Background: end-of-life care is not always in line with end-of-life preferences, so patients do not always die at their preferred place of death (PPD). This study aims to identify factors associated with patients' PPD and changes in PPD. Methods: we prospectively collected data on PPD at four time points within 6 months from 230 acutely hospitalised older patients who were part of the control group in a stepped-wedge randomised controlled trial. Associations between patient characteristics and preferences were calculated using multivariable (multinomial) logistic regression analysis. Results: the mean age of participants was 80.7 years. 47.8% of the patients had no PPD at hospital admission. Patients previously admitted to hospital preferred to die at home (home versus no preference: odds ratio [OR] 2.38, 95% confidence interval [CI] 1.15-4.92; home versus healthcare facility: OR 3.25, 95% CI 1.15-9.16). Patients with more chronic diseases preferred the healthcare facility as their PPD (healthcare facility versus no preference: OR 1.33, 95% CI 1.09-1.61; healthcare facility versus home: OR 1.21, 95% CI 1.00-1.47). 32 of 65 patients changed their preference during follow-up, and most of these had no PPD at hospital admission (home versus no preference: OR 0.005, 95% CI ≤0.001-0.095) and poorer self-rated well-being (OR 1.82, 95% CI 1.07-3.08). Conclusions: almost half of the patients had no PPD at baseline. Previous hospital admission, having more chronic diseases and living alone are associated with having a PPD. Introducing PPD could make older people aware of PPD and facilitate optimal palliative care.
OBJECTIVE: To further test the validity and clinical usefulness of the steep ramp test (SRT) in estimating exercise tolerance in cancer survivors by external validation and extension of previously published prediction models for peak oxygen consumption (Vo2peak) and peak power output (Wpeak).DESIGN: Cross-sectional study.SETTING: Multicenter.PARTICIPANTS: Cancer survivors (N=283) in 2 randomized controlled exercise trials.INTERVENTIONS: Not applicable.MAIN OUTCOME MEASURES: Prediction model accuracy was assessed by intraclass correlation coefficients (ICCs) and limits of agreement (LOA). Multiple linear regression was used for model extension. Clinical performance was judged by the percentage of accurate endurance exercise prescriptions.RESULTS: ICCs of SRT-predicted Vo2peak and Wpeak with these values as obtained by the cardiopulmonary exercise test were .61 and .73, respectively, using the previously published prediction models. 95% LOA were ±705mL/min with a bias of 190mL/min for Vo2peak and ±59W with a bias of 5W for Wpeak. Modest improvements were obtained by adding body weight and sex to the regression equation for the prediction of Vo2peak (ICC, .73; 95% LOA, ±608mL/min) and by adding age, height, and sex for the prediction of Wpeak (ICC, .81; 95% LOA, ±48W). Accuracy of endurance exercise prescription improved from 57% accurate prescriptions to 68% accurate prescriptions with the new prediction model for Wpeak.CONCLUSIONS: Predictions of Vo2peak and Wpeak based on the SRT are adequate at the group level, but insufficiently accurate in individual patients. The multivariable prediction model for Wpeak can be used cautiously (eg, supplemented with a Borg score) to aid endurance exercise prescription.
Due to the ageing population, the prevalence of musculoskeletal disorders will continue to rise, as well as healthcare expenditure. To overcome these increasing expenditures, integration of orthopaedic care should be stimulated. The Primary Care Plus (PC+) intervention aimed to achieve this by facilitating collaboration between primary care and the hospital, in which specialised medical care is shifted to a primary care setting. The present study aims to evaluate the referral decision following orthopaedic care in PC+ and in particular to evaluate the influence of diagnostic tests on this decision. Therefore, retrospective monitoring data of patients visiting PC+ for orthopaedic care was used. Data was divided into two periods; P1 and P2. During P2, specialists in PC+ were able to request additional diagnostic tests (such as ultrasounds and MRIs). A total of 2,438 patients visiting PC+ for orthopaedic care were included in the analysis. The primary outcome was the referral decision following PC+ (back to the general practitioner (GP) or referral to outpatient hospital care). Independent variables were consultation- and patient-related predictors. To describe variations in the referral decision, logistic regression modelling was used. Results show that during P2, significantly more patients were referred back to their GP. Moreover, the multivariable analysis show a significant effect of patient age on the referral decision (OR 0.86, 95% CI = 0.81– 0.91) and a significant interaction was found between the treating specialist and the period (p = 0.015) and between patient’s diagnosis and the period (p < 0.001). Despite the significant impact of the possibility of requesting additional diagnostic tests in PC+, it is important to discuss the extent to which the availability of diagnostic tests fits within the vision of PC+. In addition, selecting appropriate profiles for specialists and patients for PC+ are necessary to further optimise the effectiveness and cost of care.
