Predictors of Hyperkalemia among Patients on Maintenance Hemodialysis Transported to the Emergency Department by Ambulance
Bibliographic record
Abstract
Background: Hyperkalemia is common among patients on maintenance hemodialysis (HD) and is associated with mortality. We hypothesized that clinical characteristics available at time of paramedic assessment before emergency department (ED) ambulance transport (ambulance-ED) would associate with severe hyperkalemia (K≥6 mmol/L). Rapid identification of patients who are at risk for hyperkalemia and thereby hyperkalemia-associated complications may allow paramedics to intervene in a timely fashion, including directing emergency transport to dialysis-capable facilities. Methods: Patients on maintenance HD from a single paramedic provider region, who had at least one ambulance-ED and subsequent ED potassium from 2014 to 2018, were examined using multivariable logistic regression to create risk prediction models inclusive of prehospital vital signs, days from last dialysis, and the presence of prehospital electrocardiogram (ECG) features of hyperkalemia. We used bootstrapping with replacement to validate each model internally, and performance was assessed by discrimination and calibration. Results: Among 704 ambulance-ED visits, severe hyperkalemia occurred in 75 (11%); 26 patients with ED hyperkalemia did not have a prehospital ECG. Younger age at transport, longer HD vintage, more days from last hemodialysis session (OR=49.84; 95% CI, 7.72 to 321.77 for ≥3 days versus HD the same day [before] ED transport), and prehospital ECG changes (OR=6.64; 95% CI, 2.31 to 19.12) were independently associated with severe ED hyperkalemia. A model incorporating these factors had good discrimination (c-statistic 0.82; 95% CI, 0.76 to 0.89) and, using a cutoff of 25% probability, correctly classified patients 89% of the time. Conclusions: Characteristics available at the time of ambulance-ED were associated with severe ED hyperkalemia. An awareness of these associations may allow health care providers to define novel care pathways to ensure timely diagnosis and management of hyperkalemia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".