Providing a nurse-led complex nursing INtervention FOcused on quality of life assessment on advanced cancer patients: The INFO-QoL pilot trial.

PURPOSE Unmet needs for advanced-disease cancer patients are fatigue, pain, and emotional support. Little information is available about the feasibility of interventions focused on patient-reported outcome measurement developed according to the Medical Research Council (MRC) Framework in advanced-disease cancer patients. We aimed to pilot a nurse-led complex intervention focused on QoL assessment in advanced-disease cancer patients. METHODS The INFO-QoL study was based on an exploratory, nonequivalent comparison group, pre-test-post-test design. Study sites received either the INFO-QoL intervention or usual care. Adult advanced-disease cancer patients admitted to hospice inpatient units that gave their informed consent were included in the study. Subjects were 187 patients and their families and 19 healthcare professionals. We evaluated feasibility, acceptability, and patients' outcomes using the Integrated Palliative Care Outcome Scale. RESULTS Nineteen healthcare professionals were included. The mean competence score increased significantly over time (p < 0.001) and the mean usefulness score was high 8.63 (±1.36). In the post-test phase, 54 patients were allocated to the experimental unit and 36 in the comparison unit. Compared to the comparison unit, in the experimental unit anxiety (R2 = 0.07; 95% CI = -0.06; 0.19), family anxiety (R2 = 0.22; 95% CI = -0.03; 0.41), depression (R2 = 0.31; 95% CI = -0.05; 0.56) and sharing feelings (R2 = 0.09; 95% CI = -0.05; 0.23), were improved between pre-test and post-test phase. CONCLUSIONS The INFO-QoL was feasible and potentially improved psychological outcomes. Despite the high attrition rate, the INFO-QoL improved the quality and safety culture for patients in palliative care settings

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis