An electronic health record (EHR)-based risk calculator can predict fractures comparably to FRAX: a proof-of-concept study.

Information in the electronic health record (EHR), such as diagnoses, vital signs, utilization, medications, and laboratory values, may predict fractures well without the need to verbally ascertain risk factors. In our study, as a proof of concept, we developed and internally validated a fracture risk calculator using only information in the EHR. PURPOSE: Fracture risk calculators, such as the Fracture Risk Assessment Tool, or FRAX, typically lie outside the clinician workflow. Conversely, the electronic health record (EHR) is at the center of the clinical workflow, and many variables in the EHR could predict fractures without having to verbally ascertain FRAX risk factors. We sought to evaluate the utility of EHR variables to predict fractures and, as a proof of concept, to create an EHR-based fracture risk model. METHODS: Routine clinical data from 24,189 subjects presenting to primary care from 2010 to 2018 was utilized. Major osteoporotic fractures (MOFs) were captured by physician diagnosis codes. Data was split into training (n = 18,141) and test sets (n = 6048). We fit Cox regression models for candidate risk factors in the training set, and then created a global model using a backward stepwise approach. We then applied the model to the test set and compared the discrimination and calibration to FRAX. RESULTS: We found variables related to vital signs, utilization, diagnoses, medications, and laboratory values to be associated with incident MOF. Our final model included 19 variables, including age, BMI, Parkinson's disease, chronic kidney disease, and albumin levels. When applied to the test set, we found the discrimination (AUC 0.73 vs. 0.70, p = 0.08) and calibration were comparable to FRAX. CONCLUSION: Routinely collected data in EHR systems can generate adequate fracture predictions without the need to verbally ascertain fracture risk factors. In the future, this could allow for automated fracture prediction at the point of care to improve osteoporosis screening and treatment rates.

Electronic Health Records (EHRs) Can Identify Patients at High Risk of Fracture but Require Substantial Race Adjustments to Currently Available Fracture Risk Calculators.

BACKGROUND: Osteoporotic fracture prediction calculators are poorly utilized in primary care, leading to underdiagnosis and undertreatment of those at risk for fracture. The use of these calculators could be improved if predictions were automated using the electronic health record (EHR). However, this approach is not well validated in multi-ethnic populations, and it is not clear if the adjustments for race or ethnicity made by calculators are appropriate. OBJECTIVE: To investigate EHR-generated fracture predictions in a multi-ethnic population. DESIGN: Retrospective cohort study using data from the EHR. SETTING: An urban, academic medical center in Philadelphia, PA. PARTICIPANTS: 12,758 White, 7,844 Black, and 3,587 Hispanic patients seeking routine care from 2010 to 2018 with mean 3.8 years follow-up. INTERVENTIONS: None. MEASUREMENTS: FRAX and QFracture, two of the most used fracture prediction tools, were studied. Risk for major osteoporotic fracture (MOF) and hip fracture were calculated using data from the EHR at baseline and compared to the number of fractures that occurred during follow-up. RESULTS: MOF rates varied from 3.2 per 1000 patient-years in Black men to 7.6 in White women. FRAX and QFracture had similar discrimination for MOF prediction (area under the curve, AUC, 0.69 vs. 0.70, p=0.08) and for hip fracture prediction (AUC 0.77 vs 0.79, p=0.21) and were similar by race or ethnicity. FRAX had superior calibration than QFracture (calibration-in-the-large for FRAX 0.97 versus QFracture 2.02). The adjustment factors used in MOF prediction were generally accurate in Black women, but underestimated risk in Black men, Hispanic women, and Hispanic men. LIMITATIONS: Single center design. CONCLUSIONS: Fracture predictions using only EHR inputs can discriminate between high and low risk patients, even in Black and Hispanic patients, and could help primary care physicians identify patients who need screening or treatment. However, further refinements to the calculators may better adjust for race-ethnicity.

Understanding Type 2 Diabetes Self-Management in Racial/Ethnic Minorities: Application of the Extended Parallel Processing Model and Sensemaking Theory in a Qualitative Study.

PURPOSE: The purpose of the study was to understand the role of perceived disease threat and self-efficacy in type 2 diabetes (T2DM) patients' self-management by using the extended parallel processing model (EPPM) and sensemaking theory. METHODS: Semistructured interviews (n = 25) were conducted with T2DM patients from an urban safety-net hospital. Participants were 50% male/female median age was 55 years and 76% were Black. Participants were categorized by EPPM group based on validated questionnaires (high/low disease threat [HT/LT]; high/low self-efficacy [HE/LE]). Nine were HT/HE, 7 HT/LE, 6 LT/HE, and 3 LT/LE. Interviews were transcribed and analyzed using inductive and deductive coding. Sensemaking theory was applied to contextualize and analyze data. RESULTS: Those with HT indicated threat fluctuated throughout diagnosis but that certain triggers (eg, diabetic complications) drove changes in disease view. Those in the HT/HE group more frequently expressed disease acceptance, whereas the HT/LE group more often expressed anger or denial. HT/HE participants expressed having adequate social support and higher trust in health care providers. HT/LE participants reported limited problem-solving skills. In those with LT, the HE group took more ownership of self-management behaviors. The LT/LE group had heightened positive and negative emotional responses that appeared to limit their ability to perform self-care. They also less frequently described problem-solving skills, instead expressing reliance on medical guidance from their providers. CONCLUSIONS: EPPM and sensemaking theory are effective frameworks for understanding how perceived health threat and self-efficacy may impede T2DM self-care. A greater focus on these constructs is needed to improve care among low-income minority patients, especially those with low threat and self-efficacy.

Plasma chemokine levels correlate with the outcome of antiviral therapy in patients with hepatitis C.

Chronic infection with the hepatitis C virus (HCV) is associated with failures of T-cell-mediated immune clearance and with abnormal B-cell growth and activation. We examined the levels of chemokines that bind to CXC chemokine receptor 3 (CXCR3) to determine whether such chemokines might play a role in the failure of the immune system to clear HCV infection. Elevations in CXC ligand 9 (CXCL9), CXCL10, and CXCL11 were observed in all patients with HCV. CXCR3 expression was increased significantly on peripheral blood B lymphocytes, but not T lymphocytes, from individuals with HCV infection. Chemokine levels were measured in samples collected before, during, and after antiviral therapy from a group of 29 patients infected with HCV genotypes 1a (24 patients) and 1b (5 patients). Levels of CXCL10 and CXCL9 decreased following successful antiviral therapy; CXCL11 did not decline significantly during or in the first 6 months after therapy. The baseline level of CXCL10 (measured before the start of antiviral treatment) was greatest in patients with HCV who subsequently became nonresponders to therapy. These results suggest that plasma concentrations of immunoreactive CXCL10 may be a predictor of responsiveness or nonresponsiveness to antiviral therapy with pegylated interferon (IFN) with or without ribavirin. This observation has implications for understanding the pathogenesis of HCV infection.