Glycemic Variability is Independently Associated with Poor Prognosis in Main PICU Centers in Southwest China
Bibliographic record
Abstract
Abstract Background: Glucose variability (GV) is one of the common complications in critically ill patients, and studies investigating the role of GV in the prognosis of pediatric patients are scarce in China. And there is no consensus on the measurement of GV. Thus, this study took a prospective, multi-center cohort observational study to identify the ‘best’ index of variability in non-diabetic critically ill children and to confirm whether GV is associated with unfavorable outcomes and whether this association persists after control of hypoglycemia and hyperglycemia. Materials and Methods: Four GV indices were chosen and calculated in our study, namely, mean absolute glucose (MAG), standard deviation (SD), glycemic lability index (GLI), and another metric-average consecutive absolute change percentage (ACACP), which can be used in the real-time clinical decision. The primary outcome was 28-day mortality. Multivariate Cox regression analysis was used to identify the potential predictors for the outcome. And the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to assess the predictability of dysglycemia (glucose variability and hypo/hyperglycemia) on the unfavorable outcome. Results: Of the total of 780 participants, 12.4% (n=97) died within 28 days after PICU admission. There were statistical differences in terms of four GV indices (SD, GLI, MAG, and ACACP) between survivors and non-survivors, in which MAG obtained the largest area under the curve and showed a strong connection to ICU mortality independently. Subsequent addition of MAG to the multivariate Cox model for hyperglycemia resulted in further quantitative evolution of the model statistics (AUC 0.651 to 0.681, P=0.001; IDI: 0.017, P=0.044; NRI:0.224, P=0.186). And the impact of hyperglycemia (adjusted HR1.419, 95% CI 0.815-2.471, p=0.216) on outcome was attenuated and no longer statistically relevant after adjustment for MAG (adjusted HR 2.455, 95%CI 1.411-4.270, p=0.001). Conclusions: GV is closely associated with unfavorable outcomes, and may be a more powerful negative predictor of outcome than hypoglycemia and hyperglycemia. These findings emphasize the crucial role of GV in PICU children. GV index that contains information such as time, rate of change, etc. is the focus of future research. MAG may be a good choice. Trial registration: Chinese clinical registration (ChiCTR), ChiCTR2000030846. Registered 15 March 2020, https://www.chictr.org.cn/showproj.aspx?proj=50760.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".