Abstract 15977: Utilizing Admission Bloodwork to Risk Stratify for Acute Kidney Injury in Hospitalized Patients With NSTEMI or Unstable Angina Undergoing Invasive Coronary Angiography± PCI
Bibliographic record
Abstract
Introduction: AKI after invasive coronary angiography (CAG) or PCI is common with an estimated incidence of 7-9%. This complication incurs approximately a $9,500 higher cost of stay, with proportional length of stay increases with absolute elevations in serum creatinine (sCr). While the majority of adverse events occur within 30 days of procedure, there is evidence of increased mortality, stroke, and MACE at 1 year and 5 years following index AKI. This study aimed to identify risk factors for AKI in hospitalized NSTEMI and unstable angina undergoing CAG ± PCI. Methods: Patients who were admitted for NSTEMI or UA who underwent CAG from 2011-2022 in Northeast Ohio Cleveland Clinic hospitals were identified from the EMR. Admission and serial blood work results in addition to demographics, past medical history, and hospital course were compiled and analyzed via multivariable logistic regression using R statistical software. KIDAGO definitions of AKI were utilized to define presence of AKI. All patients declared ESRD prior to admission were excluded from analysis. Results: 4,174 cases were included in the analysis with mean age 66.5 years, 63% male, and 81% Caucasian. 7% developed AKI and there was no difference in the proportion of race, gender, or prevalence of ischemic heart disease between those who developed AKI and those who did not. Patients who developed AKI were older (70.9 vs. 66.1 years, p<0.001), and were more likely to have a history of CHF (22.2 vs 10.1%, p<0.0001), CKD (26.3 vs. 9.5%, p<0.001), CVA (22.5 vs 15.3%, p=0.001), and hypertension (66.6 vs 59.4%, p=0.016). Multivariable logistic regression was performed on admission blood work in the presence of known risk factors for AKI, Table 1. Conclusions: In patients admitted with NSTEMI or UA who undergo CAG ± PCI, admission blood work helps identify patients at risk for AKI even in the presence of traditional risk factors such as IABP, age, and history of CKD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".