Assessing risk of major adverse cardiac events among patients with chest pain and cocaine use using the HEART score.

INTRODUCTION: Chest pain (CP), a common presentation in the emergency department (ED) setting, is associated with significant morbidity and mortality if emergency clinicians miss the diagnosis of acute coronary syndrome (ACS). The HEART (History, Electrocardiogram, Age, Risk Factors, Troponin) score had been validated for risk-stratification patients who are at high risk for ACS and major adverse cardiac events (MACE). However, the use of cocaine as a risk factor of the HEART score was controversial. We hypothesized that patients with cocaine-positive (COP) would not be associated with higher risk of 30-day MACE than cocaine-negative (CON) patients. METHODS: This retrospective study included adult patients who presented to 13 EDs of a University's Medical System between August 7, 2017 to August 19, 2021. Patients who had CP and prospectively calculated HEART scores and urine toxicology tests as part of their clinical evaluation were eligible. Areas Under The Receiver Operating Curve (AUROC) were calculated for the performance of HEART score and 30-day MACE for each group. RESULTS: This study analyzed 46,210 patients' charts, 663 (1.4%) were COP patients. Mean age was statistically similar between groups but there were fewer females in the COP group (26.2% vs 53.2%, p < 0.001). Mean (+/- SD) HEART score was 3.7 (1.4) comparing to 3.1 (1.8, p < 0.001) between COP vs CON groups, respectively. Although more COP patients (54%) had moderate HEART scores (4-6) vs. CON group (35.2%, p < 0.001), rates of 30-day MACE were 1.1% for both groups. HEART score's AUROC was 0.72 for COP and 0.78 for CON groups. AUROC for the Risk Factor among COP patients, which includes cocaine, was poor (0.54). CONCLUSION: This study, which utilized prospective calculated HEART scores, demonstrated that overall performance of the HEART score was reasonable. Specifically, our analysis showed that the rate of 30-day MACE was not affected by cocaine use as a risk factor. We would recommend clinicians to consider the HEART score for this patient group.

Illicit Fentanyl Exposure Among Victims of Violence Treated at a Trauma Center.

INTRODUCTION: Opioid overdoses and violent injury are leading causes of death in the United States, yet testing for novel opioids like fentanyl remains uncommon. The purpose of this investigation is to characterize a population of victims of violence who test positive for illicit fentanyl. METHODS: Retrospective cohort study of patients treated at a level-one trauma center between January 31, 2019 and February 21, 2020. Data were extracted from the electronic medical record. Subjects were included if they had an encounter diagnosis for a violent or intentional injury, using the International Classification of Diseases, v10 (X92-Y09). We excluded patients who received licit fentanyl as a part of their care before testing. Those who tested positive for fentanyl exposure on our standard hospital urine drug screen were considered to have been exposed to illicit fentanyl. Those testing negative for fentanyl were considered controls. RESULTS: Of the 1132 patients treated for intentional injuries during the study period, 366 were included in the study (32.3%). Of these, 133 (36.3%) subjects were exposed to illicit fentanyl prehospital. There were no demographic differences between cases and controls. Cases had a lower GCS voice score on arrival (median = 4, interquartile range [IQR] = 4-5 versus median = 5, IQR = 4-5, P = 0.02), higher rates of ventilator usage (32.3% versus 21.5%, P = 0.02), and more intensive care unit admissions (27.1% versus 12.0%, P = 0.005). More than half of cases tested negative for opiates (78/133, 58.6%). Cases had more trauma center encounters (26.3% had ≥2 visits versus 15.5%). CONCLUSIONS: Exposure to illicit fentanyl was common among victims of violence in this single-center study. These patients are at increased risk of being admitted to intensive care units and repeated trauma center visits, suggesting fentanyl testing may help identify those who could benefit from violence prevention and substance abuse treatment.

Patterns of opioid prescribing in emergency departments during the early phase of the COVID-19 pandemic.

INTRODUCTION: The COVID-19 pandemic was superimposed upon an ongoing epidemic of opioid use disorder and overdose deaths. Although the trend of opioid prescription patterns (OPP) had decreased in response to public health efforts before the pandemic, little is known about the OPP from emergency department (ED) clinicians during the COVID-19 pandemic. METHODS: We conducted a pre-post study of adult patients who were discharged from 13 EDs and one urgent care within our academic medical system between 01/01/2019 and 09/30/2020 using an interrupted time series (ITS) approach. Patient characteristics and prescription data were extracted from the single unified electronic medical record across all study sites. Prescriptions of opioids were converted into morphine equivalent dose (MED). We compared the "Covid-19 Pandemic" period (C19, 03/29/2020-9/30/2020) and the "Pre-Pandemic" period (PP, 1/19/2020-03/28/2020). We used a multivariate logistic regression to assess clinical factors associated with opioid prescriptions. RESULTS: We analyzed 361,794 ED visits by adult patients, including 259,242 (72%) PP and 102,552 (28%) C19 visits. Demographic information and percentages of patients receiving opioid prescriptions were similar in both groups. The median [IQR] MED per prescription was higher for C19 patients (70 [56-90]) than for PP patients (60 [60-90], P < 0.001). ITS demonstrated a significant trend toward higher MED prescription per ED visit during the pandemic (coefficient 0.11, 95% CI 0.05-0.16, P = 0.002). A few factors, that were associated with lower likelihood of opioid prescriptions before the pandemic, became non-significant during the pandemic. CONCLUSION: Our study demonstrated that emergency clinicians increased the prescribed amount of opioids per prescription during the COVID-19 pandemic compared to the pre-pandemic period. Etiologies for this finding could include lack of access to primary care and other specialties during the pandemic, or lower volumes allowing for emergency clinicians to identify who is safe to be prescribed opioids.

Early Prediction of Intensive Care Admission in Emergency Department Patients With Asthma.

BACKGROUND: Emergency physicians must choose whether patients with asthma are admitted to a hospital ward or a higher level of care, such as an intermediate care unit (IMC) or intensive care unit (ICU). OBJECTIVE: This study aimed to determine which variables, available early during emergency department (ED) visits, are associated with IMC/ICU admission. METHODS: In this retrospective chart review (records from 2015-2018), two trained abstractors, blinded to study hypothesis, abstracted data on predictor variables and disposition (ward vs. IMC/ICU). Predictor variables were defined explicitly and abstracted from the periods of ED arrival and after treatment with 7.5 mg nebulized albuterol. Distress was defined as tripod positioning or speaking in broken sentences. "Arrival" and "after treatment" scoring systems were derived based on adjusted odds ratios (aOR) for predictor variables. We performed analyses using SASⓇ, version 9.4 (SAS Institute). RESULTS: Among 273 patients, 105 required admission to an IMC/ICU. At presentation, distress (aOR 2.1, 95% confidence interval [CI] 1.1-3.9), room air SpO2 ≥95% (aOR 0.29, 95% CI 0.14-0.62), respiratory rate > 20 breaths/min (aOR 1.9, 95% CI 1.0-3.3), and retractions (aOR 1.9, 95% CI 1.1-3.3) were associated with IMC/ICU admission. After initial bronchodilator therapy, heart rate > 120 beats/min (aOR 7.1, 95% CI 2.0-25), room air SpO2 ≥ 95% (aOR 0.15, 95% CI 0.07-0.34), and noninvasive ventilation (aOR 6.5, 95% CI 2.5-17) were associated with IMC/ICU admission. Both scoring systems stratified risk of IMC/ICU admission into low-risk (9-10%) and high-risk (70-100%) groups. CONCLUSIONS: Combinations of predictor variables, available early in a patient's stay, stratify risk of admission to an IMC/ICU bed.

Comparison of 4 tests' utility for predicting need for emergency department care in patients with alcohol-related complaints.

INTRODUCTION: Intoxication is a common presenting complaint in emergency departments (ED), but many patients with intoxication do not need emergency care. Three screens (BLINDED, Brown, and San Francisco) attempt to determine which intoxicated patients can be triaged to a lower level of care. METHODS: Observational multi-center cohort study of patients presenting to one of three ED with complaints consistent with acute intoxication. When a qualifying patient was brought to the emergency department, a team member interviewed the triaging provider. Interviews covered all three screens and the provider's gestalt. Receiver operating curve (ROC) analysis was used to determine which screen performed best. Cases were reviewed to determine need for emergency care. RESULTS: Of the 199 subjects studied, 91% (181/199) were male and were 50 years old on average (SD = 12 years). Of the 55 subjects tested (28%), their average alcohol level was 251 mg/dL (SD = 146 mg/dL). Only 117 subjects (59%) had complete information for inclusion in the final comparison of screens. Provider gestalt performed best (AUC = 0.69), but there were no meaningful differences between any of the screens (AUC = 0.62-0.66, p > 0.05 for all comparisons). Inability to sit up was sensitive for needing emergent care (88%), but it was non-specific (17%). Similarly, signs of trauma were specific (99%) for ED care, but insensitive (18%). CONCLUSIONS: The three formal screens and provider gestalt performed similarly.

Emergency Department Asthma "Spacing Trials": Institutional Variability and Time Cost.

BACKGROUND: Some admitting physicians request a medication-free interval ("spacing trial") in the emergency department (ED) to determine whether a patient with an acute exacerbation of asthma can be safely admitted to a hospital ward bed, where bronchodilators are only available every 4 h. OBJECTIVE: Our objectives were to estimate the frequency of ED spacing trials in different hospitals and their associated time cost. METHODS: This multicenter retrospective cohort study examined patients admitted for asthma from 2015 to 2018. We included all university records and a random sample of records from two community hospitals in the same urban area. Two team members abstracted data from each record using recommended methods, with group consensus to resolve differences. Proportion confidence intervals were calculated using normal binomial approximation. We calculated mean differences in ED stay associated with spacing trials, using multivariable linear regression to adjust for age, hospital type, history of intubation, initial pulse, initial respiratory rate, initial signs of distress. RESULTS: We collected data from 274 patients in the university hospital, and 71 and 70 cases from the community hospitals. An explicit spacing trial was noted in 52 of 274 (19%) university hospital records vs. 3 of 141 (2%) community hospital records, with a difference of 17% (95% confidence interval [CI] 11-23%). Delayed patient decompensation occurred in 3%, with no difference between hospitals. Spacing trials were associated with an adjusted mean of 159 min (95% CI 102-217 min) increase in ED stay. CONCLUSIONS: The practice of spacing varies widely between hospitals and is associated with substantial delay without an apparent benefit.

National Early Warning Score Is Modestly Predictive of Care Escalation after Emergency Department-to-Floor Admission.

BACKGROUND: Decompensation on the medical floor is associated with increased in-hospital mortality. OBJECTIVE: Our aim was to determine the accuracy of the National Early Warning Score (NEWS) in predicting early, unplanned escalation of care in patients admitted to the hospital from the emergency department (ED) compared to the Shock Index (SI) and the quick Sepsis-Related Organ Failure Assessment (qSOFA) score. METHODS: We conducted a retrospective cohort study of patients admitted directly from the ED to monitored or unmonitored beds (November 9, 2015 to April 30, 2018) in 3 hospitals. Interhospital transfers were excluded. Patient data, vital status, and bed assignment were extracted from the electronic medical record. Scores were calculated using the last set of vital signs prior to leaving the ED. Primary endpoint was in-hospital death or placement in an intermediate or intensive care unit within 24 h of admission from the ED. Scores were compared using the area under the receiver operating curve (AUROC). RESULTS: Of 46,018 ED admissions during the study window, 39,491 (85.8%) had complete data, of which 3.7% underwent escalation in level of care within 24 h of admission. NEWS outperformed (AUROC 0.69; 95% confidence interval [CI] 0.68-0.69) qSOFA (AUROC 0.63; 95% CI 0.62-0.63; p < 0.001) and SI (AUROC 0.60; 95% CI 0.60-0.61; p < 0.001) at predicting unplanned escalations or death at 24 h. CONCLUSIONS: This multicenter study found NEWS was superior to the qSOFA score and SI in predicting early, unplanned escalation of care for ED patients admitted to a general medical-surgical floor.

Repeat lactate level predicts mortality better than rate of clearance.

BACKGROUND: Lactate clearance has been developed into a marker of resuscitation in trauma, but no study has compared the predictive power of the various clearance calculations. Our objective was to determine which method of calculating lactate clearance best predicted 24-hour and in-hospital mortality after injury. STUDY DESIGN: Retrospective chart review of patients admitted to a Level-1 trauma center directly from the scene of injury from 2010 to 2013 who survived >15min, had an elevated lactate at admission (≥3mmol/L), followed by another measurement within 24h of admission. Lactate clearance was calculated using five models: actual value of the repeat level, absolute clearance, relative clearance, absolute rate, and relative rate. Models were compared using the areas under the respective receiver operating curves (AUCs), with an endpoint of death at 24h and in-hospital mortality. RESULTS: 3910 patients had an elevated admission lactate concentration on admission (mean=5.6±3.0mmol/L) followed by a second measurement (2.7±1.8mmol/L). Repeat absolute measurement best predicted 24-hour (AUC=0.85, 95% CI: 0.84-0.86) and in-hospital death (AUC=0.77; 95% CI, 0.76-0.78). Relative clearance was the best model of lactate clearance (AUC=0.77, 95% CI: 0.75-0.78 and AUC=0.705, 95% CI: 0.69-72, respectively) (p<0.0001 for each). A sensitivity analysis using a range of initial lactate measures yielded similar results. CONCLUSIONS: The absolute value of the repeat lactate measurement had the greatest ability to predict mortality in injured patients undergoing resuscitation.

Anomaly Detection Outperforms Logistic Regression in Predicting Outcomes in Trauma Patients.

OBJECTIVE: Recent advancements in trauma resuscitation have shown a great benefit of early identification and control of hemorrhage, which is the most common cause of death in injured patients. We introduce a new analytical approach, anomaly detection (AD), as an alternative method to the traditional logistic regression (LR) method in predicting which injured patients receive transfusions, intensive care, and other interventions. METHODS: We abstracted routinely collected prehospital vital sign data from patient records (adult patients who survived more than 15 minutes after being directly admitted to a level 1 trauma center). The vital signs of the study cohort were analyzed using both LR and AD methods. Predictions on blood transfusions generated by these approaches were compared with hospital records using the respective areas under the receiver operating characteristic curves (AUROC). RESULTS: Of the patients seen at our trauma center between January 1, 2009, and December 31, 2010, 5,464 were included. AD significantly outperformed LR, identifying which patients would receive transfusions of uncrossmatched blood, transfusion of blood between the time of admission and 6 hours later, the need for intensive care, and in-hospital mortality (mean AUROC = 0.764 and 0.720, respectively). AD and LR provided similar predictions for the patients who would receive massive transfusion. Under the stratified 10 fold times 10 cross-validation test, AD also had significantly lower AUROC variance across subgroups than LR, suggesting AD is a more stable predictions model. CONCLUSIONS: AD provides enhanced predictions for clinically relevant outcomes in the trauma patient cohort studied and may assist providers in caring for acutely injured patients in the prehospital arena.

Quarterly Reporting of Computed Tomography Ordering History Reduces the Use of Imaging in an Emergency Department.

BACKGROUND: Computed tomography (CT) is a useful and necessary part of many emergency department (ED) assessments. However, the costs of imaging and the health risks associated with radiation exposure have sparked national efforts to reduce CT ordering in EDs. STUDY OBJECTIVE: We analyzed CT ordering habits prior to and following implementation of a feedback tool at a community hospital. METHODS: In this intervention study, we identified the CT-ordering habits of physicians and mid-level care providers (physician assistants and nurse practitioners) at baseline and after implementation of a system that sent quarterly feedback reports comparing their ordering habits with those of their peers. Variability in ordering and subgroup analyses by body region were included in these reports. RESULTS: We examined the records of 104,454 patients seen between October 1, 2013 and December 31, 2014. There were 5552 or 21.0% of patients seen during the baseline period that underwent CT imaging. We observed an absolute reduction in imaging of 2.3% (95% confidence interval 1.7-2.8%) after implementation, avoiding approximately $400,000 in costs, 22 days of scanning time, and radiation exposure equivalent to 33,000 chest films annually. These changes occurred across physicians and mid-level providers, regardless of the number years of practice or board certification. CONCLUSIONS: Implementation of a feedback mechanism reduced CT use by emergency medicine practitioners, with concomitant reductions in cost and radiation exposure. The change was similar across levels of medical care. Future studies will examine the effect of the feedback reporting system at other institutions in our hospital network.

Computer Modelling Using Prehospital Vitals Predicts Transfusion and Mortality.

OBJECTIVE: Test computer-assisted modeling techniques using prehospital vital signs of injured patients to predict emergency transfusion requirements, number of intensive care days, and mortality, compared to vital signs alone. METHODS: This single-center retrospective analysis of 17,988 trauma patients used vital signs data collected between 2006 and 2012 to predict which patients would receive transfusion, require 3 or more days of intensive care, or die. Standard transmitted prehospital vital signs (heart rate, blood pressure, shock index, and respiratory rate) were used to create a regression model (PH-VS) that was internally validated and evaluated using area under the receiver operating curve (AUROC). Transfusion records were matched with blood bank records. Documentation of death and duration of intensive care were obtained from the trauma registry. RESULTS: During the course of their hospital stay, 720 of the 17,988 patients in the study population died (4%), 2,266 (12.6%) required at least a 3-day stay in the intensive care unit (ICU), 1,171 (6.5%) required transfusions, and 210 (1.2%) received massive transfusions. The PH-VS model significantly outperformed any individual vital sign across all outcomes (average AUROC = 0.82), The PH-VS model correctly predicted that 512 of 777 (65.9%) and 580 of 931 (62.3%) patients in the study population would receive transfusions within the first 2 and 6 hours of admission, respectively. CONCLUSIONS: The predictive ability of individual vital signs to predict outcomes is significantly enhanced with the model. This could support prehospital triage by enhancing decision makers' ability to match critically injured patients with appropriate resources with minimal delays.

The Severity of Bleeding and Mortality in Trauma Patients Taking Dabigatran.

BACKGROUND: Dabigatran, a direct thrombin inhibitor, has been shown to be more effective than warfarin in the prevention of stroke in patients with atrial fibrillation. Until recently, it lacked a reversal agent, and its contribution to the risk of transfusion in injured patients is unknown. OBJECTIVE: We sought to determine whether patients who sustain traumatic injuries while taking dabigatran receive more blood transfusions than matched patients taking warfarin, aspirin, clopidogrel, or controls. METHODS: This retrospective, single-center cohort consisted of injured patients who were taking dabigatran before admission to a major trauma center (January 2010-December 2013) who were compared with cohorts of patients taking warfarin, clopidogrel, or aspirin and a control group. The outcome was bleeding risk as measured by the use of blood products, with mortality as a secondary outcome. Outcomes were controlled for by age, sex, injury severity, and blunt mechanism. RESULTS: Thirty-eight patients were taking dabigatran. Compared with the general trauma population, patients taking dabigatran were more likely to be male, older, and to have higher injury severity. Patients taking dabigatran received transfusions (odds ratio [OR] 1.31 [95% confidence interval {CI} 0.56-3.04]), packed red blood cells (OR 1.43 [95% CI 0.54-3.77]), frozen plasma (OR 1.20 [95% CI 0.42-3.49]), and platelets (OR 2.01 [95% CI 0.63-6.37]) as often as matched controls. The mortality rate among patients on dabigatran was 12.5% (OR 1.51 [95% CI 0.39-5.89]) compared with 9.1% in matched controls. None of these results was statistically significant. CONCLUSIONS: In this small study, injured patients taking dabigatran were transfused as often and had similar in-hospital mortality as matched controls who were not taking anticoagulants.

The impact of a point-of-care visual clinical decision support tool on admissions for cellulitis in the University of Maryland medical system.

Introduction: Cellulitis is commonly diagnosed in emergency departments (EDs), yet roughly one third of ED patients admitted for presumed cellulitis have another, usually benign, condition instead (eg, stasis dermatitis). This suggests there is an opportunity to reduce health care resource use through improved diagnosis at the point of care. This study seeks to test whether a clinical decision support (CDS) tool interoperable with the electronic medical record (EMR) can reduce inappropriate hospital admissions and drive more appropriate and accurate care. Methods: This study was a trial of an EMR-interoperable, image-based CDS tool for evaluation of ED patients with suspected cellulitis. At the point of assigning a provisional diagnosis of cellulitis in the EMR, the clinician was randomly prompted to use the CDS. Based on the patient features entered into the CDS by the clinician, the CDS provided the clinician a list of likely diagnoses. The following were recorded: patient demographics, disposition and final diagnosis of patients, and whether antibiotics were prescribed. Logistic regression methods were used to determine the impact of CDS engagement on our primary outcome of admission for cellulitis, adjusted for patient factors. Antibiotic use was a secondary end point. Results: From September 2019 to February 2020 (or 7 months), the CDS tool was deployed in the EMR at 4 major hospitals in the University of Maryland Medical System. There were 1269 encounters for cellulitis during the study period. The engagement with the CDS was low (24.1%, 95/394), but engagement was associated with an absolute reduction in admissions (7.1%, p = 0.03). After adjusting for age greater than 65 years, female sex, non-White race, and private insurance, CDS engagement was associated with a significant reduction of admissions (adjusted OR = 0.62, 95% confidence interval (CI): 0.40-0.97, p = 0.04) and antibiotic use (Adjusted OR = 0.63, 95% CI: 0.40-0.99, p = 0.04). Conclusions: CDS engagement was associated with decreased admissions for cellulitis and decreased antibiotic use in this study, despite low levels of CDS engagement. Further research should examine the impact of CDS engagement in other practice environments and measure longer-term outcomes in patients discharged from the ED.

Assessing risk of major adverse cardiac event among COVID-19 patients using HEART score.

Coronavirus disease 2019 (COVID-19) is known to be associated with cardiovascular complications, but whether the current validated HEART score for chest pain is still applicable for these patients is unknown. This study aims to identify the impact and association of COVID-19 co-infection in patients presenting with chest pain and a calculated HEART score to the emergency departments (ED) with 30-day of major adverse cardiac event (MACE). This is a multicenter, retrospective observational study that included adult (age ≥ 18 years) patients visiting 13 different EDs with chest pain and evaluated using a HEART score. The primary outcome was the percentage of 30-day MACE, which included acute myocardial infarction, emergency percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG), or death among patients who presented with chest pain and had COVID-19 co-infection. The sensitivity and specificity of the HEART score among COVID-19 co-infection for MACE were assessed by the receiver operating curve (ROC). We analyzed records of 46,210 eligible patients, in which 327 (0.7%) patients were identified as infected with COVID-19. Patients with COVID-19 had higher mean total HEART score of 3.3 (1.7), compared to patients who did not have COVID-19 (3.1, SD 1.8, P = 0.048). The rate of MACE was similar between both groups. There were only 2 (0.6%) COVID-19 patients who had MACE, compared to 504 (1.1%) patients in control group. Total HEART score was associated with an area under the ROC (AUROC) of 0.99, while the control group's was 0.78. History was associated with high AUROC in both COVID-19 (0.74) and control groups (0.76). Older age in COVID-19 had higher AUROC (0.89) than control patients (0.63). Among patients presenting to the ED with chest pain and having COVID-19 infection, HEART score had predictive capability for MACE, similar to patients without COVID-19 infection. Further studies with more COVID-19 patients are still necessary to confirm our observation.

The actual, long-term cost of intentional injury care among a cohort of Maryland Medicaid recipients.

BACKGROUND: Intentional injury (both self-harm and interpersonal) is a major cause of morbidity and mortality, yet there are little data on the per-person cost of caring for these patients. Extant data focus on hospital charges related to the initial admission but does not include actual dollars spent or follow-up outpatient care. The Affordable Care Act has made Medicaid the primary payor of intentional injury care (39%) in the United States and the ideal source of cost data for these patients. We sought to determine the total and per-person long-term cost (initial event and following 24 months) of intentional injury among Maryland Medicaid recipients. METHODS: Retrospective cohort study of Maryland Medicaid claims was performed. Recipients who submitted claims after receiving an intentional injury, as defined by the International Classification of Diseases, Tenth Revision, between October 2015 and October 2017, were included in this study. Subjects were followed for 24 months (last participant enrolled October 2017 and followed to October 2019). Our primary outcome was the dollars paid by Medicaid. We examined subgroups of patients who harmed themselves and those who received repeated intentional injury. RESULTS: Maryland Medicaid paid $11,757,083 for the care of 12,172 recipients of intentional injuries between 2015 and 2019. The per-person, 2-year health care cost of an intentional injury was a median of $183 (SD, $5,284). These costs were highly skewed: min, $2.56; Q1 = 117.60, median, $182.80; Q3 = $480.82; and max, $332,394.20. The top 5% (≥95% percentile) required $3,000 (SD, $6,973) during the initial event and $8,403 (SD, $22,024) per served month thereafter, or 55% of the overall costs in this study. CONCLUSION: The long-term, per-person cost of intentional injury can be high. Private insurers were not included and may experience different costs in other states. LEVEL OF EVIDENCE: Economic and Value Based Evaluations; level III.

58-year-old Male with a Headache, Hand Numbness, and Phantosmia.

INTRODUCTION: A 58-year-old male presents to the emergency department with headache, hand numbness, and phantosmia. CASE PRESENTATION: Magnetic resonance imaging showed multiple acute and early subacute lesions involving the cortex and subcortical white matter of the left frontal lobe, left parietal lobe, left temporal lobe, left caudate, and left putamen. DISCUSSION: This case takes the reader through the subtle findings that led to the diagnosis and ultimately to treatment.

Coding Disparity and Specificity during Emergency Department Visits after Transitioning to the Tenth Version of the International Classification of Diseases.

The purpose of this study was to examine coding changes using the International Classification of Diseases (ICD) after the transition from ICD-9 to ICD-10. We studied a national cohort of emergency department visits from the Veterans Health Administration (VHA) before and after the transition, focusing on coding disparity and coding specificity. The cohort accounted for 2 million emergency department visits by 1.2 million patients. There were no statistical differences between the groups with respect to demographics, comorbidities, diagnoses, or use of medical services. While ICD-10 offered significantly more codes as well as more specific coding options, the ICD-10 encounters continued to use a small number of codes, were less likely to use multiple codes, and did not consistently exploit the more unique codes to create more specific diagnoses. These findings within the VHA system corresponded to similar challenges that have been documented with Medicare claims and in the private sector.