The association between arrival potassium and 30-day survival following resuscitation from out-of-hospital cardiac arrest: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: It is unknown whether there is an association between initial serum potassium level and short-term survival in out-of-hospital cardiac arrest (OHCA) survivors. The aim of this study was to describe potential associations between first recorded potassium level and 30-day survival in patients surviving OHCA. METHODS: We identified 4,894 patients who had return of spontaneous circulation (ROSC) at hospital arrival, and a registered post-OHCA serum-potassium value, using Danish nationwide registry data from 2001-2019. Potassium values were divided into seven predefined levels: < 2.5, 2.5-2.9, 3.0-3.4, 3.5-4.6, 4.7-5.5, 5.5-6.0, > 6.0 mmol/L. Thirty-day survival was estimated using a multivariable Cox regression (reference normokalemia 3.5-4.6 mmol/L). The multivariable model included age, sex, Charlson comorbidity index (including chronic kidney disease), witnessed status, performance of bystander cardiopulmonary resuscitation (CPR) and first registered heart rhythm. RESULTS: Over the 30-day follow-up period, survival rates in the seven strata were as follows: 25 (51.0%), 119 (53.6%), 512 (65.4%), 1,631 (57.9%), 220 (32.7%), 34 (22.8%), and 46 (22.7%), respectively. Thirty-day survival was significantly lower for all groups with hyperkalemia compared with normokalemia: 4.7-5.5 mmol/L: (average risk ratio (RR): 0.72, 95% confidence interval (95% CI): 0.66-0.78); 5.5-6.0 mmol/L: (average RR: 0.60, 95% CI: 0.47-0.73); > 6.0 mmol/L: (average RR: 0.56, 95% CI: 0.46-0.66). Survival did not differ significantly in patients with hypokalemia compared with normokalemia. CONCLUSIONS: In OHCA survivors, hyperkalemia was associated with reduced 30-day survival compared with normokalemia, independent of age, sex, comorbidity burden and pre-hospital OHCA-characteristics. Conversely, hypokalemia was not associated with reduced 30-day 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".