Shunt infection prevention practices in Hydrocephalus Clinical Research Network–Quality: a new quality improvement network for hydrocephalus management
Bibliographic record
Abstract
OBJECTIVE: Knowledge-based tools used to standardize perioperative care, such as the shunt infection prevention protocol of the Hydrocephalus Clinical Research Network (HCRN), have demonstrated their ability to reduce surgeon-based and center-based variations in outcomes and improve patient care. The mere presence of high-quality evidence, however, does not necessarily translate into improved patient outcomes owing to the implementation gap. To advance understanding of how knowledge-based tools are being utilized in the routine clinical care of children with hydrocephalus, the HCRN-Quality (HCRNq) network was started in 2019. With a focus on CSF shunt infection, the authors present baseline data regarding CSF shunt infection rates and current shunt infection prevention practices in use at HCRNq sites. METHODS: Baseline shunt surgery practices, infection rate, and risk factor data were prospectively collected within HCRNq. No standard infection protocol was recommended, but site use of a protocol was implied if at least 3 of 6 common shunt infection prevention practices were used in > 80% of shunt surgical procedures. Univariable and multivariable analyses of shunt infection risk factors were performed. RESULTS: Thirty sites accrued data on 2437 procedures between November 2019 and June 2021. The unadjusted infection rate across all sites was 3.9% (range 0%-13%) and did not differ among shunt insertion, shunt revision, or shunt insertion after infection. Protocol use was implied for only 15/30 centers and 60% of shunt operations. On univariable analysis, iodine/DuraPrep (OR 0.57, 95% CI 0.37-0.88, p = 0.02) and the use of an antibiotic-impregnated catheter in any segment of the shunt (or both) decreased infection risk (OR 0.53, 95% CI 0.34-0.82, p = 0.01). Iodine-based prep solutions (OR 0.56, 95% 0.36-0.86, p = 0.02) and the use of antibiotic-impregnated catheters (OR 0.52, 95% CI 0.34-0.81, p = 0.01) retained significance in the multivariable model, but no relationship between protocol use and infection risk was demonstrated in this baseline analysis. CONCLUSIONS: The authors have demonstrated that children undergoing CSF shunt surgery at HCRNq sites share similar demographic characteristics with other large North American multicenter cohorts, with similar observed baseline infection rates and risk factors. Many centers have implemented standardized shunt infection prevention practices, but considerable practice variation remains. As such, there is an opportunity to decrease shunt infection rates in these centers through continued standardization of care.
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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 | 0.027 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".