Predicting the risk of hyperkalemia in patients with chronic kidney disease starting lisinopril
Bibliographic record
Abstract
PURPOSE: Angiotensin-converting enzyme (ACE) inhibitors are recommended for patients with chronic kidney disease (CKD) because they slow disease progression. But physicians' concerns about the risk of hyperkalemia (elevated serum potassium level), a potentially fatal adverse effect, may limit optimal management with ACE-inhibitors. We synthesized known predictors of hyperkalemia into a prognostic risk score to predict the risk of hyperkalemia. METHODS: We assembled a retrospective cohort of adult patients with possible CKD (at least one estimated glomerular filtration rate (eGFR) value less than 60 ml/min/1.73 m(2)) who started an ACE-inhibitor (i.e., incident users) between 1998 and 2006 at a health maintenance organization. We followed patients for hyperkalemia: (1) potassium value >5.5 mmol/L; or (2) diagnosis code for hyperkalemia. Cox regression synthesized a priori predictors recorded in the electronic medical record into a risk score. RESULTS: We followed 5171 patients and 145 experienced hyperkalemia, a 90-day risk of 2.8%. Predictors included: age, eGFR, diabetes, heart failure, potassium supplements, potassium-sparing diuretics, and a high dose for the ACE-inhibitor (lisinopril). The risk score separated high-risk patients (top quintile, observed risk of 6.9%) from low-risk patients (bottom quintile, observed risk of 0.7%). Predicted and observed risks agreed within 1% for each quintile. The risk increased gradually in relation to declining eGFR with no apparent threshold for contraindicating ACE-inhibitors. CONCLUSIONS: The risk score separated high-risk patients (who may need more intensive laboratory monitoring) from low-risk patients. The risk score should be validated in other populations before it is ready for use in clinical practice.
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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 | 0.001 | 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".