Dataset Information

A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography.

ABSTRACT:

Background

Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI).

Hypothesis

Clinical and biomarker data can predict AKI more accurately.

Methods

Using Luminex xMAP technology, we measured 109 biomarkers in blood from 889 patients prior to undergoing coronary angiography. Procedural AKI was defined as an absolute increase in serum creatinine of ≥0.3 mg/dL, a percentage increase in serum creatinine of ≥50%, or a reduction in urine output (documented oliguria of <0.5 mL/kg per hour for >6 hours) within 7 days after contrast exposure. Clinical and biomarker predictors of AKI were identified using machine learning and a final prognostic model was developed with least absolute shrinkage and selection operator (LASSO).

Results

Forty-three (4.8%) patients developed procedural AKI. Six predictors were present in the final model: four (history of diabetes, blood urea nitrogen to creatinine ratio, C-reactive protein, and osteopontin) had a positive association with AKI risk, while two (CD5 antigen-like and Factor VII) had a negative association with AKI risk. The final model had a cross-validated area under the receiver operating characteristic curve (AUC) of 0.79 for predicting procedural AKI, and an in-sample AUC of 0.82 (P < 0.001). The optimal score cutoff had 77% sensitivity, 75% specificity, and a negative predictive value of 98% for procedural AKI. An elevated score was predictive of procedural AKI in all subjects (odds ratio = 9.87; P < 0.001).

Conclusions

We describe a clinical and proteomics-supported biomarker model with high accuracy for predicting procedural AKI in patients undergoing coronary angiography.

SUBMITTER: Ibrahim NE

PROVIDER: S-EPMC6712314 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography.

Ibrahim Nasrien E NE McCarthy Cian P CP Shrestha Shreya S Gaggin Hanna K HK Mukai Renata R Magaret Craig A CA Rhyne Rhonda F RF Januzzi James L JL

Clinical cardiology 20190108 2

<h4>Background</h4>Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI).<h4>Hypothesis</h4>Clinical and biomarker data can predict AKI more accurately.<h4>Methods</h4>Using Luminex xMAP technology, we measured 109 biomarkers in blood from 889 patients prior to undergoing coronary angiography. Procedural AKI was defined as an absolute increase in serum creatinine of ≥0.3 mg/dL, a percentage increase in serum creatinine of ≥50% ...[more]

PMID: 30582197

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Project description:BackgroundPrevious studies have shown that renal function recovery after acute kidney injury (AKI) was associated with decreased risk of all-cause mortality. However, little is known about the correlation between renal function recovery and long-term prognosis in patients with contrast-associated acute kidney injury (CA-AKI) undergoing coronary angiography (CAG).MethodsWe retrospectively enrolled 5,865 patients who underwent CAG. CA-AKI was defined as an increase in serum creatinine (SCr) ≥ 50% or ≥ 0.3 mg/dl from baseline within 72 h post procedure. Recovered CA-AKI was defined as a decrease in SCr to baseline or no CA-AKI level. The first endpoint was long-term all-cause mortality. Kaplan-Meier analysis and Cox regression analysis were used to investigate the association between kidney function recovery and long-term mortality.ResultsDuring the median follow-up period of 5.25 years, the overall long-term mortality was 20.07%, and the long-term mortality in patients with recovered CA-AKI and non-recovered CA-AKI was 17.46 and 27.44%, respectively. After multivariate Cox hazard regression, non-recovered CA-AKI was significantly associated with long-term mortality, while recovered CA-AKI was not [recovered CA-AKI vs. no CA-AKI, hazard ratio (HR) = 1.06, 95% confidence interval (CI): 0.81-1.39, p = 0.661; non-recovered CA-AKI vs. no CA-AKI, HR = 1.39, 95% CI: 1.21-1.60, p < 0.001]. In the subgroup of CAD, both recovered CA-AKI and non-recovered CA-AKI were associated with increased risk of long-term all-cause mortality. However, in other subgroup analyses, only non-recovered CA-AKI was associated with increased risk of long-term all-cause mortality.ConclusionOur results found that non-recovered CA-AKI is significantly associated with long-term mortality. In patients with CAD, recovered CA-AKI can still increase the risk of all-cause mortality. Clinicians need to pay more attention to patients suffering from CA-AKI, whose kidney function has not recovered. In addition, active prevention treatments should be taken by patients with CAD.

Project description:BackgroundAmong older adults (age ≥75 years) hospitalized for acute myocardial infarction, acute kidney injury after coronary angiography is common. Aging-related conditions may independently predict acute kidney injury, but have not yet been analyzed in large acute myocardial infarction cohorts.MethodsWe analyzed data from 2212 participants age ≥75 years in the Comprehensive Evaluation of Risk Factors in Older Patients with Acute Myocardial Infarction (SILVER-AMI) study who underwent coronary angiography. Acute kidney injury was defined using Kidney Disease Improving Global Outcomes (KDIGO) criteria (serum Cr increase ≥0.3 mg/dL from baseline or ≥1.5 times baseline). We analyzed the associations of traditional acute kidney injury risk factors and aging-related conditions (activities of daily living impairment, prior falls, cachexia, low physical activity) with acute kidney injury, and then performed logistic regression to identify independent predictors.ResultsParticipants' mean age was 81.3 years, 45.2% were female, and 9.5% were nonwhite; 421 (19.0%) experienced acute kidney injury. Comorbid diseases and aging-related conditions were both more common among individuals experiencing acute kidney injury. However, after multivariable adjustment, no aging-related conditions were retained. There were 11 risk factors in the final model; the strongest were heart failure on presentation (odds ratio [OR] 1.91; 95% confidence interval [CI], 1.41-2.59), body mass index [BMI] >30 (vs BMI 18-25: OR 1.75; 95% CI, 1.27-2.42), and nonwhite race (OR 1.65; 95% CI, 1.16-2.33). The final model achieved an area under the receiver operating characteristic curve of 0.72 and was well calibrated (Hosmer-Lemeshow P = .50). Acute kidney injury was independently associated with 6-month mortality (OR 1.98; 95% CI, 1.36-2.88) but not readmission (OR 1.26; 95% CI, 0.98-1.61).ConclusionsAcute kidney injury is common among older adults with acute myocardial infarction undergoing coronary angiography. Predictors largely mirrored those in previous studies of younger individuals, which suggests that geriatric conditions mediate their influence through other risk factors.

Dataset Information

A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography.

Background

Hypothesis

Methods

Results

Conclusions

Publications

A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography.

Similar Datasets

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