Predictors of Hyperkalemia among Patients on Maintenance Hemodialysis Transported to the Emergency Department by Ambulance
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it