P-2239. RNA Signatures for Diagnosing or Predicting Febrile Illness: A Descriptive Analysis
Bibliographic record
Abstract
Abstract Background Fever is a common sign of illness; it may self-resolve or be present in serious illnesses requiring hospitalization. Investigating the cause of fever can be extremely resource intensive requiring multiple diagnostic tests and healthcare providers thereby delaying time to diagnosis. RNA molecules present in the blood can be easily detected and reflect a patient’s disease state, making them ideal biomarkers. Our objective was to identify RNA common to signatures that diagnose and predict the outcome of febrile illnesses. Our secondary objectives were to assess whether signatures are being validated and translated. Methods We conducted a scoping review of published research articles, including preprints, from EMBASE and Medline that measured blood RNA in patients with febrile illness. Here we are analyzing a articles that used array or RNA-seq to generate original signatures. Results We identified 248 original blood RNA signatures for the diagnosis or prognosis of febrile illness. 154 were diagnostic, 56 prognostic, 15 diagnostic/prognostic and 23 exploratory. The most common illnesses studied were sepsis (n=83), viral infections (n=74) and tuberculosis (n=38). From all sepsis signatures there were 214 unique RNA and 74 RNA present in multiple signatures. The most common RNA, C3AR1 (complement receptor) and MPO (myeloperoxidase), appeared in 5 signatures. For viral infections there were 54 signatures, and IFI27 (interferon) appeared in 8 signatures. For tuberculosis there were 26 signatures, and the most common RNA were BATF2 (transcription factor) and FCGR1B (Fc receptor) each appearing in 4 signatures. 158/248 signatures were validated. Clinical sensitivity and specificity in validation groups were 54-100% and 79-97%, respectively. 9/248 studies claimed that they had filed or were filing for a patent. Conclusion Blood RNA molecules can be used for the diagnosis and prognosis of febrile illnesses. Currently, < 65% of RNA signatures are validated and < 5% of signatures are commercialized. In conducting this analysis, we encountered a lack of standardization in methods reporting. Further work to improve transparency and encourage validation is needed so RNA signatures can be adapted into simple, molecular diagnostic tests and translated for patient use. Disclosures Shaun Morris, MD, MPH, DTM&H, FRCPC, FAAP, GSK Canada: Advisor/Consultant|GSK Canada: Honoraria|Pfizer: Advisor/Consultant|Pfizer: Honoraria|Sanofi Canada: Advisor/Consultant|Sanofi Canada: Honoraria
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".