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Weissman GE, Yadav KN, Srinivasan T, Szymanski S, Capulong F, Madden V, Courtright KR, Hart JL, Asch DA, Ratcliffe SJ, Schapira MM, Halpern SD. Preferences for Predictive Model Characteristics among People Living with Chronic Lung Disease: A Discrete Choice Experiment. Med Decis Making 2020; 40:633-643. [PMID: 32532169 DOI: 10.1177/0272989x20932152] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background. Patients may find clinical prediction models more useful if those models accounted for preferences for false-positive and false-negative predictive errors and for other model characteristics. Methods. We conducted a discrete choice experiment to compare preferences for characteristics of a hypothetical mortality prediction model among community-dwelling patients with chronic lung disease recruited from 3 clinics in Philadelphia. This design was chosen to allow us to quantify "exchange rates" between different characteristics of a prediction model. We provided previously validated educational modules to explain model attributes of sensitivity, specificity, confidence intervals (CI), and time horizons. Patients reported their interest in using prediction models themselves or having their physicians use them. Patients then chose between 2 hypothetical prediction models each containing varying levels of the 4 attributes across 12 tasks. Results. We completed interviews with 200 patients, among whom 95% correctly chose a strictly dominant model in an internal validity check. Patients' interest in predictive information was high for use by themselves (n = 169, 85%) and by their physicians (n = 184, 92%). Interest in maximizing sensitivity and specificity were similar (0.88 percentage points of specificity equivalent to 1 point of sensitivity, 95% CI 0.72 to 1.05). Patients were willing to accept a reduction of 6.10 months (95% CI 3.66 to 8.54) in the predictive time horizon for a 1% increase in specificity. Discussion. Patients with chronic lung disease can articulate their preferences for the characteristics of hypothetical mortality prediction models and are highly interested in using such models as part of their care. Just as clinical care should become more patient centered, so should the characteristics of predictive models used to guide that care.
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Affiliation(s)
- Gary E Weissman
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Fostering Improvement in End-of-Life Decision Science Program, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Kuldeep N Yadav
- Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Fostering Improvement in End-of-Life Decision Science Program, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Trishya Srinivasan
- Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Fostering Improvement in End-of-Life Decision Science Program, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephanie Szymanski
- Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Fostering Improvement in End-of-Life Decision Science Program, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Florylene Capulong
- Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Fostering Improvement in End-of-Life Decision Science Program, University of Pennsylvania, Philadelphia, PA, USA
| | - Vanessa Madden
- Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Fostering Improvement in End-of-Life Decision Science Program, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Katherine R Courtright
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Fostering Improvement in End-of-Life Decision Science Program, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Joanna L Hart
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Fostering Improvement in End-of-Life Decision Science Program, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Asch
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Care Innovation, University of Pennsylvania, Philadelphia, PA, USA.,The Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia, PA, USA
| | - Sarah J Ratcliffe
- Department of Public Health Sciences and Division of Biostatistics at the University of Virginia, Charlottesville, VA, USA
| | - Marilyn M Schapira
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.,The Center for Health Equity Research and Promotion, Philadelphia VA Medical Center, Philadelphia, PA, USA
| | - Scott D Halpern
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Palliative and Advanced Illness Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Fostering Improvement in End-of-Life Decision Science Program, University of Pennsylvania, Philadelphia, PA, USA.,Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.,Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
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Jin H, Wu S. Use of Patient-Reported Data to Match Depression Screening Intervals With Depression Risk Profiles in Primary Care Patients With Diabetes: Development and Validation of Prediction Models for Major Depression. JMIR Form Res 2019; 3:e13610. [PMID: 31573900 PMCID: PMC6774232 DOI: 10.2196/13610] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 06/10/2019] [Accepted: 08/31/2019] [Indexed: 11/13/2022] Open
Abstract
Background Clinical guidelines recommend screening for depression in the general adult population but recognizes that the optimum interval for screening is unknown. Ideal screening intervals should match the patient risk profiles. Objective This study describes a predictive analytics approach for mining clinical and patient-reported data from a large clinical study for the identification of primary care patients at high risk for depression to match depression screening intervals with patient risk profiles. Methods This paper analyzed data from a large safety-net primary care study for diabetes and depression. A regression-based data mining technique was used to examine 53 demographics, clinical variables, and patient-reported variables to develop three prediction models for major depression at 6, 12, and 18 months from baseline. Predictors with the strongest predictive power that require low information collection efforts were selected to develop the prediction models. Predictive accuracy was measured by the area under the receiver operating curve (AUROC) and was evaluated by 10-fold cross-validation. The effectiveness of the prediction algorithms in supporting clinical decision making for six “typical” types of patients was demonstrated. Results The analysis included 923 patients who were nondepressed at the study baseline. Five patient-reported variables were selected in the prediction models to predict major depression at 6, 12, and 18 months: (1) Patient Health Questionnaire 2-item score; (2) the Sheehan Disability Scale; (3) previous problems with depression; (4) the diabetes symptoms scale; and (5) emotional burden of diabetes. All three depression prediction models had an AUROC>0.80, comparable with published depression prediction studies. Among the 6 “typical” types of patients, the algorithms suggest that patients who reported impaired daily functioning by health status are at an elevated risk for depression in all three periods. Conclusions This study demonstrated that leveraging patient-reported data and prediction models can help improve identification of high-risk patients and clinical decisions about the depression screening interval for diabetes patients. Implementation of this approach can be coupled with application of modern technologies such as telehealth and mobile health assessment for collecting patient-reported data to improve privacy, reducing stigma and costs, and promoting a personalized depression screening that matches screening intervals with patient risk profiles.
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Affiliation(s)
- Haomiao Jin
- Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States.,Edward R Roybal Institute on Aging, University of Southern California, Los Angeles, CA, United States
| | - Shinyi Wu
- Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States.,Edward R Roybal Institute on Aging, University of Southern California, Los Angeles, CA, United States.,Daniel J Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, United States
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