Various scoring systems attempt to predict the risk of surgical site infection (SSI) after cardiac surgery, but their discrimination is limited. Our aim was to analyze all SSI risk factors in both coronary artery bypass graft (CABG) and valve replacement patients in order to create a new SSI risk score for such individuals. A priori prospective collected data on patients that underwent cardiac surgery (n = 2020) were analyzed following recommendations from the Reporting of studies Conducted using Observational Routinely collected health Data (RECORD) group. Study participants were divided into two periods: the training sample for defining the new tool (2010–2014, n = 1298), and the test sample for its validation (2015–2017, n = 722). In logistic regression, two preoperative variables were significantly associated with SSI (odds ratio (OR) and 95% confidence interval (CI)): diabetes, 3.3/2–5.7; and obesity, 4.5/2.2–9.3. The new score was constructed using a summation system for punctuation using integer numbers, that is, by assigning one point to the presence of either diabetes or obesity. The tool performed better in terms of assessing SSI risk in the test sample (area under the Receiver-Operating Characteristic curve (aROC) and 95% CI, 0.67/055–0.76) compared to the National Nosocomial Infections Surveillance (NNIS) risk index (0.61/0.50–0.71) and the Australian Clinical Risk Index (ACRI) (0.61/0.50–0.72). A new two-variable score to preoperative SSI risk stratification of cardiac surgery patients, named Infection Risk Index in Cardiac surgery (IRIC), which outperforms other classical scores, is now available to surgeons. Personalization of treatment for cardiac surgery patients is needed.

Typically, skin lesions caused by monkeypox (MPX) begin as a monomorphic exanthema with centrifugal distribution, and can be found at different sequential stages in different regions of the body. T...

Our aim was to assess whether airborne particle counting is an immediate indicator of biocontamination in controlled environment rooms with HEPA filters in a hospital. A prospective study was carried out in a tertiary care hospital between 2016 and 2018. The study was divided in two periods and the measurements were performed in different controlled environment rooms with HEPA filters. The Environmental Biosafety Criterion (EBC) was defined as the absence of fungal and bacterial contamination. In the training period, the area under the ROC curve (aROC) of airborne particle counting and EBC was calculated for each particle size as well as the cut-off points that optimize the combination of sensitivity and specificity in the association between them. aROC is created by plotting sensitivity against 1-specificity. In the testing period, the cut-off points previously selected were validated. 328 measurements were carried out in the training period and 301 in the testing period. In the training period, an association was found between airborne particle counting and EBC. An aROC = 0.760, 95% Confidence Interval (95% CI) 0.695–0.825 was observed for 0.3 µm particles; an aROC = 0.797 (95% CI 0.734–0.860) for 0.5 µm particles; and an aROC = 0.751 (95% CI 0.673–0.829) for 5 µm particles. The cut-off points that optimized the combination of sensitivity and specificity were 9.0 × 10³ for 0.3 µm particles, 3.6 × 10³ particles for 0.5 µm, and 3.2 × 10² particles for 5 µm. In the testing period, the previous cut-off points were validated. We conclude that airborne particle counting is a useful, immediate, and preliminary measure to identify the presence of biocontamination in controlled environment rooms with HEPA filters.