P549CIED infection risk score validation using US health claims data
Bibliographic record
Abstract
Abstract Funding Acknowledgements This work was supported by Medtronic Background/Introduction: The increasing number of cardiac implantable electronic device (CIED) infections has led to increased interest in the identification of patients who may benefit from additional infection prevention measures. Purpose The purpose of this evaluation was to validate the predictive value of the Prevention of Arrhythmia Device Infection Trial (PADIT) risk score to identify patients at increased risk of CIED infection using a U.S. health claims data set. Methods A retrospective analysis using the Optum® Clinformatics® claims database was conducted to create a dataset of index procedures which either did or did not result in an infection. The study population included both commercial and Medicare Advantage patients aged ≥18 years with at least one record of a CIED procedure between January 2011 and September 2014. Major CIED infections, defined as an infection associated with system removal, invasive procedure without system removal, or death attributable to infection, were identified through diagnosis and procedure codes. The dataset was randomized (stratified by PADIT score, which included prior procedures, age, depressed renal function, immunocompromised, and procedure type) into a Development Dataset (60%) and a Validation dataset (40%). A frailty model allowing multiple procedures per patient was fit using the Development Dataset, with PADIT score as the only predictor, excluding patients with prior infection. Prior CIED infection, which was not available in the original PADIT data, was examined for additional predictive value. Results The data extraction resulted in a dataset of 53,554 index procedures among 51,583 patients, with 30,950 patients randomized to the Development Dataset. The distribution of procedures was pacemakers (52%), ICD (20%), CRT (12%), and Revision/Upgrade (16%), while prior procedures were none (62%), 1 (37%), and 2 (1%). Among patients with no history of prior CIED infection, the frailty model showed that a 1 unit increase in the PADIT score predicts higher infection risk (20%) in the U.S. claims data set (Table 1). Prior CIED infection was associated with strong additional predictive value (HR 4.77, p < 0.0001) after adjusting for PADIT score. Conclusion In the largest external validation of a CIED risk score, the PADIT risk score predicts increased CIED infection risk, identifying higher risk patients that can benefit from targeted interventions to reduce the risk of CIED infection. Prior CIED infection brings additional predictive value to the PADIT score.
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 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 | 0.015 | 0.062 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".