308. Identification of Prosthetic Hip and Knee Joint Infections in Administrative Databases
Bibliographic record
Abstract
Canada lacks a prosthetic hip and knee joint infection (PJI) registry, leaving active surveillance to be orchestrated by individual hospitals, which is limited by cost and narrow scope. Administrative databases are potentially an ideal instrument for infection surveillance, but detection algorithms relying solely on PJI diagnostic codes alone have been hampered by low specificity. There is a need to develop improved strategies to efficiently and accurately identify PJIs using health administrative databases. Combinations of International Classification of Disease, Tenth Revision, diagnostic and procedure codes were used to create testing cohorts among individuals treated at two institutions in Toronto, Ontario, from April 1, 2015 until March 31, 2016. These cohorts were compared with a reference standard of PJIs, which were identified by chart reviews of every individual who underwent a hip or knee revision operation at these institutions during the study period. The primary outcomes were the performance characteristics of each algorithm. Over the 1-year study period, there were 471 revision operations for 405 patients, of which 155 (33%) were performed for the treatment of a PJI. Of the 405 individuals, 108 (27%) had a PJI as the surgical indication; there were 57 (53%) two-stage procedures, nine (8%) single-stage procedures, 34 (31%) incision and drainage procedures with implant retention, and eight (7%) excisional arthroplasties. The combination of a revision operation code plus a PJI diagnosis code was the most robust detection method: sensitivity 0.86 (95% confidence interval, 0.79–0.91) and specificity 0.99 (0.98–1.00). Coupling codes for a revision operation and insertion of a peripherally inserted central catheter yielded a sensitivity of 0.45 (0.37–0.53) and specificity of 1.00 (0.98–1.00). PJI codes alone had a sensitivity of 1.00 (0.86–1.00) and specificity 0.50 (0.23–0.77). The combination of a revision operation procedure code and a PJI diagnosis code is sensitive and specific for the detection of a PJI in administrative databases. This is a promising avenue for national PJI surveillance and has the potential to facilitate future research in the prevention and management of PJIs. All authors: No reported disclosures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".