%0 Journal Article %J Journal of the American Geriatrics Society %D Forthcoming %T A comprehensive prognostic tool for older adults: Predicting death, ADL disability, and walking disability simultaneously. %A Lee, Alexandra K %A Diaz-Ramirez, L Grisell %A Boscardin, W John %A Smith, Alexander K %A Lee, Sei J %K ADL %K Disability %K Mortality %K predition %X

BACKGROUND: Many clinical and financial decisions for older adults depend on the future risk of disability and mortality. Prognostic tools for long-term disability risk in a general population are lacking. We aimed to create a comprehensive prognostic tool that predicts the risk of mortality, of activities of daily living (ADL) disability, and walking disability simultaneously using the same set of variables.

METHODS: We conducted a longitudinal analysis of the nationally-representative Health and Retirement Study (HRS). We included community-dwelling adults aged ≥70 years who completed a core interview in the 2000 wave of HRS, with follow-up through 2018. We evaluated 40 predictors encompassing demographics, diseases, physical functioning, and instrumental ADLs. We applied novel methods to optimize three models simultaneously while prioritizing variables that take less time to ascertain during backward stepwise elimination. The death prediction model used Cox regression and both the models for walking disability and for ADL disability used Fine and Gray competing-risk regression. We examined calibration plots and generated optimism-corrected statistics of discrimination using bootstrapping. To simulate unavailable patient data, we also evaluated models excluding one or two variables from the final model.

RESULTS: In 6646 HRS participants, 2662 developed walking disability, 3570 developed ADL disability, and 5689 died during a median follow-up of 9.5 years. The final prognostic tool had 16 variables. The optimism-corrected integrated area under the curve (iAUC) was 0.799 for mortality, 0.685 for walking disability, and 0.703 for ADL disability. At each percentile of predicted mortality risk, there was a substantial spread in the predicted risks of walking disability and ADL disability. Discrimination and calibration remained good even when missing one or two predictors from the model. This model is now available on ePrognosis (https://eprognosis.ucsf.edu/alexlee.php) CONCLUSIONS: Given the variability in disability risk for people with similar mortality risks, using individualized risks of disabilities may inform clinical and financial decisions for older adults.

%B Journal of the American Geriatrics Society %G eng %R 10.1111/jgs.17932 %0 Journal Article %J JAMA Internal Medicine %D Forthcoming %T Development and External Validation of Models to Predict Need for Nursing Home Level of Care in Community-Dwelling Older Adults With Dementia. %A Deardorff, W James %A Jeon, Sun Y %A Barnes, Deborah E %A Boscardin, W John %A Kenneth M. Langa %A Covinsky, Kenneth E %A Mitchell, Susan L %A Lee, Sei J %A Smith, Alexander K %K Community-dwelling %K Dementia %K home care %K Nursing %X

IMPORTANCE: Most older adults living with dementia ultimately need nursing home level of care (NHLOC).

OBJECTIVE: To develop models to predict need for NHLOC among older adults with probable dementia using self-report and proxy reports to aid patients and family with planning and care management.

DESIGN, SETTING, AND PARTICIPANTS: This prognostic study included data from 1998 to 2016 from the Health and Retirement Study (development cohort) and from 2011 to 2019 from the National Health and Aging Trends Study (validation cohort). Participants were community-dwelling adults 65 years and older with probable dementia. Data analysis was conducted between January 2022 and October 2023.

EXPOSURES: Candidate predictors included demographics, behavioral/health factors, functional measures, and chronic conditions.

MAIN OUTCOMES AND MEASURES: The primary outcome was need for NHLOC defined as (1) 3 or more activities of daily living (ADL) dependencies, (2) 2 or more ADL dependencies and presence of wandering/need for supervision, or (3) needing help with eating. A Weibull survival model incorporating interval censoring and competing risk of death was used. Imputation-stable variable selection was used to develop 2 models: one using proxy responses and another using self-responses. Model performance was assessed by discrimination (integrated area under the receiver operating characteristic curve [iAUC]) and calibration (calibration plots).

RESULTS: Of 3327 participants with probable dementia in the Health and Retirement Study, the mean (SD) age was 82.4 (7.4) years and 2301 (survey-weighted 70%) were female. At the end of follow-up, 2107 participants (63.3%) were classified as needing NHLOC. Predictors for both final models included age, baseline ADL and instrumental ADL dependencies, and driving status. The proxy model added body mass index and falls history. The self-respondent model added female sex, incontinence, and date recall. Optimism-corrected iAUC after bootstrap internal validation was 0.72 (95% CI, 0.70-0.75) in the proxy model and 0.64 (95% CI, 0.62-0.66) in the self-respondent model. On external validation in the National Health and Aging Trends Study (n = 1712), iAUC in the proxy and self-respondent models was 0.66 (95% CI, 0.61-0.70) and 0.64 (95% CI, 0.62-0.67), respectively. There was excellent calibration across the range of predicted risk.

CONCLUSIONS AND RELEVANCE: This prognostic study showed that relatively simple models using self-report or proxy responses can predict need for NHLOC in community-dwelling older adults with probable dementia with moderate discrimination and excellent calibration. These estimates may help guide discussions with patients and families in future care planning.

%B JAMA Internal Medicine %G eng %R 10.1001/jamainternmed.2023.6548 %0 Journal Article %J Journal of the American Geriatrics Society %D 2023 %T Development and validation of novel multimorbidity indices for older adults. %A Steinman, Michael A %A Jing, Bocheng %A Shah, Sachin J %A Rizzo, Anael %A Lee, Sei J %A Covinsky, Kenneth E %A Ritchie, Christine S %A Boscardin, W John %K claims data %K functional impairment %K multimorbidity %K prognostic models %X

BACKGROUND: Measuring multimorbidity in claims data is used for risk adjustment and identifying populations at high risk for adverse events. Multimorbidity indices such as Charlson and Elixhauser scores have important limitations. We sought to create a better method of measuring multimorbidity using claims data by incorporating geriatric conditions, markers of disease severity, and disease-disease interactions, and by tailoring measures to different outcomes.

METHODS: Health conditions were assessed using Medicare inpatient and outpatient claims from subjects age 67 and older in the Health and Retirement Study. Separate indices were developed for ADL decline, IADL decline, hospitalization, and death, each over 2 years of follow-up. We validated these indices using data from Medicare claims linked to the National Health and Aging Trends Study.

RESULTS: The development cohort included 5012 subjects with median age 76 years; 58% were female. Claims-based markers of disease severity and disease-disease interactions yielded minimal gains in predictive power and were not included in the final indices. In the validation cohort, after adjusting for age and sex, c-statistics for the new multimorbidity indices were 0.72 for ADL decline, 0.69 for IADL decline, 0.72 for hospitalization, and 0.77 for death. These c-statistics were 0.02-0.03 higher than c-statistics from Charlson and Elixhauser indices for predicting ADL decline, IADL decline, and hospitalization, and <0.01 higher for death (p < 0.05 for each outcome except death), and were similar to those from the CMS-HCC model. On decision curve analysis, the new indices provided minimal benefit compared with legacy approaches. C-statistics for both new and legacy indices varied substantially across derivation and validation cohorts.

CONCLUSIONS: A new series of claims-based multimorbidity measures were modestly better at predicting hospitalization and functional decline than several legacy indices, and no better at predicting death. There may be limited opportunity in claims data to measure multimorbidity better than older methods.

%B Journal of the American Geriatrics Society %V 71 %P 121-135 %G eng %N 1 %R 10.1111/jgs.18052 %0 Journal Article %J J Am Heart Assoc %D 2023 %T Preoperative Factors Predict Memory Decline After Coronary Artery Bypass Grafting or Percutaneous Coronary Intervention in an Epidemiological Cohort of Older Adults. %A Tang, Angelina B %A Diaz-Ramirez, L Grisell %A Smith, Alexander K %A Lee, Sei J %A Whitlock, Elizabeth L %K Aged %K Aged, 80 and over %K Coronary Artery Bypass %K Coronary Artery Bypass, Off-Pump %K Coronary Artery Disease %K Female %K Humans %K Male %K Memory Disorders %K Percutaneous Coronary Intervention %K Treatment Outcome %X

Background Durable memory decline may occur in older adults after surgical (coronary artery bypass grafting [CABG]) or nonsurgical (percutaneous coronary intervention) coronary revascularization. However, it is unknown whether individual memory risk can be predicted. We reanalyzed an epidemiological cohort of older adults to predict memory decline at ≈1 year after revascularization. Methods and Results We studied Health and Retirement Study participants who underwent CABG or percutaneous coronary intervention at age ≥65 years between 1998 and 2015 and participated in ≥1 biennial postprocedure assessment. Using a memory score based on direct and proxy cognitive tests, we identified participants whose actual postprocedure memory score was 1-2 ("mild") or >2 ("major") SDs below expected postprocedure performance. We modeled probability of memory decline using logistic regression on preoperatively known factors and evaluated model discrimination and calibration. A total of 1390 participants (551 CABG, 839 percutaneous coronary intervention) underwent CABG/percutaneous coronary intervention at 75±6 years old; 40% were women. The cohort was 83% non-Hispanic White, 8.4% non-Hispanic Black, 6.4% Hispanic ethnicity, and 1.7% from other groups masked by the HRS (Health and Retirement Study) to preserve participant confidentiality. At a median of 1.1 (interquartile range, 0.6-1.6) years after procedure, 267 (19%) had mild memory decline and 88 (6.3%) had major memory decline. Factors predicting memory decline included older age, frailty, and off-pump CABG; obesity was protective. The optimism-corrected area under the receiver operator characteristic curve was 0.73 (95% CI, 0.71-0.77). A cutoff of 50% probability of memory decline identified 14% of the cohort as high risk, and was 94% specific and 30% sensitive for late memory decline. Conclusions Preoperative factors can be used to predict late memory decline after coronary revascularization in an epidemiological cohort with high specificity.

%B J Am Heart Assoc %V 12 %G eng %N 1 %R 10.1161/JAHA.122.027849 %0 Journal Article %J The Journals of Gerontology, Series A %D 2022 %T Changes in the Hierarchy of Functional Impairment from Middle Age to Older Age. %A Brown, Rebecca T %A L Grisell Diaz-Ramirez %A W John Boscardin %A Anne Cappola %A Lee, Sei J %A Michael A Steinman %K Activities of Daily Living %K functional impairment %X

BACKGROUND: Understanding the hierarchy of functional impairment in older adults has helped illuminate mechanisms of impairment and inform interventions, but little is known about whether hierarchies vary by age. We compared the pattern of new-onset impairments in activities of daily living (ADLs) and instrumental ADLs (IADLs) from middle age through older age.

METHODS: We conducted a cohort study using nationally representative data from 32486 individuals enrolled in the Health and Retirement Study. The outcomes were new-onset impairment in each ADL and IADL, defined as self-reported difficulty performing each task, assessed yearly for 9 years. We used multi-state models and competing risks survival analysis to estimate the cumulative incidence of impairment in each task by age group (ages 50-64, 65-74, 75-84, and 85 or older).

RESULTS: The pattern of incident ADL impairments differed by age group. Among individuals ages 50-64 and 65-74 who were independent at baseline, over 9 years' follow-up, difficulties dressing and transferring were the most common impairments to develop. In individuals ages 75-84 and 85 or older who were independent at baseline, difficulties bathing, dressing, and walking were most common. For IADLs, the pattern of impairments was similar across age groups; difficulty shopping was most common followed by difficulty managing money and preparing meals. Complementary analyses demonstrated a similar pattern.

CONCLUSIONS: These findings suggest that the hierarchy of ADL impairment differs by age. These findings have implications for the development of age-specific interventions to prevent or delay functional impairment.

%B The Journals of Gerontology, Series A %V 77 %P 1577-1584 %G eng %N 8 %R 10.1093/gerona/glab250 %0 Journal Article %J JAMA Internal Medicine %D 2022 %T Development and External Validation of a Mortality Prediction Model for Community-Dwelling Older Adults With Dementia. %A Deardorff, W James %A Barnes, Deborah E %A Jeon, Sun Y %A Boscardin, W John %A Kenneth M. Langa %A Covinsky, Kenneth E %A Mitchell, Susan L %A Whitlock, Elizabeth L %A Smith, Alexander K %A Lee, Sei J %K community dwelling %K Dementia %K mortality risk %X

Importance: Estimating mortality risk in older adults with dementia is important for guiding decisions such as cancer screening, treatment of new and chronic medical conditions, and advance care planning.

Objective: To develop and externally validate a mortality prediction model in community-dwelling older adults with dementia.

Design, Setting, and Participants: This cohort study included community-dwelling participants (aged ≥65 years) in the Health and Retirement Study (HRS) from 1998 to 2016 (derivation cohort) and National Health and Aging Trends Study (NHATS) from 2011 to 2019 (validation cohort).

Exposures: Candidate predictors included demographics, behavioral/health factors, functional measures (eg, activities of daily living [ADL] and instrumental activities of daily living [IADL]), and chronic conditions.

Main Outcomes and Measures: The primary outcome was time to all-cause death. We used Cox proportional hazards regression with backward selection and multiple imputation for model development. Model performance was assessed by discrimination (integrated area under the receiver operating characteristic curve [iAUC]) and calibration (plots of predicted and observed mortality).

Results: Of 4267 participants with probable dementia in HRS, the mean (SD) age was 82.2 (7.6) years, 2930 (survey-weighted 69.4%) were female, and 785 (survey-weighted 12.1%) identified as Black. Median (IQR) follow-up time was 3.9 (2.0-6.8) years, and 3466 (81.2%) participants died by end of follow-up. The final model included age, sex, body mass index, smoking status, ADL dependency count, IADL difficulty count, difficulty walking several blocks, participation in vigorous physical activity, and chronic conditions (cancer, heart disease, diabetes, lung disease). The optimism-corrected iAUC after bootstrap internal validation was 0.76 (95% CI, 0.75-0.76) with time-specific AUC of 0.73 (95% CI, 0.70-0.75) at 1 year, 0.75 (95% CI, 0.73-0.77) at 5 years, and 0.84 (95% CI, 0.82-0.85) at 10 years. On external validation in NHATS (n = 2404), AUC was 0.73 (95% CI, 0.70-0.76) at 1 year and 0.74 (95% CI, 0.71-0.76) at 5 years. Calibration plots suggested good calibration across the range of predicted risk from 1 to 10 years.

Conclusions and Relevance: We developed and externally validated a mortality prediction model in community-dwelling older adults with dementia that showed good discrimination and calibration. The mortality risk estimates may help guide discussions regarding treatment decisions and advance care planning.

%B JAMA Internal Medicine %V 182 %P 1161-1170 %G eng %N 11 %R 10.1001/jamainternmed.2022.4326 %0 Journal Article %J Computer Methods and Programs in Biomedicine %D 2021 %T A Novel Method for Identifying a Parsimonious and Accurate Predictive Model for Multiple Clinical Outcomes. %A L Grisell Diaz-Ramirez %A Lee, Sei J %A Alexander K Smith %A Gan, Siqi %A W John Boscardin %K backward elimination %K Bayesian Information Criterion %K prognostic models %K Survival Analysis %K variable selection %X

BACKGROUND AND OBJECTIVE: Most methods for developing clinical prognostic models focus on identifying parsimonious and accurate models to predict a single outcome; however, patients and providers often want to predict multiple outcomes simultaneously. As an example, for older adults one is often interested in predicting nursing home admission as well as mortality. We propose and evaluate a novel predictor-selection computing method for multiple outcomes and provide the code for its implementation.

METHODS: Our proposed algorithm selected the best subset of common predictors based on the minimum average normalized Bayesian Information Criterion (BIC) across outcomes: the Best Average BIC (baBIC) method. We compared the predictive accuracy (Harrell's C-statistic) and parsimony (number of predictors) of the model obtained using the baBIC method with: 1) a subset of common predictors obtained from the union of optimal models for each outcome (Union method), 2) a subset obtained from the intersection of optimal models for each outcome (Intersection method), and 3) a model with no variable selection (Full method). We used a case-study data from the Health and Retirement Study (HRS) to demonstrate our method and conducted a simulation study to investigate performance.

RESULTS: In the case-study data and simulations, the average Harrell's C-statistics across outcomes of the models obtained with the baBIC and Union methods were comparable. Despite the similar discrimination, the baBIC method produced more parsimonious models than the Union method. In contrast, the models selected with the Intersection method were the most parsimonious, but with worst predictive accuracy, and the opposite was true in the Full method. In the simulations, the baBIC method performed well by identifying many of the predictors selected in the baBIC model of the case-study data most of the time and excluding those not selected in the majority of the simulations.

CONCLUSIONS: Our method identified a common subset of variables to predict multiple clinical outcomes with superior balance between parsimony and predictive accuracy to current methods.

%B Computer Methods and Programs in Biomedicine %V 204 %P 106073 %G eng %R 10.1016/j.cmpb.2021.106073 %0 Journal Article %J Medical Care %D 2021 %T A Novel Metric for Developing Easy-to-Use and Accurate Clinical Prediction Models: The Time-cost Information Criterion. %A Lee, Sei J %A Alexander K Smith %A Ramirez-Diaz, Ledif G %A Kenneth E Covinsky %A Gan, Siqi %A Chen, Catherine L %A W John Boscardin %K Bayesian Information Criterion %K Methodology %X

BACKGROUND: Guidelines recommend that clinicians use clinical prediction models to estimate future risk to guide decisions. For example, predicted fracture risk is a major factor in the decision to initiate bisphosphonate medications. However, current methods for developing prediction models often lead to models that are accurate but difficult to use in clinical settings.

OBJECTIVE: The objective of this study was to develop and test whether a new metric that explicitly balances model accuracy with clinical usability leads to accurate, easier-to-use prediction models.

METHODS: We propose a new metric called the Time-cost Information Criterion (TCIC) that will penalize potential predictor variables that take a long time to obtain in clinical settings. To demonstrate how the TCIC can be used to develop models that are easier-to-use in clinical settings, we use data from the 2000 wave of the Health and Retirement Study (n=6311) to develop and compare time to mortality prediction models using a traditional metric (Bayesian Information Criterion or BIC) and the TCIC.

RESULTS: We found that the TCIC models utilized predictors that could be obtained more quickly than BIC models while achieving similar discrimination. For example, the TCIC identified a 7-predictor model with a total time-cost of 44 seconds, while the BIC identified a 7-predictor model with a time-cost of 119 seconds. The Harrell C-statistic of the TCIC and BIC 7-predictor models did not differ (0.7065 vs. 0.7088, P=0.11).

CONCLUSION: Accounting for the time-costs of potential predictor variables through the use of the TCIC led to the development of an easier-to-use mortality prediction model with similar discrimination.

%B Medical Care %V 59 %P 418-424 %G eng %N 5 %R 10.1097/MLR.0000000000001510