Interrogating a clinical database to study treatment of hypotension in the critically ill
Bibliographic record
Abstract
OBJECTIVE: In intensive care, it is imperative to resolve hypotensive episodes (HEs) in a timely manner to minimise end-organ damage. Clinical practice guidelines generally recommend initial treatment with fluid resuscitation followed by vasoactive agent administration if patients remain hypotensive. However, the impact of such interventions on patient outcomes has not been clearly established. Hence, the objective of this study was to investigate the relationship between fluid and vasoactive agent interventions and patient outcomes, while highlighting the utility of electronic medical records in clinical research. DESIGN: Retrospective cohort study. SETTING: Intensive care units (ICUs) at a large, academic, tertiary medical center. PARTICIPANTS: Patients in Multi-parameter Intelligent Monitoring in Intensive Care II (a large electronic ICU database) who experienced a single HE during their ICU stay. 2332 patients had complete data. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome of interest was inhospital mortality. Secondary outcomes were ICU length of stay (LOS), HE duration, Hypotension Severity Index (defined as the mean arterial pressure curve area below 60 mm Hg during the HE) and rise in serum creatinine. RESULTS: Fluid resuscitation was associated with significantly shorter ICU LOS among ICU survivors (p=0.007). Vasoactive agent administration significantly decreased HE duration (p<0.001) and Hypotension Severity Index (p=0.002) but was associated with increased inhospital mortality risk (p<0.001), prolonged ICU LOS among ICU survivors (p=0.04) and rise in serum creatinine (p=0.002) after adjustment for confounders. Propensity score analyses as well as sensitivity analyses in treatment-, diagnosis- and ICU service-specific subpopulations corroborated the relationship between vasoactive agents and increased inhospital mortality. CONCLUSIONS: An adverse relationship between vasoactive agents and inhospital mortality was found in patients with hypotension. This study has implications for the care of critically ill patients with hypotension and illustrates the utility of electronic medical records in research when randomised controlled trials are difficult to conduct.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".