Open-access programs for injury categorization using ICD-9 or ICD-10.

BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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

A half-century of burn epidemiology and burn care in a rural state.

The aim of this study is to quantify the changes in incidence, severity, and mortality in burn injuries in the state of Maine over the past 50 years from both prevention and treatment perspectives. The authors analyzed the data from multiple sources, including the U.S. Census, death certificates, hospital discharge abstracts, and institutional burn registries in Maine and Boston. The average annual number of burn-related deaths decreased from 53 in 1960-1964 to 14 in 2004-2008. The Maine age-adjusted rate of burn deaths was 8.6% above the national rate in 1960 and 1.4% below it in 2006. The annual number of burn patients admitted to Maine hospitals declined by 65% from 1978 to 2009. Since 1999, 12% of hospitalized patients in Maine were treated in an American Burn Association-certified burn center in Boston. Mortality for Maine burn patients, including those treated at Boston hospitals, is directly related to age and burn severity and similar to stratified mortality in the National Burn Repository. Incidence, severity, and mortality of burn injuries in Maine have decreased dramatically over the past 5 decades. Prevention programs, legislation, and a regionalized system of burn care have all likely contributed to bringing Maine\u27s morbidity and mortality rate below the national average

Open-access programs for injury categorization using ICD-9 or ICD-10

Abstract Background 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. Methods 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). Results 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. Conclusions 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

The distribution of survival times after injury.

INTRODUCTION: The distribution of survival times after injury has been described as trimodal, but several studies have not confirmed this. The purpose of this study was to clarify the distribution of survival times after injury. METHODS: We defined survival time (t(s)) as the interval between injury time and declared death time. We constructed histograms for t(s) ≤ 150 min from the 2004-2007 Fatality Analysis Reporting System (FARS, for traffic crashes) and National Violent Death Reporting System (NVDRS, for homicides). We estimated statistical models in which death times known only within intervals were treated as interval-censored. For confirmation, we also obtained EMS response times (t(r)), prehospital times (t(p)), and hospital times (t(h)) for decedents in the 2008 National Trauma Data Bank (NTDB) with t(s) = t(p) + t(h) ≤ 150. We approximated times until circulatory arrest (t(x)) as t(r) for patients pulseless at the injury scene, t(p) for other patients pulseless at hospital admission, and t(s) for the rest; for any declared t(s), we calculated mean t(x)/t(s). We used this ratio to estimate t(x) for hospital deaths in FARS or NVDRS and provide independent support for using interval-censored methods. RESULTS: FARS and NVDRS deaths were most frequent in the first few minutes. Both showed a second peak at 35-40 min after injury, corresponding to peaks in hospital deaths. Third peaks were not present. Estimated t(x) in FARS and NVDRS did not show second peaks and were similar to estimates treating some death times as interval-censored. CONCLUSIONS: Increases in frequency of survival times at 35-40 min are primarily artifacts created because declaration of death in hospitals is delayed until completing resuscitative attempts. By avoiding these artifacts, interval censoring methods are useful for analysis of injury survival times

The Effect of Resident Participation on Appendectomy Operative Times.

OBJECTIVE: To assess the association between level of resident autonomy and operative times for appendectomies. DESIGN: A single center retrospective analysis of electronic medical record data of patients who underwent an appendectomy from 1/1/2017 to 12/31/2018. Medical record numbers s were matched with cases entered in the ACGME Resident Case Log system. Cases were stratified by resident role ( First Assistant, Surgeon Junior, Surgeon Chief, or Teaching Assistant ) and operative times were compared to cases without resident participation using student\u27s t test. SETTING: Maine Medical Center, Department of Surgery, Portland, Maine. PARTICIPANTS: Inclusion criteria: ≥5 years old, underwent appendectomy at a tertiary medical center during the study duration, and either had corresponding Case-log data or had no resident involvement. Patients who underwent appendectomy as part of a larger procedure were excluded. RESULTS: Six hundred eighty-eight patients met inclusion criteria, with residents participating in 574 (83.5%) cases. Overall mean operating time was 51 ± 21.5 minutes. Attending physicians without resident participation had the shortest OR times (43 ± 19.1 minutes). There was no difference in operating time between chief resident involvement and attending physicians without resident participation (45 ± 21; p = 0.43). Cases with residents involved as First Assistant (53 ± 18.6 minutes; p = 0.04) Surgeon Junior (52 ± 24.0 minutes; p \u3c 0.001), or Teaching Assistant (57 ± 21.6 minutes; p \u3c 0.001) were found to have longer operating times as compared to attending physicians operating without a resident. CONCLUSIONS: Operative times for appendectomies are impacted by resident role. Chief residents\u27 operative times approach that of attendings when operating as Surgeon Chief, however they are significantly longer when operating as Teaching Assistant. Involvement of junior residents in any role lengthen operating times. This suggests that surgical education influences operating room efficiency

Senior Thesis Collections

PLEASE NOTE: Where applicable, the audio has been removed from this file due to copyrighted material. The garments shown here were created in response to the Senior Thesis design challenge: create a complete collection that reflects the essence and philosophies of your personal vision