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Record W4406943010 · doi:10.1093/ofid/ofae631.595

P-394. Prevention Of Infections In Cardiac Surgery (PICS)-Prevena Study – a pilot/vanguard factorial cluster cross-over RCT

2025· article· en· W4406943010 on OpenAlexaffabout
Thomas Scheier, Richard Whitlock, Mark Loeb, P.J. Devereaux, Shun Fu Lee, Dominik Mertz

Bibliographic record

VenueOpen Forum Infectious Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineRandomized controlled trialVanguardCluster randomised controlled trialCluster (spacecraft)Cross overInternal medicineMedical physics

Abstract

fetched live from OpenAlex

Abstract Background Sternal surgical site infections (s-SSI) after cardiac surgery can lead to significant morbidity, mortality, and costs. Among cardiac surgery patients, the effects of negative pressure wound management is unknown.Table 1:Baseline CharacteristicsBMI= Body Mass Index; COPD= Chronic obstructive pulmonary disease; CABG= Coronary artery bypass graft; SD= Standard deviation Methods The PICS-PREVENA Vanguard study, a 2x2 factorial, open label, cluster-randomized crossover trial, was conducted at 2 hospitals in Ontario, Canada (NCT03402945). We randomized each site to one of four sequences of the four study arms – each arm combining either cefazolin mono vs. combination prophylaxis with vancomycin (not analyzed) with either standard wound dressing vs. the 3M Prevena incision management system (Prevena). Only diabetic or obese patients (BMI >30kg/m2) were eligible for the latter comparison. For this population, we report the feasibility and efficacy endpoints of the Prevena versus standard wound dressing interventions. Feasability was assessed by adherence to the study protocol and loss to follow-up. The primary efficacy outcome was a composite of deep and organ-space s-SSI within 90-days after surgery. We used mixed logistic regression, accounting for clusters as a random effect for analysis. Funding: KCI USA, Inc.Table 2:Outcomes at end of follow up (90 days)BMI= Body Mass Index; s-SSI= Sternal surgical site infections; SSI= surgical site infections; ICU= intensive care unit; OR= odds ratio; SD= Standard deviation; CI= confidence interval^ Mean Difference* For patients with open venous saphenous harvest Results Among the 4107 included patients, 2230 were obese or diabetic. Of these patients, 1208 underwent surgery during a standard wound dressing period, and 1022 during a Prevena allocated period (Figure and Table 1). Of the latter, 696 (68.1%) had Prevena applied. Loss to follow up was 3.6% for obese or diabetic patients. Deep and organ-space s-SSI occurred in 16 (1.6%) and 17 (1.4%) in the Prevena and standard wound dressing allocated periods, respectively (OR= 1.11, 95% CI: 0.56- 2.20). Other clinical outcomes (Table 2) did not suggest a difference: all s-SSI (3.8% vs. 3.4%, OR: 1.12, 95% CI: 0.71- 1.75), SSI on legs (0.5% vs. 0.3%, OR: 1.41, 95% CI: 0.38- 5.30), and 90-day mortality (3.8% vs. 4.7%; OR: 0.79 (95% CI: 0.52- 1.20). An on-treatment analysis showed similar results.Figure 1:Treatment assignmentBMI= Body Mass Index Conclusion The vanguard study showed challenges with introducing a novel technology as standard of care with only a 68% compliance overall, with non-compliance mostly driven by one of the sites. No conclusions should be drawn regarding the efficacy of Prevena, as this Vanguard phase was not powered for these outcomes. Disclosures Richard Whitlock, PhD, Abbott: Grant/Research Support|Atricure: Grant/Research Support|CytoSorbents: Grant/Research Support PJ Devereaux, MD, PhD, Abbott Diagnostics: Advisor/Consultant|Abbott Diagnostics: Grant/Research Support|AOP Pharma: Grant/Research Support|Astra Zeneca: Advisor/Consultant|Bayer: Advisor/Consultant|CloudDX: Monitoring Devices|Quidel Canada: Advisor/Consultant|Renibus: Advisor/Consultant|Roche Canada: Advisor/Consultant|Roche Diagnostics: Grant/Research Support|Siemens: Grant/Research Support|Trimedic: Advisor/Consultant Dominik Mertz, MD, MSc, KCI Inc. USA: Grant/Research Support

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.349
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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