Adaptation and Validation of a Chart‐Based Delirium Detection Tool for the ICU (CHART‐DEL‐ICU)
Bibliographic record
Abstract
OBJECTIVE: To adapt and validate a chart-based delirium detection tool for use in critically ill adults. DESIGN: Validation study. SETTING: Medical-surgical intensive care unit (ICU) in an academic hospital. MEASUREMENTS: A chart-based delirium detection tool (CHART-DEL) was adapted for use in critically ill adults (CHART-DEL-ICU) and compared with prospective delirium assessments (i.e., clinical assessments (reference standard) by a research nurse trained by a neuropsychiatrist and routine delirium screening tools Confusion Assessment Method (CAM-ICU)) and (Intensive Care Delirium Screening Checklist (ICDSC)). The original CHART-DEL tool was adapted to include physician-reported ICDSC score (for probable delirium) and Richmond-Agitation Sedation Scale score (for altered level of consciousness and agitation). Two trained chart abstractors blinded to all delirium assessments manually abstracted delirium-related information from medical charts and electronic medical records and rated if delirium was present (four levels: uncertain, possible, probable, definite) or absent (no evidence). RESULTS: Charts were manually abstracted for delirium-related information for 213 patients who were included in a prospective cohort study that included prospective delirium assessments. The CHART-DEL-ICU tool had excellent interrater reliability (kappa = 0.90). Compared to the reference standard, the sensitivity was 66.0% (95% CI = 59.3-72.3%) and specificity was 82.1% (95% CI = 78.0-85.7%), with a cut-point that included definite, probable, possible, and uncertain delirium. The AUC of the CHART-DEL-ICU alone is 74.1% (95% CI = 70.4-77.8%) compared with the addition of the CAM-ICU and ICDSC (CAM-ICU/CHART-DEL-ICU: 80.9% (95% CI = 77.8-83.9%), P = .01; ICDSC/CHART-DEL-ICU: 79.2% (95% CI = 75.9-82.6%), P = .03). CONCLUSION: A chart-based delirium detection tool has improved diagnostic accuracy when combined with routine delirium screening tools (CAM-ICU and ICDSC), compared to a chart-based method on its own. This presents a potential for retrospective detection of delirium from medical charts for research or to augment routine delirium screening methods to find missed cases of delirium.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".