Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories

Wong, Edwin S.; Yoon, Jean; Piegari, Rebecca; Rosland, Ann‐Marie; Fihn, Stephan D.; Chang, Ernest S.

doi:10.1007/s11606-018-4653-x

Cited by 25 publications

(21 citation statements)

References 39 publications

(41 reference statements)

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“…Scores range from 0 (lowest risk) to 99 (highest risk) and are reported in increments of 5 from 0 to 94 and by 1 from 95 to 99. 23,24 Veterans in the cohort were stratified into four categories based on their CAN score for the risk of hospitalization and/or mortality within 1 year. Those with a missing CAN score (e.g., veterans lacking an assigned primary care provider) were assigned to a fifth category.…”

Section: Methodsmentioning

confidence: 99%

Life-Sustaining Treatment Decisions Initiative: Early Implementation Results of a National Veterans Affairs Program to Honor Veterans’ Care Preferences

et al. 2020

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BACKGROUND: On July 1, 2018, the Veterans Health Administration (VA) National Center for Ethics in Health Care implemented the Life-Sustaining Treatment Decisions Initiative (LSTDI). Its goal is to identify, document, and honor LST decisions of seriously ill veterans. Providers document veterans' goals and decisions using a standardized LST template and order set. OBJECTIVE: Evaluate the first 7 months of LSTDI implementation and identify predictors of LST template completion. DESIGN: Retrospective observational study of clinical and administrative data. We identified all completed LST templates, defined as completion of four required template fields. Templates also include four non-required fields. Results were stratified by risk of hospitalization or death as estimated by the Care Assessment Need (CAN) score.

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Section: Methodsmentioning

confidence: 99%

Life-Sustaining Treatment Decisions Initiative: Early Implementation Results of a National Veterans Affairs Program to Honor Veterans’ Care Preferences

et al. 2020

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“…Scholars have identified subpopulations among high‐risk populations including high‐cost and high‐risk cohorts using data from a variety of sources including managed care plans, administrative claims, and health systems . Prior work into HN subpopulations classified subgroups using clinical conditions, risk scores, hospital procedures, and acute utilization.…”

mentioning

confidence: 99%

“…7,8 Scholars have identified subpopulations among highrisk populations including high-cost 9-11 and high-risk 12 cohorts using data from a variety of sources including managed care plans, 10 administrative claims, 11 and health systems. 9,12 Prior work into HN subpopulations classified subgroups using clinical conditions, risk scores, hospital procedures, and acute utilization. Although valuable, past work is limited by a lack of focus on post-acute care that accounts for approximately 73% of the regional variation in Medicare spending.…”

mentioning

confidence: 99%

High‐Need Phenotypes in Medicare Beneficiaries: Drivers of Variation in Utilization and Outcomes

Keeney

Bélanger

Jones

et al. 2019

J American Geriatrics Society

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OBJECTIVES High‐need (HN) Medicare beneficiaries heavily use healthcare services at a high cost. This population is heterogeneous, composed of individuals with varying degrees of medical complexity and healthcare needs. To improve healthcare delivery and decrease costs, it is critical to identify the subpopulations present within this population. We aimed to (1) identify distinct clinical phenotypes present within HN Medicare beneficiaries, and (2) examine differences in outcomes between phenotypes. DESIGN Latent class analysis was used to identify phenotypes within a sample of HN fee‐for‐service (FFS) Medicare beneficiaries aged 65 years and older using Medicare claims and post‐acute assessment data. SETTING Not applicable. PARTICIPANTS Two cross‐sectional cohorts were used to identify phenotypes. Cohorts included FFS Medicare beneficiaries aged 65 and older who survived through 2014 (n = 415 659) and 2015 (n = 416 643). MEASUREMENTS The following variables were used to identify phenotypes: acute and post‐acute care use, functional dependency in one or more activities of daily living, presence of six or more chronic conditions, and complex chronic conditions. Mortality, hospitalizations, healthcare expenditures, and days in the community were compared between phenotypes. RESULTS Five phenotypes were identified: (1) comorbid ischemic heart disease with hospitalization and skilled nursing facility use (22% of the HN sample), (2) comorbid ischemic heart disease with home care use (23%), (3) home care use (12%), (4) high comorbidity with hospitalization (32%), and (5) Alzheimer's disease/related dementias with functional dependency and nursing home use (11%). Mortality was highest in phenotypes 1 and 2; hospitalizations and expenditures were highest in phenotypes 1, 3, and 4. CONCLUSIONS Our findings represent a first step toward classifying the heterogeneity among HN Medicare beneficiaries. Further work is needed to identify modifiable utilization patterns between phenotypes to improve the value of healthcare provided to these subpopulations. J Am Geriatr Soc 68:70–77, 2019

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“…First, this analysis was performed among a cohort of Veterans and may be difficult to generalize to non-Veterans. However, our population represented a diverse cohort with a variety of comorbidities and is representative of high-risk individuals across the US [ 6 , 22 ]. Second, we did not have access to non-VA data, limiting our ability to examine utilization outside of the VA.…”

Section: Discussionmentioning

confidence: 99%

“…We chose to use 2014 data so that we could measure two-year outcomes from 2015–2016. CAN ≥75 is a recognized marker of high risk of hospitalization or death and encompasses patients who may be considered in a second tier of risk, but who have potentially more modifiable risk factors [ 22 ]. We excluded patients who died during 2014 ( n = 109,881) to simulate the cohort of high-CAN patients that would be able to be identified and clustered on December 31, 2014.…”

Section: Methodsmentioning

confidence: 99%

A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data

et al. 2021

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Background Identifying individuals at risk for future hospitalization or death has been a major priority of population health management strategies. High-risk individuals are a heterogeneous group, and existing studies describing heterogeneity in high-risk individuals have been limited by data focused on clinical comorbidities and not socioeconomic or behavioral factors. We used machine learning clustering methods and linked comorbidity-based, sociodemographic, and psychobehavioral data to identify subgroups of high-risk Veterans and study long-term outcomes, hypothesizing that factors other than comorbidities would characterize several subgroups. Methods and findings In this cross-sectional study, we used data from the VA Corporate Data Warehouse, a national repository of VA administrative claims and electronic health data. To identify high-risk Veterans, we used the Care Assessment Needs (CAN) score, a routinely-used VA model that predicts a patient’s percentile risk of hospitalization or death at one year. Our study population consisted of 110,000 Veterans who were randomly sampled from 1,920,436 Veterans with a CAN score≥75th percentile in 2014. We categorized patient-level data into 119 independent variables based on demographics, comorbidities, pharmacy, vital signs, laboratories, and prior utilization. We used a previously validated density-based clustering algorithm to identify 30 subgroups of high-risk Veterans ranging in size from 50 to 2,446 patients. Mean CAN score ranged from 72.4 to 90.3 among subgroups. Two-year mortality ranged from 0.9% to 45.6% and was highest in the home-based care and metastatic cancer subgroups. Mean inpatient days ranged from 1.4 to 30.5 and were highest in the post-surgery and blood loss anemia subgroups. Mean emergency room visits ranged from 1.0 to 4.3 and were highest in the chronic sedative use and polysubstance use with amphetamine predominance subgroups. Five subgroups were distinguished by psychobehavioral factors and four subgroups were distinguished by sociodemographic factors. Conclusions High-risk Veterans are a heterogeneous population consisting of multiple distinct subgroups–many of which are not defined by clinical comorbidities–with distinct utilization and outcome patterns. To our knowledge, this represents the largest application of ML clustering methods to subgroup a high-risk population. Further study is needed to determine whether distinct subgroups may benefit from individualized interventions.

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Identifying Latent Subgroups of High-Risk Patients Using Risk Score Trajectories

Cited by 25 publications

References 39 publications

Life-Sustaining Treatment Decisions Initiative: Early Implementation Results of a National Veterans Affairs Program to Honor Veterans’ Care Preferences

Life-Sustaining Treatment Decisions Initiative: Early Implementation Results of a National Veterans Affairs Program to Honor Veterans’ Care Preferences

High‐Need Phenotypes in Medicare Beneficiaries: Drivers of Variation in Utilization and Outcomes

A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data

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