Molecular and clinical signatures in Acute Kidney Injury define distinct subphenotypes that associate with death, kidney, and cardiovascular events
Bibliographic record
Abstract
Abstract Introduction AKI is a heterogeneous syndrome defined via serum creatinine and urine output criteria. However, these markers are insufficient to capture the biological complexity of AKI and not necessarily inform on future risk of kidney and clinical events. Methods Data from ASSESS-AKI was obtained and analyzed to uncover different clinical and biological signatures within AKI. We utilized a set of unsupervised machine learning algorithms incorporating a comprehensive panel of systemic and organ-specific biomarkers of inflammation, injury, and repair/health integrated into electronic data. Furthermore, the association of these novel biomarker-enriched subphenotypes with kidney and cardiovascular events and death was determined. Clinical and biomarker concentration differences among subphenotypes were evaluated via classic statistics. Kaplan-Meier and cumulative incidence curves were obtained to evaluate longitudinal outcomes. Results Among 1538 patients from ASSESS-AKI, we included 748 AKI patients in the analysis. The median follow-up time was 4.8 years. We discovered 4 subphenotypes via unsupervised learning. Patients with AKI subphenotype 1 (‘injury’ cluster) were older (mean age ± SD): 71.2 ± 9.4 (p<0.001), with high ICU admission rates (93.9%, p<0.001) and highly prevalent cardiovascular disease (71.8%, p<0.001). They were characterized by the highest levels of KIM-1, troponin T, and ST2 compared to other clusters (P<0.001). AKI subphenotype 2 (‘benign’ cluster) is comprised of relatively young individuals with the lowest prevalence of comorbidities and highest levels of systemic anti-inflammatory makers (IL-13). AKI Subphenotype 3 (‘chronic inflammation and low injury’) comprised patients with markedly high pro-BNP, TNFR1, and TNFR2 concentrations while presenting low concentrations of KIM-1 and NGAL. Patients with AKI subphenotype 4 (‘inflammation-injury’) were predominantly critically ill individuals with the highest prevalence of sepsis and stage 3 AKI. They had the highest systemic (IL-1B, CRP, IL-8) and kidney inflammatory biomarker activity (YKL-40, MCP-1) as well as high kidney injury levels (NGAL, KIM-1). AKI subphenotype 3 and 4 were independently associated with a higher risk of death compared to subphenotype 2. Moreover, subphenotype 3 was independently associated with CKD outcomes and CVD events. Conclusion We discovered four clinically meaningful AKI subphenotypes with statistical differences in biomarker composites that associate with longitudinal risks of adverse clinical events. Our approach is a novel look at the potential mechanisms underlying AKI and the putative role of biomarkers investigation.
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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 | 0.002 |
| 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) | 0.001 | 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".