Development and validation of a screening tool for early identification of bloodstream infection in older patients – a retrospective case-control study
Bibliographic record
Abstract
BACKGROUND: Delayed diagnosis of bloodstream infection (BSI) occurs in > 20% of older patients, with misdiagnosis in 35%. Our objective was to develop and validate a clinically useful screening tool to identify older patients with a high probability of having a BSI. METHODS: Hospitalized patients > 80 years old with BSI (n = 105/group) were evaluated for the tool development in this retrospective matched case-controlled study (learn cohort). The tool was validated in different retrospectively matched case and control patients > 80 years old (n = 120/group) and 65 to 79 years old (n = 250/group) (test cohort). Binary logistic regression was used to develop a screening tool using laboratory and clinical parameters that were significantly associated with BSI (P < 0.05; adjusted odds ratio (OR) > 1); and Classification and Regression Tree (CART) analysis was used to identify parameter breakpoints. Performance metrics were used to evaluate and validate the tool. RESULTS: The significant parameters associated with BSI were maximum temperature (Tmax)(> 37.55C)(OR = 42.575), neutrophils (> 7.95)(OR = 1.923), a change in level of consciousness (LOC) (Yes = 1, No = 0)(OR = 1.571), blood urea nitrogen (BUN)(> 10.05)(OR = 1.359), glucose (> 7.35)(OR = 1.167), albumin (< 33.5)(OR = 1.038) and alanine aminotransferase (ALT) (> 19.5)(OR = 1.005). The optimal screening tool [Ln (odds of BSI) = - 150.299 + 3.751(Tmax) + 0.654(neutrophils) + 0.452(change in LOC) + 0.307(BUN) + 0.154(glucose) + 0.038(albumin) + 0.005(ALT)] had favorable performance metrics in the learn and test cohorts (sensitivity, specificity and accuracy of 95% in the learn cohort and 77, 89, and 81% in the total test cohort); and performed better than using only temperature and neutrophil count. CONCLUSIONS: The validated tool had high predictive value which may improve early identification and management of BSI in older patients.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".