Incidence and clinical characteristics of transfusion‐associated circulatory overload using an active surveillance algorithm
Bibliographic record
Abstract
BACKGROUND: The concordance of haemovigilance criteria developed for surveillance of transfusion-associated circulatory overload (TACO) with its clinical diagnosis has not been assessed. In a pilot study to evaluate an electronic screening algorithm, we sought to examine TACO incidence and application of haemovigilance criteria in patients with post-transfusion pulmonary oedema. STUDY DESIGN AND METHODS: From June to September 2014, all transfused adult inpatients at four academic hospitals were screened with an algorithm identifying chest radiographs ordered within 12 h of blood component release. Patients with post-transfusion pulmonary oedema underwent case adjudication by an expert panel. TACO incidence was calculated, and clinical characteristics were compared with other causes of post-transfusion pulmonary oedema. RESULTS: Among 4932 transfused patients, there were 3412 algorithm alerts, 50 cases of TACO and 47 other causes of pulmonary oedema. TACO incidence was 1 case per 100 patients transfused. TACO classification based on two sets of haemovigilance criteria (National Healthcare Safety Network and proposed revised International Society for Blood Transfusion) was concordant with expert panel diagnosis in 57% and 54% of reviewed cases, respectively. Although the majority of clinical parameters did not differentiate expert panel adjudicated TACO from other cases, improved oxygenation within 24 h of transfusion did (P = 0·01). CONCLUSIONS: The incidence of TACO was similar to that observed in prior studies utilizing active surveillance. Case classification by haemovigilance criteria was frequently discordant with clinical diagnoses of TACO in patients with post-transfusion pulmonary oedema. Improvements in oxygenation within 24 h of transfusion merit further evaluation in the diagnosis of TACO.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".