Summary Purpose The purpose of this study was to investigate the adoption and actual use of a digital dietary monitoring system (DDMS) and its impact on patient satisfaction with the provided hospital care, body weight changes and health-related quality of life (HRQoL) in patients with potentially curable esophageal cancer planned for surgery. The DDMS enables patients and dietitians to monitor patients' nutritional intake and body weight during the preoperative period. Methods In this prospective observational study, the first 47 included patients received usual nutritional care, and were followed from diagnosis until surgery. After implementation of the DDMS 37 patients were followed, again from diagnosis until surgery. Main outcomes were actual use of the DDMS, by means of adoption and usage measures, overall patient satisfaction (EORTC-INPATSAT32), weight change and HRQoL (EORTC QLQ-C30 and EORTC-OG25). Outcomes were assessed immediately after diagnosis, and 6 and 12 weeks later. Results The system had an adoption rate of 64% and a usage rate of 78%. No significant effects on patient satisfaction were found at 12 weeks after diagnosis between the intervention and the usual care group. The implementation of the DDMS also had no significant effect on body weight and HRQoL over time. Conclusions Patients with potentially curable esophageal cancer planned for surgery were able to use the DDMS. However, no significant effects on patient satisfaction, body weight changes and HRQoL were observed. Further research should focus on the specific needs of patients regarding information and support to preoperatively optimize nutritional intake and nutritional status.
MULTIFILE
Background: Manual muscle mass assessment based on Computed Tomography (CT) scans is recognized as a good marker for malnutrition, sarcopenia, and adverse outcomes. However, manual muscle mass analysis is cumbersome and time consuming. An accurate fully automated method is needed. In this study, we evaluate if manual psoas annotation can be substituted by a fully automatic deep learning-based method.Methods: This study included a cohort of 583 patients with severe aortic valve stenosis planned to undergo Transcatheter Aortic Valve Replacement (TAVR). Psoas muscle area was annotated manually on the CT scan at the height of lumbar vertebra 3 (L3). The deep learning-based method mimics this approach by first determining the L3 level and subsequently segmenting the psoas at that level. The fully automatic approach was evaluated as well as segmentation and slice selection, using average bias 95% limits of agreement, Intraclass Correlation Coefficient (ICC) and within-subject Coefficient of Variation (CV). To evaluate performance of the slice selection visual inspection was performed. To evaluate segmentation Dice index was computed between the manual and automatic segmentations (0 = no overlap, 1 = perfect overlap).Results: Included patients had a mean age of 81 ± 6 and 45% was female. The fully automatic method showed a bias and limits of agreement of -0.69 [-6.60 to 5.23] cm2, an ICC of 0.78 [95% CI: 0.74-0.82] and a within-subject CV of 11.2% [95% CI: 10.2-12.2]. For slice selection, 84% of the selections were on the same vertebra between methods, bias and limits of agreement was 3.4 [-24.5 to 31.4] mm. The Dice index for segmentation was 0.93 ± 0.04, bias and limits of agreement was -0.55 [1.71-2.80] cm2.Conclusion: Fully automatic assessment of psoas muscle area demonstrates accurate performance at the L3 level in CT images. It is a reliable tool that offers great opportunities for analysis in large scale studies and in clinical applications.
In the decision-making environment of evidence-based practice, the following three sources of information must be integrated: research evidence of the intervention, clinical expertise, and the patient’s values. In reality, evidence-based practice usually focuses on research evidence (which may be translated into clinical practice guidelines) and clinical expertise without considering the individual patient’s values. The shared decision-making model seems to be helpful in the integration of the individual patient’s values in evidence-based practice. We aim to discuss the relevance of shared decision making in chronic care and to suggest how it can be integrated with evidence-based practice in nursing. We start by describing the following three possible approaches to guide the decision-making process: the paternalistic approach, the informed approach, and the shared decision-making approach. Implementation of shared decision making has gained considerable interest in cases lacking a strong best-treatment recommendation, and when the available treatment options are equivalent to some extent. We discuss that in chronic care it is important to always invite the patient to participate in the decision-making process. We delineate the following six attributes of health care interventions in chronic care that influence the degree of shared decision making: the level of research evidence, the number of available intervention options, the burden of side effects, the impact on lifestyle, the patient group values, and the impact on resources. Furthermore, the patient’s willingness to participate in shared decision making, the clinical expertise of the nurse, and the context in which the decision making takes place affect the shared decision-making process. A knowledgeable and skilled nurse with a positive attitude towards shared decision making – integrated with evidence-based practice – can facilitate the shared decision-making process. We conclude that nurses as well as other health care professionals in chronic care should integrate shared decision making with evidence- based practice to deliver patient-centred care.
