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BACKGROUND: While several studies in cerebral amyloid angiopathy (CAA) focus on cognitive function, data on neuropsychiatric symptoms (NPS) and lifelong mental activities in these patients are scarce. Since NPS are associated with functional impairment, faster cognitive decline and faster progression to death, replication studies in more diverse settings and samples are warranted. METHODS: We prospectively recruited n = 69 CAA patients and n = 18 cognitively normal controls (NC). The number and severity of NPS were assessed using the Alzheimer's Disease (AD) Assessment Scale's (ADAS) noncognitive subscale. We applied different regression models exploring associations between NPS number or severity and group status (CAA vs. NC), CAA severity assessed with magnetic resonance imaging (MRI) or cognitive function (Mini-Mental State Examination (MMSE), ADAS cognitive subscale), adjusting for age, sex, years of education, arterial hypertension, AD pathology, and apolipoprotein E status. Mediation analyses were performed to test indirect effects of lifelong mental activities on CAA severity and NPS. RESULTS: Patients with CAA had 4.86 times (95% CI 2.20-10.73) more NPS and 3.56 units (95% CI 1.94-5.19) higher expected NPS severity than NC. Higher total CAA severity on MRI predicted 1.14 times (95% CI 1.01.-1.27) more NPS and 0.57 units (95% CI 0.19-0.95) higher expected NPS severity. More severe white matter hyperintensities were associated with 1.21 times more NPS (95% CI 1.05-1.39) and 0.63 units (95% CI 0.19-1.08) more severe NPS. NPS number (MMSE mean difference - 1.15, 95% CI -1.67 to -0.63; ADAS cognitive mean difference 1.91, 95% CI 1.26-2.56) and severity (MMSE - 0.55, 95% CI -0.80 to -0.30; ADAS cognitive mean difference 0.89, 95% CI 0.57-1.21) predicted lower cognitive function. Greater lifelong mental activities partially mediated the relationship between CAA severity and NPS (indirect effect 0.05, 95% CI 0.0007-0.13), and greater lifelong mental activities led t

This study explores the role of steroid administration in identifying distressed or even mentally disordered cancer patients (so-called case finding). Charts of 12 298 cancer patients (4499 treated with prednisone equivalents) were analysed descriptively. A subset of 10 945 was further explored via latent class analysis (LCA). LCA avoids confounding by indication because it subgroups patients without prior preconceptions based on homogeneous expression of traits (i.e. the variables examined). LCA identified four subgroups: two subgroups with high dosages of prednisone equivalent (≥80 mg/day on average over all treatment days) and two with low dosages. The two subgroups with high average dosages had an increased likelihood of psychotropic drug administration, but only one was more likely to require 1:1 observation. In one subgroup, low dosages of prednisone equivlents correlated with a slightly increased probability for a psychiatric assessment and psychotropic drug administration. The subgroup least likely to receive steroid treatment was also the least likely to receive a psychiatric assessment and psychotropic drug administration. Descriptive statistics on age, sex, cumulative inpatient treatment, type of cancer, stage of cancer at first diagnosis, mental disorders, severe mental disorders and psychotropic drug administration (antidepressants, antipsychotics, benzodiazepines, anticonvulsants/mood stabilizers, opioids) are provided for patients receiving no, less and more than 80 mg of prednisone equivalent. Keywords: cancer; glucocorticoid; latent class analysis; oncology; psychiatric burden; steroid.

Background: Research findings suggest that a significant proportion of individuals diagnosed with cancer, ranging from 25% to 60%, experience distress and require access to psycho-oncological services. Until now, only contemporary approaches, such as logistic regression, have been used to determine predictors of distress in oncological patients. To improve individual prediction accuracy, novel approaches are required. We aimed to establish a prediction model for distress in cancer patients based on a back propagation neural network (BPNN). Methods: Retrospective data was gathered from a cohort of 3063 oncological patients who received diagnoses and treatment spanning the years 2011-2019. The distress thermometer (DT) has been used as screening instrument. Potential predictors of distress were identified using logistic regression. Subsequently, a prediction model for distress was developed using BPNN. Results: Logistic regression identified 13 significant independent variables as predictors of distress, including emotional, physical and practical problems. Through repetitive data simulation processes, it was determined that a 3-layer BPNN with 8 neurons in the hidden layer demonstrates the highest level of accuracy as a prediction model. This model exhibits a sensitivity of 79.0%, specificity of 71.8%, positive predictive value of 78.9%, negative predictive value of 71.9%, and an overall coincidence rate of 75.9%. Conclusion: The final BPNN model serves as a compelling proof of concept for leveraging artificial intelligence in predicting distress and its associated risk factors in cancer patients. The final model exhibits a remarkable level of discrimination and feasibility, underscoring its potential for identifying patients vulnerable to distress.

Introduction: Research suggests a global shortfall of psycho-oncological assessment and care during the COVID-19 pandemic in addition to delayed diagnosis of cancer. The present study is the first to explore the effect of the pandemic on the provision of psycho-oncological care, stage of cancer at first diagnosis, and duration of hospitalizations. Method: Retrospective latent class analysis of 4,639 electronic patient files with all types, treatment types, and stages of cancer, 370 of which were treated during the pandemic prior to availability of vaccinations. Discussion: Latent class analysis identified four subgroups based on differences in screening for distress, provision of psycho-oncological support (consultation with a psychiatrist or clinical psychologist), administration of psychotropic medication, use of 1:1 observation, stage of cancer at first diagnosis, and duration of hospitalizations. Yet, the pandemic had no effect on subgrouping. Thus, the COVID-19 pandemic had no effect on the provision of psycho-oncological support. Conclusion: Results are contrary to prior research. The efficiency and quality of procedures implemented to provide psycho-oncological support during and prior to the pandemic are critically reflected. Keywords: COVID-19; Distress; Psycho-oncology; Screening; Stage.

Introduction: International guidelines on cancer treatment recommend screening for early detection and treatment of distress. However, screening rates are insufficient. In the present study, a survey was developed to assess perceived systemic barriers to distress screening. Methods: A three-step approach was used for the study. Based on qualitative content analysis of interviews and an expert panel, an initial survey with 53 questions on barriers to screening was designed. It was completed by 98 nurses in a large comprehensive cancer center in Switzerland. From this, a short version of the survey with 24 questions was derived using exploratory principal component analysis. This survey was completed by 150 nurses in four cancer centers in Switzerland. A confirmatory factor analysis was then performed on the shortened version, yielding a final set of 14 questions. Results: The initial set of 53 questions was reduced to a set of 14 validated questions retaining 53% of the original variance. These 14 questions allow for an assessment within 2-3 min that identifies relevant barriers to distress screening from the perspective of those responsible for implementation of distress screening. Across several hospitals in Switzerland, the timing of the first distress screening, lack of capacity, patient and staff overload, and refusal of distressed patients to be referred to support services emerged as major problems. Conclusion: The validated 14 questions on barriers to screening cancer patients for distress enable clinicians and hospital administrators to quickly identify relevant issues and take action to improve screening programs. Keywords: cancer; distress screening; hospital administrators; principal component analysis; psycho-oncology.

(1) Background: International cancer treatment guidelines recommend low-threshold psycho-oncological support based on nurses’ routine distress screening (e.g., via the distress thermometer and problem list). This study aims to explore factors which are associated with declining psycho-oncological support in order to increase nurses’ efficiency in screening patients for psycho-oncological support needs. (2) Methods: Using machine learning, routinely recorded clinical data from 4064 patients was analyzed for predictors of patients declining psycho-oncological support. Cross validation and nested resampling were used to guard against model overfitting. (3) Results: The developed model detects patients who decline psycho-oncological support with a sensitivity of 89% (area under the cure of 79%, accuracy of 68.5%). Overall, older patients, patients with a lower score on the distress thermometer, fewer comorbidities, few physical problems, and those who do not feel sad, afraid, or worried refused psycho-oncological support. (4) Conclusions: Thus, current screening procedures seem worthy to be part of daily nursing routines in oncology, but nurses may need more time and training to rule out misconceptions of patients on psycho-oncological support.

Background: A timely initiation of therapy crucially impacts the prognosis of head and neck tumor patients. Previous research has shown that shortening the Time-to-Treatment Initiation (TTI) significantly improves survival and functional outcomes. In February 2020, a novel Interdisciplinary Fast-Track Diagnostic Program (IFTDP) for head and neck cancer was implemented at our institution. The aim was to optimize the diagnostic work up and staging process within three subsequent days. In this study, we aimed to assess the impact of the IFTDP on the TTI. Methods: We included patients with primary head and neck tumors undergoing the IFTDP from January 2019 to September 2020. A historical patient cohort originating from the time prior to the introduction of the IFTDP served as control group. Results: A total of 289 patients treated in curative intent either surgically or radio therapeutically were included in the study. Compared to the historical controls, overall TTI was reduced by 25% from 28 to 21 days (p=0.002). Conclusion: An IFTDP can significantly reduce TTI, which presumably affects survival and functional outcomes. Moreover, increasing the efficiency of the diagnostic workup may avoid redundancies and could contribute to an early detection of potential risk factors.

Objective: The distress thermometer (DT) is a well-established screening tool to detect clinically significant distress in cancer patients. It is often administered in combination with the problem list (PL), differentiating further between various (e.g., physical and emotional) sources of distress. The present study aimed to extend previous research on the association between distress and overall survival. Further exploratory analysis aimed to evaluate the predictive value of the PL for overall survival. Methods: Patients (n=323) with newly diagnosed lung cancer were recruited from a large cancer center. Patients were split into two groups, those with (DT score ≥5) and those without significant distress. Overall survival time was illustrated by a Kaplan Meier curve and compared with a log rank test. Univariable Cox proportional hazard models were built to control the association of distress with overall survival for age, gender, disease stage,comorbidity and their interaction terms. A multiple linear regression was used to investigate the association of the items from the problem list with survival time. Results: Patients with significant distress had a shorter survival time compared to patients without significant distress (25 vs. 43 months). Regression analysis revealed more problems with both "bathing and dressing" and "eating", as well as absence of "diarrhea" and increased "nervousness" to negatively impact overall survival time. Conclusion: Our results show that estimation of the survival function using cancer-related distress is possible. However, when using Cox regression, distress shows no significant value for survival as a predictor. Moreover, our study did not reveal an interaction effect between disease stage, comorbidity, and distress. Overall, results suggest that physical and emotional problems that arise from lung cancer may be useful to identify patients at risk for poor prognosis.

Background: Psycho-oncological support (PO) is an effective measure to reduce distress and improve the quality of life in patients with cancer. Currently, there are only a few studies investigating the (expressed) wish for PO. The aim of this study was to evaluate the number of patients who request PO and to identify predictors for the wish for PO. Methods: Data from 3063 cancer patients who had been diagnosed and treated at a Comprehensive Cancer Center between 2011 and 2019 were analyzed retrospectively. Potential predictors for the wish for PO were identified using logistic regression. As a novelty, a Back Propagation Neural Network (BPNN) was applied to establish a prediction model for the wish for PO. Results: In total, 1752 patients (57.19%) had a distress score above the cut-off and 14.59% expressed the wish for PO. Patients’ requests for pastoral care (OR = 13.1) and social services support (OR = 5.4) were the strongest predictors of the wish for PO. Patients of the female sex or who had a current psychiatric diagnosis, opioid treatment and malignant neoplasms of the skin and the hematopoietic system also predicted the wish for PO, while malignant neoplasms of digestive organs and older age negatively predicted the wish for PO. These nine significant predictors were used as input variables for the BPNN model. BPNN computations indicated that a three-layer network with eight neurons in the hidden layer is the most precise prediction model. Discussion: Our results suggest that the identification of predictors for the wish for PO might foster PO referrals and help cancer patients reduce barriers to expressing their wish for PO. Furthermore, the final BPNN prediction model demonstrates a high level of discrimination and might be easily implemented in the hospital information system.

Objective: In routine oncological treatment settings, psychological distress, including mental disorders, is overlooked in 30% to 50% of patients. High workload and a constant need to optimise time and costs require a quick and easy method to identify patients likely to miss out on psychological support. Methods: Using machine learning, factors associated with no consultation with a clinical psychologist or psychiatrist were identified between 2011 and 2019 in 7,318 oncological patients in a large cancer treatment centre. Parameters were hierarchically ordered based on statistical relevance. Nested resampling and cross validation were performed to avoid overfitting. Results: Patients were least likely to receive psycho-oncological (i.e., psychiatric/psychotherapeutic) treatment when they were not formally screened for distress, had inpatient treatment for less than 28 days, had no psychiatric diagnosis, were aged 65 or older, had skin cancer or were not being discussed in a tumour board. The final validated model was optimised to maximise sensitivity at 85.9% and achieved an area under the curve (AUC) of 0.75, a balanced accuracy of 68.5% and specificity of 51.2%. Conclusion: Beyond conventional screening tools, results might contribute to identify patients at risk to be neglected in terms of referral to psycho-oncology within routine oncological care. Keywords: cancer; machine learning; mental disorders; psycho-oncology; psychological support.

Patients with both cancer and a severe mental illness (SMI) have a higher risk of advanced stage cancer at diagnosis and poorer survival in comparison to individuals with cancer alone. The present study explores if similar disparities exist in terms of psycho-oncological support. Latent class analysis (LCA) was used to group 10,945 patients with any type of cancer, of which 72 (0.7%) had been diagnosed with a SMI (ICD10-codes F20-F22, F24, F25, F28-F31, F32.3, F33.3), and 1056 (9.6%) with another mental disorder. Subgrouping was based on presence of SMI, other mental illnesses, stage of cancer at its first detection, screening for distress and receipt of information on psycho-oncology, consultation with a psychotherapist and/or psychiatrist, prescription of different psychotropic medication, and use of a patient care attendant. Five subgroups were identified. Patients with SMI were most likely to suffer from further mental comorbidities, to be prescribed antipsychotics, antidepressants, or mood stabilizers, and be in need of a patient care attendant. In comparison to patients without SMI, the larger one of 2 subgroups of patients with SMI had a low probability to be screened for distress and informed about psycho-oncological support services. A smaller subgroup of patients with SMI was probable to be diagnosed with an advanced stage of cancer. In subgroups without patients with mental disorders, screening for distress and offering psycho-oncological support seemed to be economized unless benzodiazepines or opioids were prescribed. Contrary to published evidence, distress screening and offering psycho-oncological support is neglected in patients with SMI unless an advanced stage of cancer is being diagnosed.

BACKGROUND: Psychologic distress and manifest mental disorders are overlooked in 30-50% of patients with cancer. Accordingly, international cancer treatment guidelines recommend routine screening for distress in order to provide psychologic support to those in need. Yet, institutional and patient-related factors continue to hinder implementation. OBJECTIVE: This study aims to investigate factors, which are associated with no screening for distress in patients with cancer. METHODS: Using machine learning, factors associated with lack of distress screening were explored in 6491 patients with cancer between 2011 and 2019 at a large cancer treatment center. Parameters were hierarchically ordered based on statistical relevance. Nested resampling and cross validation were performed to avoid overfitting and to comply with assumptions for machine learning approaches. RESULTS: Patients unlikely to be screened were not discussed at a tumor board, had inpatient treatment of less than 28 days, did not consult with a psychiatrist or clinical psychologist, had no (primary) nervous system cancer, no head and neck cancer, and did have breast or skin cancer. The final validated model was optimized to maximize sensitivity at 83.9%, and achieved a balanced accuracy of 68.9, area under the curve of 0.80, and specificity of 53.9%. CONCLUSION: Findings of this study may be relevant to stakeholders at both a clinical and institutional level in order to optimize distress screening rates.