Association of Intensive Care Unit Patient-to-Clinician Ratios with Mortality Across Two U.S. Health Systems
Bibliographic record
Abstract
Abstract Rationale The association of interprofessional team member workload with intensive care unit (ICU) outcomes is understudied. Objectives To evaluate the association of patient-to-intensivist ratio (PIR), patient-to-respiratory therapist ratio (PRTR), and patient–to–clinical pharmacist ratio (PpharmR) with hospital mortality. Methods We conducted a retrospective study of adults admitted from the emergency department to an ICU with acute respiratory failure or sepsis within two U.S. healthcare systems (2013–2018). Our primary exposures were patient-to-clinician ratios (PIR, PRTR, and PpharmR) averaged over the ICU stay; our primary outcome was hospital mortality. We used multivariable mixed-effects regression, with patient-to-clinician ratios modeled as restricted cubic splines (four knots). We primarily considered each exposure separately, then included all ratios together. Results Our cohort included 45,036 patients (mean age, 66.0 [standard deviation, 16.6] years; 23,420 [52.0%] men) across 27 ICUs within 24 hospitals. Of these, 29,326 (65.1%) had acute respiratory failure, 32,434 (72.0%) had sepsis, and 9,675 (21.5%) died in the hospital. The average PIR was 9.3 (standard deviation, 3.6), and the average PRTR was 7.9 (standard deviation, 3.2); the average PpharmR was 15.0 (standard deviation, 5.5) among patients (n = 8,950 of 45,036) in ICUs with clinical pharmacists (n = 8 of 27). We found no significant association between average daily PIR (Wald test for all spline terms: P = 0.24) or PRTR (P = 0.18) and hospital mortality in the full cohort; similarly, among patients in ICUs with pharmacists, no significant association of PpharmR with mortality was observed (P = 0.08). Models including ratios together yielded similar null results. Conclusions We did not identify an association of any average daily patient-to-clinician ratio with hospital mortality for U.S. ICU patients with sepsis or respiratory failure.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.002 | 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".