Skip to main content
SpringerLink
Log in

New Frailty Index Approach Predicts COVID-19 Mortality Risk

  • RESEARCH ARTICLES
  • Published:
Advances in Gerontology Aims and scope Submit manuscript

Abstract

The relationships between blood biomarkers, frailty, and the risk of death of people diagnosed with COVID-19 is unclear. In the current investigation we decided to analyze the collective effect of multiple biomarkers (laboratory markers of inflammation, blood biochemistry deviations, comorbidity, demographics) on mortality in people diagnosed with COVID-19. We analyzed baseline data of one hundred fifty-five patients (age range from twenty-six to ninety-four) diagnosed with COVID-19. Thirty-seven parameters (including major morbidities) were used to derive the frailty index (FI) and calculate the risk of death as a function of FI and individual biomarkers. Discriminative ability was assessed by the area under the receiver-operating characteristic (ROC curves). The mean frailty index was 0.17 (SD = 0.10), FI of those who survived was 0.11 (SD = 0.078) and those who died was 0.22 (SD = 0.093). In a sex-adjusted model, the FI was a more powerful predictor for mortality than age. The ROC analysis showed that models involving FI as a feature have good discriminative ability for predicting COVID-19 mortality: AUC for age was 0.77, for the FI it was 0.82, and for the fully adjusted model (age + FI) it was 0.84. Thus, the systemic effect of multiple biological processes comprising aging are elucidated using the Frailty Index approach. Assessment of the frailty index at the time of admission of a patient with COVID-19 to the clinic can help to predict the high risks of severe disease and mortality.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

REFERENCES

  1. Wang, S., Qiu, Z., Hou, Y., Deng, X., Xu, W., Zheng, T., Wu, P., Xie, S., Bian, W., Zhang, C., Sun, Z., Liu, K., Shan, C., Lin, A., Jiang, S., Xie, Y., Zhou, Q., Lu, L., Huang, J., and Li, X., AXL is a candidate receptor for SARS-CoV-2 that promotes infection of pulmonary and bronchial epithelial cells, Cell Res., 2021, vol. 31, no. 2, pp. 126–140.

    Article  CAS  PubMed  Google Scholar 

  2. Alon, R., Sportiello, M., Kozlovski, S., Kumar, A., Reilly, E.C., Zarbock, A., Garbi, N., and Topham, D.J., Leukocyte trafficking to the lungs and beyond: Lessons from influenza for COVID-19, Nat. Rev. Immunol., 2021, vol. 21, no. 1, pp. 49–64.

    Article  CAS  PubMed  Google Scholar 

  3. Salamanna, F., Maglio, M., Landini, M.P., and Fini, M., Body Localization of ACE-2: On the trail of the keyhole of SARS-CoV-2, Front. Med. (Lausanne), 2020, vol. 7, p. 594495.

    Article  PubMed  Google Scholar 

  4. Bonaventura, A., Vecchie, A., Dagna, L., Martinod, K., Dixon, D.L., Van Tassell, B.W., Dentali, F., Montecucco, F., Massberg, S., Levi, M., and Abbate, A., Endothelial dysfunction and immunothrombosis as key pathogenic mechanisms in COVID-19, Nat. Rev. Immunol., 2021, vol. 21, no. 5, pp. 319–329.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Tanni, S.E., Fabro, A.T., de Albuquerque, A., Ferreira, E.V.M., Verrastro, C.G.Y., Sawamura, M.V.Y., Ribeiro, S.M., and Baldi, B.G., Pulmonary fibrosis secondary to COVID-19: A narrative review, Expert Rev. Respir. Med., 2021, vol. 15, no. 6, pp. 791–803.

    Article  CAS  PubMed  Google Scholar 

  6. Zhavoronkov, A., Geroprotective and senoremediative strategies to reduce the comorbidity, infection rates, severity, and lethality in gerophilic and gerolavic infections, Aging (Albany, New York), 2020, vol. 12, no. 8, pp. 6492–6510.

    CAS  Google Scholar 

  7. Mueller, A.L., McNamara, M.S., and Sinclair, D.A., Why does COVID-19 disproportionately affect older people?, Aging (Albany, New York), 2020, vol. 12, no. 10, pp. 9959–9981.

    CAS  Google Scholar 

  8. Dres, M., Hajage, D., Lebbah, S., Kimmoun, A., Pham, T., Beduneau, G., Combes, A., Mercat, A., Guidet. B., Demoule, A., and Schmidt, M., Characteristics, management, and prognosis of elderly patients with COVID-19 admitted in the ICU during the first wave: Insights from the COVID-ICU study: Prognosis of COVID-19 elderly critically ill patients in the ICU, Ann. Intensive Care, 2021, vol. 11, no. 1, p. 77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wu, Z. and McGoogan, J.M., Characteristics of and important lessons from the coronavirus disease, 2019 (COVID-19) outbreak in China: Summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention, JAMA, 2020, vol. 323, no. 13, pp. 1239–1242.

    Article  CAS  PubMed  Google Scholar 

  10. Richardson, S., Hirsch, J.S., Narasimhan, M., Crawford, J.M., McGinn, T., and Davidson, K.W., the Northwell COVID-19 Research Consortium, Barnaby, D.P., Becker, L.B., Chelico, J.D., Cohen, S.L., Cookingham, J., Coppa, K., Diefenbach, M.A., Dominello, A.J., Duer-Hefele, J., Falzon, L., Gitlin, J., Hajizadeh, N., Harvin, T.G., Hirschwerk, D.A., Kim, E.J., Kozel, Z.M., Marrast, L.M., Mogavero, J.N., Osorio, G.A., Qiu, M., and Zanos, T.P., Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area, JAMA, 2020, vol. 323, no. 20, pp. 2052–2059.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Yang, J., Zheng, Y., Gou, X., Pu, K., Chen, Z., Guo, Q., Ji, R., Wang, H., Wang, Y., and Zhou, Y., Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: A systematic review and meta-analysis, Int. J. Infect. Dis., 2020, vol. 94, pp. 91–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bartleson, J.M., Radenkovic, D., Covarrubias, A.J., Furman, D., Winer, D.A., and Verdin, E., SARS-CoV-2, COVID-19 and the ageing immune system, Nat. Aging, 2021, vol. 1, no. 9, pp. 769–782.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Barzilai, N., Appleby, J.C., Austad, S.N., Cuervo, A.M., Kaeberlein, M., Gonzalez-Billault, C., Lederman, S., Stambler, I., and Sierra, F., Geroscience in the age of COVID-19, Aging Dis., 2020, vol. 11, no. 4, pp. 725–729.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Pawelec, G., Bronikowski, A., Cunnane, S.C., Ferrucci, L., Franceschi, C., Fülöp, T., Gaudreau, P., Gladyshev, V.N., Gonos, E.S., Gorbunova, V., Kennedy, B.K., Larbi, A., Lemaître, J.F., Liu, G.H., Maier, A.B., Morais, J.A., Nóbrega, O.T., Moskalev, A., Rikkert, M.O., Seluanov, A., Senior, A.M., Ukraintseva, S., Vanhaelen, Q., Witkowski, J., and Cohen, A.A., The conundrum of human immune system “senescence,” Mech. Ageing Dev., 2020, vol. 192, p. 111357.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Moskalev, A., Stambler, I., and Caruso, C., Innate and adaptive immunity in aging and longevity: the foundation of resilience, Aging Dis., 2020, vol. 11, no. 6, pp. 1363–1373.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lagunas-Rangel, F.A., Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis, J. Med Virol., 2020, vol. 92, no. 10, pp. 1733–1734.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Rydyznski Moderbacher, C., Ramirez, S.I., Dan, J.M., Grifoni, A., Hastie, K.M., Weiskopf, D., Belanger, S., Abbott, R.K., Kim, C., Choi, J., Kato, Y., Crotty, E.G., Kim, C., Rawlings, S.A., Mateus, J., Tse, L.P.V., Frazier, A., Baric, R., Peters, B., and Greenbaum, J., Ollmann Saphire, E., Smith, D.M., Sette, A., and Crotty, S., Antigen-specific adaptive immunity to SARS-CoV-2 in acute COVID-19 and associations with age and disease severity, Cell, 2020, vol. 183, no. 4, pp. 996–1012.e19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Shah, W., Hillman, T., Playford, E.D., and Hishmeh, L., Managing the long term effects of covid-19: Summary of NICE, SIGN, and RCGP rapid guideline, BMJ, 2021, vol. 372, p. n136.

    Article  PubMed  Google Scholar 

  19. Mitnitski, A.B., Mogilner, A.J., and Rockwood, K., Accumulation of deficits as a proxy measure of aging, Scientific World Journal, 2001, vol. 1, pp. 323–336.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Stubbings, G., Farrell, S., Mitnitski, A., Rockwood, K., and Rutenberg, A., Informative frailty indices from binarized biomarkers, Biogerontology, 2020, vol. 21, no. 3, pp. 345–355.

    Article  PubMed  Google Scholar 

  21. Andrés-Esteban, E.M., Quintana-Diaz, M., Ramírez-Cervantes, K.L., Benayas-Peña, I., Silva-Obregón, A., Magallón-Botaya, R., Santolalla-Arnedo, I., Juárez-Vela, R., and Gea-Caballero, V., Outcomes of hospitalized patients with COVID-19 according to level of frailty, PeerJ., 2021, vol. 9, p. e11260.

  22. Yang, Y., Luo, K., Jiang, Y., Yu, Q., Huang, X., Wang, J., Liu, N., and Huang, P., The impact of frailty on COVID-19 outcomes: A systematic review and meta-analysis of 16 cohort studies, J. Nutr. Health Aging, 2021, vol. 25, no. 5, pp. 702–709.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Geriatric Medicine Research Collaborative, Covid Collaborative, and Welch, C., Age and frailty are independently associated with increased COVID-19 mortality and increased care needs in survivors: Results of an international multi-centre study, Age Ageing, 2021, vol. 50, no. 3, pp. 617–630.

    Article  Google Scholar 

  24. Strazhesko, I., Tkacheva, O., Kashtanova, D., Ivanov, M., Kljashtorny, V., Esakova, A., Karnaushkina, M., Guillemette, C., Hewett, A., Legault, V., Maytesian, L., Litvinova, M., Cohen, A., and Moskalev, A., Physiological health indexes predict deterioration and mortality in patients with COVID-19: A comparative study, Aging (Albany, New York), 2022, vol. 14, no. 4, pp. 1611–1626.

    CAS  Google Scholar 

  25. Zhang, X.M., Jiao, J., Cao, J., Huo, X.P., Zhu, C., Wu, X.J., and Xie, X.H., Frailty as a predictor of mortality among patients with COVID-19: A systematic review and meta-analysis, BMC Geriatr., 2021, vol. 21, no. 1, p. 186.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Vlachogiannis, N.I., Baker, K.F., Georgiopoulos, G., Lazaridis, C., van der Loeff, I.S., Hanrath, A.T., Sopova, K., Tual-Chalot, S., Gatsiou, A., Spyridopoulos, I., Stamatelopoulos, K., Duncan, C.J.A., and Stellos, K., Clinical frailty, and not features of acute infection, is associated with late mortality in COVID-19: A retrospective cohort study. J. Cachexia Sarcopenia Muscle, 2022, vol. 13, no. 3, pp. 1502–1513.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Searle, S.D., Mitnitski, A., Gahbauer, E.A., Gill, T.M., and Rockwood, K., A standard procedure for creating a frailty index, BMC Geriatr., 2008, vol. 8, p. 24.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

ACKNOWLEDGMENTS

We dedicate this article to the memory of our friend and teacher Arnold Mitnitsky, one of the creators of the Frailty Index concept. This manuscript is his last scientific work on which he worked intensively during the last months of his life.

Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

Author information

Authors and Affiliations

  1. RLE Group, Benidorm, Spain

    Alexander Fedintsev

  2. Department of Internal Medicine with a Course of Cardiology and Functional Diagnostics named after Academician V.S. Moiseev, “Peoples’ Friendship University of Russia” (RUDN University), Moscow, Russia

    Maria Karnaushkina & Maria Litvinova

  3. Department of Science, Technology and Society, Bar-Ilan University, Ramat-Gan, Israel

    Ilia Stambler

  4. Division of Geriatric Medicine, Dalhousie University, Halifax, Nova Scotia, Canada

    Arnold Mitnitski

  5. Innovative Technologies and AI for Public Health Laboratory, National Public Health Research Institute named after N.A. Semashko, Moscow, Russia

    Alexander Melerzanov

  6. Department of Medical Genetics, Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), Moscow, Russia

    Maria Litvinova

  7. Laboratory of Medical AI, Computer Sciences and Technology Department, Tsinghua University, Beijing, China

    Kirill Balbek

  8. Russian Clinical Research Center for Gerontology, Pirogov Russian National Research Medical University of the Ministry of Healthcare of the Russian Federation, Moscow, Russia

    Alexey Moskalev

Authors
  1. Alexander Fedintsev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria Karnaushkina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ilia Stambler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arnold Mitnitski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexander Melerzanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maria Litvinova
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kirill Balbek
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alexey Moskalev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexey Moskalev.

Ethics declarations

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Statement of Compliance with Standards of Research Involving Human Subjects

Study approval: Ethical approval was obtained from the Local Ethical Committee of Sechenov First Moscow State Medical University (Abstract of minutes no. 19-20 at July 2, 2020).

Consent to participate: Department of the University Clinical Hospital of the Sechenov First Moscow State Medical University (Sechenov University) Hospital for Veterans of Wars No. 3, Moscow, Russian Federation The research (protocol no. 5 at May 12, 2020) complied with the requirements of the Declaration of Helsinki. Written informed consent was obtained for participation in this study for every participant.

CONFLICT OF INTEREST

The authors of this work declare that they have no conflicts of interest.

Additional information

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fedintsev, A., Karnaushkina, M., Stambler, I. et al. New Frailty Index Approach Predicts COVID-19 Mortality Risk. Adv Gerontol 13, 26–35 (2023). https://doi.org/10.1134/S2079057024600046

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S2079057024600046

Keywords:

Search

Navigation