Factors associated with SIRS negativity at the early stage of sepsis among nonsurviving sepsis patients in ICU: Targeting “silent sepsis”
Bibliographic record
Abstract
Abstract Background Despite the very high sensitivity of the Systemic Inflammatory Response Syndrome (SIRS) score for identifying sepsis, there remains a subset of septic patients who exhibit negative SIRS scores, and unfortunately, many of these patients experience poor outcomes. This study aims to investigate the factors associated with SIRS negativity during the early stage of sepsis in deceased septic patients. Methods Adult septic patients were included from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database between 2008 and 2019. Sepsis was determined based on the Sepsis 3.0 criteria. Patients who did not survive after 28 days were assigned to the SIRS-negative or SIRS-positive group according to whether the SIRS score was less than two points within 24 hours of intensive care unit (ICU) admission. The baseline data of patients in the SIRS-negative and SIRS-positive groups were collected and compared. The factors associated with SIRS negativity in septic patients were analysed by logistic regression. The dose-response relationships of SIRS negativity with SOFA score and age were determined with a restricted cubic spline model. Results A total of 53,150 patients were screened in the MIMIC-IV database, and 2706 sepsis nonsurvivors were ultimately included, 101 of whom were negative for SIRS. There were significant differences in SOFA scores between groups (8.18 ± 3.58 vs. 9.75 ± 4.28, P < 0.001). In addition, differences in several other parameters nearly reached statistical significance, including age (76 [61 to 86] vs. 72 [60 to 82], P = 0.053), body mass index (BMI) (26 [22 to 31] vs. 27 [24 to 32], P = 0.056), and the Charlson comorbidity index (8 [6 to 9] vs. 7 [5 to 9], P = 0.052). Logistic regression analysis indicated that both SOFA score (OR = 0.93 [95% CI = 0.87-1.00], P = 0.046) and age (OR = 1.04 [95% CI = 0.88–1.15], P = 0.012) were independent factors related to SIRS negativity in septic patients. Analysis with a restricted cubic spline model showed that the odds ratio (OR) of SIRS negativity continued to increase with age, particularly for those over 80 years old (p for nonlinearity = 0.024). The odds ratio of SIRS negativity was more than 1 when the SOFA score was less than 4 (p for nonlinearity = 0.261). Conclusions For sepsis patients with poor prognoses, elderly individuals (over 80 years) are more likely to be SIRS negative when they have mild organ dysfunction damage (less than 4 SOFA scores) in the early stage of sepsis. This warranted an opportunity to provide early diagnosis for elderly population with negative SIRS score, in order to prevent poor outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".