Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs

[EN] Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Bad survival prognosis and patients' decline are working criteria to guide PC decision-making for older patients. Still, there is not a clear consensus on when to initiate early PC. This work aims to propose machine learning approaches to predict frailty and mortality in older patients in supporting PC decision-making. Predictive models based on Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) were implemented for binary 1-year mortality classification, survival estimation and 1-year frailty classification. Besides, we tested the similarity between mortality and frailty distributions. The 1-year mortality classifier achieved an Area Under the Curve Receiver Operating Characteristic (AUC ROC) of 0.87 [0.86, 0.87], whereas the mortality regression model achieved an mean absolute error (MAE) of 333.13 [323.10, 342.49] days. Moreover, the 1-year frailty classifier obtained an AUC ROC of 0.89 [0.88, 0.90]. Mortality and frailty criteria were weakly correlated and had different distributions, which can be interpreted as these assessment measurements are complementary for PC decision-making. This study provides new models that can be part of decision-making systems for PC services in older patients after their external validation.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the InAdvance project (H2020-SC1-BHC-2018-2020 No. 825750).Blanes-Selva, V.; Doñate-Martínez, A.; Linklater, G.; Garcia-Gomez, JM. (2022). Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs. Health Informatics Journal. 28(2):1-18. https://doi.org/10.1177/1460458222109259211828

Responsive and Minimalist App Based on Explainable AI to Assess Palliative Care Needs during Bedside Consultations on Older Patients

[EN] Palliative care is an alternative to standard care for gravely ill patients that has demonstrated many clinical benefits in cost-effective interventions. It is expected to grow in demand soon, so it is necessary to detect those patients who may benefit from these programs using a personalised objective criterion at the correct time. Our goal was to develop a responsive and minimalist web application embedding a 1-year mortality explainable predictive model to assess palliative care at bedside consultation. A 1-year mortality predictive model has been trained. We ranked the input variables and evaluated models with an increasing number of variables. We selected the model with the seven most relevant variables. Finally, we created a responsive, minimalist and explainable app to support bedside decision making for older palliative care. The selected variables are age, medication, Charlson, Barthel, urea, RDW-SD and metastatic tumour. The predictive model achieved an AUC ROC of 0.83 [CI: 0.82, 0.84]. A Shapley value graph was used for explainability. The app allows identifying patients in need of palliative care using the bad prognosis criterion, which can be a useful, easy and quick tool to support healthcare professionals in obtaining a fast recommendation in order to allocate health resources efficiently.This work was supported by the InAdvance project (H2020-SC1-BHC-2018-2020 grant agreement number 825750.) and the CANCERLEss project (H2020-SC1-2020-Single-Stage-RTD grant agreement number 965351), both funded by the European Union's Horizon 2020 research and innovation programme.Blanes-Selva, V.; Doñate-Martínez, A.; Linklater, G.; Garcés-Ferrer, J.; Garcia-Gomez, JM. (2021). Responsive and Minimalist App Based on Explainable AI to Assess Palliative Care Needs during Bedside Consultations on Older Patients. Sustainability. 13(17):1-11. https://doi.org/10.3390/su13179844111131

Evaluation design of the patient-centred pathways of early palliative care, supportive ecosystems and appraisal standard (InAdvance):a randomised controlled trial

BACKGROUND: Palliative care aims to contribute to pain relief, improvement with regard to symptoms and enhancement of health-related quality of life (HRQoL) of patients with chronic conditions. Most of the palliative care protocols, programmes and units are predominantly focused on patients with cancer and their specific needs. Patients with non-cancer chronic conditions may also have significantly impaired HRQoL and poor survival, but do not yet receive appropriate and holistic care. The traditional focus of palliative care has been at the end-of-life stages instead of the relatively early phases of serious chronic conditions. The ‘Patient-centred pathways of early palliative care, supportive ecosystems and appraisal standard’ (InAdvance) project implements and evaluates early palliative care in the daily clinical routine addressing patients with complex chronic conditions in the evolution towards advanced stages. The objective of the current study is to evaluate the acceptability, feasibility, effectiveness and cost-effectiveness of this novel model of palliative care in the relatively early phases in patients with chronic conditions. METHODS: In this study, a single blind randomised controlled trial design will be employed. A total of 320 participants (80 in each study site and 4 sites in total) will be randomised on a 1:1 basis to the Palliative Care Needs Assessment (PCNA) arm or the Care-as-Usual arm. This study includes a formative evaluation approach as well as a cost-effectiveness analysis with a within-trial horizon. Study outcomes will be assessed at baseline, 6 weeks, 6 months, 12 months and 18 months after the implementation of the interventions. Study outcomes include HRQoL, intensity of symptoms, functional status, emotional distress, caregiving burden, perceived quality of care, adherence to treatment, feasibility, acceptability, and appropriateness of the intervention, intervention costs, other healthcare costs and informal care costs. DISCUSSION: The InAdvance project will evaluate the effect of the implementation of the PCNA intervention on the target population in terms of effectiveness and cost-effectiveness in four European settings. The evidence of the project will provide step-wise guidance to contribute an increased evidence base for policy recommendations and clinical guidelines, in an effort to augment the supportive ecosystem for palliative care. TRIAL REGISTRATION: ISRCTN, ISRCTN24825698. Registered 17/12/2020

‘How did it come to this?’ Causal network analysis in practice and service development

Practice development has been widely used to support change in healthcare for more than 20 years (McCormack et al., 2006), a result there is a growing body of knowledge which, describes the process and context together with the factors that influence the outcomes of such developments. Learning from failure in practice and service development is fundamental if we are to identify and understand what factors can influence success. Too often the analysis of failure has been subjective and has relied upon anecdotal accounts. This article explains the use of the methodology developed by Miles and Huberman (1994) to inductively map how variables and factors interact to produce a particular outcome. Causal Network Analysis (CNA) is useful in exploring the factors, which can influence developments, as well as exploring what triggered the success or failure of a particular development

Classifying idiopathic inflammatory myopathies:comparing the performance of six existing criteria

Various criteria have been proposed to classify the inflammatory myositides (IIMs) polymyositis (PM) and dermatomyositis (DM). However, none have received universal acceptance. Our aim was to assess the performance of the main criteria used to classify IIM. Specialist consultant diagnosis was considered the gold standard

Developing learning outcomes for medical students and foundation doctors in palliative care: a national consensus-seeking initiative in Scotland

<b>Background</b> Undergraduate education in palliative care is essential if doctors are to be competent to care for dying patients and their families in a range of specialties and healthcare settings. However, creating space for this within existing undergraduate and foundation year curricula poses significant challenges. We aimed to develop consensus learning outcomes for palliative care teaching in the university medical schools in Scotland.<p></p> <b>Methods</b> The General Medical Council (GMC) outlines a number of learning outcomes with clear relevance to palliative care. Leaders from the five Scottish medical schools identified and agreed a small number of outcomes, which we judged most relevant to teaching palliative care and collated teaching resources to support these.<p></p> <b>Results</b> Consensus learning outcomes for undergraduate palliative care were agreed by our mixed group of clinician educators over a number of months. There were many secondary gains from this process, including the pooling of educational resources and best practice, and the provision of peer support for those struggling to establish curriculum time for palliative care.<p></p> <b>Discussion</b> The process and outcomes were presented to the Scottish Teaching Deans, with a view to their inclusion in undergraduate and foundation year curricula. It is through a strong commitment to achieving these learning outcomes that we will prepare all doctors for providing palliative care to the increasing numbers of patients and families that require it