Dimensionality reduction and ensemble of LSTMs for antimicrobial resistance prediction.

Bacterial resistance to antibiotics has been rapidly increasing, resulting in low antibiotic effectiveness even treating common infections. The presence of resistant pathogens in environments such as a hospital Intensive Care Unit (ICU) exacerbates the critical admission-acquired infections. This work focuses on the prediction of antibiotic resistance in Pseudomonas aeruginosa nosocomial infections at the ICU, using Long Short-Term Memory (LSTM) artificial neural networks as the predictive method.

Data and Network Analytics for COVID-19 ICU Patients: A Case Study for a Spanish Hospital.

The COVID-19 pandemic presents unprecedented challenges to the healthcare systems around the world. In 2020, Spain was among the countries with the highest Intensive Care Unit (ICU) hospitalization and mortality rates. This work analyzes data of COVID-19 patients admitted to a Spanish ICU during the first wave of the pandemic.

Artificial Intelligence to Get Insights of Multi-Drug Resistance Risk Factors during the First 48 Hours from ICU Admission.

Multi-drug resistance (MDR) is one of the most current and greatest threats to the global health system nowadays. This situation is especially relevant in Intensive Care Units (ICUs), where the critical health status of these patients makes them more vulnerable. Since MDR confirmation by the microbiology laboratory usually takes 48 h, we propose several artificial intelligence approaches to get insights of MDR risk factors during the first 48 h from the ICU admission.

Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit.

The presence of bacteria with resistance to specific antibiotics is one of the greatest threats to the global health system. According to the World Health Organization, antimicrobial resistance has already reached alarming levels in many parts of the world, involving a social and economic burden for the patient, for the system, and for society in general. Because of the critical health status of patients in the intensive care unit (ICU), time is critical to identify bacteria and their resistance to antibiotics.

Validity of the current risk assessment scale for pressure ulcers in intensive care (EVARUCI) and the Norton-MI scale in critically ill patients.

Objective: The objective of this study was to evaluate the validity of risk detection scales EVARUCI and Norton-MI (modified by INSALUD) to detect critical adult patients with the risk of developing pressure ulcers (PU) in an intensive care unit (ICU).

Design: The authors have conducted a descriptive, prospective study at the ICU in their hospital from 2008 to 2014. The evaluations of both scales were registered daily by nurses from the unit.

Prevention of Ventilator-Associated Pneumonia: The Multimodal Approach of the Spanish ICU "Pneumonia Zero" Program.

Objectives: The "Pneumonia Zero" project is a nationwide multimodal intervention based on the simultaneous implementation of a comprehensive evidence-based bundle measures to prevent ventilator-associated pneumonia in critically ill patients admitted to the ICU.

Design: Prospective, interventional, and multicenter study.

Setting: A total of 181 ICUs throughout Spain.

Combatting resistance in intensive care: the multimodal approach of the Spanish ICU "Zero Resistance" program.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2015 and co-published as a series in Critical Care. Other articles in the series can be found online at http://ccforum.com/series/annualupdate2015.

[Usefulness of failure mode and effects analysis to improve patient safety during the process of incorporating new nurses in an intensive care unit].

Objective: To analyze proactively the process of incorporating new nurses in the intensive care unit (ICU) in order to detect risk areas and establish improvements that increase critical patient safety.

Material And Methods: Once the risk area was defined, the different phases of failure mode and effects analysis (FMEA) were applied: work team selection; process design; process phases definition; failure modes, possible causes and effects analysis; risk priority for each failure, and development of ameliorating and corrective actions. The proposed actions consisted of an orientation and training program (theoretical and practical) for new nurses, a supervision plan, a progressive responsibility program and ICU participation in personnel recruitment.

[Perception of safety culture in Spanish intensive care units].

Background And Objective: Promoting a safety culture in intensive care units (ICUs) is a basic strategy to improve patient safety. The aim of this study was to measure the safety culture in Spanish ICUs.

Method: We drafted a questionnaire based on the Safety Climate Survey (SCS) and the Safety Attitude Questionnaire-ICU model (SAQ-ICU).