Hypoglycemia and Cardiac Arrest in a Critically Ill Patient on Strict Glycemic Control
Bibliographic record
Abstract
In Response: Predefined inclusion and exclusion criteria characterize a study population. If renal failure, however defined, had been an a priori exclusion criterion, then Dr. Al-Ansari's assertion that this patient should have been excluded would have been quite correct (1). Renal failure was not an exclusion criterion in the study we conducted (2). On the other hand, if Dr. Al-Ansari is suggesting that renal failure should have been one of our study's exclusion criteria, we would have to ask why. When we designed the study, there was no reason to believe that tight glycemic control might be either less effective or possibly harmful in patients with renal dysfunction. On the contrary, data available at the time suggested that tight glycemic control reduced the incidence of renal failure (3). These data were subsequently corroborated by others (4,5). Moreover, renal dysfunction on admission to intensive care has not been an exclusion criterion in either of the two randomized studies published to date (3,5), and it is not an exclusion criterion in the continuing Australasian/Canadian study (6). The patient we described in our case report weighed 57.3 kg, and she was being fed according to the usual practice in our intensive care unit. This feeding regimen was a deliberate feature of the study because we had concerns, as others have more recently echoed (7), that van den Berghe et al.'s original study may have demonstrated increased mortality in patients rendered hyperglycemic by an aggressive feeding regimen. Dr. Al-Ansari's assertion that a lower caloric intake is itself a risk factor for hypoglycemia is not supported by a recent, nested, case-control study (8). Ideally, a study should be performed, which prospectively randomized patients into two groups, both receiving tight glycemic control using the same algorithm but different target caloric intakes. We agree that the algorithm being used at the time was not ideal, and indeed our current algorithm considers the lessons that we have learned, including more frequent blood glucose measurements during periods of hypoglycemia. However, the inadequacy of the algorithm has no bearing on the phenomenon we describe or its interpretation. Dr. Al-Ansari's question regarding the frequency of blood gas analysis is difficult to interpret, and its relevance to the subject of our case report is unclear. Our nursing staff is encouraged to perform blood gas analyses when they feel it is clinically indicated. Our blood gas analyzer provides a highly reliable and accurate measure of blood potassium and glucose concentrations, as well as the usual indices of gas exchange. Given that this patient had respiratory and renal failure, was receiving tight glycemic control, and had unstable blood glucose concentrations, the frequency of blood gas analysis reported does not seem unreasonable. Finally, we are delighted that Dr. Al-Ansari gave us the opportunity to highlight a feature of our case report (2) that corroborates our interpretation of events. Figure 2 in our case report shows a striking similarity in the concentration/time profiles for glucose (middle panel) and potassium (top panel), most particularly because of the peaks recorded at 04:30 h and 06:00 h. This similarity reveals that the phenomenon of glucose-associated hyperkalemia was not only reproducible, but it also suggests a dose-response relationship, both peaks having occurred immediately after the administration of different volumes of 50% dextrose, the first of 25 mL and the second of 50 mL. Anuj Bhatia, MD Brit Cadman, MD Iain Mackenzie, MD Department of Anaesthesia and John V. Farman Intensive Care Unit Department of Pharmacy Addenbrooke's Hospital Cambridge, United Kingdom [email protected]
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".