Increasing Sepsis Rates in the United States: Results From National Inpatient Sample, 2005 to 2014
Bibliographic record
Abstract
Objectives: To examine the trends in hospitalization rates, mortality, and costs for sepsis during the years 2005 to 2014. Methods: This was a retrospective serial cross-sectional analysis of patients ≥18 years admitted for sepsis in National Inpatient Sample. Trends in sepsis hospitalizations were estimated, and age- and sex-adjusted rates were calculated for the years 2005 to 2014. Results: There were 541 694 sepsis admissions in 2005 and increased to 1 338 905 in 2014. Sepsis rates increased significantly from 1.2% to 2.7% during the years 2005 to 2014 (relative increase: 123.8%; P trend < .001). However, the relative increase changed by 105.8% ( P trend < .001) after adjusting for age and sex and maintained significance. Although total cost of hospitalization due to sepsis increased significantly from US$22.2 to US$38.1 billion ( P trend < .001), the mean hospitalization cost decreased significantly from US$46,470 to US$29,290 ( P trend < .001). Conclusions: Hospitalizations for sepsis increased during the years 2005 to 2014. Our study paradoxically found declining rates of in-hospital mortality, length of stay, and mean hospitalization cost for sepsis. These findings could be due to biases introduced by International Classification of Diseases, Ninth Revision, Clinical Modification coding rules and increased readmission rates or alternatively due to increased awareness and surveillance and changing disposition status. Standardized epidemiologic registries should be developed to overcome these biases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".