Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia

Predicting mechanical ventilation effects on six human tissue transcriptomes

BACKGROUND

Mechanical ventilation (MV) is a lifesaving therapy used for patients with respiratory failure. Nevertheless, MV is associated with numerous complications and increased mortality. The aim of this study is to define the effects of MV on gene expression of direct and peripheral human tissues.

METHODS

Classification models were applied to Genotype-Tissue Expression Project (GTEx) gene expression data of six representative tissues-liver, adipose, skin, nerve-tibial, muscle and lung, for performance comparison and feature analysis. We utilized 18 prediction models using the Random Forest (RF), XGBoost (eXtreme Gradient Boosting) decision tree and ANN (Artificial Neural Network) methods to classify ventilation and non-ventilation samples and to compare their prediction performance for the six tissues. In the model comparison, the AUC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model. We then conducted feature analysis per each tissue to detect MV marker genes followed by pathway enrichment analysis for these genes.

RESULTS

XGBoost outperformed the other methods and predicted samples had undergone MV with an average accuracy for the six tissues of 0.951 and average AUC of 0.945. The feature analysis detected a combination of MV marker genes per each tested tissue, some common across several tissues. MV marker genes were mainly related to inflammation and fibrosis as well as cell development and movement regulation. The MV marker genes were significantly enriched in inflammatory and viral pathways.

CONCLUSION

The XGBoost method demonstrated clear enhanced performance and feature analysis compared to the other models. XGBoost was helpful in detecting the tissue-specific marker genes for identifying transcriptomic changes related to MV. Our results show that MV is associated with reduced development and movement in the tissues and higher inflammation and injury not only in direct tissues such as the lungs but also in peripheral tissues and thus should be carefully considered before being implemented.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

New perspectives in the antibiotic treatment of mechanically ventilated patients with infections from Gram-negatives

Introduction: Ventilator-associated pneumonia (VAP) is a common and potentially fatal complication of mechanical ventilation that is often caused by multidrug-resistant (MDR) Gram-negative bacteria (GNB). Despite the repurposing of older treatments and the novel antimicrobials, many resistance mechanisms cannot be confronted, and novel therapies are needed.Areas covered: We searched the literature for keywords regarding the treatment of GNB infections in mechanically ventilated patients. This narrative review presents new data on antibiotics and non-antibiotic approaches focusing on Phase 3 trials against clinically significant GNB that cause VAP.Expert opinion: Ceftazidime-avibactam, meropenem-vaborbactam, and imipenem-relebactam stand out as new options for infections by Klebsiella pneumoniae carbapenemase-producing bacteria, whereas ceftolozane-tazobactam adds therapeutic flexibility in Pseudomonas aeruginosa infections with multiple resistance mechanisms. Ceftazidime-avibactam and ceftolozane-tazobactam have relevant literature. Aztreonam-avibactam holds promise for the treatment of infections by metallo-β-lactamase (MBL)-producing organisms. Recently approved cefiderocol possesses an extended antibacterial spectrum, including KPC- and MBL-producers. However, recently published data have toned down optimism about treating VAP caused by carbapenem-resistant Acinetobacter baumannii. For the latter, eravacycline may provide additional hope, pending pertinent data. Non-antibiotic treatments currently being considered as adjunct therapeutic approaches are welcome. Nevertheless, they will hopefully substitute current antimicrobials in the future.