<u>Objective:</u> 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.
<p><u> </u></p>
<p><u>Research Design and
Methods:</u> Youth (< 20 years) 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.</p>
<p> </p>
<p><u>Results:</u> The sample
included 5308 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 (<i>Se</i>) (>0.95), specificity (<i>Sp</i>)
(>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. Combining 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).
The sensitivity, specificity, and PPV for type 2 diabetes using the combined method
were >=0.91. </p>
<p> </p>
<p><u>Conclusions</u>: 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. </p>
<br>
Association between fear of hypoglycemia and physical activity in youth with type 1 diabetes: The
SEARCH
for diabetes in youth study