SARA JANE CROMER, VICTORIA CHEN, MICAH KOSS, MELTON M. FAN, CRISTINA I. FERNÁNDEZ HERNÁNDEZ, WILLIAM G. MARSHALL, EVELYN GREAUX, MIRIAM UDLER
{"title":"197-OR: 算法识别可改善临床研究招募的种族和民族多样性","authors":"SARA JANE CROMER, VICTORIA CHEN, MICAH KOSS, MELTON M. FAN, CRISTINA I. FERNÁNDEZ HERNÁNDEZ, WILLIAM G. MARSHALL, EVELYN GREAUX, MIRIAM UDLER","doi":"10.2337/db24-197-or","DOIUrl":null,"url":null,"abstract":"Introduction: Early analyses of Rare and Atypical Diabetes Network (RADIANT) study recruitment suggested that goals to recruit underrepresented groups were not being met. We tested whether a validated electronic health record (EHR) algorithm to identify people with an atypical form of diabetes improved identification of racially and ethnically minoritized individuals who may be candidates for the RADIANT study. Methods: Individuals identified by the algorithm were reviewed by research assistants, then classified by endocrinologists as atypical diabetes, a known type of diabetes, or unable to classify (more information needed). Chi-squared tests were used to compare the proportion of self-reported non-Hispanic Black (NHB) and Hispanic or Spanish-speaking (H/SS) participants enrolled through the Mass General site prior to use of the algorithm (mainly through referral by expert clinicians) and proportion of potential individuals identified by the algorithm. Results: Prior to beginning recruitment through the EHR algorithm, 53% of participants enrolled in RADIANT from the Mass General Brigham site identified as NHW, 5% as NHB, 5% as H/SS, 20% as non-Hispanic Asian (NHA). The algorithm initially identified 539 individuals with potentially atypical forms of diabetes. Of these, 452 under the age of 85 were reviewed, and 93 (20.6%) were classified as atypical and possible RADIANT candidates (v. 65.7% with a known type of diabetes and 13.7% unable to be classified). Of those with likely atypical diabetes, 39.8% identified as NHW, 22.6% as NHB, 11.8% as H/SS, and 20.4% as NHA. The algorithm identified a higher percentage of NHB individuals (p<0.001) and H/SS individuals (p<0.001) when compared to previous recruitment methods. Conclusion: Use of a validated algorithm to identify individuals with atypical diabetes in the EHR led to improved identification of candidates for the RADIANT study who are historically underrepresented in clinical and genetic research studies. Disclosure S. Cromer: Other Relationship; Johnson & Johnson Medical Devices Companies. Advisory Panel; Alexion Pharmaceuticals, Inc. Other Relationship; Wolters Kluwer Health. V. Chen: None. M. Koss: None. M.M. Fan: None. C.I. Fernández Hernández: None. W.G. Marshall: Employee; Abbott. E. Greaux: None. M. Udler: Other Relationship; Up-To-Date. Funding American Diabetes Association (7-21-JDFM-005); NIDDK (1U54DK118612)","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"79 1","pages":""},"PeriodicalIF":6.2000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"197-OR: Algorithmic Identification May Improve Racial and Ethnic Diversity of Clinical Study Recruitment\",\"authors\":\"SARA JANE CROMER, VICTORIA CHEN, MICAH KOSS, MELTON M. FAN, CRISTINA I. FERNÁNDEZ HERNÁNDEZ, WILLIAM G. MARSHALL, EVELYN GREAUX, MIRIAM UDLER\",\"doi\":\"10.2337/db24-197-or\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Introduction: Early analyses of Rare and Atypical Diabetes Network (RADIANT) study recruitment suggested that goals to recruit underrepresented groups were not being met. We tested whether a validated electronic health record (EHR) algorithm to identify people with an atypical form of diabetes improved identification of racially and ethnically minoritized individuals who may be candidates for the RADIANT study. Methods: Individuals identified by the algorithm were reviewed by research assistants, then classified by endocrinologists as atypical diabetes, a known type of diabetes, or unable to classify (more information needed). Chi-squared tests were used to compare the proportion of self-reported non-Hispanic Black (NHB) and Hispanic or Spanish-speaking (H/SS) participants enrolled through the Mass General site prior to use of the algorithm (mainly through referral by expert clinicians) and proportion of potential individuals identified by the algorithm. Results: Prior to beginning recruitment through the EHR algorithm, 53% of participants enrolled in RADIANT from the Mass General Brigham site identified as NHW, 5% as NHB, 5% as H/SS, 20% as non-Hispanic Asian (NHA). The algorithm initially identified 539 individuals with potentially atypical forms of diabetes. Of these, 452 under the age of 85 were reviewed, and 93 (20.6%) were classified as atypical and possible RADIANT candidates (v. 65.7% with a known type of diabetes and 13.7% unable to be classified). Of those with likely atypical diabetes, 39.8% identified as NHW, 22.6% as NHB, 11.8% as H/SS, and 20.4% as NHA. The algorithm identified a higher percentage of NHB individuals (p<0.001) and H/SS individuals (p<0.001) when compared to previous recruitment methods. Conclusion: Use of a validated algorithm to identify individuals with atypical diabetes in the EHR led to improved identification of candidates for the RADIANT study who are historically underrepresented in clinical and genetic research studies. Disclosure S. Cromer: Other Relationship; Johnson & Johnson Medical Devices Companies. Advisory Panel; Alexion Pharmaceuticals, Inc. Other Relationship; Wolters Kluwer Health. V. Chen: None. M. Koss: None. M.M. Fan: None. C.I. Fernández Hernández: None. W.G. Marshall: Employee; Abbott. E. Greaux: None. M. Udler: Other Relationship; Up-To-Date. 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197-OR: Algorithmic Identification May Improve Racial and Ethnic Diversity of Clinical Study Recruitment
Introduction: Early analyses of Rare and Atypical Diabetes Network (RADIANT) study recruitment suggested that goals to recruit underrepresented groups were not being met. We tested whether a validated electronic health record (EHR) algorithm to identify people with an atypical form of diabetes improved identification of racially and ethnically minoritized individuals who may be candidates for the RADIANT study. Methods: Individuals identified by the algorithm were reviewed by research assistants, then classified by endocrinologists as atypical diabetes, a known type of diabetes, or unable to classify (more information needed). Chi-squared tests were used to compare the proportion of self-reported non-Hispanic Black (NHB) and Hispanic or Spanish-speaking (H/SS) participants enrolled through the Mass General site prior to use of the algorithm (mainly through referral by expert clinicians) and proportion of potential individuals identified by the algorithm. Results: Prior to beginning recruitment through the EHR algorithm, 53% of participants enrolled in RADIANT from the Mass General Brigham site identified as NHW, 5% as NHB, 5% as H/SS, 20% as non-Hispanic Asian (NHA). The algorithm initially identified 539 individuals with potentially atypical forms of diabetes. Of these, 452 under the age of 85 were reviewed, and 93 (20.6%) were classified as atypical and possible RADIANT candidates (v. 65.7% with a known type of diabetes and 13.7% unable to be classified). Of those with likely atypical diabetes, 39.8% identified as NHW, 22.6% as NHB, 11.8% as H/SS, and 20.4% as NHA. The algorithm identified a higher percentage of NHB individuals (p<0.001) and H/SS individuals (p<0.001) when compared to previous recruitment methods. Conclusion: Use of a validated algorithm to identify individuals with atypical diabetes in the EHR led to improved identification of candidates for the RADIANT study who are historically underrepresented in clinical and genetic research studies. Disclosure S. Cromer: Other Relationship; Johnson & Johnson Medical Devices Companies. Advisory Panel; Alexion Pharmaceuticals, Inc. Other Relationship; Wolters Kluwer Health. V. Chen: None. M. Koss: None. M.M. Fan: None. C.I. Fernández Hernández: None. W.G. Marshall: Employee; Abbott. E. Greaux: None. M. Udler: Other Relationship; Up-To-Date. Funding American Diabetes Association (7-21-JDFM-005); NIDDK (1U54DK118612)
期刊介绍:
Diabetes is a scientific journal that publishes original research exploring the physiological and pathophysiological aspects of diabetes mellitus. We encourage submissions of manuscripts pertaining to laboratory, animal, or human research, covering a wide range of topics. Our primary focus is on investigative reports investigating various aspects such as the development and progression of diabetes, along with its associated complications. We also welcome studies delving into normal and pathological pancreatic islet function and intermediary metabolism, as well as exploring the mechanisms of drug and hormone action from a pharmacological perspective. Additionally, we encourage submissions that delve into the biochemical and molecular aspects of both normal and abnormal biological processes.
However, it is important to note that we do not publish studies relating to diabetes education or the application of accepted therapeutic and diagnostic approaches to patients with diabetes mellitus. Our aim is to provide a platform for research that contributes to advancing our understanding of the underlying mechanisms and processes of diabetes.