Development, evaluation and validation of a screening tool for late onset bacteremia in neonates – a pilot study
Bibliographic record
Abstract
BACKGROUND: Clinical and laboratory parameters can aid in the early identification of neonates at risk for bacteremia before clinical deterioration occurs. However, current prediction models have poor diagnostic capabilities. The objective of this study was to develop, evaluate and validate a screening tool for late onset (> 72 h post admission) neonatal bacteremia using common laboratory and clinical parameters; and determine its predictive value in the identification of bacteremia. METHODS: A retrospective chart review of neonates admitted to a neonatal intensive care unit (NICU) between March 1, 2012 and January 14, 2015 and a prospective evaluation of all neonates admitted between January 15, 2015 and March 30, 2015 were completed. Neonates with late-onset bacteremia (> 72 h after NICU admission) were eligible for inclusion in the bacteremic cohort. Bacteremic patients were matched to non-infected controls on several demographic parameters. A Pearson's Correlation matrix was completed to identify independent variables significantly associated with infection (p < 0.05, univariate analysis). Significant parameters were analyzed using iterative binary logistic regression to identify the simplest significant model (p < 0.05). The predictive value of the model was assessed and the optimal probability cut-off for bacteremia was determined using a Receiver Operating Characteristic curve. RESULTS: Maximum blood glucose, heart rate, neutrophils and bands were identified as the best predictors of bacteremia in a significant binary logistic regression model. The model's sensitivity, specificity and accuracy were 90, 80 and 85%, respectively, with a false positive rate of 20% and a false negative rate of 9.7%. At the study bacteremia prevalence rate of 51%, the positive predictive value, negative predictive value and negative post-test probability were 82, 89 and 11%, respectively. CONCLUSION: The model developed in the current study is superior to currently published neonatal bacteremia screening tools. Validation of the tool in a historic data set of neonates from our institution will be completed.
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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 | 0.001 | 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".