Conectar
Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore which patient characteristics and variables significantly influence the discharge planning of patients, based on the data available within 24 h of admission.
Prospective observational study.
A prospective cohort was conducted at a university hospital with 523 patients hospitalised for stroke. We built and trained six different machine learning (ML) models, followed by testing and tuning those models to find the best-suited predictor for discharge disposition, dichotomized into home and non-home. To evaluate the accuracy, reliability and interpretability of the best-performing models, we identified and analysed the features that had the greatest impact on the predictions.
In total, 523 patients met the inclusion criteria, with a mean age of 61 years. Of the patients with stroke, 30.01% had non-home discharge. Our model predicting non-home discharge achieved an area under the receiver operating characteristic curve of 0.95 and a precision of 0.776. After threshold was moved, the model had a recall of 0.809. Top 10 variables by importance were National Institutes of Health Stroke Scale (NIHSS) score, family income, Barthel index (BI) score, FRAIL score, fall risk, pressure injury risk, feeding method, depression, age and dysphagia.
The ML model identified higher NIHSS, BI, and FRAIL, family income, higher fall risk, pressure injury risk, older age, tube feeding, depression and dysphagia as the top 10 strongest risk predictors in identifying patients who required non-home discharge to higher levels of care. Modern ML techniques can support timely and appropriate clinical decision-making.
This study illustrates the characteristics and risk factors of non-home discharge in patients with stroke, potentially contributing to the improvement of the discharge process.
STROBE guidelines.
To identify latent profiles of competence and perceptions of spiritual care among clinical nurses and explore the possible influencing factors.
Understanding nurses' level of spiritual care competence and their perceptions and acceptance of such care is important, which could help devise nurse training programmes to address such competence in clinical nurses. However, research addressing interindividual variability in competence and perceptions among Chinese nurses is lacking.
Multicentre cross-sectional study.
Nurses working in departments with critically ill patients from 12 community, 5 secondary and 10 tertiary hospitals in Shanghai completed a demographic information questionnaire and the Chinese versions of the Spiritual Care Competence Scale, Spiritual Care-Giving Scale and Spiritual Perspectives Scale. The data were analysed using IBM SPSS v26.0 and Mplus version 8.3. Latent profile analysis identified subgroups with different levels of spiritual care competence.
In total, 1277 Chinese nurses were recruited. Four profiles of competence and perceptions of spiritual care were revealed: Low ability (23.8%), High ability (6.4%), High acceptance (34.9%) and Moderate (34.9%). The level of job position, spiritual care-related education, hospital grade and nurses' perceptions and perspectives of spiritual care predicted the probability of profile memberships in their competence.
There was heterogeneity in the characteristics of spiritual care competence. Nursing managers can implement individualised interventions, including relevant training, according to the influencing factors of different competence profiles to improve the level of such competence among nurses.
The results provide a new and expanded view of improving nurses' spiritual care competence. Interprofessional collaboration with clinicians, administrators, educators and spiritual leaders can contribute to the development of related education and training.
EQUATOR guidelines, STROBE checklist: cross-sectional studies.
All participants were clinical nurses. Participants were informed they could withdraw from the study at any time.
FreshRSS 