Open-access programs for injury categorization using ICD-9 or ICD-10
Tóm tắt
The article introduces Programs for Injury Categorization, using the International Classification of Diseases (ICD) and R statistical software (ICDPIC-R). Starting with ICD-8, methods have been described to map injury diagnosis codes to severity scores, especially the Abbreviated Injury Scale (AIS) and Injury Severity Score (ISS). ICDPIC was originally developed for this purpose using Stata, and ICDPIC-R is an open-access update that accepts both ICD-9 and ICD-10 codes. Data were obtained from the National Trauma Data Bank (NTDB), Admission Year 2015. ICDPIC-R derives CDC injury mechanism categories and an approximate ISS (“RISS”) from either ICD-9 or ICD-10 codes. For ICD-9-coded cases, RISS is derived similar to the Stata package (with some improvements reflecting user feedback). For ICD-10-coded cases, RISS may be calculated in several ways: The “GEM” methods convert ICD-10 to ICD-9 (using General Equivalence Mapping tables from CMS) and then calculate ISS with options similar to the Stata package; a “ROCmax” method calculates RISS directly from ICD-10 codes, based on diagnosis-specific mortality in the NTDB, maximizing the C-statistic for predicting NTDB mortality while attempting to minimize the difference between RISS and ISS submitted by NTDB registrars (ISSAIS). Findings were validated using data from the National Inpatient Survey (NIS, 2015). NTDB contained 917,865 cases, of which 86,878 had valid ICD-10 injury codes. For a random 100,000 ICD-9-coded cases in NTDB, RISS using the GEM methods was nearly identical to ISS calculated by the Stata version, which has been previously validated. For ICD-10-coded cases in NTDB, categorized ISS using any version of RISS was similar to ISSAIS; for both NTDB and NIS cases, increasing ISS was associated with increasing mortality. Prediction of NTDB mortality was associated with C-statistics of 0.81 for ISSAIS, 0.75 for RISS using the GEM methods, and 0.85 for RISS using the ROCmax method; prediction of NIS mortality was associated with C-statistics of 0.75–0.76 for RISS using the GEM methods, and 0.78 for RISS using the ROCmax method. Instructions are provided for accessing ICDPIC-R at no cost. The ideal methods of injury categorization and injury severity scoring involve trained personnel with access to injured persons or their medical records. ICDPIC-R may be a useful substitute when this ideal cannot be obtained.
Tài liệu tham khảo
Annest JL, Hedegaard H, Chen L, Warner M, Smalls E. Proposed framework for presenting injury data using ICD-10-CM external cause of injury codes. 2014. http://www.cdc.gov/injury/wisqars/pdf/ICD-10-CM_External_Cause_Injury_Codes-a.pdf. Accessed 12 Mar 2018
Baker SP, O'Neill B, Haddon W Jr, Long WB. The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma. 1974;14:187–96.
Barell V, Aharonson-Daniel L, Fingerhut LA, et al. An introduction to the Barell body region by nature of injury diagnosis matrix. Inj Prev. 2002;8:91–6.
Boyd CR, Tolson MA, Copes WS. Evaluating trauma care: the TRISS method. J Trauma. 1987;27:370–8.
CDC. Recommended framework for presenting injury mortality data. MMWR Morb Mortal Wkly Rep. 1997;46:1–30.
Champion HR, Copes WS, Sacco WJ, et al. A new characterization of injury severity. J Trauma. 1990;30:539–45.
Champion HR, Sacco WJ. Trauma risk assessment: review of severity scales. Emergency Medicine Annual. 1983;2:43–71.
Charlson ME, Pompei P, Ales KL, CR MK. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83.
Clark DE, Ahmad S. Estimating injury severity using the Barell matrix. Inj Prev. 2006;12:111–116
Clark DE, Osler TM, Hahn DR. ICD Programs for Injury Categorization (ICDPIC), Version 3.0. 2010. http://ideas.repec.org/c/boc/bocode/s457028.html. Accessed 12 Mar 2018
Committee on Medical Aspects of Automotive Safety, AMA. Rating the severity of tissue damage. I. The abbreviated scale. JAMA. 1971;215:277–80.
Committee on Trauma, ACS. NTDB Research Data Set, Admission Year 2015. Chicago IL: American College of Surgeons. 2017.
Copes WS, Champion HR, Sacco WJ, Lawnick MM, Keast SL, Bain LW. The injury severity score revisited. J Trauma. 1988;28:69–77.
Copes WS, Champion HR, Sacco WJ, et al. Progress in characterizing anatomic injury. J Trauma. 1990;30:1200–7.
Deyo RA, Taylor VM, Diehr P, et al. Analysis of automated administrative and survey databases to study patterns and outcomes of care. Spine. 1994;19:2083S–91S.
Di Bartolomeo S, Tillati S, Valent F, Zanier L, Barbone F. ISS mapped from ICD-9-CM by a novel freeware versus traditional coding: a comparative study. Scand J Trauma Resusc Emerg Med. 2010;18:17.
Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36:8–27.
Fleischman RJ, Mann NC, Dai M, et al. Validating the use of ICD-9 code mapping to generate injury severity scores. J Trauma Nursing. 2017;24:4–14.
Glance LG, Osler TM, Mukamel DB, Meredith W, Wagner J, Dick AW. TMPM-ICD9: a trauma mortality prediction model based on ICD-9-CM codes. Ann Surg. 2009;249:1032–9.
Greene NH, Kernic MA, Vavilala MS, Rivara FP. Validation of ICDPIC software injury severity scores using a large regional trauma registry. Inj Prev. 2015;21:325–30.
Haas B, Xiong W, Brennan-Barnes M, Gomez D, Nathens AB. Overcoming barriers to population-based injury research: development and validation of an ICD10-to-AIS algorithm. Can J Surg. 2012;55:21–6.
Hartensuer R, Nikolov B, Franz D, Weimann A, Raschke M, Juhra C, Vergleich von ICD-10 und AIS mit der Entwicklung einer Methode zur automatisierten Umwandlung. Z Orthop Unfall. 2015;153:607–12.
Hedegaard H, Johnson RL, Warner M, Chen L, Annest JL. Proposed framework for presenting injury data using the International Classification of Disease(s), tenth revision, clinical modification (ICD-10-CM) diagnosis codes. Nat Health Stat Reports. 2016;89:1–19.
Levy PS, Mullner R, Goldberg J, Gelfand H. The estimated survival probability index of trauma severity. Health Serv Res. 1978;13:28–35.
Loftis KL, Price JP, Gillich PJ, et al. Development of an expert based ICD-9-CM and ICD-10-CM map to AIS 2005 update 2008. Traffic Inj Prev. 2016;17:S1–5.
MacKenzie EJ, Steinwachs DM, Shankar B. Classifying trauma severity based on hospital discharge diagnoses. Validation of an ICD-9CM to AIS-85 conversion table. Med Care. 1989;27:412–22.
Meredith JW, Evans G, Kilgo PD, et al. A comparison of the abilities of nine scoring algorithms in predicting mortality. J Trauma. 2002;53:621–9.
Osler T, Baker SP, Long WA. Modification of the injury severity score that both improves accuracy and simplifies scoring. J Trauma. 1997;43:922–5.
Osler T, Glance L, Buzas JS, Mukamel D, Wagner J, Dick A. A trauma mortality prediction model based on the anatomic injury scale. Ann Surg. 2008;247:1041–8.
Osler T, Rutledge R, Deis J, Bedrick E. ICISS: an International Classification of Disease(s) based injury severity score. J Trauma. 1996;41:380–6.
Paladugu R, Schein M, Gardezi S, Wise L. One hundred citation classics in general surgical journals. World J Surg. 2002;26:1099–105.
Phillips B, Clark DE, Nathens AB, Shiloach M, Freel AC. Comparison of injury patient information from hospitals with records in both the National Trauma Data Bank and the Nationwide Inpatient Sample. J Trauma. 2008;64:768–79.
Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130–9.
Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1993;46:1075–9.
Sears JM, Blanar L, Bowman SM. Predicting work-related disability and medical cost outcomes: a comparison of injury severity scoring methods. Injury. 2014;45:16–22.
Van Belleghem G, Devos S, De Wit L, et al. Predicting in-hospital mortality of traffic victims: a comparison between AIS-and ICD-9-CM-related injury severity scales when only ICD-9-CM is reported. Injury. 2016;47:141–6.