Fine-grained mathematical modeling for cost-effectiveness evaluation of public health policies for cervical cancer, with application to a Colombian case study.

Background: Cervical cancer (CC) is globally ranked fourth in terms of incidence and mortality among women. Vaccination against Human Papillomavirus (HPV) and screening programs can significantly reduce CC mortality rates. Hence, executing cost-effective public health policies for prevention and surveillance is crucial.

Air microbial sampling in operating theatres by active and passive methods: equation correlation from the GISIO-ISChIA study results and comparison with the EU GGMP recommendation, towards the definition of threshold values.

The aim of this study was to calculate the equation of correlation between the microbial air contamination values obtained by active sampling (colony-forming units per cubic metre, CFU/m3) and by passive sampling (Index of microbial air contamination, IMA), by using the data from the ISChIA study, and to compare the values obtained with the recommended limits defined by the EU Guidelines to Good Manufacturing Practice (EU GGMP), 2008, for clean areas used to manufacture sterile medicinal products. Air sampling was performed during 335 elective prosthesis procedures. Correlation between CFU/m3 and IMA values was evaluated using the Spearman test; p<0.

Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project.

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients' characteristics at ICU admission. We used data from the "Italian Nosocomial Infections Surveillance in Intensive Care Units" network.

A machine learning approach to predict healthcare-associated infections at intensive care unit admission: findings from the SPIN-UTI project.

Background: Identifying patients at higher risk of healthcare-associated infections (HAIs) in intensive care units (ICUs) represents a major challenge for public health. Machine learning could improve patient risk stratification and lead to targeted infection prevention and control interventions.

Aim: To evaluate the performance of the Simplified Acute Physiology Score (SAPS) II for HAI risk prediction in ICUs, using both traditional statistical and machine learning approaches.

Cluster analysis identifies patients at risk of catheter-associated urinary tract infections in intensive care units: findings from the SPIN-UTI Network.

Background: Although preventive strategies have been proposed against catheter-associated urinary tract infections (CAUTIs) in intensive care units (ICUs), more efforts are needed to control the incidence rate.

Aim: To distinguish patients according to their characteristics at ICU admission, and to identify clusters of patients at higher risk for CAUTIs.

Methods: A two-step cluster analysis was conducted on 9656 patients from the Italian Nosocomial Infections Surveillance in Intensive Care Units project.