Validated risk rule using computerized data to identify males at high risk for fracture

Development and external validation of a 1- and 5-year fracture prediction tool based on electronic medical records data: The EPIC risk algorithm

OBJECTIVES

We aimed to develop and validate a fracture risk algorithm for the automatic identification of subjects at high risk of imminent and long-term fracture risk.

RESEARCH, DESIGN, AND METHODS

A cohort of subjects aged 50-85, between 2007 and 2017, was extracted from the Catalan information system for the development of research in primary care database (SIDIAP). Participants were followed until the earliest of death, transfer out, fracture, or 12/31/2017. Potential risk factors were obtained based on the existing literature. Cox regression was used to model 1 and 5-year risk of hip and major fracture. The original cohort was randomly split in 80:20 for development and internal validation purposes respectively. External validation was explored in a cohort extracted from the Spanish database for pharmaco-epidemiological research in primary care.

RESULTS

A total of 1.76 million people were included from SIDIAP (50.7 % women with mean age of 65.4 years). Hip and major fracture incidence rates were 3.57 [95%CI 3.53 to 3.60] and 11.61 [95%CI 11.54 to 11.68] per 1000 person-years, respectively. The derived model included 19 risk factors. Internal validity showed good results on calibration and discrimination. The 1-year C-statistic for hip and major fracture were 0.851 (95%CI 0.853 to 0.864), and 0.717 (95%CI 0.742 to 0.749) respectively. The 5-year C-statistic for hip and major fracture were 0.849 (95%CI 0.847 to 0.852) and 0.724 (95%CI 0.721 to 0.727) respectively. External validation showed good performance for hip and major fracture risk prediction.

CONCLUSIONS

We have developed and validated a clinical prediction tool for 1- and 5-year hip and major osteoporotic fracture risks using electronic primary care data. The proposed algorithm can be automatically estimated at the population level using the available primary care records. Future work is needed on the cost-effectiveness of its use for population-based screening and targeted prevention of osteoporotic fractures.

Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal

Osteoporotic fractures (OF) are a global public health problem currently. Many risk prediction models for OF have been developed, but their performance and methodological quality are unclear. We conducted this systematic review to summarize and critically appraise the OF risk prediction models. Three databases were searched until April 2021. Studies developing or validating multivariable models for OF risk prediction were considered eligible. Used the prediction model risk of bias assessment tool to appraise the risk of bias and applicability of included models. All results were narratively summarized and described. A total of 68 studies describing 70 newly developed prediction models and 138 external validations were included. Most models were explicitly developed (n=31, 44%) and validated (n=76, 55%) only for female. Only 22 developed models (31%) were externally validated. The most validated tool was Fracture Risk Assessment Tool. Overall, only a few models showed outstanding (n=3, 1%) or excellent (n=32, 15%) prediction discrimination. Calibration of developed models (n=25, 36%) or external validation models (n=33, 24%) were rarely assessed. No model was rated as low risk of bias, mostly because of an insufficient number of cases and inappropriate assessment of calibration. There are a certain number of OF risk prediction models. However, few models have been thoroughly internally validated or externally validated (with calibration being unassessed for most of the models), and all models showed methodological shortcomings. Instead of developing completely new models, future research is suggested to validate, improve, and analyze the impact of existing models.

Predicting treatment recommendations in postmenopausal osteoporosis

We designed, implemented, and tested a clinical decision support system at the Research Center for the Study of Menopause and Osteoporosis within the University of Ferrara (Italy). As an independent module of our system, we implemented an original machine learning system for rule extraction, enriched with a hierarchical extraction methodology and a novel rule evaluation technique. Such a module is used in everyday operation protocol, and it allows physicians to receive suggestions for prevention and treatment of osteoporosis. In this paper, we design and execute an experiment based on two years of data, in order to evaluate and report the reliability of our suggestion system. Our results are encouraging, and in some cases reach expected accuracies of around 90%.

Use of an electronic medical record dashboard to identify gaps in osteoporosis care

Using an electronic medical record (EMR)-based dashboard, this study explored osteoporosis care gaps in primary care. Eighty-four physicians shared their practice activities related to bone mineral density testing, 10-year fracture risk calculation and treatment for those at high risk. Significant gaps in fracture risk calculation and osteoporosis management were identified.

PURPOSE

To identify care gaps in osteoporosis management focusing on Canadian clinical practice guidelines (CPG) related to bone mineral density (BMD) testing, 10-year fracture risk calculation and treatment for those at high risk.

METHODS

The ADVANTAGE OP EMR tool consists of an interactive algorithm to facilitate assessment and management of fracture risk using CPG. The FRAX® and Canadian Association of Radiologists and Osteoporosis Canada (CAROC) tools were embedded to facilitate 10-year fracture risk calculation. Physicians managed patients as clinically indicated but with EMR reminders of guideline recommendations; participants shared practice level data on management activities after 18-month use of the tool.

RESULTS

Eighty-four physicians (54%) of 154 who agreed to participate in this study shared their aggregate practice activities. Across all practices, there were 171,310 adult patients, 40 years of age and older, of whom 17,214 (10%) were at elevated risk for fracture. Sixty-two percent of patients potentially at elevated risk for fractures did not have BMD testing completed; most common reasons for this were intention to order BMD later (48%), physician belief that BMD was not required (15%) and patient refusal (20%). For patients with BMD completed, fracture risk was calculated in 29%; 19% were at high risk, of whom 37% were not treated with osteoporosis medications as recommended by CPG.

CONCLUSION

Despite access to CPG and fracture risk calculators through the ADVANTAGE OP EMR tool, significant gaps remain in fracture risk calculation and osteoporosis management. Additional strategies are needed to address this clinical inertia among family physicians.

Limited Osteoporosis Screening Effectiveness Due to Low Treatment Rates in a National Sample of Older Men

OBJECTIVE

To determine the association between dual-energy x-ray absorptiometry (DXA) testing for osteoporosis and subsequent fractures in US male veterans without a previous fracture.

PATIENTS AND METHODS

This is a propensity score-matched observational study using Centers for Medicare and Medicaid Services and Veterans Affairs (VA) data from January 1, 2000, through December 31, 2010, with a mean follow-up time of 4.7 years (range, 0-10 years). Men receiving VA primary care aged 65 to 99 years without a previous fracture (N=2,539,812) were included. Men undergoing DXA testing were propensity score matched with untested controls in a 1:3 ratio, indicating the probability of DXA testing within the next year. Time to first clinical fracture was the primary outcome. Comorbidities, demographic characteristics, medications, DXA results, and osteoporosis treatment were defined using administrative data and natural language processing. A landmark analysis contingent on surviving to 12 months after screening was completed, accounting for competing risk of mortality.

RESULTS

During follow-up of 153,311 men tested by DXA and 390,158 controls, 56,083 (10.3%) had sustained a fracture and 111,774 (20.6%) died. Overall, DXA testing was not associated with a decrease in fractures; conclusions are limited by unmeasured confounders and low medication initiation and adherence in those meeting treatment thresholds (12% of follow-up time). In contrast, DXA testing in prespecified subgroups was associated with a lower risk of fracture in comparison to the overall population who underwent DXA testing: androgen deprivation therapy (hazard ratio [HR], 0.77; 95% CI, 0.66-0.89), glucocorticoids (HR, 0.77; 95% CI, 0.72-0.84), age 80 years and older (HR, 0.85; 0.81-0.90), 1 or more VA guideline risk factors (HR, 0.91; 95% CI, 0.87-0.95), and high Fracture Risk Assessment Tool using body mass index score (HR, 0.90; 95% CI, 0.86-0.95).

CONCLUSION

Current VA DXA testing practices are ineffective overall; interventions to improve treatment adherence are needed. Targeted DXA testing in higher-risk men was associated with a lower fracture risk.

A comparison of electronic and manual fracture risk assessment tools in screening elderly male US veterans at risk for osteoporosis

This study compares four screening tools in their ability to predict osteoporosis. We found that there was no significant difference between the tools. These results provide support for the use of automated screening tools which work in conjunction with the electronic medical record and help improve screening rates for osteoporosis.

INTRODUCTION

The purpose of this study is to compare the performance of four fracture risk assessment tools (FRATs) in identifying osteoporosis by bone mineral density (BMD) T-score: Veterans Affairs Fracture Absolute Risk Assessment Tool (VA-FARA), World Health Organization's Fracture Risk Assessment Tool (FRAX), electronic FRAX (e-FRAX), and Osteoporosis Self-Assessment Screening Tool (OST).

METHODS

We performed a cross-sectional analysis of all patients enrolled in the VA Salt Lake City bone health team (BHT) who had completed a DXA scan between February 1, 2012, and February 1, 2013. DXA scan results were obtained by chart abstraction. For calculation of FRAX, osteoporosis risk factors were obtained from a screening questionnaire completed prior to DXA. For VA-FARA and e-FRAX, risk factors were derived from the electronic medical record (EMR). Clinical risk scores were calculated and compared against the gold standard of DXA-based osteoporosis. Sensitivity, specificity, and predictive values were calculated. Receiver operator characteristic (ROC) curves were plotted, and areas under the curve (AUC) were compared.

RESULTS

A cohort of 463 patients met eligibility criteria (mean age 80.4 years). One hundred twelve patients (24%) had osteoporosis as defined by DXA T-score ≤-2.5. Sensitivity, specificity, and predictive values were calculated. ROC statistics were compared and did not reach statistical significance difference between FRATs in identifying DXA-based osteoporosis.

CONCLUSIONS

Our study suggests that all FRATs tested perform similarly in identifying osteoporosis among elderly, primarily Caucasian, male veterans. If these electronic screening methods perform similarly for fracture outcomes, they could replace manual FRAX and thus improve efficiency in identifying individuals who should be sent for DXA scan.

Calculating the Baseline Incidence in Patients Without Risk Factors: A Strategy for Economic Evaluation

Economic and epidemiological models need various inputs to estimate the occurrence of events in different subsets of the population, such as the incidence of events for patients with risk factors compared with those without. However, the baseline event incidence for patients without risk factors (incidence_no_risk) may not be reported in the literature, therefore the event incidence in the population (incidence_pop) is commonly used in its place as the baseline. However, this is problematic because incidence_pop is a weighted average of a heterogeneous population. We therefore developed a method for deriving the incidence for persons without risk factors (incidence_no_risk) by adjustment of incidence_pop. We calculated incidence_no_risk using the relative risk for events due to risk factors (RR_risk), incidence_pop, and the prevalence of the risk factor (pRF), which are typically available in the literature. Since the incidence for patient with risk factors (incidence_risk) can be expressed as incidence_risk = incidence_no_risk × RR_risk, we found that incidence_no_risk = incidence_pop/((RR_risk × pRF) + (1 - pRF)). We validated the equation by modeling the fracture incidence in high-risk patients in an osteoporosis transition-state model. With incidence_pop used as the baseline fracture incidence, the model overestimated hip fractures in the study population (10.72 fractures/1000 patient-years). After adjustment of incidence_pop using incidence_no_risk as the baseline incidence, the model accurately predicted hip fractures (2.27/1000 patient-years). Therefore, incidence_no_risk can be calculated using this method based on the event incidence for the study population, the relative risk increase associated with the risk factor, and the prevalence of the risk factor.

Comparing fracture absolute risk assessment (FARA) tools: an osteoporosis clinical informatics tool to improve identification and care of men at high risk of first fracture

BACKGROUND

Fracture absolute risk assessment (FARA) is recommended for guiding osteoporosis treatment decisions in males. The best strategy for applying FARA in the clinic setting is not known.

OBJECTIVES

We compared 2 FARA tools for use with electronic health records (EHRs) to determine which would more accurately identify patients known to be high risk for fracture. Tools evaluated were an adaptation of the World Health Organization's Fracture Risk Assessment Tool used with electronic data (eFRAX) and the Veterans Affairs (VA)-based tool, VA-FARA.

METHODS

We compared accuracies of VA-FARA and eFRAX for correctly classifying male veterans who fractured and who were seen in the VA's Sierra Pacific Network in 2002-2013. We then matched those cases to nonfracture controls to compare odds of fracture in patients classified as high risk by either tool.

RESULTS

Among 8740 patients, the mean (SD) age was 67.0 (11.1) years. Based on risk factors present in the EHR, VA-FARA correctly classified 40.1% of fracture patients as high risk (33.0% and 34.6% for hip and any major fracture, respectively); eFRAX classified 17.4% correctly (17.4% for hip and 0.2% for any major fracture). Compared with non-high-risk patients, those classified as high risk by VA-FARA were 35% more likely to fracture (95% CI = 23%-47%; P < 0.01) compared with 17% for eFRAX (95% CI = 5%-32%; P < 0.01).

CONCLUSIONS

VA-FARA is more predictive of first fracture than eFRAX using EHR data. Decision support tools based on VA-FARA may improve early identification and care of men at risk.

An evaluation of clinical risk factors for estimating fracture risk in postmenopausal osteoporosis using an electronic medical record database

SUMMARY

Many of the clinical risk factors used in fracture risk assessment (FRAX) calculator are available in electronic medical record (EMR) databases and are good sources of osteoporosis risk factor information. The EPIC EMR database showed a lower prevalence of FRAX risk factors and, consequently, proportion of patients who would be deemed "high risk."

INTRODUCTION

The FRAX tool is underutilized for osteoporosis screening. Many of the clinical risk factors for FRAX may be available in EMR databases and may enable health systems to perform fracture risk assessments. We intended to identify variables in an EMR database for calculating FRAX score in a cohort of postmenopausal women, to estimate absolute fracture risk, and to determine the proportions of women whose absolute fracture risks exceed the National Osteoporosis Foundation (NOF) thresholds.

METHODS

Our cohort was selected using an EMR database with demographic, inpatient, outpatient, and clinical information for female patients age≥50 in a family practice, internal medicine, or obstetrics/gynecology clinic in 2007-2008. The latest physician encounter was the index date. Variables, problem and medication lists, diagnosis codes, and histories from the EMR were used to populate the 11 clinical risk factor variables used in the FRAX. These risk factor prevalence and treatment-eligible proportions were compared to those of published epidemiology studies.

RESULTS

The study included 345 patients. Mean (SD) 10-year risk for any major fracture was 11.1% (6.8) when bone mineral density (BMD) was used and 11.2% (6.5) when BMI was used. About 10.1% of the cohort exceeded the NOF's 20% major fracture risk threshold and 32.5% exceeded the NOF's 3% hip fracture risk threshold when BMD was used. Overall, the number of treatment-eligible patients was slightly lower when FRAX was calculated using BMD versus BMI (13.6 and 36.8%).

CONCLUSION

Our cohort using EMR data most likely underestimated the mean 10-year probability of any major fracture compared to other cohorts in published literature. The difference may be in the nature of EMRs for supporting only passive data collection of risk factor information.

The clinical epidemiology of male osteoporosis: a review of the recent literature

Osteoporosis, a musculoskeletal disease characterized by decreased bone mineral density (BMD) and an increased risk of fragility fractures, is now recognized as an important public health problem in men. Osteoporotic fractures, particularly of the hip, result in significant morbidity and mortality in men and lead to considerable societal costs. Many national and international organizations now address screening and treatment for men in their osteoporosis clinical guidelines. However, male osteoporosis remains largely underdiagnosed and undertreated. The objective of this paper is to provide an overview of recent findings in male osteoporosis, including pathophysiology, epidemiology, and incidence and burden of fracture, and discuss current knowledge about the evaluation and treatment of osteoporosis in males. In particular, clinical practice guidelines, fracture risk assessment, and evidence of treatment effectiveness in men are addressed.