Resumen de Identifying individuals with advanced chronic conditions who may benefit from an early palliative care approach: using the NECPAL CCOMS-ICO© tool: population-based prevalence, predictive validity for mortality and predictive models
In high income countries, around 75% of the population will die due to chronic conditions. Despite only about one third of those having chronic diseases needing palliative care suffer from cancer, palliative care is mainly aimed at patients with terminal cancer in institutional settings. Nevertheless, there is strong evidence of unmet palliative needs among people with life-threatening non-malignant disease. Data in patients with advanced cancer show that early provision of specialty palliative care improves quality of life, lowers spending, and helps clarify treatment preferences and goals of care. Translating available evidence into health systems to deliver early palliative care to all people with advanced chronic conditions different than cancer in any setting of care might improve clinical outcomes decreasing costs of care in this population. Recognising transition 1, the period referred to as end of life preceding terminal phase, may enable early palliative care intervention and anticipatory palliative care planning. Nevertheless, the right moment to start palliative care -for which early identification is a prerequisite- has not been defined yet. Acknowledging limitations of available prognostic indices and predictive models, with insufficient evidence at this time to recommend their widespread use, a pragmatic approach to identify candidates for palliative care advocating a person centred approach based not on diagnosis or prognosis, but on their needs has been proposed. It is based on asking the surprise question (“Would you be surprised if this patient were to die in the next 12 months?”) and looking for one or more clinical indicators that would suggest a person might be at risk of deteriorating and dying and should be assessed for unmet needs. This pragmatic approach is the basis of most of the set of identification indicators which have been developed in recent years to recognizing transition 1 and identifying individuals likely in need of palliative care, as the NECPAL CCOMS-ICO© tool. The overall aim of this thesis was to evaluate the usefulness of the NECPAL CCOMS-ICO© tool in identifying individuals with advanced chronic conditions who may benefit from an early palliative care approach, through employing it as a tool to determine the population-based prevalence of these individuals (Study I), evaluating its predictive validity for mortality at 3, 6, 12 and 24 months to inform usefulness as screening tool for early palliative care (Study II) and identifying the indicators that were associated with mortality within 24 months to develop a predictive model for identifying individuals at high risk of death (Study III). Conclusions The NECPAL CCOMS-ICO© tool can be considered useful in identifying individuals with advanced chronic conditions who may benefit from an early palliative care approach. It can be employed to assess the population-based needs for palliative care through identifying prospectively the population-based prevalence of this population, an innovative approach which can be potentially useful for improving clinical practice. It can be used, as well as the SQ, as screening tools for early palliative care, as they present high sensitivity and high NPV, both important predictive values to identify such a vulnerable and often undetected and under-treated population. It can be employed as a first assessment to identify this population, preferably accompanied by repeated or additional tests, aiming to improve specificity. From a population-based perspective, end of life trajectories may turn out to be an excellent conceptual framework for the development of simple predictive models for identifying individuals at high risk of death, particularly in advanced frailty and organ failure, the most prevalent population-based advanced chronic conditions, for which simple and promising predictive models have been developed and should be externally validated.
