Readmission and Late Mortality After Pediatric Severe Sepsis
Bibliographic record
Abstract
OBJECTIVE: Pediatric severe sepsis remains a significant health problem with hospital mortality up to 10%. However, there is little information about later health outcomes or needs of survivors. Therefore, our goal was to evaluate the rates of and risk factors for rehospitalization and late mortality among survivors of pediatric severe sepsis. PATIENTS AND METHODS: This was a population-based retrospective cohort study of survivors of pediatric severe sepsis (age 1 month to 18 years) in Washington State over the years 1990-2004. The sentinel admission was linked to subsequent death or episodes of hospitalization. The main outcome measures were readmission and/or late death after surviving an initial hospitalization with severe sepsis. Risk factors for readmission or death were identified by using a multivariate extended Cox model. RESULTS: Overall, 7183 children were admitted with severe sepsis, 6.8% of whom died during the sentinel admission or within 28 days of discharge, whereas an additional 6.5% died subsequently. Almost half (47%) of the survivors were readmitted at least once (median: 3) after a median of 3 months, and the majority of these readmissions were emergent. Sentinel admission factors independently associated with both adverse outcomes were neurologic or hematologic organ dysfunction, government-based insurance, as well as several coexisting health conditions. In addition, age less than 1 year at the time of sepsis and bloodstream and cardiovascular infections were highly associated with subsequent readmission. CONCLUSIONS: Late death occurred with similar frequency as early death associated with hospitalization with severe sepsis. Almost half of the pediatric patients suffering from an episode of severe sepsis had at least 1 subsequent hospitalization, two thirds of which were emergent or urgent. These data suggest that late outcomes after an episode of severe sepsis are poor and call for the evaluation of interventions designed to reduce later morbidity and mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".