Association of Post-Hospitalization Vascular Biomarker Clusters with Future Heart Failure
Bibliographic record
Abstract
Background: Individual vascular biomarkers helped elucidate the connections between acute kidney injury (AKI) and future heart failure (HF). The role of combined vascular biomarkers in recently discharged patients with risk of future hospitalizations with HF is unknown. Methods: Using the ASSESS-AKI cohort, we performed an unsupervised spectral cluster analysis with 9 plasma biomarkers measured at 3 months post-hospitalization [Angiopoietin (angpt)-1, angpt-2, vascular endothelial growth factor (VEGF)-A, VEGF-C, VEGF-d, VEGF receptor 1 (R1), solubleTie-2 (sTie-2), placental growth factor (PlGF), and basic fibroblast growth factor (bFGF)] in 1,497 patients, half of whom had AKI. We used a Cox regression analysis to evaluate the associations between clusters and future hospitalizations with HF. Models were adjusted for demographics, cardiovascular disease risk factors, medications, ICU status, lung disease, sepsis, serum creatinine, proteinuria, and admission center. Results: 3 biomarker-derived clusters were identified: Cluster 1 [n=302, Vascular Injury (VI) phenotype] had higher levels of vessel injury markers, whereas Cluster 2 [n=728, Vascular Repair (VR) phenotype] had higher levels of vessel repair markers. Cluster 3 (n=467) had lower levels of both repair and injury markers (dormant phenotype). The median time to HF was 4.7 years (IQR: 2.93-5.93). Participants with the VI phenotype were twice as likely to have a HF event [aHR 2.23 (1.56, 3.18)] compared to the VR phenotype. The dormant phenotype was also significantly associated with HF [1.98 (1.22, 3.21)] compared to the VR phenotype. AKI was a significant effect modifier for the relationship between clusters and HF with an interaction P-value of <0.01. Among those with AKI, the relationship between the VI phenotype and HF was significant [aHR 2.13 (95%CI: 1.36-3.36)]. Conclusions: Vascular biomarkers can be used to derive clusters to risk-stratify patients for future HF events. Vascular panels may be used to tailor post-AKI follow-up to minimize risk of future HF. Funding: NIDDK Support
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".