Effectiveness of prophylactic intranasal photodynamic disinfection therapy and chlorhexidine gluconate body wipes for surgical site infection prophylaxis in adult spine surgery
Bibliographic record
Abstract
<h3>Background:</h3> Current measures to prevent spinal surgical site infection (SSI) lack compliance and lead to antimicrobial resistance. We aimed to examine the effectiveness of bundled preoperative intranasal photodynamic disinfection therapy (nPDT) and chlorhexidine gluconate (CHG) body wipes in the prophylaxis of spine SSIs in adults, as well as determine our institutional savings attributable to the use of this strategy and identify adverse events reported with nPDT–CHG. <h3>Methods:</h3> We performed a 14-year prospective observational interrupted time-series study in adult (age > 18 yr) patients undergoing emergent or elective spine surgery with 3 time-specific cohorts: before rollout of our institution’s nPDT–CHG program (2006–2010), during rollout (2011–2014) and after rollout (2015–2019). We used unadjusted bivariate analysis to test for temporal changes across patient and surgical variables, and segmented regression to estimate the effect of nPDT–CHG on the annual SSI incidence rates per period. We used 2 models to estimate the cost of nPDT–CHG to prevent 1 additional SSI per year and the annual cumulative cost savings through SSI prevention. <h3>Results:</h3> Over the study period, 13 493 patients (mean 964 per year) underwent elective or emergent spine surgery. From 2006 to 2019, the mean age, mean Charlson Comorbidity Index (CCI) score and mean Spine Surgical Invasiveness Index (SSII) score increased from 48.4 to 58.1 years, from 1.7 to 2.6, and from 15.4 to 20.5, respectively (<i>p</i> < 0.001). Unadjusted analysis confirmed a significant decrease in the annual number (74.6 to 26.8) and incidence (7.98% to 2.67%) of SSIs with nPDT–CHG (<i>p</i> < 0.001). After adjustment for mean age, mean CCI score and mean SSII score, segmented regression showed an absolute reduction in the annual SSI incidence rate of 3.36% per year (<i>p</i> < 0.001). The estimated annual cost to prevent 1 additional SSI per year was about $1350–$1650, and the estimated annual cumulative cost savings were $2 484 856–$2 495 016. No adverse events were reported with nPDT–CHG. <h3>Conclusion:</h3> Preoperative nPDT–CHG administration is an effective prophylactic strategy for spinal SSIs, with significant cost savings. Given its rapid action, minimal risk of antimicrobial resistance, broad-spectrum activity and high compliance rate, preoperative nPDT–CHG decolonization should be the standard of care for all patients undergoing emergent or elective spine surgery.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".