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Development and Validation of a Tool to Identify Patients With Type 2 Diabetes at High Risk of Hypoglycemia-Related Emergency Department or Hospital Use
Open accessAbstract
Importance: Hypoglycemia-related emergency department (ED) or hospital use among patients with type 2 diabetes (T2D) is clinically significant and possibly preventable. Objective: To develop and validate a tool to categorize risk of hypoglycemic-related utilization in patients with T2D. Design, Setting, and Participants: Using recursive partitioning with a split-sample design, we created a classification tree based on potential predictors of hypoglycemia-related ED or hospital use. The resulting model was transcribed into a tool for practical application and tested in 1 internal and 2 fully independent, external samples. Development and internal testing was conducted in a split sample of 206 435 patients with T2D from Kaiser Permanente Northern California (KPNC), an integrated health care system. The tool was externally tested in 1 335 966 Veterans Health Administration and 14 972 Group Health Cooperative patients with T2D. Exposures: Based on a literature review, we identified 156 candidate predictor variables (prebaseline exposures) using data collected from electronic medical records. Main Outcomes and Measures: Hypoglycemia-related ED or hospital use during 12 months of follow-up. Results: The derivation sample (n = 165 148) had a mean (SD) age of 63.9 (13.0) years and included 78 576 (47.6%) women. The crude annual rate of at least 1 hypoglycemia-related ED or hospital encounter in the KPNC derivation sample was 0.49%. The resulting hypoglycemia risk stratification tool required 6 patient-specific inputs: number of prior episodes of hypoglycemia-related utilization, insulin use, sulfonylurea use, prior year ED use, chronic kidney disease stage, and age. We categorized the predicted 12-month risk of any hypoglycemia-related utilization as high (>5%), intermediate (1%-5%), or low (<1%). In the internal validation sample, 2.0%, 10.7%, and 87.3% were categorized as high, intermediate, and low risk, respectively, with observed 12-month hypoglycemia-related utilization rates of 6.7%, 1.4%, and 0.2%, respectively. There was good discrimination in the internal validation KPNC sample (C statistic = 0.83) and both external validation samples (Veterans Health Administration: C statistic = 0.81; Group Health Cooperative: C statistic = 0.79). Conclusions and Relevance: This hypoglycemia risk stratification tool categorizes the 12-month risk of hypoglycemia-related utilization in patients with T2D using only 6 inputs. This tool could facilitate targeted population management interventions, potentially reducing hypoglycemia risk and improving patient safety and quality of life.
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“Older adults can also be stratified for future risk for hypoglycemia with validated risk calculators (e.g., Kaiser Hypoglycemia Model) (34) and with consideration of hypoglycemia risk factors (Table 6.5).”
From 13. Older Adults: Standards of Care in Diabetes—2024 (2024) · cites this paper as [34]
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“Older adults can also be stratified for future risk for hypoglycemia with validated risk calculators (e.g., Kaiser Hypoglycemia Model for adults with type 2 diabetes) (40) and with consideration of hypoglycemia risk factors (Table 6.5).”
From 13. Older Adults: Standards of Care in Diabetes—2026 (2026) · cites this paper as [40]
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“Validated tools have been developed to estimate hypoglycemia risk using predominantly electronic health record data (101-103).”
From 6. Glycemic Goals, Hypoglycemia, and Hyperglycemic Crises: Standards of Care in Diabetes-2026. (2026) · cites this paper as [101]
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Cites
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Levey, A. S., Bosch, J. P., Lewis, J. B., Greene, T., Rogers, N., Roth, D., & for the Modification of Diet in Renal Disease Study Group* (1999) · Annals of Internal Medicine · doi:10.7326/0003-4819-130-6-199903160-00002 · Cited by 15,079
Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating
Steyerberg, E. W. (2019) · Statistics for Biology and Health · doi:10.1007/978-3-030-16399-0 · Cited by 3,281
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
Changes in diabetes-related complications in the United States, 1990-2010
Gregg, E. W., Li, Y., Wang, J., Rios Burrows, N., Ali, M. K., Rolka, D., Williams, D. E., & Geiss, L. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1310799 · Cited by 1,749
Emergency hospitalizations for adverse drug events in older Americans.
Budnitz, D., Lovegrove, M., Shehab, N., & Richards, C. (2011) · New England Journal of Medicine · doi:10.1056/nejmsa1103053 · Cited by 1,748
Fonarow, G. C. (2005) · JAMA · doi:10.1001/jama.293.5.572 · Cited by 1,617
Severe hypoglycemia and risks of vascular events and death
Zoungas, S., Patel, A., Chalmers, J., de Galan, B. E., Li, Q., Billot, L., Woodward, M., Ninomiya, T., Neal, B., MacMahon, S., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1003795 · Cited by 1,471
Prognosis and prognostic research: validating a prognostic model
Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009) · BMJ · doi:10.1136/bmj.b605 · Cited by 1,436
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–150 of 150
Zheng, R., Zeng, X., Shen, R., Wang, Y., Liu, J., & Zhang, M. (2025) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s501132 · Cited by 4
Tang, Y., Liu, J., Hannachi, H., Engel, S. S., Ganz, M. L., & Rajpathak, S. N. (2018) · Diabetes Therapy · doi:10.1007/s13300-018-0512-3 · Cited by 3
Experimental pharmacological approaches to reverse impaired awareness of hypoglycemia—a review
Hashmi, H. Z., Khowaja, A. A., & Moheet, A. A. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1349004 · Cited by 5
RSSDI guidelines for management of type 2 diabetes mellitus in older adults
Jagadeesha, A., Viswanathan, V., Agarwal, S., Maheshwari, A., Virmani, A. K., Nagendra, L., Amarnath, A., Chawla, M. S., Jethwani, P., Parikh, R. M., et al. (2026) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-025-01613-8 · Cited by 4
Mirghani, H. O. (2019) · BMC Research Notes · doi:10.1186/s13104-019-4145-z · Cited by 3
Tseng, C., Aron, D. C., Soroka, O., Lu, S., Myers, C. E., & POGACH, L. M. (2019) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2019.107475 · Cited by 3
Artificial intelligence in diabetes management
Thomas, A., Gopi, V. P., & Francis, B. (2024) · Advances in Artificial Intelligence · doi:10.1016/b978-0-443-19073-5.00002-1 · Cited by 3
Bhagra, S., Ducharme‐Smith, A., Juntunen, M. B., Liedl, C. P., Golembiewski, E. H., Sundt, W. J. S., Krpata, T. S., Lampman, M. A., Espinoza, A. L., & McCoy, R. R. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322177 · Cited by 3
Corchuelo, J. C., Guerrero‐Pérez, F., de la Jordana, P. G.-S., & Pérez-Maraver, M. (2019) · Endocrinología Diabetes y Nutrición (English ed ) · doi:10.1016/j.endien.2018.06.007 · Cited by 4
Chen, M., Parker, M. M., Karter, A. J., Grant, R. W., & Gilliam, L. K. (2025) · Contemporary Clinical Trials · doi:10.1016/j.cct.2025.107885 · Cited by 2
Self-citationGilliam, L. K., Parker, M. M., & Karter, A. J. (2025) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2025.0021 · Cited by 2
Self-citationXu, H., Yu, H., Cheng, Z., Mu, C., Bao, D., Li, X., & Xing, Q. (2024) · Journal of Diabetes Investigation · doi:10.1111/jdi.14135 · Cited by 2
Ralston, J. D., Anderson, M. L., Ng, J., Bashir, A., Ehrlich, K. J., Burns-Hunt, D., Cotton, M., Hansell, L. D., Hsu, C. W., Hunt, H., et al. (2024) · Contemporary Clinical Trials · doi:10.1016/j.cct.2024.107456 · Cited by 2
Self-citationHannah, K. L., Nemlekar, P. M., Bushman, J. S., & Norman, G. J. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15982 · Cited by 2
Fasina, I., Agyei, O., Kim, J., Montgomery, E., Powers, S., & Plyler, D. (2021) · INNOVATIONS in pharmacy · doi:10.24926/iip.v12i4.3997 · Cited by 1
Wang, H., Lu, K.-P., Chung, C., Tseng, Y., Lee, T.-Y., Horng, J., Chang, T., Wu, M., Lin, T., Liu, T.-P., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.13.990978 · Cited by 2
A scoping review of the predictive models of diabetes complicationsPreprint
Ndjaboué, R., Ngueta, G., Rochefort-Brihay, C., Delorme, S., Guay, D., Ivers, N. M., Shah, B. R., Straus, S. E., Yu, C. H. Y., Comeau, S., et al. (2021) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/pnv25 · Cited by 2
Reaven, P. D., Newell, M., Rivas, S., Zhou, X., Norman, G. J., & Zhou, J. J. (2023) · doi:10.2337/figshare.21960560 · Cited by 2
Reaven, P. D., Newell, M., Rivas, S., Zhou, X., Norman, G. J., & Zhou, J. J. (2023) · doi:10.2337/figshare.21960560.v1 · Cited by 3
Stratifying Risk of Nonadherence in Lingual Strengthening Dysphagia Rehabilitation
Vitale, K., Powell, W. R., Krekeler, B. N., Yee, J. P., & Rogus‐Pulia, N. M. (2023) · American Journal of Speech-Language Pathology · doi:10.1044/2023_ajslp-22-00338 · Cited by 1
Qarni, A. A. A., AlSubaiee, M., Husain, M., Alkhazmari, G., AlQahtani, A., Aldossary, I., Gado, W., Albahrani, Z., AlMukhaylid, S., Alarfaj, A., et al. (2023) · Series of Endocrinology Diabetes and Metabolism · doi:10.54178/jsedmv5i2004 · Cited by 1
Cahyaningsih, I., Asiri, A., de Vos, S., Bos, J., Schuiling‐Veninga, C. C. M., Taxis, K., & Denig, P. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13195855 · Cited by 1
Thai, T. K., CHRISTINE BOARD, Nugent, J. R., Johnston, J. L., Huynh, E. Y., Chen, C. H., Chan, A. H., Grant, R. W., Gilliam, L. K., & Gopalan, A. (2024) · Contemporary Clinical Trials · doi:10.1016/j.cct.2024.107751 · Cited by 1
Zhang, Y., Hu, X., Xu, W., Chen, Y., Guo, X., Liu, S.-H., & Gao, L. (2025) · World Journal of Diabetes · doi:10.4239/wjd.v16.i9.104290 · Cited by 1
Genome-Wide Association Study of Hypoglycemia in Adults With Diabetes in the Million Veteran Program
Raghavan, S., LITKOWSKI, E. M., Jensen, A. E., CHAREST, B. R., Wang, Z., Hui, Q., Chen, H., Rhee, M. K., Leong, A., Meigs, J. B., et al. (2025) · Diabetes · doi:10.2337/db25-0304 · Cited by 1
Liu, Y., Guo, C., Li, X., Li, S., Huang, J., Zhao, L., Zhu, Y., Liu, X., Wang, B., Lin, R., et al. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1685969 · Cited by 2
Kim, S., Sang, H., Park, J., Woo, S., Cho, E.-H., Kim, C. H., Kim, D. J., Jeong, C., Park, T. S., Hwang, Y., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0352342 · Cited by 1
Mizu, D., Matsuoka, Y., Huh, J.-Y., & Ariyoshi, K. (2021) · Acute Medicine & Surgery · doi:10.1002/ams2.637 · Cited by 1
LACY, M. E., Whitmer, R. A., Lee, S. J., Rushakoff, R. J., & Pletcher, M. J. (2021) · Clinical Diabetes · doi:10.2337/cd20-0057 · Cited by 1
The Patient Faints in the Waiting Area with a Suspected Hypoglycemic Event
Chmieliauskaite, M., Grosh, M. D., Syed, A. Z., & Pinto, A. (2023) · Dental Clinics of North America · doi:10.1016/j.cden.2023.05.010 · Cited by 1
Manejo do Tratamento Medicamentoso por Pacientes Diabéticos
Moura, R. B. R., Valente, F. B. G., & Nascimento, S. S. (2024) · Revista JRG de Estudos Acadêmicos · doi:10.55892/jrg.v7i14.928 · Cited by 1
Paucity of Published Data and Protocols for Hypoglycemia Management in Long-term Care
Krecic, M. R., Meyer, J. H., & Chavanu, K. J. (2024) · The Senior Care Pharmacist · doi:10.4140/tcp.n.2024.373 · Cited by 1
Part Two: Diabetes Part 2: A Review of Insulin Therapy in Older Patients
Phillippe, H. M., & Pogge, E. K. (2022) · The Senior Care Pharmacist · doi:10.4140/tcp.n.2022.181 · Cited by 1
Chandran, K., Tai, K. P., Toh, M. P. H. S., Phng, F. W. L., Seah, E.-J. D., & Wu, C. X. (2019) · Journal of Endocrinology and Metabolism · doi:10.14740/jem563 · Cited by 1
Lee, S.-Y., Saleem, M., Land, A. M., DeLaney, E. W., Garretson, A. R., Patel, M., Hall, A. G., Westrick, S., Ovalle, F., Cherrington, A. L., et al. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106604
Predicting severe hypoglycaemia — a step forward
Zaccardi, F., & Khunti, K. K. (2017) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2017.138
Ismailov, S. I., & Vodovskaya, A. (2018) · INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine) · doi:10.22141/2224-0721.14.5.2018.142691
Developing the Hypoglycemia-Nursing Early Warning Score System ( Hy-Newss ): A Pilot Study
Wibisono, A. H., Kurniasih, D., Sumartiyah, E., Putri, R. N., Kurnia, D. A., Gultom, Y., & Dahlia, D. (2021) · Journal of Public Health Research · doi:10.4081/jphr.2021.2145
Machine Learning Approaches for Type 2 Diabetes Prediction and Care ManagementPreprint
Lim, A., Singh, A. P., Chiam, J., Eckert, C., Kumar, V., Ahmad, M. A., & Teredesai, A. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07820 · Cited by 3
Diabetologische Notfälle im Krankenhaus
Seibold, J., Ramshorn-Zimmer, A., Ebert, T., & Tönjes, A. (2022) · Die Diabetologie · doi:10.1007/s11428-022-00875-8
Mizu, D., Matsuoka, Y., Huh, J. Y., & Ariyoshi, K. (2020) · doi:10.21203/rs.3.rs-87519/v1
Sechs Parameter sagen Unterzuckerung voraus
rb (2018) · CME · doi:10.1007/s11298-018-6578-4
Behandlung alter Menschen mit Typ-1- oder insulinbehandeltem Typ-2-Diabetes
Meitner-Schellhaas, B., & Haak, T. (2023) · Die Diabetologie · doi:10.1007/s11428-023-01068-7
Suzuki, Y., Miya, A., Nakamura, A., Handa, T., Kameda, H., & Atsumi, T. (2025) · Diabetology International · doi:10.1007/s13340-025-00803-3
Lee, S.-Y., Saleem, M., Land, A. M., Delaney, E. W., Garretson, A., Patel, M., Hall, A. G., Westrick, S. C., Ovalle, F., Cherrington, A., et al. (2025) · doi:10.2196/preprints.72624
Wiles, J. (2025) · Springer eBooks · doi:10.1007/978-3-031-88835-9_7
Medications for the Management of Hypoglycemia
Giruzzi, M. E. (2025) · American Diabetes Association eBooks · doi:10.2337/9781580408462-ch28
Chronic Kidney Disease and Risk Management
(2025) · American Diabetes Association eBooks · doi:10.2337/9781580408462-ch18
Ralston, J. D., Anderson, M. L., Ng, J., Bashir, A., Ehrlich, K. J., Burns-Hunt, D., Cotton, M., Hansell, L. D., Hsu, C., Hunt, H., et al. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-026-10491-7
Self-citationGobbel, G. T., Miller, D. R., Noriega, R., McDannold, S. E., Matheny, M. E., & Borzecki, A. M. (2026) · doi:10.2196/preprints.105917
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Metadata
- Authors
- Karter, A. J., Warton, E. M., Lipska, K. J., Ralston, J. D., Moffet, H. H., Jackson, G. G., Huang, E. S., & Miller, D. R.
- Affiliations
- University of California, San Francisco (US)
- Kaiser Permanente (US)
- Yale University (US)
- Kaiser Permanente Washington Health Research Institute (US)
- University of Chicago (US)
- Veterans Health Administration (US)
- Corresponding author
- Karter, Andrew J.
- ORCID
- Venue
- JAMA Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2017-10-01
- Volume
- 177
- Issue
- 10
- Pages
- 1461
- ISSN
- 2168-6106 (ISSN-L)
- PMC
- PMC5624849
- OpenAlex
- W2749343537
- Semantic Scholar
- 865993e54ca35e1a709ba058bb73942c855a57a4
- Cited by
- 150
- References
- 62
- Citations by year
- 2026: 7 · 2025: 18 · 2024: 21 · 2023: 18 · 2022: 16 · 2021: 17 · 2020: 19 · 2019: 17 · 2018: 14 · 2017: 3
- FWCI
- 11.47
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- JAMA Internal Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UL1TR001863)
- National Institute on Aging (K23AG048359)
- National Institute of Diabetes and Digestive and Kidney Diseases (K24DK105340, R01DK103721, R01DK081796, P30DK092949, P30DK092924)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Patient Safety and Medication Errors
- Keywords
- type 2 diabetes, hypoglycemia, hospital utilization, risk stratification tools, classification trees, insulin use, chronic kidney disease, risk prediction model, external validation, Kaiser Permanente, Veterans Health Administration, Group Health Cooperative, electronic health records, hypoglycemia prevention
- MeSH terms
- Diabetes Mellitus, Type 2, Emergency Service, Hospital, Female, Follow-Up Studies, Humans, Hypoglycemia, Hypoglycemic Agents, Male, Prospective Studies, Quality of Life, Risk Factors, United States, Incidence, Risk Assessment, Delivery of Health Care, Integrated, Electronic Health Records
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