A systematic review of validated methods for identifying transfusion‐related sepsis using administrative and claims data
Bibliographic record
Abstract
PURPOSE: To systematically review algorithms to identify transfusion-related sepsis or septicemia in administrative data, with a focus on studies that have examined the validity of the algorithms. METHODS: A literature search was conducted using PubMed, the database of the Iowa Drug Information Service (IDIS/Web), and Embase. A Google Scholar search was conducted because of difficulty identifying relevant studies. Reviews were conducted by two investigators to identify studies using data sources from the USA or Canada, because these data sources were most likely to reflect the coding practices of Mini-Sentinel data sources. RESULTS: No studies that were identified that used administrative data to identify sepsis or septicemia related to transfusion of blood products. Thus, four studies that studied the validity of algorithms to identify sepsis and two that studied algorithms to identify allogeneic blood transfusion are described in this review. Two studies found acceptable positive predictive values of 80% and 89% for algorithms to identify sepsis in hospitalized patients. One study reported a negative predictive value of 80% in hospitalized patients, and another, a sensitivity of 75%. One study of veterans receiving surgery reported much worse performance characteristics. Two studies reported near-perfect specificity of codes for allogeneic red blood cell transfusion, but sensitivity ranged from 21% to 83%. CONCLUSIONS: There is no information to assess the validity of algorithms to identify transfusion-related sepsis or septicemia. Codes to identify sepsis performed well in most studies. Algorithms to identify transfusions need further research that includes a broader range of transfusion types.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".