Dataset Information

Use of administrative and electronic health record data for development of automated algorithms for childhood diabetes case ascertainment and type classification: the SEARCH for Diabetes in Youth Study.

ABSTRACT: BACKGROUND:The performance of automated algorithms for childhood diabetes case ascertainment and type classification may differ by demographic characteristics. OBJECTIVE:This study evaluated the potential of administrative and electronic health record (EHR) data from a large academic care delivery system to conduct diabetes case ascertainment in youth according to type, age, and race/ethnicity. SUBJECTS:Of 57?767 children aged <20?yr as of 31 December 2011 seen at University of North Carolina Health Care System in 2011 were included. METHODS:Using an initial algorithm including billing data, patient problem lists, laboratory test results, and diabetes related medications between 1 July 2008 and 31 December 2011, presumptive cases were identified and validated by chart review. More refined algorithms were evaluated by type (type 1 vs. type 2), age (<10 vs. ?10?yr) and race/ethnicity (non-Hispanic White vs. 'other'). Sensitivity, specificity, and positive predictive value were calculated and compared. RESULTS:The best algorithm for ascertainment of overall diabetes cases was billing data. The best type 1 algorithm was the ratio of the number of type 1 billing codes to the sum of type 1 and type 2 billing codes ?0.5. A useful algorithm to ascertain youth with type 2 diabetes with 'other' race/ethnicity was identified. Considerable age and racial/ethnic differences were present in type-non-specific and type 2 algorithms. CONCLUSIONS:Administrative and EHR data may be used to identify cases of childhood diabetes (any type), and to identify type 1 cases. The performance of type 2 case ascertainment algorithms differed substantially by race/ethnicity.

SUBMITTER: Zhong VW

PROVIDER: S-EPMC4229415 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Use of administrative and electronic health record data for development of automated algorithms for childhood diabetes case ascertainment and type classification: the SEARCH for Diabetes in Youth Study.

Zhong Victor W VW Pfaff Emily R ER Beavers Daniel P DP Thomas Joan J Jaacks Lindsay M LM Bowlby Deborah A DA Carey Timothy S TS Lawrence Jean M JM Dabelea Dana D Hamman Richard F RF Pihoker Catherine C Saydah Sharon H SH Mayer-Davis Elizabeth J EJ

Pediatric diabetes 20140609 8

<h4>Background</h4>The performance of automated algorithms for childhood diabetes case ascertainment and type classification may differ by demographic characteristics.<h4>Objective</h4>This study evaluated the potential of administrative and electronic health record (EHR) data from a large academic care delivery system to conduct diabetes case ascertainment in youth according to type, age, and race/ethnicity.<h4>Subjects</h4>Of 57 767 children aged <20 yr as of 31 December 2011 seen at Universit ...[more]

PMID: 24913103

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Project description:BackgroundPeripheral artery disease (PAD) affects 8 to 10 million Americans, who face significantly elevated risks of both mortality and major limb events such as amputation. Unfortunately, PAD is relatively underdiagnosed, undertreated, and underresearched, leading to wide variations in treatment patterns and outcomes. Efforts to improve PAD care and outcomes have been hampered by persistent difficulties identifying patients with PAD for clinical and investigatory purposes.ObjectiveThe aim of this study is to develop and validate a model-based algorithm to detect patients with peripheral artery disease (PAD) using data from an electronic health record (EHR) system.MethodsAn initial query of the EHR in a large health system identified all patients with PAD-related diagnosis codes for any encounter during the study period. Clinical adjudication of PAD diagnosis was performed by chart review on a random subgroup. A binary logistic regression to predict PAD was built and validated using a least absolute shrinkage and selection operator (LASSO) approach in the adjudicated patients. The algorithm was then applied to the nonsampled records to further evaluate its performance.ResultsThe initial EHR data query using 406 diagnostic codes yielded 15,406 patients. Overall, 2500 patients were randomly selected for ground truth PAD status adjudication. In the end, 108 code flags remained after removing rarely- and never-used codes. We entered these code flags plus administrative encounter, imaging, procedure, and specialist flags into a LASSO model. The area under the curve for this model was 0.862.ConclusionsThe algorithm we constructed has two main advantages over other approaches to the identification of patients with PAD. First, it was derived from a broad population of patients with many different PAD manifestations and treatment pathways across a large health system. Second, our model does not rely on clinical notes and can be applied in situations in which only administrative billing data (eg, large administrative data sets) are available. A combination of diagnosis codes and administrative flags can accurately identify patients with PAD in large cohorts.

Project description:BackgroundHistorically, counting influenza recorded in administrative health outcome databases has been considered insufficient to estimate influenza attributable morbidity and mortality in populations. We used database record linkage to evaluate whether modern databases have similar limitations.MethodsPerson-level records were linked across databases of laboratory notified influenza, emergency department (ED) presentations, hospital admissions and death registrations, from the population (?6.9 million) of New South Wales (NSW), Australia, 2005 to 2008.ResultsThere were 2568 virologically diagnosed influenza infections notified. Among those, 25% of 40 who died, 49% of 1451 with a hospital admission and 7% of 1742 with an ED presentation had influenza recorded on the respective database record. Compared with persons aged ?65 years and residents of regional and remote areas, respectively, children and residents of major cities were more likely to have influenza coded on their admission record. Compared with older persons and admitted patients, respectively, working age persons and non-admitted persons were more likely to have influenza coded on their ED record. On both ED and admission records, persons with influenza type A infection were more likely than those with type B infection to have influenza coded. Among death registrations, hospital admissions and ED presentations with influenza recorded as a cause of illness, 15%, 28% and 1.4%, respectively, also had laboratory notified influenza. Time trends in counts of influenza recorded on the ED, admission and death databases reflected the trend in counts of virologically diagnosed influenza.ConclusionsA minority of the death, hospital admission and ED records for persons with a virologically diagnosed influenza infection identified influenza as a cause of illness. Few database records with influenza recorded as a cause had laboratory confirmation. The databases have limited value for estimating incidence of influenza outcomes, but can be used for monitoring variation in incidence over time.

Project description:Background. While often first treated in the emergency department (ED), identification of sepsis is difficult. Electronic medical record (EMR) clinical decision tools offer a novel strategy for identifying patients with sepsis. The objective of this study was to test the accuracy of an EMR-based, automated sepsis identification system. Methods. We tested an EMR-based sepsis identification tool at a major academic, urban ED with 64,000 annual visits. The EMR system collected vital sign and laboratory test information on all ED patients, triggering a "sepsis alert" for those with ?2 SIRS (systemic inflammatory response syndrome) criteria (fever, tachycardia, tachypnea, leukocytosis) plus ?1 major organ dysfunction (SBP ? 90 mm Hg, lactic acid ?2.0 mg/dL). We confirmed the presence of sepsis through manual review of physician, nursing, and laboratory records. We also reviewed a random selection of ED cases that did not trigger a sepsis alert. We evaluated the diagnostic accuracy of the sepsis identification tool. Results. From January 1 through March 31, 2012, there were 795 automated sepsis alerts. We randomly selected 300 cases without a sepsis alert from the same period. The true prevalence of sepsis was 355/795 (44.7%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value of the sepsis alert was 44.7% (95% CI [41.2-48.2%]). Pneumonia and respiratory infections (38%) and urinary tract infection (32.7%) were the most common infections among the 355 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (26.1%), traumatic (25.7%), and cardiovascular (20.0%) conditions. Rates of hospital admission were: true-positive sepsis alert 91.0%, false-positive alert 83.0%, no sepsis alert 5.7%. Conclusions. This ED EMR-based automated sepsis identification system was able to detect cases with sepsis. Automated EMR-based detection may provide a viable strategy for identifying sepsis in the ED.

Dataset Information

Use of administrative and electronic health record data for development of automated algorithms for childhood diabetes case ascertainment and type classification: the SEARCH for Diabetes in Youth Study.

Publications

Use of administrative and electronic health record data for development of automated algorithms for childhood diabetes case ascertainment and type classification: the SEARCH for Diabetes in Youth Study.

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