Prescribed targets for titration of vasopressors in septic shock: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Without robust clinical evidence to guide titration of vasopressors in septic shock, it is unclear how dosing of these potent medications occurs. We sought to measure the proportion of vasopressor prescriptions for septic shock that were missing explicit targets and to describe the targets that we identified. METHODS: We conducted a multicentre, retrospective cohort study involving 9 intensive care units (ICUs) located at 3 academic hospitals in Canada and Australia. We reviewed charts of consecutive patients aged 18 years or older who were admitted to the ICU for a presumptive diagnosis of sepsis. Other inclusion criteria were hypotension (systolic arterial pressure ≤ 90 mm Hg or mean arterial pressure [MAP] ≤ 65 mm Hg) and continuous infusion of vasopressors for at least 1 hour within the initial 48 hours of ICU stay, the period of observation for this study. RESULTS: We included data from 369 patient charts. At least 1 target was specified in 99% of charts. The most common targets were MAP measurements (73%). The median initial MAP target was 65 (range 55-90) mm Hg. In multivariable regression models, hospital site and older age of the patient, but not comorbidities of the patient, were associated with MAP targets. In 40% of patients, the treating team modified the initial target at least once. INTERPRETATION: This study suggests that an explicit blood pressure target accompanies nearly every vasopressor prescription and that patient characteristics have little influence on its value. Identification of a titration strategy that will maximize benefit and minimize harm constitutes a research priority.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".