The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Predicting Psychiatric Hospitalizations among Elderly Veterans with a History of Mental Health Disease
Open accessAbstract
INTRODUCTION: Patient Aligned Care Team (PACT) care managers are tasked with identifying aging Veterans with psychiatric disease in attempt to prevent psychiatric crises. However, few resources exist that use real-time information on patient risk to prioritize coordinating appropriate care amongst a complex aging population. OBJECTIVE: To develop and validate a model to predict psychiatric hospital admission, during a 90-day risk window, in Veterans ages 65 or older with a history of mental health disease. METHODS: This study applied a cohort design to historical data available in the Veterans Affairs (VA) Corporate Data Warehouse (CDW). The Least Absolute Shrinkage and Selection Operator (LASSO) regularization regression technique was used for model development and variable selection. Individual predicted probabilities were estimated using logistic regression. A split-sample approach was used in performing external validation of the fitted model. The concordance statistic (C-statistic) was calculated to assess model performance. RESULTS: Prior to modeling, 61 potential candidate predictors were identified and 27 variables remained after applying the LASSO method. The final model's predictive accuracy is represented by a C-statistic of 0.903. The model's predictive accuracy during external validation is represented by a C-statistic of 0.935. Having a previous psychiatric hospitalization, psychosis, bipolar disorder, and the number of mental-health related social work encounters were strong predictors of a geriatric psychiatric hospitalization. CONCLUSION: This predictive model is capable of quantifying the risk of a geriatric psychiatric hospitalization with acceptable performance and allows for the development of interventions that could potentially reduce such risk.
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“the number of psychiatric hospitalizations has been used in other outcome studies55,56 of mental health conditions among veterans.”
From Treatment Engagement and Outcomes of Mindfulness-Based Cognitive Therapy for Veterans with Psychiatric Disorders (2019) · cites this paper as [56]
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Regression shrinkage and selection via the lasso
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Measuring Diagnoses: ICD Code Accuracy
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Medical Comorbidity in Schizophrenia
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Insights From Advanced Analytics At The Veterans Health Administration
Fihn, S. D., Francis, J., Clancy, C. M., Nielson, C., Nelson, K. M., Rumsfeld, J. S., Cullen, T., Bates, J., & Graham, G. L. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0054 · Cited by 368
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Reconciling Statistical and Clinicians’ Predictions of Suicide Risk
Simon, G. E., Matarazzo, B. B., Walsh, C. G., Smoller, J. W., Boudreaux, E. D., Yarborough, B. J. H., Shortreed, S. M., Coley, R. Y., Ahmedani, B. K., Doshi, R., et al. (2021) · Psychiatric Services · doi:10.1176/appi.ps.202000214 · Cited by 44
Marchand, W. R., Yabko, B., Herrmann, T., Curtis, H., & Lackner, R. (2019) · The Journal of Alternative and Complementary Medicine · doi:10.1089/acm.2018.0511 · Cited by 17
Gould, C. E., Carlson, C., Alfaro, A. J., TRIVEDI, R. B., Savell, A. D., Mehta, P. S., Burningham, Z. R., Peeples, A., & Filips, J. (2023) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2023.01.005 · Cited by 11
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Metadata
- Authors
- Burningham, Z., Leng, J., Peters, C. B., Huynh, T., Halwani, A., Rupper, R., Hicken, B., & Sauer, B. C.
- Affiliations
- University of Utah (US)
- VA Salt Lake City Healthcare System (US)
- Corresponding author
- Burningham, Zachary
- Venue
- eGEMs (Generating Evidence & Methods to improve patient outcomes)
- Publisher
- Ubiquity Press, Ltd.
- Published
- 2018-05-17
- Volume
- 6
- Issue
- 1
- Pages
- 7
- ISSN
- 2327-9214 (ISSN-L)
- PMC
- PMC5982950
- OpenAlex
- W2803719419
- Cited by
- 3
- References
- 22
- Citations by year
- 2023: 1 · 2021: 1 · 2019: 1
- FWCI
- 0.59
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- eGEMs (Generating Evidence & Methods to improve patient outcomes) (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Psychiatric care and mental health services
- Health Sciences › Medicine › Psychiatry and Mental health › Mental Health Treatment and Access
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- psychiatric hospitalization, geriatric mental health, older veterans, risk prediction model, Patient Aligned Care Team, psychosis, bipolar disorder, LASSO, external validation
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