Inferior Frontal Sulcal Hyperintensities on Brain MRI Are Associated with Amyloid Positivity beyond Age-Results from the Multicentre Observational DELCODE Study.

Inferior frontal sulcal hyperintensities (IFSHs) on fluid-attenuated inversion recovery (FLAIR) sequences have been proposed to be indicative of glymphatic dysfunction. Replication studies in large and diverse samples are nonetheless needed to confirm them as an imaging biomarker. We investigated whether IFSHs were tied to Alzheimer's disease (AD) pathology and cognitive performance. We used data from 361 participants along the AD continuum, who were enrolled in the multicentre DELCODE study. The IFSHs were rated visually based on FLAIR magnetic resonance imaging. We performed ordinal regression to examine the relationship between the IFSHs and cerebrospinal fluid-derived amyloid positivity and tau positivity (Aß42/40 ratio ≤ 0.08; pTau181 ≥ 73.65 pg/mL) and linear regression to examine the relationship between cognitive performance (i.e., Mini-Mental State Examination and global cognitive and domain-specific performance) and the IFSHs. We controlled the models for age, sex, years of education, and history of hypertension. The IFSH scores were higher in those participants with amyloid positivity (OR: 1.95, 95% CI: 1.05-3.59) but not tau positivity (OR: 1.12, 95% CI: 0.57-2.18). The IFSH scores were higher in older participants (OR: 1.05, 95% CI: 1.00-1.10) and lower in males compared to females (OR: 0.44, 95% CI: 0.26-0.76). We did not find sufficient evidence linking the IFSH scores with cognitive performance after correcting for demographics and AD biomarker positivity. IFSHs may reflect the aberrant accumulation of amyloid ß beyond age.

Predictive Factors Associated with Declining Psycho-Oncological Support in Patients with Cancer.

(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.

A short screening tool identifying systemic barriers to distress screening in cancer care.

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.

Steriod-associated psychiatric burden in cancer patients.

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.

Early Impact of the COVID-19 Pandemic on Psycho-Oncological Support: A Latent Class Analysis.

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.

Clinically Significant Distress and Physical Problems Detected on a Distress Thermometer are Associated With Survival Among Lung Cancer Patients.

BACKGROUND: The distress thermometer is a well-established screening tool to detect clinically significant distress in cancer patients. It is often administered in combination with the problem list, differentiating further between various (e.g., physical and emotional) sources of distress. OBJECTIVE: The present study aimed to extend previous research on the association between distress and overall survival. A further exploratory analysis aimed to evaluate the predictive value of the problem list for overall survival. METHODS: Patients (n = 323) with newly diagnosed lung cancer were recruited from a large cancer center. Patients were split into 2 groups, those with (distress thermometer score ≥5) and those without significant distress. The 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. 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 than 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 associated with the overall survival time. CONCLUSIONS: 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 among 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 of poor prognosis (on the basis of Kaplan-Meier estimator).

Reading Wishes from the Lips: Cancer Patients' Need for Psycho-Oncological Support during Inpatient and Outpatient Treatment.

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.

Severe mental illness in cancer is associated with disparities in psycho-oncological 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.

Towards identifying cancer patients at risk to miss out on psycho-oncological treatment via machine learning.

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.

Distinct psycho-oncological support inclinations and needs in patients with cancer: A large sample latent class analysis approach.

BACKGROUND: In patients with cancer, the routine recording of distress symptoms has been widely established in recent years. Psycho-oncological support has proven to reduce distress and increase quality of life. Despite high levels of distress as well as physical and emotional challenges in patients with cancer, a significant proportion forgoes psycho-oncological services. METHODS: A cross-sectional retrospective evaluation was carried out. Latent class analysis was used to examine the relationship between distress, physical and emotional challenges, and desire for psycho-oncological services in 2191 patients with cancer. RESULTS: Latent class analysis revealed four homogeneous subgroups: a) patients with high distress, high physical and low emotional challenges and no desire for psycho-oncology, b) patients with high distress, low physical and high emotional challenges and no desire for psycho-oncology, c) patients with high distress, high physical and emotional challenges and a desire for psycho-oncology, d) patients with low distress, low physical and emotional challenges and no desire for psycho-oncology. CONCLUSION: The identification of these subgroups of patients with cancer is useful for health care providers in order to focus their efforts in patients with cancer. It might contribute to a more tailored treatment offer for specific subgroups whose needs have so far been insufficiently met.

Uncovering Barriers to Screening for Distress in Patients With Cancer via Machine Learning.

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.

Mental disorders, length of hospitalization, and psychopharmacy-New approaches to identify barriers to psychological support for patients with cancer.

BACKGROUND: Despite abundant evidence that emotional distress is frequent in cancer patients and associated with adverse health outcomes, distress screening rates and adequate referrals to psychological support programs among those in need are insufficient in many cancer centers. We therefore aimed to analyze patient- and treatment-related barriers to distress screening and referrals to psychological support as a mandatory component of best-practice cancer care. METHOD: In the present explorative study, latent class analysis was used to identify homogeneous subgroups among 4837 patients diagnosed with cancer between 2011 and 2019. RESULTS: Four subgroups were identified. Patients with a mental disorder and psychopharmacology were least probable to be screened for distress. Together with patients aged 65 or older and male patients, they were also less likely to receive psychological support. Patients hospitalized for 28 days or longer were most likely to be both screened and to receive psychological support. CONCLUSIONS: Clinicians and researchers are recommended not neglect patients with mental disorders and psychopharmacological treatment as well as male and elderly patients when screening for distress and providing access to psychological support.