Response by Bascom and Seder to Letter Regarding Article, “Derivation and Validation of the CREST Model for Very Early Prediction of Circulatory Etiology Death in Patients Without ST-Segment–Elevation Myocardial Infarction After Cardiac Arrest”
Bibliographic record
Abstract
We appreciate the commentary by Dr Voicu et al about our recent manuscript 1 proposing a simple scoring system to assess the risk of circulatory etiology death after resuscitation from cardiac arrest.Identification of patients likely to die from circulatory etiology death is important, not only to identify those who may benefit from mechanical circulatory support, but also to select for early angiography and revascularization and to choose temperature targets, experimental neuroprotective measures, or other individualized treatments.Triage of resuscitated patients should be based on the competing risks of circulatory and neurological etiology death, maximizing the effectiveness of various treatments by offering them to the patients most likely to benefit.This early risk stratification also facilitates selection of appropriate subpopulations for clinical trials.We were interested to hear of work predicting circulatory etiology death after resuscitation using admission pH and shock. 2 Our study population differs in that we excluded patients with ST-segment-elevation myocardial infarction.In the United States the treatment pathway for such patients is established, 3 unlike those with VT/VF and presumed cardiac etiology but no ST-segment-elevation myocardial infarction.4 Additionally, their study included patients from a single center, likely treated under a single protocol, whereas ours involved 44 centers in multiple countries with associated variations in practices and outcomes that may have contributed to the lower c-statistic in our cohort.We acknowledge potential to strengthen our model by the addition of pH, which was not available in the cohort we evaluated.In previous studies, however, low pH at admission is associated with both circulatory and neurological death, 5 and may not help distinguish between these poor outcomes.Furthermore, the timing of pH measurement is important; an (unpublished) analysis at our center showed that persistent or worsening metabolic acidosis at 4 hours after resuscitation was an independent predictor of poor outcome, usually because of circulatory collapse, and performed better than admission pH, which tracked closely with duration of ischemia.The definition of shock used in our registry is derived from the American College of Cardiology (ACC) definition, and applied during the first 4 hours of admission.We agree that this reflects a severe phenotype, and is like the INTERMACS class 1 definition (Interagency Registry for Mechanically Assisted Circulatory Support), but we emphasize the assessment of competing risks before initiating mechanical circulatory support, as bleeding associated with advanced circulatory assist devices could outweigh potential benefits if the brain injury is severe.Clearly these competing risks are already being assessed informally at the bedside, as our cohort included no patients with shock and severe brain injury who received advanced circulatory support.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.031 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".