 80(22.0%) patients had low FT3, and 59(16.3%) patients developed CI-AKI. The incidence of CI-AKI and in-hospital mortality was significantly higher in patients with low FT3 than normal (31.3% vs 12.0%; 15.0% vs 3.2%, respectively, both p < 0.0001). Multivariate logistic regression analysis indicated that low FT3 was an independent predictor of CI-AKI (odds ratio [OR] = 2.62, 95%CI:1.35-5.07, p < 0.05). In addition, low FT3 was associated with an increased risk of all-cause mortality during a mean follow-up period of 20 months (hazard ratio [HR] = 2.54, 95%CI:1.15-5.60, p < 0.05).

CONCLUSION

Low FT3 was associated with CI-AKI, short- and long-term mortality in STEMI patients after pPCI.