Dashboarding to Monitor Machine-Learning-Based Clinical Decision Support Interventions.

BACKGROUND: Existing monitoring of machine-learning-based clinical decision support (ML-CDS) is focused predominantly on the ML outputs and accuracy thereof. Improving patient care requires not only accurate algorithms but also systems of care that enable the output of these algorithms to drive specific actions by care teams, necessitating expanding their monitoring. OBJECTIVES: In this case report, we describe the creation of a dashboard that allows the intervention development team and operational stakeholders to govern and identify potential issues that may require corrective action by bridging the monitoring gap between model outputs and patient outcomes. METHODS: We used an iterative development process to build a dashboard to monitor the performance of our intervention in the broader context of the care system. RESULTS: Our investigation of best practices elsewhere, iterative design, and expert consultation led us to anchor our dashboard on alluvial charts and control charts. Both the development process and the dashboard itself illuminated areas to improve the broader intervention. CONCLUSION: We propose that monitoring ML-CDS algorithms with regular dashboards that allow both a context-level view of the system and a drilled down view of specific components is a critical part of implementing these algorithms to ensure that these tools function appropriately within the broader care system.

Quantifying and reducing inequity in average treatment effect estimation.

BACKGROUND: Across studies of average treatment effects, some population subgroups consistently have lower representation than others which can lead to discrepancies in how well results generalize. METHODS: We develop a framework for quantifying inequity due to systemic disparities in sample representation and a method for mitigation during data analysis. Assuming subgroup treatment effects are exchangeable, an unbiased sample average treatment effect estimator will have lower mean-squared error, on average across studies, for subgroups with less representation when treatment effects vary. We present a method for estimating average treatment effects in representation-adjusted samples which enables subgroups to optimally leverage information from the full sample rather than only their own subgroup's data. Two approaches for specifying representation adjustment are offered-one minimizes average mean-squared error for each subgroup separately and the other balances minimization of mean-squared error and equal representation. We conduct simulation studies to compare the performance of the proposed estimators to several subgroup-specific estimators. RESULTS: We find that the proposed estimators generally provide lower mean squared error, particularly for smaller subgroups, relative to the other estimators. As a case study, we apply this method to a subgroup analysis from a published study. CONCLUSIONS: We recommend the use of the proposed estimators to mitigate the impact of disparities in representation, though structural change is ultimately needed.

Effectiveness of an Emergency Department-Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls Among Older Adults: Protocol for a Quasi-Experimental Study.

BACKGROUND: Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients. OBJECTIVE: The goal of this paper is to describe a research protocol for evaluating the effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of ML and artificial intelligence to augment medical decision-making. METHODS: To assess the effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rates at 3 different EDs. We will use a quasi-experimental design known as a sharp regression discontinuity with regard to intent-to-treat, since the intervention is administered to patients whose risk score falls above a threshold. A conditional logistic regression model will be built to describe 6-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of a return visit for a fall and the 95% CI will be estimated by comparing those identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile. RESULTS: The ML-CDS tool under study has been implemented at 2 of the 3 EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 15% (45/339) of patients have scheduled an appointment with the clinic. CONCLUSIONS: This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end data set allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multisite implementation plan will demonstrate applicability to a broad population and the possibility to adapt the intervention to other EDs and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and a staggered implementation strategy allows for the identification of secular trends that could affect causal associations and allow mitigation as necessary. TRIAL REGISTRATION: ClinicalTrials.gov NCT05810064; https://www.clinicaltrials.gov/study/NCT05810064. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48128.

Differential Effects of an Emergency Department-to-Home Care Transitions Intervention in an Older Adult Population: A Latent Class Analysis.

BACKGROUND: Older adults frequently return to the emergency department (ED) within 30 days of a visit. High-risk patients can differentially benefit from transitional care interventions. Latent class analysis (LCA) is a model-based method used to segment the population and test intervention effects by subgroup. OBJECTIVES: We aimed to identify latent classes within an older adult population from a randomized controlled trial evaluating the effectiveness of an ED-to-home transitional care program and test whether class membership modified the intervention effect. RESEARCH DESIGN: Participants were randomized to receive the Care Transitions Intervention or usual care. Study staff collected outcomes data through medical record reviews and surveys. We performed LCA and logistic regression to evaluate the differential effects of the intervention by class membership. SUBJECTS: Participants were ED patients (age 60 y and above) discharged to a community residence. MEASURES: Indicator variables for the LCA included clinically available and patient-reported data from the initial ED visit. Our primary outcome was ED revisits within 30 days. Secondary outcomes included ED revisits within 14 days, outpatient follow-up within 7 and 30 days, and self-management behaviors. RESULTS: We interpreted 6 latent classes in this study population. Classes 1, 4, 5, and 6 showed a reduction in ED revisit rates with the intervention; classes 2 and 3 showed an increase in ED revisit rates. In class 5, we found evidence that the intervention increased outpatient follow-up within 7 and 30 days (odds ratio: 1.81, 95% CI: 1.13-2.91; odds ratio: 2.24, 95% CI: 1.25-4.03). CONCLUSIONS: Class membership modified the intervention effect. Population segmentation is an important step in evaluating a transitional care intervention.

Detection of differential depressive symptom patterns in a cohort of perinatal women: an exploratory factor analysis using a robust statistics approach.

Background: Postpartum depression can take many forms. Different symptom patterns could have divergent implications for how we screen, diagnose, and treat postpartum depression. We sought to utilise a recently developed robust estimation algorithm to automatically identify differential patterns in depressive symptoms and subsequently characterise the individuals who exhibit different patterns. Methods: Depressive symptom data (N = 548) were collected from women with neuropsychiatric illnesses at two U.S. urban sites participating in a longitudinal observational study of stress across the perinatal period. Data were collected from Emory University between 1994 and 2012 and from the University of Arkansas for Medical Sciences between 2012 and 2017. We conducted an exploratory factor analysis of Beck Depression Inventory (BDI) items using a robust expectation-maximization algorithm, rather than a conventional expectation-maximization algorithm. This recently developed method enabled automatic detection of differential symptom patterns. We described differences in symptom patterns and conducted unadjusted and adjusted analyses of associations of symptom patterns with demographics and psychiatric histories. Findings: 53 (9.7%) participants were identified by the algorithm as having a different pattern of reported symptoms compared to other participants. This group had more severe symptoms across all items-especially items related to thoughts of self-harm and self-judgement-and differed in how their symptoms related to underlying psychological constructs. History of social anxiety disorder (OR: 4.0; 95% CI [1.9, 8.1]) and history of childhood trauma (for each 5-point increase, OR: 1.2; 95% CI [1.1, 1.3]) were significantly associated with this differential pattern after adjustment for other covariates. Interpretation: Social anxiety disorder and childhood trauma are associated with differential patterns of severe postpartum depressive symptoms, which might warrant tailored strategies for screening, diagnosis, and treatment to address these comorbid conditions. Funding: There are no funding sources to declare.

Quantifying aberrant approach-avoidance conflict in psychopathology: A review of computational approaches.

Making effective decisions during approach-avoidance conflict is critical in daily life. Aberrant decision-making during approach-avoidance conflict is evident in a range of psychological disorders, including anxiety, depression, trauma-related disorders, substance use disorders, and alcohol use disorders. To help clarify etiological pathways and reveal novel intervention targets, clinical research into decision-making is increasingly adopting a computational psychopathology approach. This approach uses mathematical models that can identify specific decision-making related processes that are altered in mental health disorders. In our review, we highlight foundational approach-avoidance conflict research, followed by more in-depth discussion of computational approaches that have been used to model behavior in these tasks. Specifically, we describe the computational models that have been applied to approach-avoidance conflict (e.g., drift-diffusion, active inference, and reinforcement learning models), and provide resources to guide clinical researchers who may be interested in applying computational modeling. Finally, we identify notable gaps in the current literature and potential future directions for computational approaches aimed at identifying mechanisms of approach-avoidance conflict in psychopathology.

Multimethod Process Evaluation of a Community Paramedic Delivered Care Transitions Intervention for Older Emergency Department Patients.

OBJECTIVE: We assessed fidelity of delivery and participant engagement in the implementation of a community paramedic coach-led Care Transitions Intervention (CTI) program adapted for use following emergency department (ED) visits. METHODS: The adapted CTI for ED-to-home transitions was implemented at three university-affiliated hospitals in two cities from 2016 to 2019. Participants were aged ≥60 years old and discharged from the ED within 24 hours of arrival. In the current analysis, participants had to have received the CTI. Community paramedic coaches collected data on program delivery and participant characteristics at each transition contact via inventories and assessments. Participants provided commentary on the acceptability of the adapted CTI. Using a multimethod approach, the CTI implementation was assessed quantitatively for site- and coach-level differences. Qualitatively, barriers to implementation and participant satisfaction with the CTI were thematically analyzed. RESULTS: Of the 863 patient participants, 726 (84.1%) completed their home visits. Cancellations were usually patient-generated (94.9%). Most planned follow-up visits were successfully completed (94.6%). Content on the planning for red flags and post-discharge goal setting was discussed with high rates of fidelity overall (95% and greater), while content on outpatient follow-up was lower overall (75%). Differences in service delivery between the two sites existed for the in-person visit and the first phone follow-up, but the differences narrowed as the study progressed. Participants showed a 24.6% increase in patient activation (i.e., behavioral adoption) over the 30-day study period (p < 0.001).Overall, participants reported that the program was beneficial for managing their health, the quality of coaching was high, and that the program should continue. Not all participants felt that they needed the program. Community paramedic coaches reported barriers to CTI delivery due to patient medical problems and difficulties with phone visit coordination. Coaches also noted refusal to communicate or engage with the intervention as an implementation barrier. CONCLUSIONS: Community paramedic coaches delivered the adapted CTI with high fidelity across geographically distant sites and successfully facilitated participant engagement, highlighting community paramedics as an effective resource for implementing such patient-centered interventions.

Addressing heterogeneous populations in latent variable settings through robust estimation.

Individuals routinely differ in how they present with psychiatric illnesses and in how they respond to treatment. This heterogeneity, when overlooked in data analysis, can lead to misspecified models and distorted inferences. While several methods exist to handle various forms of heterogeneity in latent variable models, their implementation in applied research requires additional layers of model crafting, which might be a reason for their underutilization. In response, we present a robust estimation approach based on the expectation-maximization (EM) algorithm. Our method makes minor adjustments to EM to enable automatic detection of population heterogeneity and to recognize individuals who are inadequately explained by the assumed model. Each individual is associated with a probability that reflects how likely their data were to have been generated from the assumed model. The individual-level probabilities are simultaneously estimated and used to weight each individual's contribution in parameter estimation. We examine the utility of our approach for Gaussian mixture models and linear factor models through several simulation studies, drawing contrasts with the EM algorithm. We demonstrate that our method yields inferences more robust to population heterogeneity or other model misspecifications than EM does. We hope that the proposed approach can be incorporated into the model-building process to improve population-level estimates and to shed light on subsets of the population that demand further attention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The electrophysiological masking level difference: effects of age and mediation of hearing and cognition.

OBJECTIVE: Evaluate the conceptual framework that age effects on the electrophysiological binaural masking level difference (MLD) are partially mediated by age-related hearing loss and/or global cognitive function via mediation analysis. DESIGN: Participants underwent a series of audiometric tests. The MLD was measured via cortical auditory evoked potentials using a speech stimulus (/ɑ/) in speech-weighted background noise. We used mediation analyses to determine the total effect, natural direct effects, and natural indirect effects, which are displayed as regression coefficients ([95% CI]; p value). STUDY SAMPLE: Twenty-eight individuals aged 19-87 years (mean [SD]: 53.3 [25.2]), recruited from the community. RESULTS: Older age had a significant total effect on the MLD (-0.69 [95% CI: -0.96, -0.45]; p < 0.01). Neither pure tone average (-0.11 [95% CI: -0.43, 0.24; p = 0.54] nor global cognitive function (-0.02 [95% CI: -0.13, 0.02]; p = 0.55) mediated the relationship of age and the MLD and effect sizes were small. Results were insensitive to use of alternative hearing measures or inclusion of interaction terms. CONCLUSIONS: The electrophysiological MLD may be an age-sensitive measure of binaural temporal processing that is minimally affected by age-related hearing loss and global cognitive function.

Scalar Assessment of the Family Caregiver Activation in Transitions Tool: An Exploratory Factor Analysis.

The Family Caregiver Activation in Transitions (FCAT) tool in its current, non-scalar form is not pragmatic for clinical use as each item is scored and intended to be interpreted individually. The purpose of the current study was to create a scalar version of the FCAT to facilitate better care communications between hospital staff and family caregivers. We also assessed the scale's validity by comparing the scalar version of the measure against patient health measures. Data were collected from 463 family caregiver-patient dyads from January 2016 to July 2019. An exploratory factor analysis was performed on the 10-item FCAT, resulting in a statistically homogeneous six-item scale focused on current caregiving activation factors. The measure was then compared against patient health measures, with no significant biases found. The six-item scalar FCAT can provide hospital staff insight into the level of caregiver activation occurring in the patient's health care and help tailor care transition needs for family caregiver-patient dyads. [Journal of Gerontological Nursing, 48(12), 35-42.].

Reward-based reinforcement learning is altered among individuals with a history of major depressive disorder and psychomotor retardation symptoms.

Reward-based reinforcement learning impairments are common in major depressive disorder, but it is unclear which aspects of reward-based reinforcement learning are disrupted in remitted major depression (rMDD). Given that the neurobiological substrates that implement reward-based RL are also strongly implicated in psychomotor retardation (PmR), the present study sought to test whether reward-based reinforcement learning is altered in rMDD individuals with a history of PmR. Three groups of individuals (1) rMDD with past PmR (PmR+, N = 34), (2) rMDD without past PmR (PmR-, N = 44), and (3) healthy controls (N = 90) completed a reward-based reinforcement learning task. Computational modeling was applied to test for group differences in model-derived parameters - specifically, learning rates and reward sensitivity. Compared to controls, rMDD PmR + exhibited lower learning rates, but not reduced reward sensitivity. By contrast, rMDD PmR- did not significantly differ from controls on either of the model-derived parameters. Follow-up analyses indicated that the results were not due to current psychopathology symptoms. Results indicate that a history of PmR predicts altered reward-based reinforcement learning in rMDD. Abnormal reward-related reinforcement learning may reflect a scar of past depressive episodes that contained psychomotor symptoms, or a trait-like deficit that preceded these episodes.

Care transitions intervention reduces ED revisits in cognitively impaired patients.

Introduction: About half of older adults with impaired cognition who are discharged home from the emergency department (ED) return for further care within 30 days. We tested the effect of an adapted Care Transitions Intervention (CTI) at reducing ED revisits in this vulnerable population. Methods: We conducted a pre-planned subgroup analysis of community-dwelling, cognitively impaired older (age ≥60 years) participants from a randomized controlled trial testing the effectiveness of the CTI adapted for ED-to-home transitions. The parent study recruited ED patients from three university-affiliated hospitals from 2016 to 2019. Subjects eligible for this sub-analysis had to: (1) have a primary care provider within these health systems; (2) be discharged to a community residence; (3) not receive care management or hospice services; and (4) be cognitively impaired in the ED, as determined by a score >10 on the Blessed Orientation Memory Concentration Test. The primary outcome, ED revisits within 30 days of discharge, was abstracted from medical records and evaluated using logistic regression. Results: Of our sub-sample (N = 81, 36 control, 45 treatment), 57% were female and the mean age was 78 years. Multivariate analysis, adjusted for the presence of moderate to severe depression and inadequate health literacy, found that the CTI significantly reduced the odds of a repeat ED visit within 30 days (odds ratio [OR] 0.25, 95% confidence interval [CI] 0.07 to 0.90) but not 14 days (OR 1.01, 95% CI 0.26 to 3.93). Multivariate analysis of outpatient follow-up found no significant effects. Discussion: Community-dwelling older adults with cognitive impairment receiving the CTI following ED discharge experienced fewer ED revisits within 30 days compared to usual care. Further studies must confirm and expand upon this finding, identifying features with greatest benefit to patients and caregivers.

Effectiveness of a care transitions intervention for older adults discharged home from the emergency department: A randomized controlled trial.

BACKGROUND: Improving care transitions following emergency department (ED) visits may reduce post-ED adverse events among older adults (e.g., ED revisits, decreased function). The Care Transitions Intervention (CTI) improves hospital-to-home transitions; however, its effectiveness at improving post-ED outcomes is unknown. We tested the effectiveness of the CTI with community-dwelling older adult ED patients, hypothesizing that it would reduce revisits and increase performance of self-management behaviors during the 30 days following discharge. METHODS: We conducted a randomized controlled trial among patients age ≥ 60 discharged home from one of three EDs in two states. Intervention participants received a minimally modified CTI, with a home visit 24 to 72 h postdischarge and one to three phone calls over 28 days. We collected demographic, health status, and psychosocial data at the initial ED visit. Medication adherence and knowledge of red flag symptoms were assessed via phone survey. Care use and comorbidities were abstracted from medical records. We performed multivariate regressions for intention-to-treat and per-protocol (PP) analyses. RESULTS: Participant characteristics (N = 1,756) were similar across groups: mean age 72.4 ± 8.6 years and 53% female. Of those randomized to the intervention, 84% completed the home visit. Overall, 12.4% of participants returned to the ED within 30 days. The CTI did not significantly affect odds of 30-day ED revisits (adjusted odds ratio [AOR] = 0.97, 95% confidence interval [CI] = 0.72 to 1.30) or medication adherence (AOR = 0.89, 95% CI = 0.60 to 1.32). Participants receiving the CTI (PP) had increased odds of in-person follow-up with outpatient clinicians during the week following discharge (AOR = 1.24, 95% CI = 1.01 to 1.51) and recalling at least one red flag from ED discharge instructions (AOR = 1.34 95% CI = 1.05 to 1.71). CONCLUSIONS: The CTI did not reduce 30-day ED revisits but did significantly increase key care transition behaviors (outpatient follow-up, red flag knowledge). Additional research is needed to explore if patients with different conditions benefit more from the CTI and whether decreasing ED revisits is the most appropriate outcome for all older adults.

Value estimation and latent-state update-related neural activity during fear conditioning predict posttraumatic stress disorder symptom severity.

Learning theories of posttraumatic stress disorder (PTSD) purport that fear-learning processes, such as those that support fear acquisition and extinction, are impaired. Computational models designed to capture specific processes involved in fear learning have primarily assessed model-free, or trial-and-error, reinforcement learning (RL). Although previous studies indicated that aspects of model-free RL are disrupted among individuals with PTSD, research has yet to identify whether model-based RL, which is inferential and contextually driven, is impaired. Given empirical evidence of aberrant contextual modulation of fear in PTSD, the present study sought to identify whether model-based RL processes are altered during fear conditioning among women with interpersonal violence (IPV)-related PTSD (n = 85) using computational modeling. Model-free, hybrid, and model-based RL models were applied to skin conductance responses (SCR) collected during fear acquisition and extinction, and the model-based RL model was found to provide the best fit to the SCR data. Parameters from the model-based RL model were carried forward to neuroimaging analyses (voxel-wise and independent component analysis). Results revealed that reduced activity within visual processing regions during model-based updating uniquely predicted higher PTSD symptoms. Additionally, after controlling for model-based updating, greater value estimation encoding within the left frontoparietal network during fear acquisition and reduced value estimation encoding within the dorsomedial prefrontal cortex during fear extinction predicted greater PTSD symptoms. Results provide evidence of disrupted RL processes in women with assault-related PTSD, which may contribute to impaired fear and safety learning, and, furthermore, may relate to treatment response (e.g., poorer response to exposure therapy).

Frontoparietal network activity during model-based reinforcement learning updates is reduced among adolescents with severe sexual abuse.

Trauma and trauma-related disorders are characterized by impaired learning processes, including reinforcement learning (RL). Identifying which aspects of learning are altered by trauma is critical endeavor, as this may reveal key mechanisms of impairment and potential intervention targets. There are at least two types of RL that have been delineated using computational modeling: model-free and model-based RL. Although these RL processes differentially predict decision-making behavior, most research has examined the impact of trauma on model-free RL. Currently unclear whether model-based RL, which involves building abstract and nuanced representations of stimulus-outcome relationships, is impaired among individuals with a history of trauma. The present study sought to test the hypothesis of impaired model-based RL among adolescent females exposed to assaultive trauma. Participants (n = 60; 29 without a history of assault and 31 with a history of assault with and without PTSD) completed a three-arm bandit task during fMRI acquisition. Two computational models compared the degree to which participants' task behavior fit the use of a model-free versus model-based RL strategy. Although a history of assaultive trauma did not predict poorer model-based RL, greater sexual abuse severity predicted less use of model-based compared to model-free RL. Additionally, severe sexual abuse predicted less left frontoparietal network encoding of model-based RL updates. Altered model-based RL, which supports goal-directed behavior, may be an important route through which clinical impairment emerges among individuals with a history of severe sexual abuse and should be examined further in future studies.

Dynamics of data-driven microstates in bipolar disorder.

Many of the existing models of mood in bipolar disorder can largely be divided into two camps, tracking mood as either a discrete or continuous variable. Both groups rely upon certain assumptions, with most considering only aggregate scores on clinical instruments. In this study, we propose a novel framework that combines elements from both discrete and continuous mood models, using a machine learning pipeline to detect subtle patterns across individuals. Latent factors are constructed from assessments at the item level, then clustered into groups referred to as microstates. Transitions between microstates are captured via a discrete-time Markov chain, allowing for characterization of mood's dynamic nature. Key findings include a factor mapping heavily onto irritability and aggression, as well as a hierarchical pattern of microstates within depression and mania. Validity of these results is confirmed by reproduction in an unseen data set from a separate subject cohort.

A digital self-report survey of mood for bipolar disorder.

OBJECTIVES: Bipolar disorder (BP) is commonly researched in digital settings. As a result, standardized digital tools are needed to measure mood. We sought to validate a new survey that is brief, validated in digital form, and able to separately measure manic and depressive severity. METHODS: We introduce a 6-item digital survey, called digiBP, for measuring mood in BP. It has three depressive items (depressed mood, fidgeting, fatigue), two manic items (increased energy, rapid speech), and one mixed item (irritability); and recovers two scores (m and d) to measure manic and depressive severity. In a secondary analysis of individuals with BP who monitored their symptoms over 6 weeks (n = 43), we perform a series of analyses to validate the digiBP survey internally, externally, and as a longitudinal measure. RESULTS: We first verify a conceptual model for the survey in which items load onto two factors ("manic" and "depressive"). We then show weekly averages of m and d scores from digiBP can explain significant variation in weekly scores from the Young Mania Rating Scale (R2  = 0.47) and SIGH-D (R2  = 0.58). Lastly, we examine the utility of the survey as a longitudinal measure by predicting an individual's future m and d scores from their past m and d scores. CONCLUSIONS: While further validation is warranted in larger, diverse populations, these validation analyses should encourage researchers to consider digiBP for their next digital study of BP.

Predicting hearing aid use in adults: the Beaver Dam Offspring Study.

OBJECTIVE: The purpose of this study was to (i) develop a model that predicts hearing aid (HA) use and (ii) determine if model fit is improved by adding factors not typically collected in audiological evaluations. DESIGN: Two models were created and evaluated. The "clinical" model used factors typically collected during audiologic clinical evaluations. The "expanded" model considered additional clinical, health and lifestyle factors to determine if the model fit could be improved (compared to clinical model). Models were created with least absolute shrinkage and selection operator (LASSO) logistic regression with 10-fold cross validation. Predictive ability was evaluated via receiver operating characteristic curves and concordance statistics (c-statistics). STUDY SAMPLE: This study included 275 participants from the Beaver Dam Offspring Study, a prospective longitudinal cohort study of aging, with a treatable level of hearing loss and no HA use at baseline. RESULTS: The clinical and expanded models report predictors important for HA use. The c-statistics of the clinical (0.80) and expanded (0.79) models were not significantly different (p = 0.41). CONCLUSIONS: Similar predictive abilities of models suggest audiological evaluations perform well in predicting HA use.