Development of an evidence-based nursing practice program for preventing unplanned endotracheal extubations in the intensive care unit: A Delphi method study.

AIMS: This study aims to develop an evidence-based nursing practice program to prevent unplanned endotracheal extubation (UEE) among adult patients in the intensive care unit (ICU). DESIGN: This study uses the Delphi method to develop an evidence-based nursing practice program. METHODS: A comprehensive review of 18 databases and evidence-based websites was conducted to gather, assess and synthesize evidence on preventing UEEs in adult patients. Using this synthesized evidence, a questionnaire was formulated for further investigation. Subsequently, input was solicited from experts through Delphi surveys to establish an evidence-based nursing practice protocol for preventing UEEs in adult ICU patients. Consistency in consultation results guided subsequent rounds of consultation. RESULTS: The developed program comprised 43 evidence items categorized into nine dimensions, including risk assessment for unplanned extubation, sedation, analgesia, delirium, balloon management, psychosocial care, early extubation, catheter immobilization and protective restraints. Two rounds of expert inquiry yielded recovery rates of 94.7% and 100% for the first and second questionnaires, respectively. Kendall W values ranged from .224 to .353 (p < .001). CONCLUSION: This study developed an evidence-based nursing practice program to prevent UEE in adult ICU patients, employing evidence-based practices and Delphi expert consultation methods. However, further validation of the program's effectiveness is warranted. REPORTING METHOD: Findings were reported according to the Standards for Reporting Qualitative Research checklist. PATIENT OR PUBLIC CONTRIBUTION: Nurses contributed to the study by participating in investigations. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: The program developed in this study offers an evidence-based framework for preventing unplanned extubation in hospitals, thereby reducing its incidence and enhancing the quality of nursing care.

Development and validation of early prediction models for new-onset functional impairment in patients after being transferred from the ICU.

A significant number of intensive care unit (ICU) survivors experience new-onset functional impairments that impede their activities of daily living (ADL). Currently, no effective assessment tools are available to identify these high-risk patients. This study aims to develop an interpretable machine learning (ML) model for predicting the onset of functional impairment in critically ill patients. Data for this study were sourced from a comprehensive hospital in China, focusing on adult patients admitted to the ICU from August 2022 to August 2023 without prior functional impairments. A least absolute shrinkage and selection operator (LASSO) model was utilized to select predictors for inclusion in the model. Four models, logistic regression, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were constructed and validated. Model performance was assessed using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Additionally, the DALEX package was employed to enhance the interpretability of the final models. The study ultimately included 1,380 patients, with 684 (49.6%) exhibiting new-onset functional impairment on the seventh day after leaving the ICU. Among the four models evaluated, the SVM model demonstrated the best performance, with an AUC of 0.909, accuracy of 0.838, sensitivity of 0.902, specificity of 0.772, PPV of 0.802, and NPV of 0.886. ML models are reliable tools for predicting new-onset functional impairments in critically ill patients. Notably, the SVM model emerged as the most effective, enabling early identification of patients at high risk and facilitating the implementation of timely interventions to improve ADL.

Study on the determinants of suckling mice neurovirulence of dengue 2 virus.

pDVWS501 was a genomic-length cDNA clone of dengue 2 virus, through which infectious virus (MON501) could be rescued. MON501 was neurovirulent in mice, whose E residues 62 and 203 were Lys and Asn, respectively. Two genomic-length cDNA clones (TB62 and TB203) were constructed by pointed mutation of pDVWS501 with OL-PCR, E62 of TB62 and E203 of TB203 were converted to Glu and Asp, respectively. RNA transcripts of pDVWS501, TB62 and TB203 were produced in vitro and electroporated into BHK-21 cells. The cultures were collected after 7 days and used as inoculum to infect C6/36 cells. The existence of rescued dengue viruses in the culture was proved by RT-PCR, and the typical cytopathic effect (CPE) of C6/36 caused by dengue virus emerged after 2-5 days' inoculation. Sequence analysis further confirmed the existence of recovered and recombinant DEN2 viruses, whose 5' termini had an additional non-virus nucleotides "G", while the 3' terminal sequences remained the same as natural. The neurovirulence of three viruses was evaluated in 1-day-old mice by the intracerebral route with 10(5)-10(2) TCID50. Compared with MON501 group, the number of infected mice with the signs of encephalitis in HFT62 and HFT203 groups was less, and the surviving time was longer. The properties of these mutants demonstrated that E62 and E203 are determinants of suckling mice neurovirulence.