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To construct a nursing programme to manage intra-abdominal pressure (IAP) in neurocritical care patients.
The consensus of 16 experts was collected using a two-round Delphi method.
First, we developed the early stages of the nursing programme for managing IAP in neurocritical care patients through preliminary study and relevant literature internationally. A questionnaire was then distributed to a panel of 16 experts, each with over 10 years of experience in respiratory critical care, neurocritical care or comprehensive intensive care unit treatment. Between April and May 2024, these experts reviewed the preliminary programme and provided feedback and recommendations for modifications.
Two rounds of expert consultation were conducted. After the first round of expert feedback, 22 items were revised. In the second round, eight additional items were revised. The questionnaire recovery rates in both rounds of correspondence were 88.9% and 100%, and the authority coefficients were 0.869 and 0.888, respectively. The Kendall W values ranged from 0.127 to 0.336 (p < 0.001). Consensus was reached on six Level 1 entries, 17 Level 2 entries, and 50 Level 3 entries.
A panel of 16 experts approved the proposed nursing approach for managing IAP in neurocritical care.
This nursing protocol offers a systematic approach for managing IAP throughout all stages of care in neurocritical settings. Moreover, this programme can guide neurocritical care nurses in maintaining optimal IAP at critical times. The protocol could potentially be used in training nurses on IAP regulation and enhancing their management skills in this specialised area of care, along with preventing IAP-related health issues.
The study is reported in accordance with the Guidance on Conducting and Reporting DElphi Studies (CREDES) recommendations.
No patient or public contribution.
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.
FreshRSS 