Practical Risk Stratification Score for Prediction of Contrast-Induced Nephropathy After Primary Percutaneous Coronary Intervention in Patients With Acute ST-Segment Elevation Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Contrast-induced nephropathy (CIN) is a common complication of percutaneous coronary intervention (PCI). This study aimed to develop a new risk stratification score that is simpler and more practical than the standard Mehran risk score (MRS) in prediction of CIN after primary PCI in ST-segment elevation myocardial infarction (STEMI) patients. METHODS: A prognostic prediction research with clinical risk score development was conducted. All STEMI patients who underwent primary PCI at the Central Chest Institute from June 1, 2017 to June 30, 2018 were included. Multivariable logistic regression analysis was used to identify independent predictors of CIN with a significant P value < 0.05. Logistic coefficients of each predictor were used for score weighting and transformation. Predictive performance was validated and compared between newly-derived risk score and the MRS by non-parametric receiver operating characteristic (ROC) regression. RESULTS: A total of 217 patients, 43 (19.8%) with CIN and 174 (80.2%) without CIN, were included for score derivation. A total of 13 potential predictors were explored under multivariable logistic regression model and were subsequently eliminated. The new risk score was based on three final predictors which were ejection fraction of less than 40%, triple-vessel disease as findings from angiogram, and the use of intra-aortic balloon pump (IABP). With only three predictor variables, the score predicted the risk of CIN with good discriminative ability (area under the receiver operating characteristic curve (AuROC): 0.83, 95% confidence interval (CI): 0.76 - 0.90) which was higher than that of the MRS (AuROC: 0.78, 95% CI: 0.69 - 0.87). The score was categorized into low-risk (positive predictive value (PPV): 9.9, 95% CI: 5.4 - 14.4) and high-risk (PPV: 56.5, 95% CI: 42.4 - 70.8) groups at the cut-off point of 2. CONCLUSIONS: The newly developed score was proved to have good predictive performance with fewer numbers of predictors and could be practically applied for risk stratification of CIN in STEMI patients who required emergent primary PCI.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".