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116 Considering life impact outcomes on the early identification of patients in need of palliative care on the development of predictive models
  1. Mateos MJ Caballero,
  2. MS Giménez Campos,
  3. V Blanes-Selva,
  4. A Fernandes,
  5. JM Garcí­a-Giménez,
  6. A Duarte-Martinez and
  7. ME Gas López
  1. Joint Research Unit in ICT applied to reengineering socio-sanitary process.Departament de Salut Valencia La Fe; Departament de Salut Valencia La Fe; Biomedical Data Science Lab ITACA Institute.Universitat Politecnica de Valencia; Santa Casa da Misericordi


Introduction Stratification and predictive models have been proposed to support clinical decisions or to identify vulnerability-based sub-populations of complex patients. In particular, predicting mortality also has been one of the initiatives proposed on the prediction of palliative care (PC) needs. However, models considering life impact outcomes, such as negative effects due to illnesses, may be more able to contribute to identifying older patients who may benefit from early PC. The aim of this study was to review the current state of the art on health indicators which could suggest a declining trajectory of end-of-life in patients aged >65 with chronic disease(s).

Method A literature review was carried out in PubMed-Medline (February 20, 2019). Two research questions (RQ) were formulated (RQ1: mortality or survival prediction; RQ2: identification of patients with PC needs). Study selection was performed independently by two reviewers under the following inclusion criteria: subject sample aged >65, with chronic disease profile, published in English. Outcomes were categorised using the Core Outcome Measures in Effectiveness Trials-COMET taxonomy, specifically the core area: Life Impact.

Results The keyword search gathered 712 papers for RQ1 and 696 for RQ2. After screening papers and eliminating duplicates, 101 studies were considered for retrieving information. We identified 1723 outcomes candidates but, after decoding abbreviations and concealing terms, only 484 remained. Of these, 83 terms were aligned with the core area Life Impact, being grouped under the following subdomains: cognitive (n=13), physical (n=28), social (n=16) and emotional (n=10) functioning; quality of life (n=1); perceived health status (n=6) and care delivered (n=9).

Conclusion The literature review allowed us to collect updated life impact outcomes related to repercussions of illnesses which may guide the development of predictive modelling beyond a mortality approaching paradigm under the clinical setting mentioned.

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