Multiplex proteomics as risk predictor of infection in patients treated with hemodialysis—A prospective multicenter study
Bibliographic record
Abstract
INTRODUCTION: Severe infection is a major problem in hemodialysis patients. Multiplex proteomics might reveal novel insights into disease mechanisms increasing the risk of infection and might also be used as a risk prediction tool. The aims of this study were (1) to evaluate associations between 92 proteins assessed by a proximity extension assay and the development of severe infection in patients on hemodialysis and (2) to develop a risk prediction model for severe infection using prespecified clinical variables and proteomics. METHODS: Prospective, observational multicenter cohort study with 5-year follow-up. Patients receiving in-center hemodialysis in five facilities in Denmark were included. The primary composite endpoint was death caused by infection, bacteremia, and infections requiring hospitalization of at least 2 days or prolonging a hospital stay. FINDINGS: Of 331 patients included 210 patients reached the primary endpoint during follow-up. In adjusted Cox regression analyses, 14 plasma proteins were associated with severe infection. Correcting for multiple testing revealed only cathepsin-L1 and interleukin-6 significantly associated with the primary outcome. Cathepsin-L1-hazard ratio: 1.64 (95% confidence interval [CI] 1.24-2.17) and interleukin-6-hazard ratio: 1.16 (95% CI 1.05-1.29). Apparent C-statistics of the risk prediction model using clinical variables was 0.605, addition of cathepsin-L1 and interleukin-6 to the model improved discrimination slightly: C = 0.625. DISCUSSION: Proteomic profiling identified cathepsin-L1 and interleukin-6 as markers for infectious risk in hemodialysis patients. Further studies are needed to replicate the results and to examine possible causality. The developed risk prediction models need considerable improvement before implementation in clinical practice is meaningful.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| 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".