Conectar
The objective of this study was to construct and validate a structural equation model (SEM) to identify factors associated with sleep quality in awake patients in the intensive care unit (ICU) and to assist in the development of clinical intervention strategies.
In this cross-sectional study, 200 awake patients who were cared for in the ICU of a tertiary hospital in China were surveyed via several self-report questionnaires and wearable actigraphy sleep monitoring devices. Based on the collected data, structural equation modelling analysis was performed using SPSS and AMOS statistical analysis software. The study is reported using the STROBE checklist.
The fit indices of the SEM were acceptable: χ2/df = 1.676 (p < .001) and RMSEA = .058 (p < 0.080). Anxiety/depression had a direct negative effect on the sleep quality of awake patients cared for in the ICU (β = −.440, p < .001). In addition, disease-freeness progress had an indirect negative effect on the sleep quality of awake patients cared for in the ICU (β = −.142, p < .001). Analgesics had an indirect negative effect on the sleep quality of awake patients cared for in the ICU through pain and sedatives (β = −.082, p < .001). Sedation had a direct positive effect on the sleep quality of conscious patients cared for in the ICU (β = .493; p < .001).
The results of the SEM showed that the sleep quality of awake patients cared for in the ICU is mainly affected by psychological and disease-related factors, especially anxiety, depression and pain, so we can improve the sleep quality of patients through psychological intervention and drug intervention.
To assess the prognostic accuracy of the surprise question (SQ) when used by nurses working in hospital wards to determine 1-year mortality in acutely hospitalised older patients.
The predictive accuracy of the SQ, when used by general nurses caring for older hospitalised patients, has not been comprehensively studied.
A prospective cohort study.
This cohort study recruited consecutive 10,139 older patients (aged ≥65 years) who were admitted to Taipei City Hospital and were evaluated for the needs of palliative care in 2015. All patients were followed up for 12 months or until their death. The c-statistic value was calculated to indicate the predictive accuracy of the SQ and Palliative Care Screening Tool (PCST).
Of all participants, 18.8% and 18.6% had a SQ response of ‘no’ and a PCST score ≥4, respectively. After controlling for other covariates, an SQ response of ‘no’ (adjusted hazard ratio [aHR], 2.05; 95% confidence interval [CI], 1.83–2.31) and a PCST score ≥4 (AHR = 1.50; 95% CI: 1.29–1.75) were found to be the independent predictors for patients' 12-month mortality. The C-statistic values of the SQ and the PCST at recognising patients in their last year of life were .663 and .670, respectively. Moreover, there was moderate concordance (k = .44) between the SQ and the PCST in predicting 12-month mortality.
SQ response of ‘no’ and a PCST score ≥4 were independent predictors of 12-month mortality in older patients.
The SQ, when used by nurses working in hospital wards, is effective in identifying older patients nearing the end of life, as well as in providing advance care planning for patients.
Patients' palliative care needs at admission were assessed by general nurses using the SQ and PCST.
FreshRSS 