Predicting Time to Death After Withdrawal of Life-Sustaining Measures Using Vital Sign Variability: Derivation and Validation
Bibliographic record
Abstract
To develop a predictive model using vital sign (heart rate and arterial blood pressure) variability to predict time to death after withdrawal of life-supporting measures. DESIGN: Retrospective analysis of observational data prospectively collected as part of the Death Prediction and Physiology after Removal of Therapy study between May 1, 2014, and May 1, 2018. SETTING: Adult ICU. PATIENTS: Adult patients in the ICU with a planned withdrawal of life-supporting measures and an expectation of imminent death. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Vital sign waveforms and clinical data were prospectively collected from 429 patients enrolled from 20 ICUs across Canada, the Czech Republic, and the Netherlands. Vital sign variability metrics were calculated during the hour prior to withdrawal. Patients were randomly assigned to the derivation cohort (288 patients) or the validation cohort (141 patients), of which 103 and 54, respectively, were eligible for organ donation after circulatory death. Random survival forest models were developed to predict the probability of death within 30, 60, and 120 minutes following withdrawal using variability metrics, features from existing clinical models, and/or the physician's prediction of rapid death. A model employing variability metrics alone performed similarly to a model employing clinical features, whereas the combination of variability, clinical features, and physician's prediction achieved the highest area under the receiver operating characteristics curve of all models at 0.78 (0.7-0.86), 0.79 (0.71-0.87), and 0.8 (0.72-0.88) for 30-, 60- and 120-minute predictions, respectively. CONCLUSIONS: Machine learning models of vital sign variability data before withdrawal of life-sustaining measures, combined with clinical features and the physician's prediction, are useful to predict time to death. The impact of providing this information for decision support for organ donation merits further investigation.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".