Frailty Indicator Using a Well- Established Frailty Phenotype Jodi - - PowerPoint PPT Presentation

Development of a Claims-Based Frailty Indicator Using a Well- Established Frailty Phenotype

Jodi B. Segal, MD, MPH, Hsien Yen-Chang, PhD, Yu Du, MS, Michelle Carson, PhD, Jeremy Walston, MD, Ravi Varadhan, PhD

Funding: National Institute on Aging, R21 AG048494-01

Background

• In 2001, Fried et al described a frailty phenotype in the

Cardiovascular Health Study (CHS)

• The phenotype is manifest when 3 or more of the following

are present: – Low grip strength – Low energy – Slowed walking speed – Low physical activity – Unintentional weight loss (>=10 lb)

• Simple measures, but not routinely measured or recorded

in most clinical encounters

Goals

• To develop a Claims-based Frailty Indicator that will

identify people with frailty using only administrative claims data

• To validate this measure of frailty as a predictor of

clinical outcomes

• To compare performance of this measure as a predictor
• f clinical outcomes to the original frailty phenotype

measure

Methods

• Data: Cardiovascular Health Study (CHS) cohort

previously linked to Medicare claims – 5,201 men and women from 4 U.S. communities (starting in 1989) – 687 more African Americans joined later – Examined annually through 1999, with phone calls every six months after 1999 – Medicare data from 1992-2013 linked to CHS data

• Frailty reference standard

– >3 indicators = Frail – Fewer than 3 = Non-frail

Methods

Candidate variable selection

– Used literature to identify variables previously used to classify individuals as frail or disabled with claims data or EMR – Added other variables from AHRQ’s Clinical Classifications Software (CCS) using ICD-9-CM codes – Operationalized them to be identifiable in claims data

Methods

Used claims from 6 months before the 5th and 9th year study visits (individuals contributed twice) Visit 2 (5th yr) Visit 5 (9th yr)

Measured frailty Measured frailty

claims claims

Methods

Modeling frailty – Adaptive Lasso technique with logistic regression – Optimal Lasso penalty chosen via 10-fold cross- validation to maximize area under the receiver

• perator curve

– Also tested a gradient boosting method, a random forest method, and logic (Boolean) regression – We required continuous enrollment in Medicare Parts A & B during the 6 month windows of interest

Methods

Internal Validation – Operationalized outcomes of fracture, death, nursing home admission, hospitalization and disability

• ccurrence in the 5 years after 5th year study visit

– Modeled the risk of events for frail and non-frail classified at a cutoff that had good specificity and acceptable sensitivity, also as a continuous variable Visit 2 claims claims data cohort data

5 year interval

Results

Characteristics of Participants in CHS with Continuous Enrollment (n=4454)

VARIABLE % Age 72 years (mean) White 84.1% Black 15.4% Other 0.46% Female 58.8% Married 66.2% Widowed 24.6% Divorced 3.89% Separated 0.90% Never Married 4.47%

Results

Variables Retained in Claims-Based Frailty Indicator

B- coefficient Variable 1.24 Impaired mobility 0.54 Depression 0.50 Congestive Heart Failure 0.50 Parkinson’s disease

• 0.49

White race 0.43 Arthritis (any type) 0.33 Cognitive impairment 0.31 Charlson comorbidity index (>0, 0) 0.28 Stroke 0.24 Paranoia 0.23 Chronic skin ulcer 0.21 Pneumonia

• 0.19

Male sex 0.18 Skin and soft tissue infection 0.14 Mycoses 0.09 Age (in 5 year categories) 0.09 Admission in past 6 months 0.08 Gout or other crystal-induced arthropathy 0.08 Falls 0.05 Musculoskeletal problems 0.05 Urinary tract infection

Results

At cutoff of 0.12, Sens=0.66 Spec=0.73 PPV =0.24 NPV=0.94 At cutoff of 0.3, Sens =0.16 Spec=0.98 PPV = 0.46 NPV = 0.90

Area under ROC=0.75

For every 10% absolute increase in predicted probability of frailty, the

• dds of actual frailty

increase by 2.4 - fold

Classification

Actual Predicted 1 Total 3519 258 3912 1 324 156 542 Total 3843 414 4454 Actual frailty vs. Predicted frailty (p>=0.2) - Enrollment-Eligible Patients

Results: Internal Validation

Outcome Measure N Unadjusted [95% C.I.] Adjusted [95% C.I.]*

Death within 5 years OR 4453 3.81 [3.15 – 4.62] 1.81 [1.41 – 2.31] Time to death HR 4453 3.18 [2.72 – 3.71] 1.61 [1.30 – 2.00] Admission within 5 years OR 2875 2.18 [1.67 – 2.86] 1.46 [1.07 – 1.99] Time to first admission HR 2875 1.71 [1.46 – 1.99] 1.30 [1.06 – 1.58] Fracture within 5 years** OR 4255 1.18 [0.95 - 1.45] 0.97 [0.76 – 1.25] Nursing home admission OR 4209 3.80 [2.96 - 4.88] 1.45 [1.04 – 2.01] Disability within 5 years OR 4184 3.56 [2.90 – 4.37] 2.15 [1.69 – 2.74]

*adjusted for sex and age (in years), **counting first fracture per year of each eligible body part,***two stage model –estimate reported is impact of frailty among people with any fractures, OR=odds ratio, HR=hazard ratio

For Every 10% Increase in Predicted Probability of Frailty

Outcome Measure N Unadjusted [95% C.I.] Adjusted [95% C.I.]*

Death within 5 years OR 4453 1.86 [1.71 – 2.00] 1.49 [1.33 – 1.68] Time to death HR 4453 1.55 [1.47 – 1.63] 1.34 [1.23 – 1.45] Admission within 5 years OR 2875 1.64 [1.55 – 2.04] 1.93 [1.60 – 2.32] Time to first admission HR 2875 1.42 [1.36 – 1.51] 1.52 [1.41 – 1.64] Fracture within 5 years** OR 4255 1.08 [1.00 – 1.17] 0.96 [0.86 – 1.08] Nursing home admission OR 4209 1.95 [1.69– 2.02] 1.38 [1.21 – 1.58] Disability within 5 years OR 4184 2.06 [1.87 – 2.26] 2.04 [1.77 – 2.36]

*adjusted for sex and age (in years), **counting first fracture per year of each eligible body part,***two stage model –estimate reported is impact of frailty among people with any fractures, OR=odds ratio, HR=hazard ratio

Comparing Age (5 years)* and Predicted Frailty** (10% increase)

*sex and frailty adjusted, **sex and age adjusted 0.1 1 10 Measure of Association Age by 5 years Predicted Frailty by 10% Time to death (HR) Admission within 5 years (OR) Fracture within 5 years (OR) Nursing home admission (OR) Time to first admission (HR) Disability within 5 years (OR) Death with 5 years (OR)

Comparison to Measured Frailty (Phenotype)

Outcome Measure N Claims-based Indicator Unadjusted [95% C.I.]* Measured Frailty Phenotype Unadjusted [95% C.I.]*

Death within 5 years OR 4453 3.81 [3.15 – 4.62] 3.26 [2.62 – 4.05] Time to death HR 4453 3.18 [2.72 – 3.71] 2.82 [2.36 – 3.37] Admission within 5 years OR 2875 2.18 [1.67 – 2.86] 2.76 [2.02 – 3.77] Time to first admission HR 2875 1.71 [1.46 – 1.99] 1.95 [1.65 – 2.30] Fracture within 5 years** OR 4255 1.18 [0.95 – 1.45] 1.21 [0.96 – 1.53] Nursing home admission OR 4209 3.80 [2.96 – 4.88] 3.35 [2.52 – 4.45] Disability within 5 years OR 4184 3.56 [2.90 – 4.37] 4.30 [3.40 – 5.45]

Comparison to Measured Frailty (Phenotype)

Outcome Measure N Claims-based Indicator Adjusted [95% C.I.]* Measured Frailty Phenotype Adjusted [95% C.I.]*

Death within 5 years OR 4453 1.81 [1.41 – 2.31] 2.46 [1.94 – 3.11] Time to death HR 4453 1.61 [1.30 – 2.00] 2.07 [1.70 – 2.53] Admission within 5 years OR 2875 1.46 [1.07 – 1.99] 2.46 [1.79 - 3.39] Time to first admission HR 2875 1.30 [1.06 – 1.58] 1.76 [1.47 – 2.10] Fracture within 5 years** OR 4255 0.97 [0.76 – 1.25] 1.09 [0.85 – 1.38] Nursing home admission OR 4209 1.45 [1.04 – 2.01] 2.08 [1.53 - 2.83] Disability within 5 years OR 4184 2.15 [1.69 – 2.74] 3.39 [2.66 – 4.32]

*adjusted for sex and age (in years), **counting first fracture per year of each eligible body part,***two stage model –estimate reported is impact of frailty among people with any fractures, OR=odds ratio, HR=hazard ratio, IRR=incidence rate ratio

0.1 1 10 Measure of Association Time to death (HR) Admission within 5 years (OR) Fracture within 5 years (OR) Nursing home admission (OR) Time to first admission (HR) Disability within 5 years (OR) Death with 5 years (OR)

Frailty Phenotype Claims based Frailty Indicator

Comparing CFI and Phenotype, Unadjusted

Comparing CFI and Phenotype, Age and Sex Adjusted

0.1 1 10 Measure of Association Time to death (HR) Admission within 5 years (OR) Fracture within 5 years (OR) Nursing home admission (OR) Time to first admission (HR) Disability within 5 years (OR) Death with 5 years (OR)

Frailty Phenotype Claims based Frailty Indicator

Additional Results

• Random forest method with slightly higher area

under ROC curve, at the expense of transparency

• Higher area under ROC curve than commonly used

Frailty Index (count of comorbidities)

• Charlson Comorbidity Index alone is nicely

predictive of some of the tested outcomes

Conclusions

• Adaptive lasso technique produced a model that

identifies individuals as frail using claims data alone

• Claims-based Frailty Index predicts outcomes

within same data set similarly to the frailty phenotype

Limitations

• Not yet validated in an external data set
• Perhaps there are other covariates that

would further improve classification

Implications

• Claims-only index built against the accepted

reference standard for measured frailty has good predictive value

• Claims-based Indicator of Frailty has broad

uses for:

– Risk adjustment – Exploration of heterogeneity of treatment effect – Population health management – Emergency preparedness

THANK YOU

What already exists?

Next Steps

• Assess predictive validity in a population
• f frail individuals who are not old

(probably patients with renal failure)

• Assess heterogeneity of treatment

response to cancer therapies attributable to frailty (compare to more commonly used performance measures)

Individuals with Inpatient Visits as Classified by Claims-based Frailty Index

10 20 30 40 50 60 1 2 3 4 5 6 7

Percentage of Individuals Number of Hospital Admissions in Five Years Frail Non-frail