S15.3 Reducing the global burden of infectious diseases through precision infection management (PIM)
Bibliographic record
Abstract
<h3></h3> The global rise in the prevalence of antibiotic resistant bacteria is a problem so serious that it threatens all modern medicine. One major factor contributing to the problem is the limited information available to clinicians regarding the risks posed by individual strains of pathogens. Titrating clinical interventions according to these risks would enable the more judicious use of antibiotics, would reduce the time necessary to control serious infections, and would minimize antibiotic-associated treatment complications. We are developing a new Precision Infection Management (PIM) strategy for achieving these objectives. Our approach links the complete proteomic, metabolomic, and genomic sequences of 50,000 microbial isolates to birth-to-death medical records and integrates microbial risk-factors into a hand-held smartphone app for clinicians.50,000 isolates spanning two decades of clinical diagnostic work in southern Alberta are being cultured in 96-well format and DNA, protein, and metabolites are being extracted using an automated workflow. Full genome sequences are being collected by the Broad Institute, quantitative proteomics analyses are being acquired using a TMT11plex workflow, and metabolomics data are being acquired using our metabolic preference assay. De-identified patient data have been collected from Alberta Health Services and these records are being linked to each clinical isolate to enable microbe/clinical outcome association studies. All data are being stored on a new secure data hub, ResistanceDB, which supports complex multi-omics data mining, machine learning, and microbial risk assignment. We are currently establishing the microbiology, analytical, and informatics pipelines necessary to support the comprehensive analyses of 50,000 microbial isolates. We have recently launched our pilot ResistanceDB hub, and have collected genomics, proteomics, and metabolomics data on a pilot set of 1,000 clinically-linked microbial isolates. We have established automated data capture and archival systems to collect data from each of the ‘omics pipelines and have invested significant time in evaluating practical methods for analyzing thousands of microbial samples. We are currently using our computational resources to survey the current state of microbial populations and document their evolution over the last decade. Our preliminary analyses show a dramatic rise in the prevalence of resistant organisms over this time. We have developed several new visualization tools for projecting microbial population-level changes over time and we are currently working to understand how these microbial dynamics have affected patient outcome. Our quantitative proteomics methods are currently capturing over 1,000 microbial proteins, including most of the know virulence factors and our metabolomics assay is capturing more than 250 metabolites from a transect of central carbon metabolism. We are currently transitioning into scale-up of the program and are building the informatics tools that will ultimately allow our microbial risk scores to be made available to clinicians via a smart-phone enabled app. The primary objective of this work is to enable the precision management of infections using isolate-specific virulence data. This PIM approach will inform clinical decision-making and infection management practices in point-of-care settings resulting in, (1) a reduction in the number of people who develop life-threatening infections, (2) a reduction in the number of side effects that result from over-treating benign infections, and (3), an extended service-life for our existing antibiotics by dramatically reducing the over-use of these drugs. We introduce a new Precision Infection Management (PIM) strategy for titrating clinical care according to the risks posed by each individual infection. <h3>Disclosure</h3> No significant relationships.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".