Multicenter International Cohort Validation of a Modified Sequential Organ Failure Assessment Score Using the Richmond Agitation-sedation Scale
Bibliographic record
Abstract
OBJECTIVE: In a multicenter, international cohort, we aimed to validate a modified Sequential Organ Failure Assessment (mSOFA) using the Richmond Agitation-Sedation Scale, hypothesized as comparable to the Glasgow Coma Scale (GCS)-based Sequential Organ Failure Assessment (SOFA). SUMMARY BACKGROUND DATA: The SOFA score, whose neurologic component is based on the GCS, can predict intensive care unit (ICU) mortality. But, GCS is often missing in lieu of other assessments, such as the also reliable and validated Richmond Agitation Sedation Scale (RASS). Single-center data suggested an RASS-based SOFA (mSOFA) predicted ICU mortality. METHODS: Our nested cohort within the prospective 2016 Fourth International Study of Mechanical Ventilation contains 4120 ventilated patients with daily RASS and GCS assessments (20,023 patient-days, 32 countries). We estimated GCS from RASS via a proportional odds model without adjustment. ICU mortality logistic regression models and c-statistics were constructed using SOFA (measured GCS) and mSOFA (measured RASS-estimated GCS), adjusted for age, sex, body-mass index, region (Europe, USA-Canada, Latin America, Africa, Asia, Australia-New Zealand), and postoperative status (medical/surgical). RESULTS: Cohort-wide, the mean SOFA=9.4+/-2.8 and mean mSOFA = 10.0+/-2.3, with ICU mortality = 31%. Mean SOFA and mSOFA similarly predicted ICU mortality (SOFA: AUC = 0.784, 95% CI = 0.769-0.799; mSOFA: AUC = 0.778, 95% CI = 0.763-0.793, P = 0.139). Across models, other predictors of mortality included higher age, female sex, medical patient, and African region (all P < 0.001). CONCLUSIONS: We present the first SOFA modification with RASS in a "real-world" international cohort. Estimating GCS from RASS preserves predictive validity of SOFA to predict ICU mortality. Alternative neurologic measurements like RASS can be viably integrated into severity of illness scoring systems like SOFA.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".