Sepsis-Associated Mortality, Resource Use, and Healthcare Costs: A Propensity-Matched Cohort Study*
Bibliographic record
Abstract
OBJECTIVES: To examine long-term mortality, resource utilization, and healthcare costs in sepsis patients compared to hospitalized nonsepsis controls. DESIGN: Propensity-matched population-based cohort study using administrative data. SETTING: Ontario, Canada. PATIENTS: We identified a cohort of adults (≥ 18) admitted to hospitals in Ontario between April 1, 2012, and March 31, 2016, with follow-up to March 31, 2017. Sepsis patients were flagged using a validated International Classification of Diseases, 10th Revision-coded algorithm (Sepsis-2 definition), including cases with organ dysfunction (severe sepsis) and without (nonsevere). Remaining hospitalized patients were potential controls. Cases and controls were matched 1:1 on propensity score, age, sex, admission type, and admission date. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Differences in mortality, rehospitalization, hospital length of stay, and healthcare costs were estimated, adjusting for remaining confounders using Cox regression and generalized estimating equations. Of 270,669 sepsis cases, 196,922 (73%) were successfully matched: 64,204 had severe and 132,718 nonsevere sepsis (infection without organ dysfunction). Over follow-up (median 2.0 yr), severe sepsis patients had higher mortality rates than controls (hazard ratio, 1.66; 95% CI, 1.63-1.68). Both severe and nonsevere sepsis patients had higher rehospitalization rates than controls (hazard ratio, 1.53; 95% CI, 1.50-1.55 and hazard ratio, 1.41; 95% CI, 1.40-1.43, respectively). Incremental costs (Canadian dollar 2018) in sepsis cases versus controls at 1-year were: $29,238 (95% CI, $28,568-$29,913) for severe and $9,475 (95% CI, $9,150-$9,727) for nonsevere sepsis. CONCLUSIONS: Severe sepsis was associated with substantially higher long-term risk of death, rehospitalization, and healthcare costs, highlighting the need for effective postdischarge care for sepsis survivors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".