In-hospital hyperglycemia but not diabetes mellitus alone is associated with increased in-hospital mortality in community-acquired pneumonia (CAP): a systematic review and meta-analysis of observational studies prior to COVID-19
Bibliographic record
Abstract
The objective of this review was to quantify the association between diabetes, hyperglycemia, and outcomes in patients hospitalized for community-acquired pneumonia (CAP) prior to the COVID-19 pandemic by conducting a systematic review and meta-analysis. Two investigators independently screened records identified in the PubMed (MEDLINE), EMBASE, CINAHL, and Web of Science databases. Cohort and case–control studies quantitatively evaluating associations between diabetes and in-hospital hyperglycemia with outcomes in adults admitted to hospital with CAP were included. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale, effect size using random-effects models, and heterogeneity using I2 statistics. Thirty-eight studies met the inclusion criteria. Hyperglycemia was associated with in-hospital mortality (adjusted OR 1.28, 95% CI 1.09 to 1.50) and intensive care unit (ICU) admission (crude OR 1.82, 95% CI 1.17 to 2.84). There was no association between diabetes status and in-hospital mortality (adjusted OR 1.04, 95% CI 0.72 to 1.51), 30-day mortality (adjusted OR 1.13, 95% CI 0.77 to 1.67), or ICU admission (crude OR 1.91, 95% CI 0.74 to 4.95). Diabetes was associated with increased mortality in all studies reporting >90-day postdischarge mortality and with longer length of stay only for studies reporting crude (OR 1.50, 95% CI 1.11 to 2.01) results. In adults hospitalized with CAP, in-hospital hyperglycemia but not diabetes alone is associated with increased in-hospital mortality and ICU admission. Diabetes status is associated with increased >90-day postdischarge mortality. Implications for management are that in-hospital hyperglycemia carries a greater risk for in-hospital morbidity and mortality than diabetes alone in patients admitted with non-COVID-19 CAP. Evaluation of strategies enabling timely and effective management of in-hospital hyperglycemia in CAP is warranted.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".