期刊
DIABETES CARE
卷 43, 期 10, 页码 2418-2425出版社
AMER DIABETES ASSOC
DOI: 10.2337/dc20-0063
关键词
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资金
- South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina, National Institutes of Health (NIH)/National Center for Advancing Translational Sciences (NCATS) grants [UL1 TR000062, UL1 TR001450]
- University of Washington, NIH/NCATS grant [UL1 TR00423]
- University of Colorado Pediatric Clinical and Translational Research Center, NIH/NCATS [UL1 TR000154]
- Barbara Davis Center for Diabetes at the University of Colorado Denver (Diabetes Endocrinology Research Center NIH grant) [P30 DK57516]
- University of Cincinnati, NIH/NCATS grants [UL1 TR000077, UL1 TR001425]
- Centers for Disease Control and Prevention [00097, DP-05-069, DP-10-001, 1U18DP006131, U18DP006133, U18DP006134, U18DP006136, U18DP006138, U18DP006139]
- National Institute of Diabetes and Digestive and Kidney Diseases, NIH
- National Institute of Diabetes and Digestive and Kidney Diseases, NIH [1UC4DK108173]
- Kaiser Permanente Southern California [U18DP006133, U48/CCU919219, U01 DP000246, U18DP002714]
- University of Colorado Denver [U18DP006139, U48/CCU819241-3, U01 DP000247, U18DP000247-06A1]
- Cincinnati Children's Hospital Medical Center [U18DP006134, U48/CCU519239, U01DP000248, 1U18DP002709]
- University of North Carolina at Chapel Hill [U18DP006138, U48/CCU419249, U01 DP000254, U18DP002708]
- Seattle Children's Hospital [U18DP006136, U58/CCU019235-4, U01 DP000244, U18DP002710-01]
- Wake Forest University School of Medicine [U48/CCU919219, U18DP006131, U18 DP006131 S1, U01 DP000250, 200-2010-35171]
OBJECTIVE Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification. RESEARCH DESIGN AND METHODS Youth (<20 years old) with potential evidence of diabetes (N= 8,682) were identified from EHRs at three children's hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule-based algorithm with targeted chart reviews where the algorithm performed poorly. RESULTS The sample included 5,308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs. 0.936). Type 1 diabetes was classified well by both methods: sensitivity (Se) (>0.95), specificity (Sp) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combination of the rule-based method with chart reviews (n= 695, 7.9%) of persons predicted to have non-type 1 diabetes resulted in perfect PPV for the cases reviewed while increasing overall accuracy (0.983). TheSe,Sp, and PPV for type 2 diabetes using the combined method were >= 0.91. CONCLUSIONS An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.
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