Characteristics, Outcomes, and Cost Patterns of High-Cost Patients in the Intensive Care Unit
Bibliographic record
Abstract
Background . ICU care is costly, and there is a large variation in cost among patients. Methods . This is an observational study conducted at two ICUs in an academic centre. We compared the demographics, clinical data, and outcomes of the highest decile of patients by total costs, to the rest of the population. Results . A total of 7,849 patients were included. The high-cost group had a longer median ICU length of stay (26 versus 4 days,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>P</mml:mi><mml:mo><</mml:mo><mml:mn>0.001</mml:mn></mml:mrow></mml:math>) and amounted to 49% of total costs. In-hospital mortality was lower in the high-cost group (21.1% versus 28.4%,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>P</mml:mi><mml:mo><</mml:mo><mml:mn>0.001</mml:mn></mml:mrow></mml:math>). Fewer high-cost patients were discharged home (23.9% versus 45.2%,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>P</mml:mi><mml:mo><</mml:mo><mml:mn>0.001</mml:mn></mml:mrow></mml:math>), and a large proportion were transferred to long-term care (35.1% versus 12.1%,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:mi>P</mml:mi><mml:mo><</mml:mo><mml:mn>0.001</mml:mn></mml:mrow></mml:math>). Patients with younger age or a diagnosis of subarachnoid hemorrhage, acute respiratory failure, or complications of procedures were more likely to be high cost. Conclusions . High-cost users utilized half of the total costs. While cost is associated with LOS, other drivers include younger age or admission for respiratory failure, subarachnoid hemorrhage, or after a procedural complication. Cost-reduction interventions should incorporate strategies to optimize critical care use among these patients.
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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 | 0.017 |
| 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".