Epidemiology of Vasopressin Use for Adults with Septic Shock
Bibliographic record
Abstract
RATIONALE: Vasopressin may be used to treat vasodilatory hypotension in septic shock, but it is not recommended by guidelines as a first- or second-line agent. Little is known about how often the drug is used currently in septic shock. OBJECTIVES: We conducted this study to describe patterns of vasopressin use in a large cohort of U.S. adults with septic shock and to identify patient and hospital characteristics associated with vasopressin use. METHODS: This was a retrospective cohort study of adults admitted to U.S. hospitals with septic shock in the Premier healthcare database (July 2008 to June 2013). We performed multilevel mixed-effects logistic regression with hospitals as a random effect to identify factors associated with use of vasopressin alone or in combination with other vasopressors on at least 1 day of hospital admission. We calculated quotients of Akaike Information Criteria (AIC) to determine relative contributions of patient and hospital characteristics to observed variation. MEASUREMENTS AND MAIN RESULTS: Among 584,421 patients with septic shock in 532 hospitals, 100,923 (17.2%) received vasopressin. A total of 6.1% of patients receiving vasopressin received vasopressin alone, and 93.9% received vasopressin in combination with other vasopressors (up to five vasopressors in 15 different combinations). The mean monthly rate of vasopressin use increased from 14.5 to 19.6% over the study period, representing an average annual relative increase of 8% (P < 0.001). The median hospital rate of vasopressin use for septic shock was 11.7% (range, 0-69.7%). Patient demographic and clinical characteristics, including patient age (adjusted odds ratio, 0.71 for age > 85 yr compared with the reference group of age < 50 yr; 95% confidence interval, 0.69-0.74) and acute respiratory dysfunction (adjusted odds ratio, 3.25; 95% confidence interval, 3.20-3.31), were responsible for the majority of observed variation in vasopressin use (quotient of AICs, 0.56). However, hospital of admission also contributed substantially to observed variation (quotient of AICs, 0.37). CONCLUSIONS: Approximately one-fifth of patients with septic shock received vasopressin, but rarely as a single vasopressor. The use of vasopressin has increased over time. The likelihood of receiving vasopressin was strongly associated with the specific hospital to which each patient was admitted.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".