Assessing patients’ needs in the follow-up after treatment for colorectal cancer—a mixed-method study

Purpose: The accessibility of cancer care faces challenges due to the rising prevalence of colorectal cancer (CRC) coupled with a shrinkage of healthcare professionals—known as the double aging phenomenon. To ensure sustainable and patient-centred care, innovative solutions are needed. This study aims to assess the needs of CRC patients regarding their follow-up care. Methods: This study uses a mixed-method approach divided in three phases. The initial phase involved focus group sessions, followed by semi-structured interviews to identify patients’ needs during follow-up. Open analysis was done to define main themes and needs for patients. In the subsequent quantitative phase, a CRC follow-up needs questionnaire was distributed to patients in the follow-up. Results: After two focus groups (n = 14) and interviews (n = 5), this study identified six main themes. Findings underscore the importance of providing assistance in managing both physical and mental challenges associated with cancer. Participants emphasised the need of a designated contact person and an increased focus on addressing psychological distress. Furthermore, patients desire individualised feedback on quality of life questionnaires, and obtaining tailored information. The subsequent questionnaire (n = 96) revealed the priority of different needs, with the highest priority being the need for simplified radiology results. A possible approach to address a part of the diverse needs could be the implementation of a platform; nearly 70% of patients expressed interest in the proposed platform. Conclusions: CRC patients perceive substantial room for improvement of their follow-up care. Findings can help to develop a platform fulfilling the distinct demands of CRC patients during follow-up.</p

A posteriori error analysis and adaptive non-intrusive numerical schemes for systems of random conservation laws

In this article we consider one-dimensional random systems of hyperbolic conservation laws. We first establish existence and uniqueness of random entropy admissible solutions for initial value problems of conservation laws which involve random initial data and random flux functions. Based on these results we present an a posteriori error analysis for a numerical approximation of the random entropy admissible solution. For the stochastic discretization, we consider a non-intrusive approach, the Stochastic Collocation method. The spatio-temporal discretization relies on the Runge--Kutta Discontinuous Galerkin method. We derive the a posteriori estimator using continuous reconstructions of the discrete solution. Combined with the relative entropy stability framework this yields computable error bounds for the entire space-stochastic discretization error. The estimator admits a splitting into a stochastic and a deterministic (space-time) part, allowing for a novel residual-based space-stochastic adaptive mesh refinement algorithm. We conclude with various numerical examples investigating the scaling properties of the residuals and illustrating the efficiency of the proposed adaptive algorithm

On the feasibility of automatically selecting similar patients in highly individualized radiotherapy dose reconstruction for historic data of pediatric cancer survivors

Purpose: The aim of this study is to establish the first step toward a novel and highly individualized three-dimensional (3D) dose distribution reconstruction method, based on CT scans and organ delineations of recently treated patients. Specifically, the feasibility of automatically selecting the CT scan of a recently treated childhood cancer patient who is similar to a given historically treated child who suffered from Wilms' tumor is assessed.Methods: A cohort of 37 recently treated children between 2- and 6-yr old are considered. Five potential notions of ground-truth similarity are proposed, each focusing on different anatomical aspects. These notions are automatically computed from CT scans of the abdomen and 3D organ delineations (liver, spleen, spinal cord, external body contour). The first is based on deformable image registration, the second on the Dice similarity coefficient, the third on the Hausdorff distance, the fourth on pairwise organ distances, and the last is computed by means of the overlap volume histogram. The relationship between typically available features of historically treated patients and the proposed ground-truth notions of similarity is studied by adopting state-of-the-art machine learning techniques, including random forest. Also, the feasibility of automatically selecting the most similar patient is assessed by comparing ground-truth rankings of similarity with predicted rankings.Results: Similarities (mainly) based on the external abdomen shape and on the pairwise organ distances are highly correlated (Pearson rp ≥ 0.70) and are successfully modeled with random forests based on historically recorded features (pseudo-R2 ≥ 0.69). In contrast, similarities based on the shape of internal organs cannot be modeled. For the similarities that random forest can reliably model, an estimation of feature relevance indicates that abdominal diameters and weight are the most important. Experiments on automatically selecting similar patients lead to coarse, yet quite robust results: the most similar patient is retrieved only 22% of the times, however, the error in worst-case scenarios is limited, with the fourth most similar patient being retrieved.Conclusions: Results demonstrate that automatically selecting similar patients is feasible when focusing on the shape of the external abdomen and on the position of internal organs. Moreover, whereas the common practice in phantom-based dose reconstruction is to select a representative phantom using age, height, and weight as discriminant factors for any treatment scenario, our analysis on abdominal tumor treatment for children shows that the most relevant features are weight and the anterior-posterior and left-right abdominal diameters

Molecular characterization of MRSA collected during national surveillance between 2008 and 2019 in the Netherlands

BACKGROUND: Although the Netherlands is a country with a low endemic level, methicillin-resistant Staphylococcus aureus (MRSA) poses a significant health care problem. Therefore, high coverage national MRSA surveillance has been in place since 1989. To monitor possible changes in the type-distribution and emergence of resistance and virulence, MRSA isolates are molecularly characterized.METHODS: All 43,321 isolates from 36,520 persons, collected 2008-2019, were typed by multiple-locus variable number tandem repeats analysis (MLVA) with simultaneous PCR detection of the mecA, mecC and lukF-PV genes, indicative for PVL. Next-generation sequencing data of 4991 isolates from 4798 persons were used for whole genome multi-locus sequence typing (wgMLST) and identification of resistance and virulence genes.RESULTS: We show temporal change in the molecular characteristics of the MRSA population with the proportion of PVL-positive isolates increasing from 15% in 2008-2010 to 25% in 2017-2019. In livestock-associated MRSA obtained from humans, PVL-positivity increases to 6% in 2017-2019 with isolates predominantly from regions with few pig farms. wgMLST reveals the presence of 35 genogroups with distinct resistance, virulence gene profiles and specimen origin. Typing shows prolonged persistent MRSA carriage with a mean carriage period of 407 days. There is a clear spatial and a weak temporal relationship between isolates that clustered in wgMLST, indicative for regional spread of MRSA strains.CONCLUSIONS: Using molecular characterization, this exceptionally large study shows genomic changes in the MRSA population at the national level. It reveals waxing and waning of types and genogroups and an increasing proportion of PVL-positive MRSA.</p