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

User-centred design of a clinical decision support system for palliative care: Insights from healthcare professionals

[EN] Objective:Although clinical decision support systems (CDSS) have many benefits for clinical practice, they also have several barriers to their acceptance by professionals. Our objective in this study was to design and validate The Aleph palliative care (PC) CDSS through a user-centred method, considering the predictions of the artificial intelligence (AI) core, usability and user experience (UX). Methods:We performed two rounds of individual evaluation sessions with potential users. Each session included a model evaluation, a task test and a usability and UX assessment. Results:The machine learning (ML) predictive models outperformed the participants in the three predictive tasks. System Usability Scale (SUS) reported 62.7 +/- 14.1 and 65 +/- 26.2 on a 100-point rating scale for both rounds, respectively, while User Experience Questionnaire - Short Version (UEQ-S) scores were 1.42 and 1.5 on the -3 to 3 scale. Conclusions:The think-aloud method and including the UX dimension helped us to identify most of the workflow implementation issues. The system has good UX hedonic qualities; participants were interested in the tool and responded positively to it. Performance regarding usability was modest but acceptable.The authors 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 grant number 825750) and the CANCERLESS project (H2020-SC1-2020-Single-Stage-RTD grant number 965351), both funded by the European Union¿s Horizon 2020 research and innovation programme. Also, it was partially supported by the ALBATROSS project (National Plan for Scientific and Technical Research and Innovation 2017¿ 2020, grant number PID2019-104978RB-I00)Blanes-Selva, V.; Asensio-Cuesta, S.; Doñate-Martínez, A.; Pereira Mesquita, F.; Garcia-Gomez, JM. (2023). User-centred design of a clinical decision support system for palliative care: Insights from healthcare professionals. Digital Health. 9:1-13. https://doi.org/10.1177/20552076221150735113

Functional requirements to mitigate the Risk of Harm to Patients from Artificial Intelligence in Healthcare

The Directorate General for Parliamentary Research Services of the European Parliament has prepared a report to the Members of the European Parliament where they enumerate seven main risks of Artificial Intelligence (AI) in medicine and healthcare: patient harm due to AI errors, misuse of medical AI tools, bias in AI and the perpetuation of existing inequities, lack of transparency, privacy and security issues, gaps in accountability, and obstacles in implementation. In this study, we propose fourteen functional requirements that AI systems may implement to reduce the risks associated with their medical purpose: AI passport, User management, Regulation check, Academic use only disclaimer, data quality assessment, Clinicians double check, Continuous performance evaluation, Audit trail, Continuous usability test, Review of retrospective/simulated cases, Bias check, eXplainable AI, Encryption and use of field-tested libraries, and Semantic interoperability. Our intention here is to provide specific high-level specifications of technical solutions to ensure continuous good performance and use of AI systems to benefit patients in compliance with the future EU regulatory framework.Comment: 14 pages, 1 figure, 1 tabl

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

Aplicación de The Community Assessment Risk Screen en centros de atención primaria del Sistema Sanitario Valenciano

Objetivo: Aplicar la herramienta The Community Assessment Risk Screen (CARS) para detectar pacientes mayores con riesgo de reingreso hospitalario y estudiar la viabilidad de su inclusión en los sistemas de información sanitaria. Diseño: Estudio de cohortes retrospectivo. Emplazamiento: Departamentos de salud 6, 10 y 11 de la Comunidad Valenciana. Participantes: Pacientes de 65 años o más atendidos en diciembre de 2008 en 6 centros de salud. La muestra fue de 500 pacientes (error muestral = ± 4,37%, fracción de muestreo = 1/307). Mediciones: Instrumento CARS formado por 3 ítems: diagnósticos (enfermedades cardiacas, diabetes, infarto de miocardio, ictus, EPOC, cáncer), número de fármacos prescritos e ingresos hospitalarios o visitas a urgencias en los 6 meses previos. Los datos procedían de SIA-Abucasis, GAIA y CMBD, y fueron contrastados con profesionales de atención primaria. La variable de resultado fue el ingreso durante 2009. Resultados: Los niveles de riesgo del CARS están relacionados con el futuro reingreso (p < 0,001). El valor de la sensibilidad y la especificidad es de 0,64, el instrumento identifica mejor a los pacientes con baja probabilidad de ser hospitalizados en el futuro (valor predictivo negativo = 0,91; eficacia diagnóstica = 0,67), pero tiene un valor predictivo positivo del 0,24. Conclusiones: El CARS original no identifica adecuadamente a la población con alto riesgo de reingreso hospitalario. No obstante, si fuese revisado y mejora su valor predictivo positivo, podría ser incorporado en los sistemas informáticos de atención primaria, siendo útil en el cribado y la segmentación inicial de la población de pacientes crónicos con riesgo de rehospitalización

The Experiences and Views on Palliative Care of Older People with Multimorbidities, Their Family Caregivers and Professionals in a Spanish Hospital

The increasing prevalence of complex chronic diseases in the population over 65 years of age is causing a major impact on health systems. This study aims to explore the needs and preferences of the multimorbid patient and carers to improve the palliative care received. The perspective of professionals who work with this profile of patients was also taken into account. A qualitative study was conducted using semi-structured interviews with open-ended questions. Separate topic guides were developed for patients, careers and health professionals. We included 12 patients, 11 caregivers and 16 health professionals in Spain. The results showed multiple unmet needs of patients and families/caregivers, including feelings of uncertainty, a sense of fear, low awareness and knowledge about palliative care in non-malignant settings, and a desire to improve physical, psychosocial and financial status. A consistent lack of specialized psychosocial care for both patients and caregivers was expressed and professionals highlighted the need for holistic needs assessment and effective and early referral pathways to palliative care. There is a lack of institutional support for multimorbid older patients in need of palliative care and important barriers need to be addressed by health systems to face the significant increase in these patients