BACKGROUND: As emergency general surgery (EGS) evolves, an EGS patient-tracking database (EGS registry [EGSR]) similar to the National Trauma Data Bank (NTDB) will be essential to study outcomes and improve care. The goal of this study was to establish diagnostic ICD-9 codes to define EGS patients. The hypothesis was that creating standardized ICD-9-based inclusion criteria would facilitate patient identification for an EGSR and aid in its ongoing development. STUDY DESIGN: We conducted a retrospective review of EGS admissions over a 9-month period to define ICD-9 diagnostic codes of patients admitted to our EGS service. Subsequently, prospective data were collected into the EGSR by testing ICD-9-based inclusion criteria over 1 month. Patient, hospital, and severity scoring variables, as well as quality assurance information, were identified. RESULTS: We identified 959 admissions to the EGS service over 9 months with 306 ICD-9 diagnosis codes that define EGS patients; the prospective population of the EGSR confirmed feasibility of ICD-9-based inclusion criteria. The EGSR captures 107 data points and 33 comorbidities per patient over 11 categories, akin to the 10 NTDB categories. CONCLUSIONS: Following the model of the NTDB, we have successfully completed creation and initial implementation of an EGSR by using ICD-9-based inclusion criteria. Our comprehensive EGSR creates a prospective data-collection modality to capture and define EGS patients. A uniform patient-tracking EGSR, akin to the NTDB, will advance the science of acute care surgery, improve EGS patient outcomes, and facilitate multi-institutional collaboration.
BACKGROUND: As emergency general surgery (EGS) evolves, an EGS patient-tracking database (EGS registry [EGSR]) similar to the National Trauma Data Bank (NTDB) will be essential to study outcomes and improve care. The goal of this study was to establish diagnostic ICD-9 codes to define EGS patients. The hypothesis was that creating standardized ICD-9-based inclusion criteria would facilitate patient identification for an EGSR and aid in its ongoing development. STUDY DESIGN: We conducted a retrospective review of EGS admissions over a 9-month period to define ICD-9 diagnostic codes of patients admitted to our EGS service. Subsequently, prospective data were collected into the EGSR by testing ICD-9-based inclusion criteria over 1 month. Patient, hospital, and severity scoring variables, as well as quality assurance information, were identified. RESULTS: We identified 959 admissions to the EGS service over 9 months with 306 ICD-9 diagnosis codes that define EGS patients; the prospective population of the EGSR confirmed feasibility of ICD-9-based inclusion criteria. The EGSR captures 107 data points and 33 comorbidities per patient over 11 categories, akin to the 10 NTDB categories. CONCLUSIONS: Following the model of the NTDB, we have successfully completed creation and initial implementation of an EGSR by using ICD-9-based inclusion criteria. Our comprehensive EGSR creates a prospective data-collection modality to capture and define EGS patients. A uniform patient-tracking EGSR, akin to the NTDB, will advance the science of acute care surgery, improve EGS patient outcomes, and facilitate multi-institutional collaboration.
Authors: Mary O Whipple; Samantha J McAllister; Terry H Oh; Connie A Luedtke; Loren L Toussaint; Ann Vincent Journal: Clin Transl Sci Date: 2013-04-19 Impact factor: 4.689
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Authors: Heena P Santry; John C Madore; Courtney E Collins; M Didem Ayturk; George C Velmahos; L D Britt; Catarina I Kiefe Journal: J Trauma Acute Care Surg Date: 2015-01 Impact factor: 3.313
Authors: Robert D Becher; Andrew B Peitzman; Jason L Sperry; Jared R Gallaher; Lucas P Neff; Yankai Sun; Preston R Miller; Michael C Chang Journal: World J Emerg Surg Date: 2016-02-24 Impact factor: 5.469
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