Best practices to reduce microbial contamination during media-fill testing process in hospital pharmacies: a scoping review of literature.

Unlabelled: The Media-Fill Test is a crucial procedure in the pharmaceutical industry, especially for the production of sterile drugs that do not undergo terminal sterilization (meaning they are prepared directly under aseptic conditions).The primary goal of the Media-Fill Test is to evaluate the overall effectiveness of the aseptic process in preventing microbial contamination. It doesn't serve to test the sterility of an individual production batch, but rather to: Validate the aseptic process, Qualify personnel, Identify critical points and Verify the environment.

Institutionalization of Health Technology Assessment of medical devices: a cluster analysis of EU, EEA, and EFTA countries.

Objectives: Health technology assessment of medical devices (HTA-MDs) presents unique challenges compared to pharmaceuticals. Total MD expenditure continues to grow in Europe, and countries typically conduct their own HTA-MDs evaluations, with varying institutionalization arrangements. European Union's (EU's) HTA Regulation aims to establish collaborative clinical assessments across Member States, potentially expediting the path from EU safety certification of MDs to pricing and reimbursement decisions.

Mortality and admissions for cerebrovascular and cardiovascular diseases after the Accumoli-Amatrice 2016 earthquake.

Objective: The aim of this study is to evaluate if the mortality and hospital admissions for cerebrovascular and cardiovascular diseases (CCVDs), with a specific focus on acute myocardial infarction (AMI), significantly increased within 12 months after the 2016 Accumoli-Amatrice earthquake in the 17 most hit municipalities.

Methods: A diff-in-diff regression model was applied to the annual occurrences to assess the differences in the mortality and hospitalization for selected CCVDs before and after the Earthquake in the area most hit by the earthquake compared to a surrounding area. A focus analysis on acute myocardial infarction (AMI) was performed.

Ranking Nursing Diagnoses by Predictive Relevance for Intensive Care Unit Transfer Risk in Adult and Pediatric Patients: A Machine Learning Approach with Random Forest.

: In hospital settings, the wide variability of acute and complex chronic conditions-among both adult and pediatric patients-requires advanced approaches to detect early signs of clinical deterioration and the risk of transfer to the intensive care unit (ICU). Nursing diagnoses (NDs), standardized representations of patient responses to actual or potential health problems, reflect nursing complexity. However, most studies have focused on the total number of NDs rather than the individual role each diagnosis may play in relation to outcomes such as ICU transfer.

A Comparison of Machine Learning-Based Models and a Simple Clinical Bedside Tool to Predict Morbidity and Mortality After Gastrointestinal Cancer Surgery in the Elderly.

Frailty in the elderly population is associated with increased vulnerability to stressors, including surgical interventions. This study compared machine learning (ML) models with a clinical bedside tool, the Gastrointestinal Surgery Frailty Index (GiS-FI), for predicting mortality and morbidity in elderly patients undergoing gastrointestinal cancer surgery. In a multicenter analysis of 937 patients aged ≥65 years, the performance of various predictive models including Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), Stepwise Regression, K-Nearest Neighbors, Neural Network, and Support Vector Machine algorithms were evaluated.