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Record W3094731894 · doi:10.1017/ice.2020.796

Getting to the Heart of the Matter: Epidemiology of Surgical Site Infections Following Open Heart Surgery in Children

2020· article· en· W3094731894 on OpenAlexaff
Matthew A. Magyar, Allyson Shephard, Pat Bedard, Ken Tang, Gyaandeo Maharajh, Nisha Thampi

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineOdds ratioIncidence (geometry)Cardiac surgeryConfidence intervalEpidemiologyUnivariate analysisCardiopulmonary bypassLogistic regressionBathingEmergency medicineSurgeryInternal medicineMultivariate analysis

Abstract

fetched live from OpenAlex

Background: Surgical site infections (SSIs) following open heart surgery involving cardiopulmonary bypass (CPB) among pediatric patients are healthcare-associated infections associated with significant morbidity and mortality. At a pediatric acute-care facility, an increase in SSI incidence prompted an epidemiologic review. We describe the incidence of cardiac SSIs at our hospital; we identified risk factors and areas of practice variation to inform improvement initiatives. Methods: SSI cases following CPB at our hospital have been identified through routine surveillance using NHSN definitions since January 2016. An increase in cases was noted in mid-2018, prompting a common cause analysis with stakeholders across the preoperative, intraoperative, and postoperative care continuum. Areas of practice variability were identified, and an epidemiologic review was performed to determine risk factors among cases compared to noncases between January 2016 and August 2018. The rate of SSIs and 95% confidence intervals were estimated, and univariate logistic regressions were fitted to estimate unadjusted odds ratios (ORs) for the association between each of the predetermined preoperative, intraoperative, and postoperative factors and developing an SSI. Results: Overall, 139 patients underwent surgery involving CPB between January 1, 2016, and August 31, 2018. Preoperative bathing was infrequently documented (9% among cases vs 5% among noncases; P = .56). Operating room observations identified frequent door openings and equipment crowding. Moreover, 11 patients (7.9%) developed a cardiac SSI, with 6 (14.3%) occurring in the first 8 months of 2018 (P = .067). There were no predominant pathogens; 3 of 11 cases were associated with methicillin-susceptible Staphylococcus aureus. Also, 9 cases were classified as deep incisional or organ-space SSI. Each hour increase in total CPB duration was associated with a 63% increase in odds of developing an SSI (OR, 1.626; 95% CI, 1.041–2.539). Each additional day of intubation (OR, 2.400; 95% CI, 1.203–4.788) and peritoneal dialysis (OR, 1.767; 95% CI, 1.070–2.919) during the first 3 days postoperatively were also associated with increased SSI risk. Postoperative documentation of wound assessment occurred in 60% of patients, with no difference between cases and noncases (55% vs 67%; P = .42). Conclusions: Using a mixed-methods approach, preoperative bathing, increased operating room traffic, and postoperative care around wounds and invasive devices were identified as areas of improvement toward safer surgical care. Although no unique organism or process explained the increased rate, determining risk factors and areas of practice variability through stakeholder engagement provided insight into opportunities to prevent SSIs. Funding: None Disclosures: None

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.310
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes1
Has abstractyes

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