Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO)
Background There is an urgent need to update diabetes prediction, which has relied on the United Kingdom Prospective Diabetes Study (UKPDS) that dates back to 1970 s’ European populations. Objective The objective of this study was to develop a risk engine with multiple risk equations using a recent...
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| Veröffentlicht in: | PharmacoEconomics 2018-09, Vol.36 (9), p.1125-1134 |
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| creator | Shao, Hui Fonseca, Vivian Stoecker, Charles Liu, Shuqian Shi, Lizheng |
| description | Background
There is an urgent need to update diabetes prediction, which has relied on the United Kingdom Prospective Diabetes Study (UKPDS) that dates back to 1970 s’ European populations.
Objective
The objective of this study was to develop a risk engine with multiple risk equations using a recent patient cohort with type 2 diabetes mellitus reflective of the US population.
Methods
A total of 17 risk equations for predicting diabetes-related microvascular and macrovascular events, hypoglycemia, mortality, and progression of diabetes risk factors were estimated using the data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (
n
= 10,251). Internal and external validation processes were used to assess performance of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine. One-way sensitivity analysis was conducted to examine the impact of risk factors on mortality at the population level.
Results
The BRAVO risk engine added several risk factors including severe hypoglycemia and common US racial/ethnicity categories compared with the UKPDS risk engine. The BRAVO risk engine also modeled mortality escalation associated with intensive glycemic control (i.e., glycosylated hemoglobin |
| doi_str_mv | 10.1007/s40273-018-0662-1 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9115843</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A714601210</galeid><sourcerecordid>A714601210</sourcerecordid><originalsourceid>FETCH-LOGICAL-c537t-ea1e8478afe8d012cdac2d9a0650fba9c9752a157e290f9d2387f156270869cc3</originalsourceid><addsrcrecordid>eNp1Ustu1TAQjRCIPuAD2CBLbIpEisd5OGaBlD54SIWLLrRby9eZBLeJXeykUlf8Os69pbQI5IVHM-ec0bFPkjwDug-U8tchp4xnKYUqpWXJUniQbANwkbLYf7iuacpLQbeSnRDOKaVlxtnjZIsJzoqKw3by87O7wp4sTbggx7YzFknrPDkyaoUjBvLFu85jCMZZomxDPjk_qt6M18RYcvq1fkMOJtM3xnavyBJ7Na6rOoSZM5cz6SwymvWILKZRuyEK7x0s67PFyyfJo1b1AZ_e3LvJ6bvjb4cf0pPF-4-H9Umqi4yPKSrAKueVarFqKDDdKM0aoWhZ0HalhBa8YAoKjkzQVjQsq3gLRck4rUqhdbabvN3oXk6rARuNdvSql5feDMpfS6eMvD-x5rvs3JUUAEWVZ1Fg70bAux8ThlEOJmjse2XRTUEymhVs_g8RoS_-gp67ydtoTzIQkIHIizuoTvUojW1d3KtnUVlzyMvoEmhE7f8DFU-Dg9HOYmti_x4BNgTtXQge21uPQOWcGrlJjYypkXNqJETO87uPc8v4HZMIYBtAiCPbof_j6P-qvwD2yMsH</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2191319459</pqid></control><display><type>article</type><title>Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO)</title><source>MEDLINE</source><source>Springer Online Journals</source><creator>Shao, Hui ; Fonseca, Vivian ; Stoecker, Charles ; Liu, Shuqian ; Shi, Lizheng</creator><creatorcontrib>Shao, Hui ; Fonseca, Vivian ; Stoecker, Charles ; Liu, Shuqian ; Shi, Lizheng</creatorcontrib><description>Background
There is an urgent need to update diabetes prediction, which has relied on the United Kingdom Prospective Diabetes Study (UKPDS) that dates back to 1970 s’ European populations.
Objective
The objective of this study was to develop a risk engine with multiple risk equations using a recent patient cohort with type 2 diabetes mellitus reflective of the US population.
Methods
A total of 17 risk equations for predicting diabetes-related microvascular and macrovascular events, hypoglycemia, mortality, and progression of diabetes risk factors were estimated using the data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (
n
= 10,251). Internal and external validation processes were used to assess performance of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine. One-way sensitivity analysis was conducted to examine the impact of risk factors on mortality at the population level.
Results
The BRAVO risk engine added several risk factors including severe hypoglycemia and common US racial/ethnicity categories compared with the UKPDS risk engine. The BRAVO risk engine also modeled mortality escalation associated with intensive glycemic control (i.e., glycosylated hemoglobin < 6.5%). External validation showed a good prediction power on 28 endpoints observed from other clinical trials (slope = 1.071,
R
2
= 0.86).
Conclusion
The BRAVO risk engine for the US diabetes cohort provides an alternative to the UKPDS risk engine. It can be applied to assist clinical and policy decision making such as cost-effective resource allocation in USA.</description><identifier>ISSN: 1170-7690</identifier><identifier>EISSN: 1179-2027</identifier><identifier>DOI: 10.1007/s40273-018-0662-1</identifier><identifier>PMID: 29725871</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Blood pressure ; Cardiovascular diseases ; Cardiovascular Diseases - epidemiology ; Clinical trials ; Comorbidity ; Complications and side effects ; Computer simulation ; Costs ; Decision making ; Decision Support Systems, Clinical - statistics & numerical data ; Diabetes ; Diabetes Mellitus, Type 2 - epidemiology ; Diabetes Mellitus, Type 2 - mortality ; Disease Progression ; Ethnicity ; Female ; Health Administration ; Health Economics ; Health risk assessment ; Hemoglobin ; Humans ; Hypoglycemia ; Literature reviews ; Male ; Medicine ; Medicine & Public Health ; Methods ; Models, Statistical ; Mortality ; Original Research Article ; Patient outcomes ; Pharmacoeconomics and Health Outcomes ; Population ; Prognosis ; Public Health ; Quality of life ; Quality of Life Research ; Risk Factors ; Studies ; Type 2 diabetes ; United States - epidemiology</subject><ispartof>PharmacoEconomics, 2018-09, Vol.36 (9), p.1125-1134</ispartof><rights>Springer International Publishing AG, part of Springer Nature 2018</rights><rights>COPYRIGHT 2018 Springer</rights><rights>Copyright Springer Nature B.V. Sep 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c537t-ea1e8478afe8d012cdac2d9a0650fba9c9752a157e290f9d2387f156270869cc3</citedby><cites>FETCH-LOGICAL-c537t-ea1e8478afe8d012cdac2d9a0650fba9c9752a157e290f9d2387f156270869cc3</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/s40273-018-0662-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40273-018-0662-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,776,780,881,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29725871$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shao, Hui</creatorcontrib><creatorcontrib>Fonseca, Vivian</creatorcontrib><creatorcontrib>Stoecker, Charles</creatorcontrib><creatorcontrib>Liu, Shuqian</creatorcontrib><creatorcontrib>Shi, Lizheng</creatorcontrib><title>Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO)</title><title>PharmacoEconomics</title><addtitle>PharmacoEconomics</addtitle><addtitle>Pharmacoeconomics</addtitle><description>Background
There is an urgent need to update diabetes prediction, which has relied on the United Kingdom Prospective Diabetes Study (UKPDS) that dates back to 1970 s’ European populations.
Objective
The objective of this study was to develop a risk engine with multiple risk equations using a recent patient cohort with type 2 diabetes mellitus reflective of the US population.
Methods
A total of 17 risk equations for predicting diabetes-related microvascular and macrovascular events, hypoglycemia, mortality, and progression of diabetes risk factors were estimated using the data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (
n
= 10,251). Internal and external validation processes were used to assess performance of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine. One-way sensitivity analysis was conducted to examine the impact of risk factors on mortality at the population level.
Results
The BRAVO risk engine added several risk factors including severe hypoglycemia and common US racial/ethnicity categories compared with the UKPDS risk engine. The BRAVO risk engine also modeled mortality escalation associated with intensive glycemic control (i.e., glycosylated hemoglobin < 6.5%). External validation showed a good prediction power on 28 endpoints observed from other clinical trials (slope = 1.071,
R
2
= 0.86).
Conclusion
The BRAVO risk engine for the US diabetes cohort provides an alternative to the UKPDS risk engine. It can be applied to assist clinical and policy decision making such as cost-effective resource allocation in USA.</description><subject>Blood pressure</subject><subject>Cardiovascular diseases</subject><subject>Cardiovascular Diseases - epidemiology</subject><subject>Clinical trials</subject><subject>Comorbidity</subject><subject>Complications and side effects</subject><subject>Computer simulation</subject><subject>Costs</subject><subject>Decision making</subject><subject>Decision Support Systems, Clinical - statistics & numerical data</subject><subject>Diabetes</subject><subject>Diabetes Mellitus, Type 2 - epidemiology</subject><subject>Diabetes Mellitus, Type 2 - mortality</subject><subject>Disease Progression</subject><subject>Ethnicity</subject><subject>Female</subject><subject>Health Administration</subject><subject>Health Economics</subject><subject>Health risk assessment</subject><subject>Hemoglobin</subject><subject>Humans</subject><subject>Hypoglycemia</subject><subject>Literature reviews</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Methods</subject><subject>Models, Statistical</subject><subject>Mortality</subject><subject>Original Research Article</subject><subject>Patient outcomes</subject><subject>Pharmacoeconomics and Health Outcomes</subject><subject>Population</subject><subject>Prognosis</subject><subject>Public Health</subject><subject>Quality of life</subject><subject>Quality of Life Research</subject><subject>Risk Factors</subject><subject>Studies</subject><subject>Type 2 diabetes</subject><subject>United States - epidemiology</subject><issn>1170-7690</issn><issn>1179-2027</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1Ustu1TAQjRCIPuAD2CBLbIpEisd5OGaBlD54SIWLLrRby9eZBLeJXeykUlf8Os69pbQI5IVHM-ec0bFPkjwDug-U8tchp4xnKYUqpWXJUniQbANwkbLYf7iuacpLQbeSnRDOKaVlxtnjZIsJzoqKw3by87O7wp4sTbggx7YzFknrPDkyaoUjBvLFu85jCMZZomxDPjk_qt6M18RYcvq1fkMOJtM3xnavyBJ7Na6rOoSZM5cz6SwymvWILKZRuyEK7x0s67PFyyfJo1b1AZ_e3LvJ6bvjb4cf0pPF-4-H9Umqi4yPKSrAKueVarFqKDDdKM0aoWhZ0HalhBa8YAoKjkzQVjQsq3gLRck4rUqhdbabvN3oXk6rARuNdvSql5feDMpfS6eMvD-x5rvs3JUUAEWVZ1Fg70bAux8ThlEOJmjse2XRTUEymhVs_g8RoS_-gp67ydtoTzIQkIHIizuoTvUojW1d3KtnUVlzyMvoEmhE7f8DFU-Dg9HOYmti_x4BNgTtXQge21uPQOWcGrlJjYypkXNqJETO87uPc8v4HZMIYBtAiCPbof_j6P-qvwD2yMsH</recordid><startdate>20180901</startdate><enddate>20180901</enddate><creator>Shao, Hui</creator><creator>Fonseca, Vivian</creator><creator>Stoecker, Charles</creator><creator>Liu, Shuqian</creator><creator>Shi, Lizheng</creator><general>Springer International Publishing</general><general>Springer</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>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>4T-</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88C</scope><scope>88E</scope><scope>88G</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>L.0</scope><scope>M0C</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2M</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20180901</creationdate><title>Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO)</title><author>Shao, Hui ; Fonseca, Vivian ; Stoecker, Charles ; Liu, Shuqian ; Shi, Lizheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c537t-ea1e8478afe8d012cdac2d9a0650fba9c9752a157e290f9d2387f156270869cc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Blood pressure</topic><topic>Cardiovascular diseases</topic><topic>Cardiovascular Diseases - epidemiology</topic><topic>Clinical trials</topic><topic>Comorbidity</topic><topic>Complications and side effects</topic><topic>Computer simulation</topic><topic>Costs</topic><topic>Decision making</topic><topic>Decision Support Systems, Clinical - statistics & numerical data</topic><topic>Diabetes</topic><topic>Diabetes Mellitus, Type 2 - epidemiology</topic><topic>Diabetes Mellitus, Type 2 - mortality</topic><topic>Disease Progression</topic><topic>Ethnicity</topic><topic>Female</topic><topic>Health Administration</topic><topic>Health Economics</topic><topic>Health risk assessment</topic><topic>Hemoglobin</topic><topic>Humans</topic><topic>Hypoglycemia</topic><topic>Literature reviews</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Methods</topic><topic>Models, Statistical</topic><topic>Mortality</topic><topic>Original Research Article</topic><topic>Patient outcomes</topic><topic>Pharmacoeconomics and Health Outcomes</topic><topic>Population</topic><topic>Prognosis</topic><topic>Public Health</topic><topic>Quality of life</topic><topic>Quality of Life Research</topic><topic>Risk Factors</topic><topic>Studies</topic><topic>Type 2 diabetes</topic><topic>United States - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shao, Hui</creatorcontrib><creatorcontrib>Fonseca, Vivian</creatorcontrib><creatorcontrib>Stoecker, Charles</creatorcontrib><creatorcontrib>Liu, Shuqian</creatorcontrib><creatorcontrib>Shi, Lizheng</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>Docstoc</collection><collection>ABI/INFORM Complete</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</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>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ABI/INFORM Global</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>ProQuest Health Management</collection><collection>Medical Database</collection><collection>ProQuest Psychology Journals</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</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 One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>PharmacoEconomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shao, Hui</au><au>Fonseca, Vivian</au><au>Stoecker, Charles</au><au>Liu, Shuqian</au><au>Shi, Lizheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO)</atitle><jtitle>PharmacoEconomics</jtitle><stitle>PharmacoEconomics</stitle><addtitle>Pharmacoeconomics</addtitle><date>2018-09-01</date><risdate>2018</risdate><volume>36</volume><issue>9</issue><spage>1125</spage><epage>1134</epage><pages>1125-1134</pages><issn>1170-7690</issn><eissn>1179-2027</eissn><abstract>Background
There is an urgent need to update diabetes prediction, which has relied on the United Kingdom Prospective Diabetes Study (UKPDS) that dates back to 1970 s’ European populations.
Objective
The objective of this study was to develop a risk engine with multiple risk equations using a recent patient cohort with type 2 diabetes mellitus reflective of the US population.
Methods
A total of 17 risk equations for predicting diabetes-related microvascular and macrovascular events, hypoglycemia, mortality, and progression of diabetes risk factors were estimated using the data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (
n
= 10,251). Internal and external validation processes were used to assess performance of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine. One-way sensitivity analysis was conducted to examine the impact of risk factors on mortality at the population level.
Results
The BRAVO risk engine added several risk factors including severe hypoglycemia and common US racial/ethnicity categories compared with the UKPDS risk engine. The BRAVO risk engine also modeled mortality escalation associated with intensive glycemic control (i.e., glycosylated hemoglobin < 6.5%). External validation showed a good prediction power on 28 endpoints observed from other clinical trials (slope = 1.071,
R
2
= 0.86).
Conclusion
The BRAVO risk engine for the US diabetes cohort provides an alternative to the UKPDS risk engine. It can be applied to assist clinical and policy decision making such as cost-effective resource allocation in USA.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>29725871</pmid><doi>10.1007/s40273-018-0662-1</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1170-7690 |
| ispartof | PharmacoEconomics, 2018-09, Vol.36 (9), p.1125-1134 |
| issn | 1170-7690 1179-2027 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9115843 |
| source | MEDLINE; Springer Online Journals |
| subjects | Blood pressure Cardiovascular diseases Cardiovascular Diseases - epidemiology Clinical trials Comorbidity Complications and side effects Computer simulation Costs Decision making Decision Support Systems, Clinical - statistics & numerical data Diabetes Diabetes Mellitus, Type 2 - epidemiology Diabetes Mellitus, Type 2 - mortality Disease Progression Ethnicity Female Health Administration Health Economics Health risk assessment Hemoglobin Humans Hypoglycemia Literature reviews Male Medicine Medicine & Public Health Methods Models, Statistical Mortality Original Research Article Patient outcomes Pharmacoeconomics and Health Outcomes Population Prognosis Public Health Quality of life Quality of Life Research Risk Factors Studies Type 2 diabetes United States - epidemiology |
| title | Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO) |
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