<p>Correlation of acidosis-adjusted potassium level and cardiovascular outcomes in diabetic ketoacidosis: a systematic review</p>
Bibliographic record
Abstract
BACKGROUND: During the progress and resolution of a diabetic ketoacidosis (DKA) episode, potassium levels are significantly affected by the extent of acidosis. However, none of the current guidelines take into account acidosis during resuscitation of potassium level in DKA management, which may increase the risk of cardiovascular adverse events. OBJECTIVE: To assess literature regarding the adjustment of potassium level using pH to calculate pH-adjusted corrected potassium level, and to observe the relationship of cardiovascular outcomes with reported potassium level and pH-adjusted corrected potassium in DKA. METHODOLOGY: Seven databases were searched from inception to January 2018 for studies which had reported people with diabetes developing diabetic ketoacidosis, in relation to prevalence or incidence, fluid resuscitation or potassium supplementation treatment, treatment or cardiovascular outcomes, and experimentation with DKA management or insulin. Quality of studies was evaluated using Cochrane Risk of Bias and Newcastle Ottawa Scale. RESULTS: Forty-seven studies were included in qualitative synthesis out of a total of 10,292 retrieved studies. Forty-one studies discussed the potassium level and blood pH at the time of admission, ten studies discussed cardiovascular outcomes, and only four studies concurrently discussed potassium level, pH, and cardiovascular outcomes. Only two studies were graded as good on the Newcastle Ottawa Scale. The reported potassium level was well within normal range (5.8 mmol/L), whereas pH rendered patients to be moderately acidotic (7.13). Surprisingly, none of the included studies mentioned pH-adjusted corrected potassium level and, hence, this was calculated later. Although mean corrected potassium was within the normal range (3.56 mmol/L), 13 studies had corrected potassium below 3.5 mmol/L and five had it below 3.0 mmol/L. Nevertheless, with the exception of one study, none discussed cardiovascular outcomes in the context of potassium or pH-adjusted potassium level. CONCLUSION: The evidence surrounding cardiovascular outcomes during DKA episodes in light of a pH-adjusted corrected potassium level is scarce. A prospective observational, or preferably, an experimental study in this regard will ensure we can modify and enhance safety of existing DKA treatment protocols.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".