THE IMPACT AND COST ANALYSIS OF PROPHYLACTIC INTRANASAL PHOTODYNAMIC DISINFECTION THERAPY (NPDT) AND CHLORHEXIDINE GLUCONATE (CHG) BODY WIPES ON SURGICAL SITE INFECTION IN ADULT SPINE SURGERY
Bibliographic record
Abstract
A surgical site infection (SSI) following instrumented spine surgery is a devastating complication. Antibiotic nasal decolonization is primarily used to prevent SSIs. This therapy lacks compliance and leads to microbial resistance. To examine the effectiveness of intranasal photodisinfection therapy (nPDT) combined with chlorhexidine body wipes (CHG) for the prophylaxis of SSIs in adult patients undergoing spine surgery. Prospective observational surveillance data was collected from January 1st, 2006 to December 31st, 2019 including patient demographics, surgical variables and cases of SSIs confirmed by our institutional SSI surveillance program. SSI data was segmented into three regions: pre-rollout (2006 to 2010); nPDT/CHG rollout (2010 to 2014); and post-rollout (2015 to 2019). Pearson correlation was used to test for historical changes across individual patient and surgical variables. An interrupted time series analysis using segmented regression adjusted for patient and surgical variables was used to estimate the effect of the nPDT/CHG rollout on SSI incidence rates per region. Two models were developed to estimate the cost of nPDT/CHG to prevent one additional SSI per year and determine the mean cumulative institutional cost savings. A total of 13,493 patients underwent spine surgery over the study period, with a mean of 964 surgical cases per calendar year. Mean age, mean charlson comorbidity index (CCI) and mean surgical spine invasiveness index (SSII) increased from 48.4 to 58.1 years; 1.7 to 2.6 and 15.4 to 20.5 over the study period (p < 0.001). Unadjusted analysis identified a significant decrease in the mean number and mean incidence of SSIs per year from 74.6 to 26.8 and 7.98% to 2.67% between pre-rollout and post-rollout (p < 0.001). Segmented regression demonstrated an absolute percent reduction of 3.36% in SSI incidence per year during the nPDT/CHG rollout adjusted for mean age, CCI and SSII. The cost of nPDT/CHG to prevent one SSI was $1,350 to $1,650 CAD per year. The mean cumulative institutional cost savings was $2,484,856 to $2,495,016 CAD per year. No AEs were reported with the use of nPDT/CHG. Preoperative nPDT/CHG is an effective preoperative preventative strategy for reducing the incidence and cost of SSIs following spine surgery. Given the demonstrated success and the potential cost savings, we recommend the routine use of nPDT/CHG for SSI prevention in adult 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".