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Identifying Common Predictors of Multiple Adverse Outcomes Among Elderly Adults With Type-2 Diabetes

Bibliographic Data

ID9099302
AuthorsSamuel Kabue (Hill Physicians Medical Group, Population Health, San Ramon, corresponding author), Vincent Liu (0000-0003-1987-9521, Division of Research, Kaiser Permanente Northern California, Oakland, CA), Wendy Dyer (0000-0002-8861-5549, Division of Research, Kaiser Permanente Northern California, Oakland, CA), Marsha A Raebel (0000-0002-8485-7161, Kaiser Permanente), Marsha Raebel (Institute of Health Research, Kaiser Permanente Colorado, Denver, CO), Greg Nichols (Health Services Research, Kaiser Permanente North West, Oakland, CA), Greg A Nichols (Kaiser Permanente), Julie Schmittdiel (Division of Research, Kaiser Permanente Northern California, Oakland, CA), Julie A Schmittdiel (0000-0001-8995-5345, Kaiser Permanente)
Year2019
Volume57
Issue9
Pages702-709
Publication date2019-09-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000001159
PMID31356411
OpenAlexW2966541524
LanguageEN
Citations received1
References cited26

OBJECTIVE: As part of a multidisciplinary team managing patients with type-2 diabetes, pharmacists need a consistent approach of identifying and prioritizing patients at highest risk of adverse outcomes. Our objective was to identify which predictors of adverse outcomes among type-2 diabetes patients were significant and common across 7 outcomes and whether these predictors improved the performance of risk prediction models. Identifying such predictors would allow pharmacists and other health care providers to prioritize their patient panels. RESEARCH DESIGN AND METHODS: Our study population included 120,256 adults aged 65 years or older with type-2 diabetes from a large integrated health system. Through an observational retrospective cohort study design, we assessed which risk factors were associated with 7 adverse outcomes (hypoglycemia, hip fractures, syncope, emergency department visit or hospital admission, death, and 2 combined outcomes). We split (50:50) our study cohort into a test and training set. We used logistic regression to model outcomes in the test set and performed k-fold validation (k=5) of the combined outcome (without death) within the validation set. RESULTS: The most significant predictors across the 7 outcomes were: age, number of medicines, prior history of outcome within the past 2 years, chronic kidney disease, depression, and retinopathy. Experiencing an adverse outcome within the prior 2 years was the strongest predictor of future adverse outcomes (odds ratio range: 4.15-7.42). The best performing models across all outcomes included: prior history of outcome, physiological characteristics, comorbidities and pharmacy-specific factors (c-statistic range: 0.71-0.80). CONCLUSIONS: Pharmacists and other health care providers can use models with prior history of adverse event, number of medicines, chronic kidney disease, depression and retinopathy to prioritize interventions for elderly patients with type-2 diabetes

Adverse effect · Cohort · Cohort study · Diabetes mellitus · Emergency department · Intensive care medicine · Logistic regression · Observational study · Polypharmacy · Population · Psychiatry · Retrospective cohort study · Type 2 diabetes · Advanced Causal Inference Techniques · Emergency Medicine · Internal Medicine · Medicine · Pharmaceutical Practices and Patient Outcomes · Pharmacovigilance and Adverse Drug Reactions

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Unique citing works1
Citations per year0,17
Citation span2020 - 2020 (1)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 1

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