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-other | 2019 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

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

Similar Datasets

Project description:BACKGROUND:Among 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. METHODS:We 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. RESULTS:Participants' 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). CONCLUSIONS:Acute 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.

Project description:Background The commercial coronary computed tomographic angiography artificial intelligence (CCTA-AI) platform has made great progress in clinical application. However, research is needed to elucidate the current stage of commercial AI platforms and the role of radiologists. This study compared the diagnostic performance of the commercial CCTA-AI platform with that of a reader based on a multicenter and multidevice sample. Methods A total of 318 patients with suspected coronary artery disease (CAD) who underwent both CCTA and invasive coronary angiography (ICA) were included in a multicenter and multidevice validation cohort between 2017 and 2021. The commercial CCTA-AI platform was used to automatically assess coronary artery stenosis by using ICA findings as the gold standard. The CCTA reader was completed by radiologists. The diagnostic performance of the commercial CCTA-AI platform and CCTA reader was evaluated at the patient and segment levels. The cutoff values of models 1 and 2 were 50% and 70% stenosis, respectively. Results It took 20.4 seconds to accomplish post-processing per patient when using the CCTA-AI platform, which was significantly shorter than the time taken to complete this task with the CCTA reader (1,112.1 s). In the patient-based analysis, the area under the curve (AUC) was 0.85 using the CCTA-AI platform and 0.61 using the CCTA reader in model 1 (stenosis ratio: 50%). In contrast, the AUC was 0.78 using the CCTA-AI platform and 0.64 using the CCTA reader in model 2 (stenosis ratio: 70%). In the segment-based analysis, the AUCs of CCTA-AI were slightly better than those of the readers. The negative predictive value (NPV) increased from model 1 to model 2. Furthermore, the diagnostic performance was better for larger-diameter arteries. Conclusions The commercial CCTA-AI platform may provide a feasible solution for the diagnosis of coronary artery stenosis, and it has a diagnostic performance that is slightly better than that of a radiologist with a moderate level of experience (5–10 years of experience).

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets