Predicting the risk of hyperkalemia in patients with chronic kidney disease starting lisinopril
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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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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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 it