Timing of Onset, Burden, and Postdischarge Mortality of Persistent Critical Illness in Scotland, 2005–2014: A Retrospective, Population-Based, Observational Study
Bibliographic record
Abstract
We aimed to understand the prevalence, timing of onset, resource use, and long-term outcomes of patients who developed persistent critical illness in a national dataset. DESIGN: Retrospective cohort. Using a physiologic risk adjustment model from ICU admission, we examined the relative ability of acute (related to reason for ICU presentation) and antecedent (demographics, comorbidities) characteristics to discriminate hospital mortality models. Persistent critical illness was defined as the point during an ICU stay when, at the population-level, patients' acute diagnoses and physiologic disturbance are no longer more accurate at discriminating who survives than are baseline demographics and comorbidity. We examined the change across ICU stay in the relative discrimination of those characteristics, and short-term (in-hospital and 30 d after admission) and medium-term (90 d after admission) survival. Finally, we analyzed the changes in the population definition of persistent critical illness over time. SETTING: Patients admitted as level 3 to Scottish ICUs between 2005 and 2014. PATIENTS: Seventy-two-thousand two-hundred fifty-three adult level 3 ICU admissions in 23 ICUs across Scotland. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The onset of persistent critical illness, occurs at an average of 5.0 days (95% CI, 3.9-6.4 d) across this dataset. The crossing point increased across the decade, by an average of 0.36 days (95% CI, 0.22-0.50 d) per year. In this dataset, 24,425 (33.8%) remained in the ICU long enough to meet this greater than 5-day definition of persistent critical illness. The care of such patients involved 72.3% ICU days used by any level 3 patient; 46.5% of all Scottish ICU bed-days were after day 5. Although rates of 30 days after admission survival rose dramatically during the decade under study, these rates were similar for those with shorter or longer ICU stays, as were the rates of 90-day survival among those who survived at least 30 days. CONCLUSIONS: Persistent critical illness occurred in one in three ICU patients in Scotland. These minority of patients accounted for disproportionate hospital resources but did not have worse 30- or 90-day postadmission survival.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".