Atrial fibrillation as a new prognosis factor in chronic patients after hospitalization: the CHRONIBERIA index

  1. Suarez-Dono, Javier
  2. Novo-Veleiro, Ignacio
  3. Gude-Sampedro, Francisco
  4. Marinho, Ricardo
  5. Xavier-Pires, Sara
  6. Rocha, Diana
  7. Araújo-Correia, João
  8. Moreira, Cecília
  9. Beires, Francisca
  10. Pérez, Danay
  11. David, Filipa
  12. Vasco-Barreto, J.
  13. Del Corral-Beamonte, Esther
  14. Piñeiro-Fernández, Juan-Carlos
  15. Casariego-Vales, Emilio
  16. Diez-Manglano, Jesús
  17. Pose-Reino, Antonio
Revista:
Scientific Reports

ISSN: 2045-2322

Ano de publicación: 2023

Volume: 13

Número: 1

Tipo: Artigo

DOI: 10.1038/S41598-023-30610-2 PMID: 36906719 SCOPUS: 2-s2.0-85149927683 GOOGLE SCHOLAR lock_openAcceso aberto editor

Outras publicacións en: Scientific Reports

Obxectivos de Desenvolvemento Sustentable

Resumo

A collaborative project in different areas of Spain and Portugal was designed to find out the variables that influence the mortality after discharge and develop a prognostic model adapted to the current healthcare needs of chronic patients in an internal medicine ward. Inclusion criteria were being admitted to an Internal Medicine department and at least one chronic disease. Patients’ physical dependence was measured through Barthel index (BI). Pfeiffer test (PT) was used to establish cognitive status. We conducted logistic regression and Cox proportional hazard models to analyze the influence of those variables on one-year mortality. We also developed an external validation once decided the variables included in the index. We enrolled 1406 patients. Mean age was 79.5 (SD = 11.5) and females were 56.5%. After the follow-up period, 514 patients (36.6%) died. Five variables were identified as significantly associated with 1 year mortality: age, being male, lower BI punctuation, neoplasia and atrial fibrillation. A model with such variables was created to estimate one-year mortality risk, leading to the CHRONIBERIA. A ROC curve was made to determine the reliability of this index when applied to the global sample. An AUC of 0.72 (0.7–0.75) was obtained. The external validation of the index was successful and showed an AUC of 0.73 (0.67–0.79). Atrial fibrillation along with an advanced age, being male, low BI score, or an active neoplasia in chronic patients could be critical to identify high risk multiple chronic conditions patients. Together, these variables constitute the new CHRONIBERIA index.

Referencias bibliográficas

  • Targets and beyond: Reaching new frontiers in evidence. Eur. Health Rep. (2015).
  • Ministerio de Sanidad, Consumo y Bienestar Social - Portal Estadístico del SNS - Encuesta Europea de Salud en España 2014. https://www.mscbs.gob.es/estadEstudios/estadisticas/EncuestaEuropea/Enc_Eur_Salud_en_Esp_2014.htm.
  • Montes Santiago, J., Casariego Vales, E., De Toro Santos, M. & Mosquera, E. La asistencia a pacientes crónicos y pluripatológicos. Magnitud e iniciativas para su manejo: La Declaración de Sevilla. Situación y propuestas en Galicia. Galicia Clínica 73, 7 (2012).
  • Vogelmeier, C. F. et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Arch. Bronconeumol. 53, 128–149 (2017).
  • Salerno, F. et al. MELD score is better than Child-Pugh score in predicting 3-month survival of patients undergoing transjugular intrahepatic portosystemic shunt. J. Hepatol. 36, 494–500 (2002).
  • Gage, B. F. et al. Validation of clinical classification schemes for predicting stroke: Results from the National Registry of Atrial Fibrillation. JAMA 285, 2864–2870 (2001).
  • Von Korff, M., Wagner, E. H. & Saunders, K. A chronic disease score from automated pharmacy data. J. Clin. Epidemiol. 45, 197–203 (1992).
  • Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J. Chronic Dis. 40, 373–383 (1987).
  • Quan, H. et al. Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am. J. Epidemiol. 173, 676–682 (2011).
  • de Groot, V., Beckerman, H., Lankhorst, G. J. & Bouter, L. M. How to measure comorbidity. A critical review of available methods. J. Clin. Epidemiol. 56, 221–229 (2003).
  • Bernabeu-Wittel, M. et al. Development of a new predictive model for polypathological patients. The PROFUND index. Eur. J. Intern. Med. 22, 311–317 (2011).
  • Yourman, L. C., Lee, S. J., Schonberg, M. A., Widera, E. W. & Smith, A. K. Prognostic indices for older adults: A systematic review. JAMA 307, 182 (2012).
  • Bernabeu-Wittel, M., Ollero-Baturone, M., Nieto-Martín, D., García-Morillo, S. & Goicoechea-Salazar, J. Patient-centered care for older adults with multiple chronic conditions: These are the polypathological patients!. J. Am. Geriatr. Soc. 61, 475–476 (2013).
  • Ministerio de Sanidad, Consumo y Bienestar Social. Estrategia para el Abordaje de la Cronicidad en el Sistema Nacional de Salud. Disponible en: https://www.mscbs.gob.es/organizacion/sns/planCalidadSNS/pdf/Evaluacion_E._Cronicidad_Final.pdf.
  • Suárez-Dono, J. et al. CRONIGAL: Prognostic index for chronic patients after hospital admission. Eur. J. Intern. Med. 36, 25–31 (2016).
  • Díez-Manglano, J. et al. Differential characteristics in polypathological inpatients in internal medicine departments and acute geriatric units: The PLUPAR study. Eur. J. Intern. Med. 24, 767–771 (2013).
  • Mahoney, F. I. & Barthel, D. W. Functional evaluation: The Barthel index. Md. State Med. J. 14, 61–65 (1965).
  • Pfeiffer, E. A short portable mental status questionnaire for the assessment of organic brain deficit in elderly patients. J. Am. Geriatr. Soc. 23, 433–441 (1975).
  • Genovese, stefano et al. Putting people at the centre: Integrated care for chronic diseases in Europe" EGIDE Policy Paper – 2020. (2020).
  • McPhail, S. M. Multimorbidity in chronic disease: Impact on health care resources and costs. Risk Manag. Healthc. Policy 9, 143–156 (2016).
  • Picco, L. et al. Economic burden of multimorbidity among older adults: Impact on healthcare and societal costs. BMC Health Serv. Res. 16, 173 (2016).
  • Smith, S. M., Wallace, E., O’Dowd, T. & Fortin, M. Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst. Rev. 3, CD006560 (2016).
  • Hindricks, G. et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association of Cardio-Thoracic Surgery (EACTS). Eur. Heart J. https://doi.org/10.1093/eurheartj/ehaa612 (2020).
  • Nardi, R. et al. Prognostic value for mortality of the new FADOI-COMPLIMED score(s) in patients hospitalized in medical wards. PLoS ONE 14, e0219767 (2019).
  • Dramé, M. et al. Derivation and validation of a mortality-risk index from a cohort of frail elderly patients hospitalised in medical wards via emergencies: The SAFES study. Eur. J. Epidemiol. 23, 783–791 (2008).
  • Mitchell, S. L., Miller, S. C., Teno, J. M., Davis, R. B. & Shaffer, M. L. The advanced dementia prognostic tool: A risk score to estimate survival in nursing home residents with advanced dementia. J. Pain Symptom Manage. 40, 639–651 (2010).
  • Di Bari, M. et al. Prognostic stratification of older persons based on simple administrative data: Development and validation of the ‘Silver Code’, to be used in emergency department triage. J. Gerontol. A. Biol. Sci. Med. Sci. 65, 159–164 (2010).
  • Teno, J. M. et al. Prediction of survival for older hospitalized patients: The HELP survival model. Hospitalized Elderly Longitudinal Project. J. Am. Geriatr. Soc. 48, S16-24 (2000).
  • Pilotto, A. et al. Development and validation of a multidimensional prognostic index for one-year mortality from comprehensive geriatric assessment in hospitalized older patients. Rejuvenation Res. 11, 151–161 (2008).
  • Sanz, F. et al. A composite of functional status and pneumonia severity index improves the prediction of pneumonia mortality in older patients. J. Gen. Intern. Med. 33, 437–444 (2018).
  • Socorro García, A., de la Puente, M., Perdomo, B., López Pardo, P. & Baztán, J. J. Functional status and mortality at month and year in nonagenarians hospitalized due to acute medical illness. Eur. J. Intern. Med. 26, 705–708 (2015).
  • Vallejo Maroto, I. et al. Recomendaciones sobre la valoración integral y multidimensional del anciano hospitalizado. Posicionamiento de la Sociedad Española de Medicina Interna. Rev. Clínica Esp. https://doi.org/10.1016/j.rce.2020.10.003 (2020).
  • Díez-Manglano, J. & Clemente-Sarasa, C. The nutritional risk and short-, medium- and long-term mortality of hospitalized patients with atrial fibrillation. Aging Clin. Exp. Res. 31, 1775–1781 (2019).
  • Wolf, P. A., Mitchell, J. B., Baker, C. S., Kannel, W. B. & D’Agostino, R. B. Impact of atrial fibrillation on mortality, stroke, and medical costs. Arch. Intern. Med. 158, 229–234 (1998).
  • Gullón, A. et al. Baseline functional status as the strongest predictor of in-hospital mortality in elderly patients with non-valvular atrial fibrillation: Results of the NONAVASC registry. Eur. J. Intern. Med. 47, 69–74 (2018).
  • Gullón, A. et al. Influence of frailty on anticoagulant prescription and clinical outcomes after 1-year follow-up in hospitalized older patients with atrial fibrillation. Intern. Emerg. Med. https://doi.org/10.1007/s11739-018-1938-3 (2018).