Blood Cultures and Molecular Diagnostics in Intensive Care Units to Diagnose Sepsis: A Bayesian Latent Class Model Analysis
Bibliographic record
Abstract
BACKGROUND: Confirmation of sepsis by standard blood cultures (STD) is often inconclusive due to slow growth and low positivity. Molecular diagnostics (MOL) are faster and may have higher positivity, but test performance can be inaccurately estimated if STD methods are used as comparators. Bayesian latent class models (LCMs) can evaluate diagnostic methods when there is no "gold standard." Intensive care unit studies that have used LCMs to combine and compare STD and MOL method performance and estimate the prevalence of sepsis have not been described. PATIENTS AND METHODS: Results from an ICU sepsis study that used both tests simultaneously were analyzed. Bayesian LCMs combined prior prevalence of sepsis, prior diagnostic characteristics of the two methods, and the study results to estimate the posterior prevalence and diagnostic characteristics. Sensitivity analyses were performed using objective (published studies) and subjective (expert opinion) prior parameters. Positive predictive values (PPVs) of the prevalence of sepsis were estimated for all combinations of test results. RESULTS: The range of posterior estimates was: sepsis prevalence (0.38-0.88), sensitivities (STD: 0.2-0.35, MOL: 0.56-0.86), and specificities (STD: 0.87-0.99, MOL: 0.72-0.95). The PPV (sepsis) of both tests being positive was (0.72-0.99). CONCLUSION: LCMs combined two imperfect methods to estimate prevalence, PPV, and diagnostic characteristics. The posterior estimates (STD sensitivity < MOL and STD specificity > MOL) seem to reflect the clinical experience appropriately. The high PPV when both methods show positive results can be useful for ruling in disease. HOW TO CITE THIS ARTICLE: Sampath S, Baby J, Krishna B, Dendukuri N, Thomas T. Blood Cultures and Molecular Diagnostics in Intensive Care Units to Diagnose Sepsis: A Bayesian Latent Class Model Analysis. Indian J Crit Care Med 2021;25(12):1402-1407.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| 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".