Diagnostic accuracy of delirium diagnosis in pediatric intensive care: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Delirium is common in adult intensive care, with validated tools for measurement, known risk factors and adverse neurocognitive outcomes. We aimed to determine what is known about pediatric delirium in the pediatric intensive care unit (PICU). METHODS: We conducted a systematic search for and review of studies of the accuracy of delirium diagnosis in children in the PICU. Secondary aims were to determine the prevalence, risk factors and outcomes associated with pediatric delirium. We created screening and data collection tools based on published recommendations. RESULTS: After screening 145 titles and abstracts, followed by 35 full-text publications and reference lists of included publications, 9 reports of 5 studies were included. Each of the five included studies was on a single index test: (1) the Pediatric Anesthesia Emergence Delirium Scale (PAED; for ages 1 to 17 years), (2) the Pediatric Confusion Assessment Method for the Intensive Care Unit (p-CAM-ICU; for ages ≥ 5 years), (3) the Cornell Assessment of Pediatric Delirium (CAP-D; a modification of the PAED designed to detect hypoactive delirium), (4) the revised Cornell Assessment of Pediatric Delirium (CAP-D(R)) and (5) clinical suspicion. We found that all five studies had a high risk of bias on at least one domain in the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Sample size, sensitivity, specificity, and effectiveness (correct classification divided by total tests done) were: PAED 144, 91%, 98%, <91% (>16% of scores required imputation for missing data); p-CAM 68, 78%, 98%, 96%; CAP-D 50, 91%, 100%, 89%; CAP-D (R) 111, and of assessments 94%, 79%, <82% (it is not clear if any assessments were not included); and clinical suspicion 877, N/A (only positive predictive value calculable, 66%). Prevalence of delirium was 17%, 13%, 28%, 21%, and 5% respectively. Only the clinical suspicion study researchers statistically determined any risk factors for delirium (pediatric risk of mortality, pediatric index of mortality, ventilation, age) or outcomes of delirium (length of stay and mortality). CONCLUSION: High-quality research to determine the accuracy of delirium screening tools in the PICU are required before prevalence, risk factors and outcomes can be determined and before a routine screening tool can be recommended. Direct comparisons of the p-CAM-ICU and CAP-D(R) should be performed.
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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 | 0.016 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".