Int J Med Inform - The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects.

Tópicos

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Resumo

RPOSE: To evaluate the impact of insufficient longitudinal data on the accuracy of a high-throughput clinical phenotyping (HTCP) algorithm for identifying (1) patients with type 2 diabetes mellitus (T2DM) and (2) patients with no diabetes.METHODS: Retrospective study conducted at Mayo Clinic in Rochester, Minnesota. Eligible subjects were Olmsted County residents with =1 Mayo Clinic encounter in each of three time periods: (1) 2007, (2) from 1997 through 2006, and (3) before 1997 (N = 54,283). Diabetes relevant electronic medical record (EMR) data about diagnoses, laboratories, and medications were used. We employed the HTCP algorithm to categorize individuals as T2DM cases and non-diabetes controls. Considering the full 11 years (1997-2007) as the gold standard, we compared gold-standard categorizations with those using data for 10 subsequent intervals, ranging from 1998-2007 (10-year data) to 2007 (1-year data). Positive predictive values (PPVs) and false-negative rates (FNRs) were calculated. McNemar tests were used to determine whether categorizations using shorter time periods differed from the gold standard. Statistical significance was defined as P < 0.05.RESULTS: We identified 2770 T2DM cases and 21,005 controls when the algorithm was applied using 11-year data. Using 2007 data alone, PPVs and FNRs, respectively, were 70% and 25% for case identification and 59% and 67% for control identification. All time frames differed significantly from the gold standard, except for the 10-year period.CONCLUSIONS: The accuracy of the algorithm reduced remarkably as data were limited to shorter observation periods. This impact should be considered carefully when designing/executing HTCP algorithms.

Resumo Limpo

rpose evalu impact insuffici longitudin data accuraci highthroughput clinic phenotyp htcp algorithm identifi patient type diabet mellitus tdm patient diabetesmethod retrospect studi conduct mayo clinic rochest minnesota elig subject olmst counti resid mayo clinic encount three time period n diabet relev electron medic record emr data diagnos laboratori medic use employ htcp algorithm categor individu tdm case nondiabet control consid full year gold standard compar goldstandard categor use data subsequ interv rang year data year data posit predict valu ppvs falseneg rate fnrs calcul mcnemar test use determin whether categor use shorter time period differ gold standard statist signific defin p result identifi tdm case control algorithm appli use year data use data alon ppvs fnrs respect case identif control identif time frame differ signific gold standard except year periodconclus accuraci algorithm reduc remark data limit shorter observ period impact consid care designingexecut htcp algorithm

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