Drug Safety

pp 1–7 | Cite as

Identifying Data Elements to Measure Frailty in a Dutch Nationwide Electronic Medical Record Database for Use in Postmarketing Safety Evaluation: An Exploratory Study

  • Janet SultanaEmail author
  • Ingrid Leal
  • Marcel de Wilde
  • Maria de Ridder
  • Johan van der Lei
  • Miriam Sturkenboom
  • Gianluca Trifiro’
Short Communication



The role of frailty in postmarketing drug safety is increasingly acknowledged. Few European electronic medical records (EMRs) have been used to explore frailty in observational drug safety research.


The aim of this study was to identify data elements, beyond multimorbidity and polypharmacy, that could potentially contribute to measuring frailty among older adults in the Dutch nationwide Integrated Primary Care Information (IPCI) database.


Persons aged between 65 and 90 years in the IPCI database were identified from 2008 to 2013. Clinical non-disease, non-drug measurements that could potentially contribute to measuring frailty were identified and selected if they were recorded in > 0.005% of patients and could be included in at least one of three definitions of frailty: the frailty phenotype model, the cumulative deficit model, and direct evaluations of frailty through standardized frailty scores. The frequency of these measures was calculated.


Overall, 314,191 (17% of the source population) elderly persons were identified. Of these, 7948 (2.53%) had one or more of 12 clinical measurements identified that could potentially contribute to measuring frailty, such as clinical evaluations of cognition, mobility, and cachexia, as well as direct measures of frailty, such as the Groningen Frailty Index. Three of five measurements required for the frailty phenotype were identified in < 0.5% of the population: cachexia, reduced walking speed, and reduced physical activity; weakness and fatigue were not identified. The measurements outlined above may be appropriate for the cumulative deficit definition of frailty, provided that at least 30 deficits, including comorbidities and drug utilization, are evaluated in total. The most commonly recorded item identified that could potentially be used in a cumulative frailty model was the Mini-Mental State Examination score (N= 2850; 0.91%); the only recorded direct measurement of frailty was the Groningen Frailty Index (N = 2382; 0.76%).


Non-disease, non-drug clinical data that could potentially contribute to a frailty model was not commonly recorded in the IPCI; less than 3% of a cohort of elderly persons had these data recorded, suggesting that the use of these data in postmarketing drug safety evaluation may be limited.


Compliance with Ethical Standards

Review board approval

This study was approved by the IPCI Review Board (IPCI Raad van Toezicht).


This work was supported by the Italian Health Ministry (Grant number GR-2009-1607316: Assessment of the Safety of Antipsychotic Drugs in Elderly with Dementia: An International, Population-Based Study Using Healthcare Databases).

Conflicts of interest

Janet Sultana, Ingrid Leal, Marcel de Wilde, Maria de Ridder, Johan van der Lei, Miriam Sturkenboom and Gianluca Trifiro have no conflicts of interest that are directly relevant to the content of this study.

Author contributions

All authors contributed to this study. GT conceived the study; GT and JS designed the study; MS provided the data; IL, MdR and MdW carried out the data extraction and analysis; and JS, IL, MdR, MdW, JvdL, MS and GT contributed to the data interpretation and drafting of the paper.

Supplementary material

40264_2018_785_MOESM1_ESM.docx (19 kb)
Supplementary material 1 (DOCX 18 kb)
40264_2018_785_MOESM2_ESM.docx (22 kb)
Supplementary material 2 (DOCX 22 kb)
40264_2018_785_MOESM3_ESM.docx (20 kb)
Supplementary material 3 (DOCX 22 kb)


  1. 1.
    Sultana J, Cutroneo P, Trifirò G. Clinical and economic burden of adverse drug reactions. J Pharmacol Pharmacother. 2013;4(Suppl 1):S73–7.CrossRefGoogle Scholar
  2. 2.
    Trifiró G, Sultana J, Spina E. Are the safety profiles of antipsychotic drugs used in dementia the same? An updated review of observational studies. Drug Saf. 2014;37(7):501–20.CrossRefGoogle Scholar
  3. 3.
    Kim DH, Schneeweiss S. Measuring frailty using claims data for pharmacoepidemiologic studies of mortality in older adults: evidence and recommendations. Pharmacoepidemiol Drug Saf. 2014;23(9):891–901.CrossRefGoogle Scholar
  4. 4.
    Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Cardiovascular Health Study Collaborative Research Group, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–56.CrossRefGoogle Scholar
  5. 5.
    Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007;62(7):722–7.CrossRefGoogle Scholar
  6. 6.
    Lee L, Patel T, Hillier Loretta M, Maulkhan N, Slonim K, et al. Identifying frailty in primary care: a systematic review. Geriatr Gerontol Int. 2017;17(10):1358–77.Google Scholar
  7. 7.
    Clegg A, Bates C, Young J, Ryan R, Nichols L, Ann Teale E, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016;45(3):353–60.CrossRefGoogle Scholar
  8. 8.
    Hippisley-Cox J, Coupland C. Development and validation of QMortality risk prediction algorithm to estimate short term risk of death and assess frailty: cohort study. BMJ. 2017;358:j4208.CrossRefGoogle Scholar
  9. 9.
    Onder G, Vetrano DL, Marengoni A, Bell JS, Johnell K, Palmer K, Optimising Pharmacotherapy through Pharmacoepidemiology Network (OPPEN). Accounting for frailty when treating chronic diseases. Eur J Intern Med. 2018;56:49–52.CrossRefGoogle Scholar
  10. 10.
    Santos-Eggimann B, Cuénoud P, Spagnoli J, Junod J. Prevalence of frailty in middle-aged and older community-dwelling Europeans living in 10 countries. J Gerontol A Biol Sci Med Sci. 2009;64(6):675–81.CrossRefGoogle Scholar
  11. 11.
    Pilotto A, Gallina P, Panza F, Copetti M, Cella A, Cruz-Jentoft A, MPI_AGE Project Investigators, et al. Relation of statin use and mortality in community-dwelling frail older patients with coronary artery disease. Am J Cardiol. 2016;118(11):1624–30.CrossRefGoogle Scholar
  12. 12.
    Pilotto A, Gallina P, Copetti M, Pilotto A, Marcato F, Mello AM, Multidimensional Prognostic Index_Age Project Investigators, et al. Warfarin treatment and all-cause mortality in community-dwelling older adults with atrial fibrillation: a retrospective observational study. J Am Geriatr Soc. 2016;64(7):1416–24.CrossRefGoogle Scholar
  13. 13.
    Stow D, Matthews FE, Barclay S, Iliffe S, Clegg A, De Biase S, et al. Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study. Age Ageing. (epub 13 Mar 2018)
  14. 14.
    McAlister FA, Lethebe BC, Lambe C, Williamson T, Lowerison M. Control of glycemia and blood pressure in British adults with diabetes mellitus and subsequent therapy choices: a comparison across health states. Cardiovasc Diabetol. 2018;17(1):27.CrossRefGoogle Scholar
  15. 15.
    Ravindrarajah R, Hazra NC, Charlton J, Jackson SHD, Dregan A, Gulliford MC. Incidence and mortality of fractures by frailty level over 80 years of age: cohort study using UK electronic health records. BMJ Open. 2018;8(1):e018836.CrossRefGoogle Scholar
  16. 16.
    Sultana J, Fontana A, Giorgianni F, Basile G, Patorno E, Pilotto A, et al. Can information on functional and cognitive status improve short-term mortality risk prediction among community-dwelling older persons? A population-based study using a UK primary care database. Clin Epidemiol. 2017;10:31–9.CrossRefGoogle Scholar
  17. 17.
    Ravindrarajah R, Dregan A, Hazra NC, Hamada S, Jackson SHD, Gulliford MC. Declining blood pressure and intensification of blood pressure management among people over 80 years: cohort study using electronic health records. J Hypertens. 2017;35(6):1276–82.CrossRefGoogle Scholar
  18. 18.
    Ravindrarajah R, Hazra NC, Hamada S, Charlton J, Jackson SHD, Dregan A, et al. Systolic blood pressure trajectory, frailty, and all-cause mortality > 80 years of age: cohort study using electronic health records. Circulation. 2017;135(24):2357–68.CrossRefGoogle Scholar
  19. 19.
    Trifirò G, Gambassi G, Sen EF, Caputi AP, Bagnardi V, Brea J, et al. Association of community-acquired pneumonia with antipsychotic drug use in elderly patients: a nested case-control study. Ann Intern Med. 2010;152(7):418–25 (W139–40).CrossRefGoogle Scholar
  20. 20.
    Trifirò G, Verhamme KM, Ziere G, Caputi AP, Stricker BH, Sturkenboom MC. All-cause mortality associated with atypical and typical antipsychotics in demented outpatients. Pharmacoepidemiol Drug Saf. 2007;16(5):538–44.CrossRefGoogle Scholar
  21. 21.
    Blok CG, de Ridder MA, Verhamme KM, Moorman PW. Hypertension in older patients, a retrospective cohort study. BMC Geriatr. 2016;16:142.CrossRefGoogle Scholar
  22. 22.
    Trifirò G, Mokhles MM, Dieleman JP, van Soest EM, Verhamme K, Mazzaglia G, et al. Risk of cardiac valve regurgitation with dopamine agonist use in Parkinson’s disease and hyperprolactinaemia: a multi-country, nested case–control study. Drug Saf. 2012;35(2):159–71.CrossRefGoogle Scholar
  23. 23.
    Sultana J, Leal I, de Ridder M, Sturkenboom M, Trifiró G. Antipsychotic use in dementia patients in a general practice setting: a Dutch population-based study. Epidemiol Psychiatr Sci. 2016;25(4):403–6.CrossRefGoogle Scholar
  24. 24.
    Drubbel I, Bleijenberg N, Kranenburg G, Eijkemans RJ, Schuurmans MJ, de Wit NJ, et al. Identifying frailty: do the frailty index and Groningen frailty indicator cover different clinical perspectives? A cross-sectional study. BMC Fam Pract. 2013;14:64.CrossRefGoogle Scholar
  25. 25.
    Creavin ST, Wisniewski S, Noel-Storr AH, Trevelyan CM, Hampton T, Rayment D, et al. Mini-Mental State Examination (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations. Cochrane Database Syst Rev. 2016;1:CD011145.Google Scholar
  26. 26.
    Gilbert T, Neuburger J, Kraindler J, Keeble E, Smith P, Ariti C, et al. Development and validation of a Hospital Frailty Risk Score focusing on older people in acutecare settings using electronic hospital records: an observational study. Lancet. 2018;391(10132):1775–82.CrossRefGoogle Scholar
  27. 27.
    Soong J, Poots AJ, Scott S, Donald K, Bell D. Developing and validating a risk prediction model for acute care based on frailty syndromes. BMJ Open. 2015;5(10):e008457.CrossRefGoogle Scholar
  28. 28.
    Kim DH, Schneeweiss S, Glynn RJ, Lipsitz LA, Rockwood K, Avorn J. Measuring frailty in medicare data: development and validation of a claims-based frailty index. J Gerontol A Biol Sci Med Sci. 2018;73(7):980–7.CrossRefGoogle Scholar
  29. 29.
    Cuthbertson CC, Kucharska-Newton A, Faurot KR, Stürmer T, Jonsson Funk M, Palta P, et al. Controlling for frailty in pharmacoepidemiologic studies of older adults: validation of an existing medicare claims-based algorithm. Epidemiology. 2018;29(4):556–61.CrossRefGoogle Scholar
  30. 30.
    Segal JB, Chang H-Y, Du Y, Walston JD, Carlson MC, Varadhan R. Development of a claims-based frailty indicator anchored to a well-established frailty phenotype. Med Care. 2017;55(7):716–22.CrossRefGoogle Scholar
  31. 31.
    Martinez BK, Sood NA, Bunz TJ, Coleman CI. Effectiveness and safety of apixaban, dabigatran, and rivaroxaban versus warfarin in frail patients with nonvalvular atrial fibrillation. J Am Heart Assoc. 2018;7(8):e008643.CrossRefGoogle Scholar
  32. 32.
    Hope AA, Gong MN, Guerra C, Wunsch H. Frailty before critical illness and mortality for elderly medicare beneficiaries. J Am Geriatr Soc. 2015;63(6):1121–8.CrossRefGoogle Scholar
  33. 33.
    McIsaac DI, Bryson GL, van Walraven C. Association of frailty and 1-year postoperative mortality following major elective noncardiacsurgery: a population-based cohort study. JAMA Surg. 2016;151(6):538–45.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Policlinico UniversitarioUniversity of MessinaMessinaItaly
  2. 2.Department of Medical InformaticsErasmus Medical CentreRotterdamThe Netherlands
  3. 3.Julius Centre for Global Health, Utrecht University Medical CentreUtrechtThe Netherlands

Personalised recommendations