Predictors of Hyperkalemia Among Chronic Hemodialysis Patients Transported to the Emergency Department
Bibliographic record
Abstract
Background: Chronic hemodialysis (HD) patients often present to the emergency department (ED) with hyperkalemia, which in turn, is associated with morbidity and mortality. In this study we sought to identify pre-hospital predictors of hyperkalemia in patients transported to the ED via ambulance (ambulance-ED). Methods: We analyzed all ambulance-ED transports in a cohort of chronic hemodialysis patients from 2014-2018 (using a province-wide emergency medical services database). The outcome was severe hyperkalemia using the first blood draw after ambulance-ED transport and defined as ≥6 mmol/L. Characteristics of interest included vital signs prior to transport, days from last dialysis and prehospital electrocardiograms (ECGs) interpreted by paramedics prior to transport. The association between prehospital factors and hyperkalemia was analyzed using adjusted logistic regression. Results: A total of 270 dialysis patients had 704 ambulance-ED transports followed by an ED potassium blood draw. Severe hyperkalemia occurred after 75 (11%) transports. In an adjusted parsimonious model (Table 1, N=609), age, dialysis vintage, bradycardia and days from last dialysis were associated with severe hyperkalemia. Among those with prehospital ECGs (N=377), presence of a prehospital ECG abnormality (i.e. peaked t-waves and/or first-degree atrioventricular block) was strongly associated with ED hyperkalemia (odds ratio 6.64, 95% confidence interval 2.31-19.12). Overall, 45% of hyperkalemic patients versus 24% of non-hyperkalemic patients required re-transport to another hospital to facilitate dialysis in a monitored setting after initial presentation. Conclusions: A longer interval from last dialysis and prehospital ECG changes are strongly associated with hyperkalemia after transport to the ED. Having an awareness of these associations may allow healthcare providers to define novel care pathways to ensure timely diagnosis and management of hyperkalemia. - Adjusted predictors of severe hyperkalemia after transport to the emergency department (n=609)* Adjusted OR OR 95% CI P Age at transport (each year) 0.97 0.95-0.99 0.009 Male Sex 1.14 0.61-2.15 0.681 Hemodialysis vintage (each year) 1.18 1.08-1.28 <0.001 Days from last HD relative to ED transport Same day but prior to ED transport Reference One 5.11 1.08-24.14 0.040 Two 18.49 4.99-68.49 <0.001 Three or more 24.43 5.48-109.04 <0.001 Heart Rate prior to ED transport 60-99 (beats/min) Reference <60 (beats/min) 3.15 1.05-9.45 0.040 ≥100 (beats/min) 0.83 0.43-1.58 0.562 OR, Odds ratio; CI, confidence intervals; HD, hemodialysis; ED emergency department*Included variables had a P<0.10 in univariable logistic regression analysis
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".