To develop a risk score for surgical site infections (SSIs) after coronary artery bypass grafting (CABG).

Retrospective study.

University hospital.

A derivation sample of 7,090 consecutive isolated or combined CABG patients and 2 validation samples (2,660 total patients).

Predictors of SSIs were identified by multivariable analyses from the derivation sample, and a risk stratification tool (additive and logistic) for all SSIs after CABG (acronym, ASSIST) was created. Accuracy of prediction was evaluated with C-statistic and compared 1:1 (using the Hanley-McNeil method) with most relevant risk scores for SSIs after CABG. Both internal (1,000 bootstrap replications) and external validation were performed.

SSIs occurred in 724 (10.2%) cases and 2 models of ASSIST were created, including either baseline patient characteristics alone or combined with other perioperative factors. Female gender, body mass index >29.3 kg/m2, diabetes, chronic obstructive pulmonary disease, extracardiac arteriopathy, angina at rest, and nonelective surgical priority were predictors of SSIs common to both models, which outperformed (P < .0001) 6 specific risk scores (10 models) for SSIs after CABG. Although ASSIST performed differently in the 2 validation samples, in both, as well as in the derivation data set, the combined model outweighed (albeit not always significantly) the preoperative-only model, both for additive and logistic ASSIST.

In the derivation data set, ASSIST outperformed specific risk scores in predicting SSIs after CABG. The combined model had a higher accuracy of prediction than the preoperative-only model both in the derivation and validation samples. Additive and logistic ASSIST showed equivalent performance.

Some manufacturers provide information on similar-fit model pairings of filtering facepiece respirators (FFRs), suggesting that fit-test outcome of one model helps predict the other. This guidance may be useful during crisis capacity when FFR supplies and/or fit tests are constrained. The purpose of this study was to compare quantitative fit-test (QNFT) results and concordance between a pair of similar-fit 3M Aura FFRs: the 3M 9320A+ and 3M 1870+.

All participants completed online training and a QNFT with both respirators. The order of the respirator being examined first was randomly allocated. The outcomes included QNFT pass rate, concordance between the 2 models, overall and individual fit factors, and percentage of male and female participants who passed or failed the QNFT.

We recruited 1,000 participants (668 females and 332 males). The QNFT pass rate, overall fit factors, and individual fit factors were significantly higher for the 3M 9320A+ than the 3M 1870+ FFR. The concordance between the models was “fair” (κ coefficient, 0.38). Male participants who passed a QNFT with either of the FFRs had 96% chance of passing the QNFT for the alternate model. Female participants who passed the 3M 1870+ had 97% chance of passing the QNFT for the 3M 9320A+ model. However, ∼1 in 12 females who passed the QNFT for the 3M 9320A+ failed the QNFT for the 3M 1870+ FFR.

Similar-fit paired FFR models may provide a consequentially different level of respiratory protection, especially for women. Our findings are important for FFR stockpiling and fit-testing strategies, especially during crisis capacity.

To describe changes in the environmental microbiota of a new neonatal intensive care unit (NICU) and potential implications for infection prevention and control (IPC) efforts.

Prospective observational study.

A newly constructed level IV neonatal cardiac intensive care unit (NCICU) before and after patient introduction and the original NICU prior to patient transfer.

Environmental samples were obtained from the original NICU prior to patient transfer to a new NCICU. Serial sampling of patient rooms and provider areas of the new NICU was conducted immediately prior to patient introduction and over an 11-month study period. Microbiota at each sampling point were characterized using Illumina sequencing of the V3/V4 region of the 16S rRNA gene. Microbiota characteristics (α and β diversity and differential abundance) were compared based on time, location, and clinical factors (room-level antibiotic use and patient turnover).

An immediate increase in the environmental differential abundance of gut anaerobes were seen after patient introduction. There was an increase in the relative abundance of Staphylococcus spp, Klebsiella spp, Pseudomonas spp, and Streptococcus spp over time. The new NCICU consistently showed more diverse microbiota and remained distinct from the original NICU. The microbiota of the provider areas of the NCICU eventually formed a cluster separate from the patient rooms. Patient turnover increased room-level microbiota diversity.

Microbiota characteristics of the new NICU were distinct from the original ICU despite housing similar patients. Patient and provider areas developed distinct microbiota profiles. Non–culture-based methods may be a useful adjunct to current IPC practice.