Implementation of automatic data extraction from an enterprise database warehouse (EDW) for validating pediatric VTE dec
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Implementation of automatic data extraction from an enterprise database warehouse (EDW) for validating pediatric VTE decision rule: a prospective observational study in a critical care population Rachael F. Schultz1 · Anjali Sharathkumar2 · Soyang Kwon3 · Karl Doerfer4 · George Lales5 · Rukhmi Bhat6
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Multiple clinical risk prediction tools for hospital acquired venous thromboembolism (HA-VTE) have been developed. The objectives of this study were to develop and assess the feasibility of data extraction from Electronic Medical Records (EMR) from an enterprise database warehouse (EDW) and to test the validity of a previously developed Pediatric Clot Decision Rule (PCDR). This single-center prospective observational cohort study was conducted between March 2016 and March 2017 and included eligible patients admitted to the intensive care units. Risk score was calculated using the PCDR tool. Sensitivity, specificity, positive and negative predicted value (PPV and NPV) were calculated based on a cut‐point of 3. A total of 2822 children were eligible for analysis and 5.1% (95% CI 4.2–6.2) children had a PCDR score of 3. Children with PCDR score of ≥ 3 had a 3 times higher odd of developing VTE compared to those with scores 21 years, n=116 N= 968 Number of eligible pa ents N= 2822
Total number of children without VTE N= 2718
Number of children with thrombosis N= 141
Excluded pa ents with arterial thrombosis N= 37 Total number of children with venous thrombosis N= 104
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was then added to allow interval, sequential data extraction for the prospective component of the study. Monitoring for data quality Throughout the study, data were manually checked by the investigators to ensure automatic import was accurate. Figure 1 illustrates the process map of building the research database. The functionality of calculating the PCDR score was built within the research database once the required variables were entered. Phase II and III: data collection Demographic and clinical data on all consecutive children were collected to generate a PCDR score [19]. Clinical data consisted of disease specific information and pertinent clinical variables. VTE events were identified by ICD-9 codes 451—phlebitis and thrombophlebitis, 452—portal vein thrombosis, 453—other venous embolism and thrombosis, 444—arterial embolism and thrombosis, 415.1—pulmonary embolism and infarction, 416.2—chronic pulmonary embolism, 434.0—cerebral thrombosis, 434.1—cerebral embolism, V12.51—personal history of venous thrombosis and embolism. Based on this information, following variables were automatically populated: age at admission, length of hospital stay, and if directly admitted to the critical care unit. It was recognized that few VTE events were missed as clinicians did not update the problem list. Additionally, data on immobility was difficult to capture objectively as it was a non-discrete variable and could not be automatically imported. These issues were addressed during p
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