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Predict Frailty

Frailty can be managed and reduced between 35 and 45%.

Problem

Frailty is an aging-related state of decreased physiological reserve that results in increased vulnerability to poor health outcomes, worsening mobility and disability, hospitalizations, and mortality (1). Long recognized within the field of geriatrics as a clinical syndrome, frailty occurs in approximately 25% of people aged 65 and over (2). Frailty also contributes greatly to cost of care. The estimated annual cost directly attributed to frailty is more than $14,000 per patient after controlling for other variables (3).

Size of the Problem

  • 25% frailty occurs in approximately 25% of people aged 65 and over (2).
  • Half as likely frail patients are half as likely to be discharged to home (11).
  • 20% frail patients are roughly 20% more likely to be readmitted within 12 months (11).
  • $14,000 is the estimated annual cost of care per patient attributed to frailty (3).

Why it matters

People who are frail have a decreased ability to maintain or return to homeostasis after stressful events or aggressive interventions. This often leads to loss of independence (4). Compared to non-frail patients, they are 15% more likely to develop inpatient complications, and in the year following treatments for critical illness, have 30% increased disability in daily activities (1,5). For older adults, frailty may be a better predictor of mortality than age. It doubles the risk of in-hospital mortality and increases the risk of one-year mortality by 50% (5,6).

Even though frailty is a measurable phenotype that can be identified with standardized measures, such as unintentional weight loss of 10 pounds in the past year, these performance measures are not routinely captured in clinical encounters (7). Frailty is also not captured in administrative claims databases. But, validated claims-based algorithms have shown good discrimination of frailty and high predictive ability with adverse health outcomes. These algorithms present a powerful tool to enable provider identification and risk assessment of frail patients (7,8).

Solution

Early identification of frailty via predictive analytics is vital, as the evidence shows that frailty can be managed and reduced. Interventions designed to improve nutrition, stimulate cognition, and promote physical activity can reduce frailty between 35 and 45% (9,10). Frailty also has greater reversibility than disability, and prioritizing screening can provide critical information to identify poor prognosis and reversible risk factors.

Datasources

  • Medical Claims: Data extracted from health insurance medical claims with details about dates and place of service, diagnosis codes, key procedures, use of medical equipment, and provider specialties.
  • ADT Records: Data from Admit, Discharge, and Transfer feeds and patient data notification services that synchronize patient demographic, diagnostic, and visit information across healthcare systems.
  • Social Needs Assessments: Self-reported data that identify an individual's specific needs and the acute social and economic challenges they are experiencing.

Citations

  1. Bellal, Joseph, et al. “Superiority of Frailty Over Age in Predicting Outcomes Among Geriatric Trauma Patients: A Prospective Analysis” JAMA Surgery, vol. 8, no. 149, Aug. 2014, pp. 766-772, doi:10.1001/jamasurg.2014.296.
  2. Muscedere, John, et al. “The Impact of Frailty on Intensive Care Unit Outcomes: A Systematic Review and Meta-Analysis.” Intensive Care Medicine, vol. 43, no. 8, 2017, pp. 1105-1122, 10.1007/00134-017-4867-0.
  3. Simpson, Kit N., et al. “Effect of frailty on resource use and cost for Medicare patients” Future Medicine, vol. 7, no. 8, 29 May 2018. De 19/10.2217/cer-2018-0029.
  4. Hubbard, Ruth E., et al. “Frailty Status at Admission to Hospital Predicts Multiple Adverse Outcomes.” Age and Ageing, vol. 46, no. 5, 22 May 2017, pp. 801-806, 10.-1093/ageing/afx081.
  5. Brummel, Nathan E., et al. “Frailty and Subsequent Disability and Mortality among Patients with Critical lllness/* American Journal of Respiratory and Critical Care Medicine, vol. 196, no. 1, 2 Dec. 2016, pp. 64-72, doi:10.1164/rccm.201605-09390C.
  6. Bagshaw, Sean M., et al. “Association between frailty and short- and long-term outcomes among critically ill patients: a multicentre prospective cohort study” Canadian Medical Association Journal, vol. 186, no. 2, 25 Nov. 2013, pp.95-102. doi:10.1503/cmaj:130639.
  7. Segal, Jodi B, et al. “Development of a Claims-Based Frailty Indicator Anchored to a Well-Established Frailty Phenotype.” Medical Care, vol. 85, no. 7, 2017, pp. 716-722, 10.:1097/MLR.0000000000000729.
  8. Cuthbertson, Carmen C, et al. “Controlling for Frailty in Pharmacoepidemiologic Studies of Older Adults: Validation of an Existing Medicare Claims-Based Algorithm.” Epidemiology (Cambridge, Mass.), vol. 29, no. 4, 2018, pp. 556-561, 10.1097/EDE.0000000000000833.
  9. Apóstolo, Joáo, et al. “Predicting Risk and Outcomes for Frail Older Adults: An Umbrella Review of Frailty Screening Tools.” JB! Database of Systematic Reviews and Implementation Reports, vol. 15, no. 4, 2017, pp. 1154-1208, 10.11124/JBISRIR-2016-003018.
  10. Ng, Tze Pin, et al. “Nutritional, Physical, Cognitive, and Combination Interventions and Frailty Reversal Among Older Adults: A Randomized Controlled Trial.* The American Journal of Medicine, vol. 128, no. 11, 1Nov. 2015, pp.1225-1236, doi:10.1016/j.amjmed.2015.06.017.
  11. Castillo-Angeles, Manuel, et al. “Association of Frailty With Morbidity and Mortality in Emergency General Surgery By Procedural Risk Level.” JAMA Surgery, 25 Nov. 2020, 10.1001/jamasurg.2020.5397.

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