Mood and Psychotic Disorders as Prognostic Factors for Sepsis and Septic Shock Mortality: A Meta-Analysis
Bibliographic record
Abstract
Abstract Rationale: There are various mechanisms by which mental disorders and their treatments may influence sepsis-related-mortality including a dysregulated inflammatory response, pro-inflammatory state, and baseline higher risk of infection among patients with mental disorders. With greater than a 2-fold increased risk of death of any cause and 14.2% of deaths worldwide attributable to mental health, mental disorders are increasingly emerging as an important prognostic factor for mortality. Despite investigations into mortality incidence among patients with mental illness, to date, there are no systematic reviews investigating the impact of mental illness on sepsis and septic shock mortality. Therefore, this study intends to estimate the impact of mental disorders on sepsis and septic shock mortality. Methods: MEDLINE, EMBASE, and PubMed were searched for studies investigating the influence of mental disorders on sepsis or septic shock mortality.Studies were included if they met the following criteria: 1) observational study (prospective or retrospective cohort, case-control), 2) measured deaths among patients with sepsis/septic shock (events, RR, HR, OR), and 3) investigated any clinical mental illness (based on DSM, ICD). Results: Ten full-texts met inclusion criteria with 12 274 586 participants with 13.5% (n = 1 463 389) diagnosed with a mental disorder at baseline. Generalized mental disorders (OR 0.76, 95%CI 0.71-0.81, p < 0.001, k = 3), mood disorders (OR 0.79, 95%CI 0.74-0.82, p < 0.001, k = 3), and psychotic disorders (OR 0.56, 95%CI 0.42-0.75, p < 0.001, k = 2) associated with reduced in-hospital mortality. Mood disorders (HR 0.78, 95%CI 0.72-0.84, p < 0.001, k = 3) and depression in isolation (HR 0.80, 95%CI 0.70-0.90, k = 2) were associated with reduced 90-day mortality. Conclusion: Our meta-analysis found evidence associating various mental disorders with reduced mortality in patients with sepsis and septic shock. Specifically, mental disorders may be paradoxically a protective factor due to a pro-inflammatory state and dysregulated immune response, offsetting an otherwise immunocompromised state. Our meta-analysis is limited by retrospective data analysis, underreporting of baseline characteristics, adjustment for important confounders (e.g., psychotropic medication use), and significant statistical heterogeneity.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".