Worldwide prevalence and associated factors of nursing staff turnover: A systematic review and meta-analysis.

AIMS: To systematically assess the worldwide prevalence of nurse turnover and discuss its influencing factors. DESIGN: Systematic review and meta-analysis. METHODS: PubMed, the Cochrane Library, Web of Science, CINAHL, China Knowledge Resource Integrated Database, Wanfang Database were searched from their commencement date to 25 March 2021. Two authors independently reviewed the studies. Stata 15.0 software package was used for statistical analysis, with estimates of data on the prevalence of nurse turnover using a random-effects model. This review was performed according to the Joanna Briggs Institute (JBI) manual for evidence synthesis and the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement. PROSPERO Registration Number:CRD42020208873. RESULTS: A total of 15 studies covering 852,317 nurses were included in the analyses. The pooled prevalence of nurse turnover was 18% (95% CI: 11% to 26%, I2 = 99.86%, p < 0.0001). Geographic regions (Asia), published years (2001 to 2010) and respondents (new nurses) were significantly associated with the prevalence of nurse turnover. Additionally, several risk factors for turnover were identified in the literature, involving demographic factors (young, single, have short working hours, lower level of education and male nurses), organizational factors (small-scale hospitals, low salary levels, larger workload, developed region and absence of labour union), satisfaction (dissatisfaction with organization, profession, job and competence).

Dynamic Nomogram for Subsyndromal Delirium in Adult Intensive Care Unit: A Prospective Cohort Study.

Purpose: To develop a dynamic nomogram of subsyndromal delirium (SSD) in intensive care unit (ICU) patients and internally validate its efficacy in predicting SSD. Patients and Methods: Patients who met the inclusion and exclusion criteria in the ICU of a tertiary hospital in Zhejiang from September 2021 to June 2022 were selected as the research objects. The patient data were randomly divided into the training set and validation set according to the ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to screen the predictors of SSD, and R software was used to construct a dynamic nomogram. Receiver operating characteristic (ROC) curve, calibration band and decision curve were used to evaluate the discrimination, calibration and clinical effectiveness of the model. Results: A total of 1000 eligible patients were included, including 700 in the training set and 300 in the validation set. Age, drinking history, C reactive protein level, APACHE II, indwelling urinary catheter, mechanical ventilation, cerebrovascular disease, respiratory failure, constraint, dexmedetomidine, and propofol were predictors of SSD in ICU patients. The ROC curve values of the training set was 0.902 (95% confidence interval: 0.879-0.925), the best cutoff value was 0.264, the specificity was 78.4%, and the sensitivity was 88.0%. The ROC curve values of the validation set was 0.888 (95% confidence interval: 0.850-0.930), the best cutoff value was 0.543, the specificity was 94.9%, and the sensitivity was 70.9%. The calibration band showed good calibration in the training and validation set. Decision curve analysis showed that the net benefit in the model was significantly high. Conclusion: The dynamic nomogram has good predictive performance, so it is a precise and effective tool for medical staff to predict and manage SSD in the early stage.

Effects of the Implementation of Intelligent Technology for Hand Hygiene in Hospitals: Systematic Review and Meta-analysis.

BACKGROUND: The World Health Organization recommends regular hand hygiene monitoring and feedback to improve hand hygiene behaviors and health care-associated infection rates. Intelligent technologies for hand hygiene are increasingly being developed as alternative or supplemental monitoring approaches. However, there is insufficient evidence regarding the effect of this type of intervention, with conflicting results in the literature. OBJECTIVE: We conduct a systematic review and meta-analysis to evaluate the effects of using intelligent technology for hand hygiene in hospitals. METHODS: We searched 7 databases from inception to December 31, 2022. Two reviewers independently and blindly selected studies, extracted data, and assessed the risk of bias. A meta-analysis was performed using the RevMan 5.3 and STATA 15.1 software. Sensitivity and subgroup analyses were also conducted. Overall certainty of evidence was appraised using the Grading of Recommendations Assessment, Development, and Evaluation approach. The systematic review protocol was registered. RESULTS: The 36 studies comprised 2 randomized controlled trials and 34 quasi-experimental studies. The included intelligent technologies involved 5 functions: performance reminders，electronic counting and remote monitoring，data processing，feedback，and education. Compared with usual care, the intelligent technology intervention for hand hygiene improved health care workers' hand hygiene compliance (risk ratio 1.56, 95% CI 1.47-1.66; P<.001), reduced health care-associated infection rates (risk ratio 0.25, 95% CI 0.19-0.33; P<.001), and was not associated with multidrug-resistant organism detection rates (risk ratio 0.53, 95% CI 0.27-1.04; P=.07). Three covariates, including publication year, study design, and intervention, were not factors of hand hygiene compliance or hospital-acquired infection rates analyzed by meta-regression. Sensitivity analysis showed stable results except for the pooled outcome of multidrug-resistant organism detection rates. The caliber of 3 pieces of evidence suggested a dearth of high-caliber research. CONCLUSIONS: Intelligent technologies for hand hygiene play an integral role in hospital. However, low quality of evidence and important heterogeneity were observed. Larger clinical trials are required to evaluate the impact of intelligent technology on multidrug-resistant organism detection rates and other clinical outcomes.

Critical care nurses' experiences of physical restraint in intensive care units: A qualitative systematic review and meta-synthesis.

INTRODUCTION: Physical restraint is widely used in intensive care units. Critical care nurses are the primary decision makers and practitioners of physical restraint. However, little is known about the qualitative evidence of their experience of physical restraint. AIMS: To aggregate, synthesise and interpret the qualitative evidence of studies that explored critical care nurses' experiences of physical restraint in intensive care units. METHODS: A qualitative systematic review and meta-synthesis was conducted following Joanna Briggs Institute methodology and reported following the ENTREQ statement. Qualitative studies published in the English and Chinese were systematically searched in eight databases. PubMed, Web of Science, CINAHL, EMBASE, PsycINFO, China National Knowledge Infrastructure, Wan Fang, and Chinese Biomedical Literature Database from inception to November 2021. Two reviewers independently assessed the study eligibility and performed the data extraction and the quality appraisal. A meta-aggregative approach was used to synthesise findings. The review protocol was registered prospectively with PROSPERO (CRD42021278671). RESULTS: Thirteen studies were included. A total of 48 intact and definite codes were extracted and classified into ten sub-categories. Four themes were finally identified: intention, alternatives, determinants, and reflection. CONCLUSION: The intention of critical care nurses to use physical restraint is primarily driven by patient safety. Nurses will consider alternatives; however, many determinants urge nurses to implement physical restraint. Nurses prefer to sacrifice patients' comfort to ensure their safety. When nurses reflect on what they have done, some experience moral distress, but most rationalise their decision making. Further studies should explore the safety of alternatives, reduce the use of physical restraint, and pay more attention to nurses' moral distress through these insights. RELEVANCE TO CLINICAL PRACTICE: Developing alternatives, providing relevant training for nurses, physicians, patients, and families to facilitate a restraint-free culture, and developing high-quality guidelines and regulations are essential strategies to reduce the use of physical restraint in ICUs.

Development and implementation of a novel decision support tool on physical restraint use in critically ill adult patients.

AIM: To investigate whether a novel decision support tool would effectively minimize physical restraint use in critically ill adult patients. DESIGN: A nonequivalent quasi-experimental design combined with a descriptive qualitative approach was used. METHODS: A Restraint Decision Tree was developed based on a qualitative study that explored the barriers to employ the Restraint Decision Wheel. During the quasi-experimental study, patients admitted to the unit between October 2017 and March 2018 were enrolled as the control group receiving the Restraint Decision Wheel (n = 528), whereas patients between April 2018 and September 2018 were enrolled as the intervention group receiving the Restraint Decision Tree (n = 564). The physical restraint rate, accidental catheter removal rate and nurses' satisfaction were compared. RESULTS: The Restraint Decision Tree significantly decreased physical restraint use. No significant difference in the rate of accidental catheter removal was found. Nurses reported increased satisfaction with the restraint decision-making. CONCLUSIONS: The Restraint Decision Tree could minimize physical restraint use. Physicians' involvement in the restraint decision-making and nurses' competence in delirium assessment may be essential for successful implementation of the Restraint Decision Tree.

Value of pressure injury assessment scales for patients in the intensive care unit: Systematic review and diagnostic test accuracy meta-analysis.

OBJECTIVES: To review and examine the evidence of the value of pressure injury risk assessment scales in intensive care patients. RESEARCH METHODOLOGY: We searched MEDLINE, Embase, CINAHL, Web of Science, the Cochrane Library, China Biomedical Literature Service System, VIP Database and CNIK from inception to February 2019. Two reviewers independently assessed articles' eligibility and risk of bias using the Quality Assessment of Diagnostic Accuracy Studies-II (QUADAS-2). We used a hierarchical summary receiver operating characteristics (HSROC) model to conduct the meta-analysis of diagnostic accuracy. RESULT: Twenty-four studies were included, involving 16 scales and 15,199 patients in intensive care settings. Results indicated that the top four risk assessment scales were the Cubbin & Jackson Index (SEN = 0.84, SPE = 0.84, AUC = 0.90), the EVRUCI scale (SEN = 0.84, SPE = 0.68, AUC = 0.82), the Braden scale (SEN = 0.78, SPE = 0.61, AUC = 0.78), the Waterlow scale (SEN = 0.63, SPE = 0.46, AUC = 0.56). The Norton scale and the other eleven scales were tested in less than two studies and need to be further researched. CONCLUSION: The Braden scale, most frequently used in hospitals, is not the best risk assessment tool for critically ill patients. The Cubbin & Jackson Index has good diagnostic test accuracy. However, low quality of evidence and important heterogeneity were observed.

Risk predictive models for delirium in the intensive care unit: a systematic review and meta-analysis.

BACKGROUND: An emerging approach to prevent delirium in an intensive care unit is the use of risk prediction models. At present, there is no scientific comparison of the predictive effect of the prediction model. This systematic review and meta-analysis aimed to compare the performance of available delirium risk prediction models for intensive care units. METHODS: As of June 1st, 2019, articles on delirium prediction models of the intensive care patients were searched in the Cochrane Library, PubMed, Embase, Web of Science, CINAHL, ProQuest, and four Chinese databases. Studies describing the development or validation of risk prediction models for predicting delirium in ICU patients were included. The Prediction model Risk of Bias Assessment Tool (PROBAST) was used to assess the quality of included studies. A meta-analysis of the predictive performance was performed using the forest plot package in R3.6.1. RESULTS: A total of 21 studies with 14 models were included in this article. PRE-DELIRIC, E-PREDELIRIC, and recalibrated PRE-DELIRIC model were the most popular models, which had been externally validated in at least two studies. The pooled area under the receiver operator characteristic curve (AUC) were 0.844 (95% CI: 0.793-0.896), 0.763 (95% CI: 0.680-0.846) and 0.776 (95% CI: 0.738-0.813) respectively. Most of the other models were with C-statistics above 0.7. CONCLUSIONS: The E-PRE-DELIRIC model, PRE-DELIRIC model, or both are recommended to predict ICU delirium risk. However, the recommendation should be considered with caution because of substantial heterogeneity. The protocol was registered with PROSPERO (CRD42019130802).

Decision-making among the substitute decision makers in intensive care units: An investigation of decision control preferences and decisional conflicts.

AIMS: To explore decision control preferences and decisional conflicts and to analyse their association among the surrogate decision makers in the intensive care unit. DESIGN: The study carried out a cross-sectional survey among the surrogates. METHODS: The participants were 115 surrogate decision makers of critical patients, from August to September 2019. A Chi-squared test and logistic regression were used to assess decision control preferences and decisional conflicts, and Spearman's rank correlation coefficient was employed to examine their association. RESULTS: Of the 115 surrogate decision makers, 51.3% preferred a collaborative role, and 63.48% were somewhat unsure about making decisions. Logistic regression analysis identified decision control preferences was associated with surrogates' age, education level, and personality traits, while decisional conflicts was associated with surrogates' age, education level, character, medical expense burden, and Acute Physiology and Chronic Health Evaluation-II score. Cohen's kappa statistics showed a bad concordance of decision-making expectations and actuality, with kappa values of 0.158 (p < .05). Wherein surrogates who experienced discordance between their preferred and actual roles, have relatively higher decisional conflicts. CONCLUSION: This study identified individual differences of surrogate decision makers in decision control preferences and decisional conflicts. These results imply that incorporation of the individual decision preferences and communication styles into care plans is an important first step to develop high quality decision support. IMPACT: This research is a contribution to the limited study on decision control preferences and decisional conflicts among surrogate decision makers of critically ill patients. Moreover based on the investigation of understanding the status and related factors of decision preferences and decisional conflicts set the stage for developing effective decision support interventions.