The effect of pressure injury prevention care bundles on pressure injuries in hospital patients: A complex intervention systematic review and meta-analysis.

BACKGROUND: Numerous interventions for pressure injury prevention have been developed, including care bundles. OBJECTIVE: To systematically review the effectiveness of pressure injury prevention care bundles on pressure injury prevalence, incidence, and hospital-acquired pressure injury rate in hospitalised patients. DATA SOURCES: The Medical Literature Analysis and Retrieval System Online (via PubMed), the Cumulative Index to Nursing and Allied Health Literature, EMBASE, Scopus, the Cochrane Library and two registries were searched (from 2009 to September 2023). STUDY ELIGIBILITY CRITERIA: Randomised controlled trials and non-randomised studies with a comparison group published in English after 2008 were included. Studies reporting on the frequency of pressure injuries where the number of patients was not the numerator or denominator, or where the denominator was not reported, and single subgroups of hospitalised patients were excluded. Educational programmes targeting healthcare professionals and bundles targeting specific types of pressure injuries were excluded. PARTICIPANTS AND INTERVENTIONS: Bundles with ≥3 components directed towards patients and implemented in ≥2 hospital services were included. STUDY APPRAISAL AND SYNTHESIS METHODS: Screening, data extraction and risk of bias assessments were undertaken independently by two researchers. Random effects meta-analyses were conducted. The certainty of the body of evidence was assessed using Grading of Recommendations, Assessment, Development and Evaluation. RESULTS: Nine studies (seven non-randomised with historical controls; two randomised) conducted in eight countries were included. There were four to eight bundle components; most were core, and only a few were discretionary. Various strategies were used prior to (six studies), during (five studies) and after (two studies) implementation to embed the bundles. The pooled risk ratio for pressure injury prevalence (five non-randomised studies) was 0.55 (95 % confidence intervals 0.29-1.03), and for hospital-acquired pressure injury rate (five non-randomised studies) it was 0.31 (95 % confidence intervals 0.12-0.83). All non-randomised studies were at high risk of bias, with very low certainty of evidence. In the two randomised studies, the care bundles had non-significant effects on hospital-acquired pressure injury incidence density, but data could not be pooled. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: Whilst some studies showed decreases in pressure injuries, this evidence was very low certainty. The potential benefits of adding emerging evidence-based components to bundles should be considered. Future effectiveness studies should include contemporaneous controls and the development of a comprehensive, theory and evidence-informed implementation plan. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42023423058. TWEETABLE ABSTRACT: Pressure injury prevention care bundles decrease hospital-acquired pressure injuries, but the certainty of this evidence is very low.

Comparing the effect of the Model of Therapeutic Engagement in cardiac rehabilitation on the sense of coherence and adherence to treatment: a randomized clinical trial.

PURPOSE: The study aimed to compare the effectiveness of a traditional cardiac rehabilitation (CR) program with an enhanced program incorporating the model of therapeutic engagement (MTE) and extended remote support for patients undergoing coronary artery bypass graft (CABG) patients. MATERIALS AND METHODS: In a randomized controlled trial, 88 CABG patients were assigned to experimental and control groups. The experimental group received integrated MTE cardiac rehabilitation, and assessments were conducted at three time points: pre-CR, one month later, and three months post-CR. The study measured medication adherence (MARS-5) and sense of coherence (SoC-13) scales. RESULTS: The study found no significant differences in demographic factors between the experimental and control groups. However, significant differences were observed in MARS and individuals' SoC scores over time in the experimental group, with notable improvements (p < 0.001). The control group showed significant changes only up to one month. Group effects were evident, with consistent increases in the experimental group's outcomes at each assessment point. CONCLUSION: Integrating the MTE into CR programs offers benefits in terms of medication adherence and individuals' sense of coherence, which warrants further investigation and clinical implementation.

Cardiac rehabilitation (CR) is recognized as one of the most effective interventions for secondary prevention, but its accessibility is limited in middle-income countries (MICs).This study represents one of the first theoretically-informed CR trials in a MIC that incorporates the model of therapeutic engagement (MTE) combined with extended remote support services into CR program.The MTE model, as a theoretical framework, was highly suitable for CR settings and demonstrated favorable outcomes.This approach has the potential to greatly benefit cardiac patients, particularly those who may initially show hesitance or reluctance towards engaging in CR.

Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review.

BACKGROUND AND OBJECTIVE: Early identification of patients at risk of deterioration can prevent life-threatening adverse events and shorten length of stay. Although there are numerous models applied to predict patient clinical deterioration, most are based on vital signs and have methodological shortcomings that are not able to provide accurate estimates of deterioration risk. The aim of this systematic review is to examine the effectiveness, challenges, and limitations of using machine learning (ML) techniques to predict patient clinical deterioration in hospital settings. METHODS: A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) guidelines using EMBASE, MEDLINE Complete, CINAHL Complete, and IEEExplore databases. Citation searching was carried out for studies that met inclusion criteria. Two reviewers used the inclusion/exclusion criteria to independently screen studies and extract data. To address any discrepancies in the screening process, the two reviewers discussed their findings and a third reviewer was consulted as needed to reach a consensus. Studies focusing on use of ML in predicting patient clinical deterioration that were published from inception to July 2022 were included. RESULTS: A total of 29 primary studies that evaluated ML models to predict patient clinical deterioration were identified. After reviewing these studies, we found that 15 types of ML techniques have been employed to predict patient clinical deterioration. While six studies used a single technique exclusively, several others utilised a combination of classical techniques, unsupervised and supervised learning, as well as other novel techniques. Depending on which ML model was applied and the type of input features, ML models predicted outcomes with an area under the curve from 0.55 to 0.99. CONCLUSIONS: Numerous ML methods have been employed to automate the identification of patient deterioration. Despite these advancements, there is still a need for further investigation to examine the application and effectiveness of these methods in real-world situations.

Individuals' Intention to Engage in Outpatient Cardiac Rehabilitation Programs: Prediction Based on an Enhanced Model.

Motivation is an important factor in encouraging individuals to attend rehabilitation and underpins many approaches to engagement. The aims of this study were to develop an accurate model able to predict individual intention to engage in outpatient cardiac rehabilitation (CR) programs based on the first stage of the Model of Therapeutic Engagement integrated into a socio-environmental context. The cross-sectional study in the cardiology ward of an Australian hospital included a total of 217 individuals referred to outpatient CR. Through an ordinal logistic regression, the effect of random forest (RF)-selected profile features on individual intention to engage in outpatient CR was explored. The RF based on the conditional inference trees predicted the intention to engage in outpatient CR with high accuracy. The findings highlighted the significant roles of individuals' 'willingness to consider the treatment', 'perceived self-efficacy' and 'perceived need for rehabilitation' in their intention, while the involvement of 'barriers to engagement' and 'demographic and medical factors' was not evident.

Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation.

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R(2) were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R(2) confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.