Atrial fibrillation in older patients and artificial intelligence: a quantitative demonstration of a link with some of the geriatric multidimensional assessment tools—a preliminary report


Atrial fibrillation (AF) associates with disability and frailty. Aim of this study was to evaluate in older AF patients, using artificial intelligence (AI), the relations between geriatric tools and daily standing and resting periods. We enrolled thirty-one > 65 years patients undergoing electrical cardioversion of AF (age: 79 ± 6 years; women: 41.9%; CHA2DS2-VASc: 3.7 ± 1.2; MMSE: 27.7 ± 2.7; GDS: 3.0 ± 2.8). The data of the first day following the procedure were analyzed using machine-learning techniques in a specifically designed cloud platform. Standing, activity, time (582 ± 139 min) was directly associated with MMSE and inversely with GDS. Sleep length was 472 ± 230 min. Light sleep, the longer resting phase, was inversely related to GDS. The Chest Effort Index, a measure of obstructive sleep apnea, grew with GDS. In conclusion, AI devices can be routinely used in improving older subjects’ evaluation. A correlation exists between standing time, MMSE, and depressive symptoms. GDS associates to length and quality of sleep.

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  1. 1.

    Fumagalli S, Pelagalli G, Montorzi RF et al (2020) The CHA(2)DS(2)-VASc score and geriatric multidimensional assessment tools in elderly patients with persistent atrial fibrillation undergoing electrical cardioversion. A link with arrhythmia relapse? Eur J Intern Med.

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Calzolari I, Fumagalli S, Marchionni N et al (2009) Polyunsaturated fatty acids and cardiovascular disease. Curr Pharm Des 15:4094–4102

    CAS  Article  Google Scholar 

  3. 3.

    Sim I (2019) Mobile devices and health. N Engl J Med 381:956–968.

    Article  PubMed  Google Scholar 

  4. 4.

    Attia ZI, Noseworthy PA, Lopez-Jimenez F et al (2019) An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 394:861–867.

    Article  PubMed  Google Scholar 

  5. 5.

    Fumagalli S, Nieuwlaat R, Tarantini F et al (2012) Characteristics, management and prognosis of elderly patients in the Euro Heart Survey on atrial fibrillation. Aging Clin Exp Res 24:517–523.

    Article  PubMed  Google Scholar 

  6. 6.

    Ikegami S, Takahashi J, Uehara M et al (2019) Physical performance reflects cognitive function, fall risk, and quality of life in community-dwelling older people. Sci Rep 9:12242.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Loomer L, Downer B, Thomas KS (2019) Relationship between functional improvement and cognition in short-stay nursing home residents. J Am Geriatr Soc 67:553–557.

    Article  PubMed  Google Scholar 

  8. 8.

    Gill TM, Murphy TE, Gahbauer EA et al (2020) Factors associated with insidious and noninsidious disability. J Gerontol A Biol Sci Med Sci.

    Article  PubMed  Google Scholar 

  9. 9.

    Frasure-Smith N, Lesperance F, Habra M et al (2009) Elevated depression symptoms predict long-term cardiovascular mortality in patients with atrial fibrillation and heart failure. Circulation 120:134–140.

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Fenger-Grøn M, Vestergaard CH, Frost L et al (2020) Depression and uptake of oral anticoagulation therapy in patients with atrial fibrillation: a Danish nationwide cohort study. Med Care 58:216–224.

    Article  PubMed  Google Scholar 

  11. 11.

    Fumagalli S, Cardini F, Roberts AT et al (2015) Psychological effects of treatment with new oral anticoagulants in elderly patients with atrial fibrillation: a preliminary report. Aging Clin Exp Res 27:99–102.

    Article  PubMed  Google Scholar 

  12. 12.

    Traaen GM, Øverland B, Aakerøy L et al (2020) Prevalence, risk factors, and type of sleep apnea in patients with paroxysmal atrial fibrillation. Int J Cardiol Heart Vasc 26:100447.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Lee SH, Lee YJ, Kim S et al (2017) Depressive symptoms are associated with poor sleep quality rather than apnea-hypopnea index or hypoxia during sleep in patients with obstructive sleep apnea. Sleep Breath 21:997–1003.

    Article  PubMed  Google Scholar 

  14. 14.

    Vanek J, Prasko J, Genzor S et al (2020) Obstructive sleep apnea, depression and cognitive impairment. Sleep Med 72:50–58.

    Article  PubMed  Google Scholar 

  15. 15.

    Ambagtsheer RC, Shafiabady N, Dent E et al (2020) The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set. Int J Med Inform 136:104094.

    CAS  Article  PubMed  Google Scholar 

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Correspondence to Stefano Fumagalli.

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Fumagalli, S., Pelagalli, G., Franci Montorzi, R. et al. Atrial fibrillation in older patients and artificial intelligence: a quantitative demonstration of a link with some of the geriatric multidimensional assessment tools—a preliminary report. Aging Clin Exp Res (2020).

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  • Artificial intelligence
  • Atrial fibrillation
  • Elderly
  • Geriatric multidimensional assessment
  • Mini-mental state examination
  • Geriatric depression scale