RISK FACTORS FOR THE DEVELOPMENT OF HOSPITAL-ACQUIRED PEDIATRIC VENOUS THROMBOEMBOLISM: DEALING WITH POTENTIALLY CAUSAL AND CONFOUNDING RISK FACTORS USING DIRECTED ACYCLIC GRAPH (DAG) ANALYSIS
Bibliographic record
Abstract
Abstract Introduction Hospital-acquired venous thromboembolism (HA-VTE) in children comprises multiple risk factors that should not be evaluated separately due to collinearity and multiple cause and effect relationships. This is one of the first case-control study of pediatric HA-VTE risk factors using Directed Acyclic Graph (DAG) analysis. Material and Methods Retrospective, case-control study with 22 cases of radiologically proved HA-VTE and 76 controls matched by age, sex, unit of admission, and period of hospitalization. Descriptive statistics was used to define distributions of continuous variables, frequencies, and proportions of categorical variables, with a comparison between cases and controls. Due to many potential risk factors of HA-VTE, a directed acyclic graph (DAG) model was created to identify confounding, reduce bias, and increase precision on the analysis. The final model consisted of a DAG-based conditional logistic regression. The study was approved by the Institutional Review Board (CAAE 58056516.0.0000.5264). Results In the initial univariable model, the following variables were selected as potential risk factors for HA-VTE: length of stay (LOS, days), ICU admission in the last 30 days, LOS in ICU, infection, central venous catheter (CVC), L-asparaginase, heart failure, liver failure and nephrotic syndrome. The final model (table 1) revealed LOS (OR=1.108, 95%CI=1.024-1.199, p=0.011), L-asparaginase (OR=27.184, 95%CI=1.639-450.982, p=0.021), and nephrotic syndrome (OR=31.481, 95%CI=1.182-838.706, p=0.039) as independent risk factors for HA-VTE. Conclusion The DAG-based approach was useful to clarify the influence of confounders and multiple causalities of HA-VTE. Interestingly, CVC placement - a known thrombotic risk factor highlighted in several studies - was considered a confounder, while LOS, L-asparaginase use and nephrotic syndrome were confirmed as risk factors to HA-VTE. Large confidence intervals are related to the sample size, however the results were significant. Highlights HA-VTE comprises multiple risk factors that should not be evaluated separately due to collinearity and confounding Directed Acyclic Graph (DAG) helps to clarify collinearity and confounding related to multiple cause and effect relationships that exist in HA-VTE risk factors This is a novel study using DAG-based logistic regression to evaluate risk factors for HA-VTE in children We reported the importance of medical conditions on the genesis of HA-VTE using a DAG-based approach, which makes it possible to clarify the influence of confounders and multiple causalities, such as catheter, a significant risk factor highlighted in several studies
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".