Association between e-alert implementation for detection of acute kidney injury and outcomes: a systematic review
Bibliographic record
Abstract
Background: Electronic alerts (e-alerts) for acute kidney injury (AKI) in hospitalized patients are increasingly being implemented; however, their impact on outcomes remains uncertain. Methods: We performed a systematic review. Electronic databases and grey literature were searched for original studies published between 1990 and 2016. Randomized, quasi-randomized, observational and before-and-after studies that included hospitalized patients, implemented e-alerts for AKI and described their impact on one of care processes, patient-centred outcomes or resource utilization measures were included. Results: Our search yielded six studies ( n = 10 165 patients). E-alerts were generally automated, triggered through electronic health records and not linked to clinical decision support. In pooled analysis, e-alerts did not improve mortality [odds ratio (OR) 1.05; 95% confidence intervals (CI), 0.84-1.31; n = 3 studies; n = 3425 patients; I 2 = 0%] or reduce renal replacement therapy (RRT) use (OR 1.20; 95% CI, 0.91-1.57; n = 2 studies; n = 3236 patients; I 2 = 0%). Isolated studies reported improvements in selected care processes. Pooled analysis found no significant differences in prescribed fluid therapy. Conclusions: In the available studies, e-alerts for AKI do not improve survival or reduce RRT utilization. The impact of e-alerts on processes of care was variable. Additional research is needed to understand those aspects of e-alerts that are most likely to improve care processes and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".