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Multisystem chronic illness prognostication in non-oncologic integrated care
  1. Pablo E Bretos-Azcona1,2,
  2. Cristina Ibarrola Guillén3,
  3. Eduardo Sánchez-Iriso1,2,
  4. Juan M Cabasés Hita1,2,
  5. Javier Gorricho4 and
  6. Julián Librero López2,5
  1. 1 Universidad Pública de Navarra, Pamplona, Spain
  2. 2 IdiSNA, Pamplona, Navarra, Spain
  3. 3 Parlamento Foral de Navarra, Pamplona, Navarra, Spain
  4. 4 Servicio Navarro de Salud - Osasunbidea, Pamplona, Navarra, Spain
  5. 5 Navarrabiomed, Pamplona, Navarra, Spain
  1. Correspondence to Pablo E Bretos-Azcona, Universidad Pública de Navarra, Pamplona 31006, Spain; pbretosazcona{at}gmail.com

Abstract

Objectives To develop a mortality-predictive model for correct identification of patients with non-cancer multiple chronic conditions who would benefit from palliative care, recognise predictive indicators of death and provide with tools for individual risk score calculation.

Design Retrospective observational study with multivariate logistic regression models.

Participants All patients with high-risk multiple chronic conditions incorporated into an integrated care strategy that fulfil two conditions: (1) they belong to the top 5% of the programme’s risk pyramid according to the adjusted morbidity groups stratification tool and (2) they suffer simultaneously at least three selected chronic non-cancer pathologies (n=591).

Main outcome measure 1 year mortality since patient inclusion in the programme.

Results Among study participants, 201 (34%) died within the 1 year follow-up. Variables found to be independently associated to 1 year mortality were the Barthel Scale (p<0.001), creatinine value (p=0.032), existence of pressure ulcers (p=0.029) and patient global status (p<0.001). The area under the curve (AUC) for our model was 0.751, which was validated using bootstrapping (AUC=0.751) and k-fold cross-validation (10 folds; AUC=0.744). The Hosmer-Lemeshow test (p=0.761) showed good calibration.

Conclusions This study develops and validates a mortality prediction model that will guide transitions of care to non-cancer palliative care services. The model determines prognostic indicators of death and provides tools for the estimation of individual death risk scores for each patient. We present a nomogram, a graphical risk calculation instrument, that favours a practical and easy use of the model within clinical practices.

  • chronic conditions
  • terminal care

Data availability statement

No data are available.

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Data availability statement

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Footnotes

  • Contributors PEB-A, CIG, ES-I, JMCH, JGM and JLL contributed to the design of the study, interpreted the results, critically reviewed the manuscript and gave final approval for it. JGM acquired the data for the study. PEB-A was responsible for statistical analyses and wrote the draft of the manuscript.

  • Funding PEB-A thanks the Government of Navarra for their financial support under the PhD scholarship scheme 'Ayudas predoctorales; Plan de Formación y de I+D 2018'.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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