A comparison of electronic and manual fracture risk assessment tools in screening elderly male US veterans at risk for osteoporosis
Summary 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...
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| Veröffentlicht in: | Osteoporosis international 2017-11, Vol.28 (11), p.3107-3111 |
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| creator | Williams, S. T. Lawrence, P. T. Miller, K. L. Crook, J. L. LaFleur, J. Cannon, G. W. Nelson, R. E. |
| description | Summary
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. |
| doi_str_mv | 10.1007/s00198-017-4172-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1924888782</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1924888782</sourcerecordid><originalsourceid>FETCH-LOGICAL-c372t-c1f252be188c68022bfae7763de00526a35db48ba2b483ca915b9e9cf70593753</originalsourceid><addsrcrecordid>eNp1kU1rFTEUhoNY7PXqD3AjATfdTJuPySRZlqJVKHShBXchkzlTps4k15yM0HX_uClTFQpukkWe901yHkLecXbKGdNnyBi3pmFcNy3XopEvyI63UjbCduol2TErdWNb_v2YvEa8YzVjrX5FjoXRqmuV3pGHcxrScvB5whRpGinMEEpOcQrUx4EuPq5-pmP2oawZaOV-UI8IiAvEQktKM9IpUgwZIE7xtjYMkOf7Gp2B3nylv6BA9hGpL1t8TJkmLJAOKSec8A05Gv2M8PZp35ObTx-_XXxurq4vv1ycXzVBalGawEehRA_cmNAZJkQ_etC6kwMwpkTnpRr61vRe1FUGb7nqLdgwaqbqIJTck5Ot95DTzxWwuGXCAPPsI6QVHbeiNcZoIyr64Rl6l9Yc6-ucYMywTto63D3hGxXqPzDD6A55Wny-d5y5R0NuM-SqIfdoyMmaef_UvPYLDH8Tf5RUQGwA1qN4C_nf1f9v_Q1ckJ1I</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2008063909</pqid></control><display><type>article</type><title>A comparison of electronic and manual fracture risk assessment tools in screening elderly male US veterans at risk for osteoporosis</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Williams, S. T. ; Lawrence, P. T. ; Miller, K. L. ; Crook, J. L. ; LaFleur, J. ; Cannon, G. W. ; Nelson, R. E.</creator><creatorcontrib>Williams, S. T. ; Lawrence, P. T. ; Miller, K. L. ; Crook, J. L. ; LaFleur, J. ; Cannon, G. W. ; Nelson, R. E.</creatorcontrib><description>Summary
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.</description><identifier>ISSN: 0937-941X</identifier><identifier>EISSN: 1433-2965</identifier><identifier>DOI: 10.1007/s00198-017-4172-3</identifier><identifier>PMID: 28756457</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Absorptiometry, Photon - methods ; Aged ; Aged, 80 and over ; Bone Density - physiology ; Bone mineral density ; Cross-Sectional Studies ; Dual energy X-ray absorptiometry ; Electronic Health Records ; Electronic medical records ; Endocrinology ; Fractures ; Geriatrics ; Health risk assessment ; Humans ; Male ; Mass Screening - methods ; Medical records ; Medical screening ; Medicine ; Medicine & Public Health ; Original Article ; Orthopedics ; Osteoporosis ; Osteoporosis - diagnosis ; Osteoporosis - physiopathology ; Osteoporotic Fractures - etiology ; Osteoporotic Fractures - physiopathology ; Rheumatology ; Risk assessment ; Risk Assessment - methods ; Risk Factors ; ROC Curve ; Self-assessment ; Sensitivity and Specificity ; Statistical analysis ; Veterans ; Veterans Health</subject><ispartof>Osteoporosis international, 2017-11, Vol.28 (11), p.3107-3111</ispartof><rights>International Osteoporosis Foundation and National Osteoporosis Foundation 2017</rights><rights>Osteoporosis International is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-c1f252be188c68022bfae7763de00526a35db48ba2b483ca915b9e9cf70593753</citedby><cites>FETCH-LOGICAL-c372t-c1f252be188c68022bfae7763de00526a35db48ba2b483ca915b9e9cf70593753</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00198-017-4172-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00198-017-4172-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28756457$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Williams, S. T.</creatorcontrib><creatorcontrib>Lawrence, P. T.</creatorcontrib><creatorcontrib>Miller, K. L.</creatorcontrib><creatorcontrib>Crook, J. L.</creatorcontrib><creatorcontrib>LaFleur, J.</creatorcontrib><creatorcontrib>Cannon, G. W.</creatorcontrib><creatorcontrib>Nelson, R. E.</creatorcontrib><title>A comparison of electronic and manual fracture risk assessment tools in screening elderly male US veterans at risk for osteoporosis</title><title>Osteoporosis international</title><addtitle>Osteoporos Int</addtitle><addtitle>Osteoporos Int</addtitle><description>Summary
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.</description><subject>Absorptiometry, Photon - methods</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Bone Density - physiology</subject><subject>Bone mineral density</subject><subject>Cross-Sectional Studies</subject><subject>Dual energy X-ray absorptiometry</subject><subject>Electronic Health Records</subject><subject>Electronic medical records</subject><subject>Endocrinology</subject><subject>Fractures</subject><subject>Geriatrics</subject><subject>Health risk assessment</subject><subject>Humans</subject><subject>Male</subject><subject>Mass Screening - methods</subject><subject>Medical records</subject><subject>Medical screening</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Original Article</subject><subject>Orthopedics</subject><subject>Osteoporosis</subject><subject>Osteoporosis - diagnosis</subject><subject>Osteoporosis - physiopathology</subject><subject>Osteoporotic Fractures - etiology</subject><subject>Osteoporotic Fractures - physiopathology</subject><subject>Rheumatology</subject><subject>Risk assessment</subject><subject>Risk Assessment - methods</subject><subject>Risk Factors</subject><subject>ROC Curve</subject><subject>Self-assessment</subject><subject>Sensitivity and Specificity</subject><subject>Statistical analysis</subject><subject>Veterans</subject><subject>Veterans Health</subject><issn>0937-941X</issn><issn>1433-2965</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp1kU1rFTEUhoNY7PXqD3AjATfdTJuPySRZlqJVKHShBXchkzlTps4k15yM0HX_uClTFQpukkWe901yHkLecXbKGdNnyBi3pmFcNy3XopEvyI63UjbCduol2TErdWNb_v2YvEa8YzVjrX5FjoXRqmuV3pGHcxrScvB5whRpGinMEEpOcQrUx4EuPq5-pmP2oawZaOV-UI8IiAvEQktKM9IpUgwZIE7xtjYMkOf7Gp2B3nylv6BA9hGpL1t8TJkmLJAOKSec8A05Gv2M8PZp35ObTx-_XXxurq4vv1ycXzVBalGawEehRA_cmNAZJkQ_etC6kwMwpkTnpRr61vRe1FUGb7nqLdgwaqbqIJTck5Ot95DTzxWwuGXCAPPsI6QVHbeiNcZoIyr64Rl6l9Yc6-ucYMywTto63D3hGxXqPzDD6A55Wny-d5y5R0NuM-SqIfdoyMmaef_UvPYLDH8Tf5RUQGwA1qN4C_nf1f9v_Q1ckJ1I</recordid><startdate>20171101</startdate><enddate>20171101</enddate><creator>Williams, S. T.</creator><creator>Lawrence, P. T.</creator><creator>Miller, K. L.</creator><creator>Crook, J. L.</creator><creator>LaFleur, J.</creator><creator>Cannon, G. W.</creator><creator>Nelson, R. E.</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7RV</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope></search><sort><creationdate>20171101</creationdate><title>A comparison of electronic and manual fracture risk assessment tools in screening elderly male US veterans at risk for osteoporosis</title><author>Williams, S. T. ; Lawrence, P. T. ; Miller, K. L. ; Crook, J. L. ; LaFleur, J. ; Cannon, G. W. ; Nelson, R. E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-c1f252be188c68022bfae7763de00526a35db48ba2b483ca915b9e9cf70593753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Absorptiometry, Photon - methods</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Bone Density - physiology</topic><topic>Bone mineral density</topic><topic>Cross-Sectional Studies</topic><topic>Dual energy X-ray absorptiometry</topic><topic>Electronic Health Records</topic><topic>Electronic medical records</topic><topic>Endocrinology</topic><topic>Fractures</topic><topic>Geriatrics</topic><topic>Health risk assessment</topic><topic>Humans</topic><topic>Male</topic><topic>Mass Screening - methods</topic><topic>Medical records</topic><topic>Medical screening</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Original Article</topic><topic>Orthopedics</topic><topic>Osteoporosis</topic><topic>Osteoporosis - diagnosis</topic><topic>Osteoporosis - physiopathology</topic><topic>Osteoporotic Fractures - etiology</topic><topic>Osteoporotic Fractures - physiopathology</topic><topic>Rheumatology</topic><topic>Risk assessment</topic><topic>Risk Assessment - methods</topic><topic>Risk Factors</topic><topic>ROC Curve</topic><topic>Self-assessment</topic><topic>Sensitivity and Specificity</topic><topic>Statistical analysis</topic><topic>Veterans</topic><topic>Veterans Health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Williams, S. T.</creatorcontrib><creatorcontrib>Lawrence, P. T.</creatorcontrib><creatorcontrib>Miller, K. L.</creatorcontrib><creatorcontrib>Crook, J. L.</creatorcontrib><creatorcontrib>LaFleur, J.</creatorcontrib><creatorcontrib>Cannon, G. W.</creatorcontrib><creatorcontrib>Nelson, R. E.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>Osteoporosis international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Williams, S. T.</au><au>Lawrence, P. T.</au><au>Miller, K. L.</au><au>Crook, J. L.</au><au>LaFleur, J.</au><au>Cannon, G. W.</au><au>Nelson, R. E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comparison of electronic and manual fracture risk assessment tools in screening elderly male US veterans at risk for osteoporosis</atitle><jtitle>Osteoporosis international</jtitle><stitle>Osteoporos Int</stitle><addtitle>Osteoporos Int</addtitle><date>2017-11-01</date><risdate>2017</risdate><volume>28</volume><issue>11</issue><spage>3107</spage><epage>3111</epage><pages>3107-3111</pages><issn>0937-941X</issn><eissn>1433-2965</eissn><abstract>Summary
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.</abstract><cop>London</cop><pub>Springer London</pub><pmid>28756457</pmid><doi>10.1007/s00198-017-4172-3</doi><tpages>5</tpages></addata></record> |
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| subjects | Absorptiometry, Photon - methods Aged Aged, 80 and over Bone Density - physiology Bone mineral density Cross-Sectional Studies Dual energy X-ray absorptiometry Electronic Health Records Electronic medical records Endocrinology Fractures Geriatrics Health risk assessment Humans Male Mass Screening - methods Medical records Medical screening Medicine Medicine & Public Health Original Article Orthopedics Osteoporosis Osteoporosis - diagnosis Osteoporosis - physiopathology Osteoporotic Fractures - etiology Osteoporotic Fractures - physiopathology Rheumatology Risk assessment Risk Assessment - methods Risk Factors ROC Curve Self-assessment Sensitivity and Specificity Statistical analysis Veterans Veterans Health |
| title | A comparison of electronic and manual fracture risk assessment tools in screening elderly male US veterans at risk for osteoporosis |
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