Rates and predictors of ACE inhibitor discontinuation subsequent to elevated serum creatinine: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: ACE inhibitors (ACEI) are underutilised despite cardiovascular benefits, in part due to concerns of known transient elevations in serum creatinine (SCr) after initiation. Our objectives were to evaluate rates and predictors of ACEI discontinuation after SCr elevation post-ACEI initiation since limited data are available that examine this issue. SETTING: Primary and tertiary Veterans healthcare system in Los Angeles, California, USA PARTICIPANTS: 3039 outpatients initiating an ACEI with a SCr measured within 6 months prior to and approximately 3 months after initiating an ACEI. Patients were divided into three groups (SCr <1.5, 1.5–2 and >2). PRIMARY AND SECONDARY OUTCOME MEASURES: Rates and factors associated with ACEI discontinuation subsequent to SCr elevation after ACEI initiation and for patients with baseline SCr >2 mg/dL, the change in SCr associated with chronic use. Predictors were identified using multivariate logistic regression modelling. RESULTS: At 3 months follow-up, for those with an increase in SCr, the mean increase post-ACEI initiation was 26%, ranging from −0.01 mg/dL to 0.42 mg/dL varying according to a level of baseline renal function. ACEI discontinuation was higher in patients with elevated baseline SCr (19/165, 11.5%) compared with those with SCr <1.5 (135/2497, 5.4%), and those with SCr 1.5–2.0 (28/377, 7.4%). Male patients, and those with heart failure were less likely to discontinue ACEI after an elevation of SCr post-ACEI initiation, while those taking non-steroidal anti-inflammatory drugs, diuretics and β-blockers were more likely to discontinue ACEI. CONCLUSIONS: SCr increases <30% on average within 3 months of ACEI initiation, with subsequent discontinuation rates varying by baseline SCr. Elevation in SCr was not associated with ACEI discontinuation rates. In patients with SCr >2 mg/dL at baseline, despite an acute increase in SCr after ACEI initiation, chronic ACEI use was associated with a decrease in SCr in most patients.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".