IGFBP7 as a preoperative predictor of congestive acute kidney injury after cardiac surgery
Bibliographic record
Abstract
OBJECTIVE: Congestive acute kidney injury (c-AKI) refers to AKI in the presence of right ventricular failure (RVF) and is a highly morbid complication of cardiac surgery. However, treatment has traditionally been reactive rather than proactive due to limited modalities to predict this complication. The objective of this study was to investigate the ability of insulin-like growth-factor binding protein 7 (IGFBP7), to predict c-AKI, AKI and RVF in patients undergoing cardiac surgery, as compared to N-terminal prohormone B-type natriuretic peptide (NT-pro-BNP) and pulmonary artery pulsatility index (PAPi). METHODS: This prospective nested case-control study consisted of 350 adult patients who underwent elective cardiac surgery. The outcomes were c-AKI, AKI and RVF. Unadjusted and adjusted conditional logistic regression models and areas under the receiver operating characteristic curve (AUC) were used to assess the predictive performance of each marker. RESULTS: For the prediction of c-AKI, the unadjusted IGPBP7 model had an AUC of 0.81, as compared with 0.51 for NT-pro-BNP and 0.61 for PAPi. The adjusted c-AKI models had AUCs of 0.90 for IGFBP7, 0.87 for NT-pro-BNP and 0.77 for PAPi. For AKI and RVF, the predictive performance of IGFBP7 was moderate and exceeded that of NT-pro-BNP and PAPi in univariable analysis. IGFBP7 remained a robust independent predictor of all outcomes in multivariable analysis, whereas the other markers did not. CONCLUSIONS: IGFBP7 is a promising biomarker for prediction of AKI, RVF and c-AKI and could have value for preoperative optimisation and risk stratification of patients undergoing cardiac surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".