Outcomes In Emergency Department Patients Research Articles

Can Artificial Intelligence Be Utilized to Predict Real-Time Adverse Outcomes in Individuals Arriving at the Emergency Department With Hyperglycemic Crises?: Implications for APRN Practice.

This column on translating research into practice is crafted to offer advanced practice registered nurses an analysis of current research topics that hold practical relevance for emergency care settings. The article titled "Using Artificial Intelligence to Predict Adverse Outcomes in Emergency Department Patients With Hyperglycemic Crises in Real Time," authored by C. Hsu etal. (2023), investigates through a randomized control trial, the effectiveness of artificial intelligence as a practical tool compared with the traditional predicting hyperglycemic crisis death score to clinically predict adverse outcomes in individuals presenting to the emergency department with hyperglycemic crises. The results are discussed in the context of averting adverse outcomes associated with sepsis/septic shock, intensive care unit admission, and all-cause mortality within a 1-month time frame.

Incidence and outcomes of emergency department patients requiring emergency general surgery: a 5-year retrospective cohort study.

Patients undergoing emergency general surgery are at high risk of complications and death. Our objectives were to estimate the incidence of emergency general surgery in a Swiss University Hospital, to describe the characteristics and outcomes of patients undergoing such procedures, and to study the impact of age on clinical outcomes. This was a retrospective cohort study of adult patients who visited the emergency department (ED) of Geneva University Hospitals between January 2015 and December 2019. Routinely collected data were extracted from electronic medical records. The primary outcome was the incidence of emergency general surgery among patients visiting the emergency department, defined as general surgery within three days of emergency department admission. We also assessed demographic characteristics, mortality, intensive care unit admission and patient disposition. Multivariable log-binomial regression was used to study the associations of age with intensive care unit (ICU) admission, one-year mortality and dependence at discharge. Age was modelled as a continuous variable using restricted cubic splines and we compared older patients (75thpercentile) with younger patients (25thpercentile). Between January 2015 and December 2019, a total of 310,914 emergency department visits met our inclusion criteria. Among them, 3592 patients underwent emergency general surgery within 3 days of emergency department admission, yielding an annual incidence of 116 events per 10,000 emergency department visits (95% CI: 112-119), with a higher incidence in females and young patients. Overall, 5.3% of patients were admitted to ICU, 7.8% were dependent on rehabilitation or assisted living at discharge and 4.8% were dead after one year. Older patients had a higher risk of ICU admission (adjusted risk ratio (aRR) 2.9 [1.5-5.4]), dependence at discharge (aRR 15.3 [5.5-42.4]) and one-year mortality (aRR 5.4 [2.2-13.4]). Emergency department visits resulting in emergency general surgery are frequent, but their incidence decreases with patient age. Mortality, ICU admission and dependence at discharge following emergency general surgery are more frequent in older patients. Taking into account the increased risk for older patients, a shared process is appropriate for making more informed decisions about their options for care.

Radiologists predict differential resource utilization but not clinical outcome in emergency department patients imaged with ultrasound for right upper quadrant pain.

Radiologists with diverse training, specialization, and habits interpret imaging in the Emergency Department. It is necessary to understand if their variation predicts differential value. The purpose of this study was to determine whether attending radiologist variation predicts major clinical outcomes in adult Emergency Department patients imaged with ultrasound for right upper quadrant pain. Consecutive ED patients imaged with ultrasound for RUQ pain from 10/8/2016 to 8/10/2022 were included (N = 7097). The primary outcome was prediction of hospital admission by signing attending radiologist. Secondary outcomes included: ED and hospital length of stay (LOS), 30-day mortality, 30-day re-presentation rate, subspecialty consultation, advanced imaging follow up (HIDA, MRI, CT), and intervention (ERCP, drainage or surgery). Sample size was determined a priori (detectable effect size: w = 0.06). Data were adjusted for demographic data, Elixhauser comorbidities, number of ED visits in prior year, clinical data, and system factors (38 covariates). P-values were corrected for multiple comparisons (false discovery rate-adjusted p-values). The included ultrasounds were read by 35 radiologists (median exams/radiologist: 145 [74.5-241.5]). Signing radiologist did not predict hospitalization (p = 0.85), abdominopelvic surgery or intervention within 30 days, re-presentation to the Emergency Department within 30 days, or subspecialty consultation. Radiologist did predict difference in Emergency Department length of stay (p < 0.001) although this difference was small and imprecise. HIDA was mentioned variably by radiologists (range 0-19%, p < 0.001), and mention of HIDA in the ultrasound report increased 10-fold the odds of HIDA being performed in the next 72h (odds ratio 10.4 [8.0-13.4], p < 0.001). Radiologist variability did not predict meaningful outcome differences for patients with right upper quadrant pain undergoing ultrasound in the Emergency Department, but when radiologists mention HIDA in their reports, it predicts a 10-fold increase in the odds a HIDA is performed. Radiologists are relied on for interpretation that shapes subsequent patient care, and it is important to consider how radiologist variability can influence both outcome and resource utilization.

895. Multisite Derivation and Validation of Machine Learning Models to Predict Severe Influenza Outcomes in Emergency Department Patients

Abstract Background Millions of Americans are infected by influenza each year. Most experience mild and self-limited illness but a minority progress to severe disease and death. Emergency departments (ED) serve as a frontline for the US health system and must distinguish those at risk for clinical deterioration from those who can be safely discharged to the community. Methods We developed random forest machine learning (ML) models to estimate risk for two severe outcomes in ED patients with influenza: need for inpatient care within 72 hours and need for critical care within 24 hours. Both were defined by multidisciplinary consensus using markers of cardiovascular and respiratory dysfunction captured in the electronic health record (EHR). Predictor data were limited to those recorded prior to ED disposition decision (i.e., discharge, hospitalize) and included patient demographics, ED complaint, active medical problems, vital signs and trends, supplemental oxygen use and selected laboratory results. Our cohort was comprised of adult patients who visited one of five EDs in our university health system between 01/01/2017 and 05/18/2022. A development cohort comprised of encounters that occurred before December 2019 was used for model derivation and 10-fold out of sample validation; models were externally validated in the cohort of encounters that occurred afterward. Results Among 8,032 patients with confirmed influenza, incidence of critical care needs at 24 hours was 6.2% (n = 502) and incidence of inpatient care needs at 72 hours was 16.1% (n = 1295). The most common reasons for ED visit were symptoms of upper or lower respiratory tract infection, fever, and shortness of breath. During external validation, models exhibited excellent predictive performance with AUCs of 0.90 (0.87 - 0.94) for critical and 0.89 (95% CI 0.86 -0.92) for inpatient care needs; both were well-calibrated (Brier scores 0.025 and 0.036). Important predictors of severe disease included shortness of breath, increasing respiratory rate and a high number of comorbid diseases. Predictive Model Performance During External Validation. Random forest models predicted risk for critical care needs within 24 hours (red) and inpatient care needs within 72 hours (blue). Overall predictive performance is shown using receiver operating characteristic curves with area under the curve (AUC) in panels A and B; model calibration curves with Brier scores are shown in C and D; predicted probability distributions are shown with kernel density estimation plots in E and F. Conclusion These findings demonstrate that EHR data and ML can be used to risk-stratify patients with influenza early in their hospital course. An important future aim is to integrate ML-driven predictions into clinical decision support at the point of care. Disclosures Jeremiah S. Hinson, MD, PhD, Beckman Coulter: Advisor/Consultant|Beckman Coulter: Grant/Research Support|Beckman Coulter: TriageGO Katherine ZJ Fenstermacher, PhD, Cepheid: Grant/Research Support Richard E. Rothman, MD, PhD, CEPHEID: Advisor/Consultant|Cepheid: Grant/Research Support|Danaher: Grant/Research Support Oluwakemi Badaki-Makun, MD, PhD, Beckman Coulter: Grant/Research Support Scott Levin, PhD, Beckman Coulter: Licenses|Beckman Coulter: Employment|Beckman Coulter: Stocks/Bonds

Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule.

Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect. Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP). The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817). The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.

Reducing physical restraints in pediatrics: A quality improvement mixed-methods analysis of implementing a clinical debriefing process after behavioural health emergencies in a Children's Hospital

IntroductionAn increasing number of pediatric patients with mental and behavioral health (MBH) conditions present to Emergency Department (ED) and inpatient settings with behavioral events that require physical restraint (PR). PR usage is associated with adverse outcomes. Clinical debriefing (CD) programs have been associated with improved performance but have not been studied in this population. After implementing an MBH-CD program in our Children's Hospital, we aimed to decrease the baseline (7/2018-3/2021) rate of a second PR episode (2PR) by 50 % in the ED and inpatient settings over two years. MethodsA multidisciplinary team implemented an MBH-CD process in April 2021 for hospital teams to use immediately after behavioral events. We included patients ≤18 years old, with an ED or inpatient discharge MBH diagnosis, between July 2018 and June 2023. Pre- and post-implementation secondary outcomes included the ED median duration of PR and the ED PR time per 1000 h of ED care. ED and inpatient mean length of stay (LOS) and mean monthly visits (MMV) in pre- and post-implementation were also compared. Qualitative analysis identified major themes. ResultsPost-implementation, the ED significantly decreased 2PR rate by 67 %; in inpatients, no significant change was demonstrated. Median duration of ED PR decreased from 112 to 71 min (p = 0.006) and ED PR time significantly decreased by 82 % (14.8 to 2.7 h per 1000 h). In the post-implementation period, mean LOS (ED and inpatient) and MMV (ED only) were significantly higher. Fifty-one percent of 494 behavioral alerts were debriefed. Median debriefing duration was 6 min (IQR 4,10). Common themes included cooperation and coordination (23 %) and clinical standards (14 %). DiscussionClinical debriefing implementation was associated with significant improvement in ED patient outcomes. Inpatient outcomes were unchanged, but debriefings in both settings should enable frontline teams to continuously identify opportunities to improve future outcomes.

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time

BackgroundHyperglycemic crises are associated with high morbidity and mortality. Previous studies have proposed methods to predict adverse outcomes of patients in hyperglycemic crises; however, artificial intelligence (AI) has never been used to predict adverse outcomes. We implemented an AI model integrated with the hospital information system (HIS) to clarify whether AI could predict adverse outcomes.MethodsWe included 2,666 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. The patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from the electronic medical records were collected. The performance of the multilayer perceptron (MLP), logistic regression, random forest, Light Gradient Boosting Machine (LightGBM), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms was compared. We selected the best algorithm to construct an AI model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. The outcomes between the non-AI and AI groups were compared after implementing the HIS and predicting the hyperglycemic crisis death (PHD) score.ResultsThe MLP had the best performance in predicting the three adverse outcomes, compared with the random forest, logistic regression, SVM, KNN, and LightGBM models. The areas under the curves (AUCs) using the MLP model were 0.852 for sepsis or septic shock, 0.743 for ICU admission, and 0.796 for all-cause mortality. Furthermore, we integrated the AI predictive model with the HIS to assist decision making in real time. No significant differences in ICU admission or all-cause mortality were detected between the non-AI and AI groups. The AI model performed better than the PHD score for predicting all-cause mortality (AUC 0.796 vs. 0.693).ConclusionsA real-time AI predictive model is a promising method for predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies recruiting more patients are warranted.

Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness

BackgroundFor surveillance of episodic illness, the emergency department (ED) represents one of the largest interfaces for generalizable data about segments of the US public experiencing a need for unscheduled care. This protocol manuscript describes the development and operation of a national network linking symptom, clinical, laboratory and disposition data that provides a public database dedicated to the surveillance of acute respiratory infections (ARIs) in EDs.MethodsThe Respiratory Virus Laboratory Emergency Department Network Surveillance (RESP-LENS) network includes 26 academic investigators, from 24 sites, with 91 hospitals, and the Centers for Disease Control and Prevention (CDC) to survey viral infections. All data originate from electronic medical records (EMRs) accessed by structured query language (SQL) coding. Each Tuesday, data are imported into the standard data form for ARI visits that occurred the prior week (termed the index file); outcomes at 30 days and ED volume are also recorded. Up to 325 data fields can be populated for each case. Data are transferred from sites into an encrypted Google Cloud Platform, then programmatically checked for compliance, parsed, and aggregated into a central database housed on a second cloud platform prior to transfer to CDC.ResultsAs of August, 2023, the network has reported data on over 870,000 ARI cases selected from approximately 5.2 million ED encounters. Post-contracting challenges to network execution have included local shifts in testing policies and platforms, delays in ICD-10 coding to detect ARI cases, and site-level personnel turnover. The network is addressing these challenges and is poised to begin streaming weekly data for dissemination.ConclusionsThe RESP-LENS network provides a weekly updated database that is a public health resource to survey the epidemiology, viral causes, and outcomes of ED patients with acute respiratory infections.

Outcomes of Emergency Department Patients With Opioid Use Disorder Utilizing a Virtual Addiction Bridge Clinic: A Case Series.

Within the last decade, there has been a dramatic increase in the rate of emergency department (ED) visits and death from opioid overdose. Those who present to the ED are at high risk for subsequent morbidity and mortality. Despite effective treatment, many patients do not get rapidly connected to outpatient care. The aim of this investigation was to describe outpatient treatment engagement after ED discharge among patients with opioid use disorder (OUD) enrolled in a virtual Addiction Bridge Clinic (ABC). This was a retrospective case series describing an ED-initiated referral for rapid telehealth follow-up among patients with OUD. The primary outcome was addiction treatment engagement among those who completed the initial virtual ABC visit (engaged in ABC) vs. those who did not complete an ABC visit (Not engaged in ABC) at 1 week, 1 month, and 3 and 6 months timepoint intervals after the initial ED presentation. Of the N = 201 patients referred to the ABC between March and December 2021, a majority were Black (71%) and male (77%). Of the 201 referrals, 85 (42%) completed an initial ABC telehealth visit. Subsequent treatment engagement was 26% at 1 week, 26% at 1 month, 22% at 3 months, and 18% at 6 months after the index ED visit. A telehealth-enabled virtual addiction bridge clinic is one potential approach to reduce barriers to rapid treatment access. Strategies are needed to improve subsequent addiction treatment engagement after a virtual addiction bridge clinic visit.

The Effect of Early Severe Hyperoxia in Adults Intubated in the Prehosptial Setting or Emergency Department: A Scoping Review

BackgroundThe detrimental effects of hyperoxia exposure have been well-described in patients admitted to intensive care units. However, data evaluating the effects of short-term, early hyperoxia exposure in patients intubated in the prehospital setting or emergency department (ED) have not been systematically reviewed. ObjectiveOur aim was to quantify and describe the existing literature examining the clinical outcomes in ED patients exposed to hyperoxia within the first 24 h of mechanical ventilation. MethodsThis review was performed in concordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. Two rounds of review using Rayyan QCRI software were performed for title and abstract screening and full-text search. Of the 2739 articles, 27 articles were retrieved after initial screening, of which 5 articles were excluded during the full-text screening, leaving 22 articles for final review and data extraction. ResultsOf 22 selected publications, 9 described patients with traumatic brain injury, 6 with cardiac arrest, 3 with multisystem trauma, 1 with stroke, 2 with septic shock, and 1 was heterogeneous. Three studies were randomized controlled trials. The available data have widely heterogeneous definitions of hyperoxia exposure, outcomes, and included populations, limiting conclusions. ConclusionsThere is a paucity of data that examined the effects of severe hyperoxia exposure in the acute, post-intubation phase of the prehospital and ED settings. Further research with standardized definitions is needed to provide more detailed guidance regarding early oxygen titration in intubated patients.

Outcomes of acute heart failure patients managed in the emergency department.

Acute heart failure is a serious condition commonly seen in the emergency department (ED). The HEARTRISK6 Scale has been recently developed to identify the risk of poor outcomes but has not been tested. We sought to describe the management and outcomes of ED patients with acute heart failure and to evaluate the potential impact of the HEARTRISK6 Scale. We conducted a health records review of 300 consecutive acute heart failure patients presenting to two tertiary care EDs. Two evaluators abstracted clinical variables, ED management and treatment details, and patient outcomes using the electronic health records platform (EPIC) and attending physicians verified the data. The primary outcome measure was a short-term serious outcome, as shown in Results. In addition, the HEARTRISK6 score was calculated retrospectively. We included 300 patients with mean age of 78.5years, 51.0% male, 56.3% arrival by ambulance, and 67.0% admitted to hospital. 25.3% experienced a short-term serious outcome 1) after admission (N = 201): non-invasive ventilation 14.9%, intubation 1.5%, major cardiac procedure 5.0%, myocardial infarction 2.0%, death 8.5%; 2) after ED discharge (N = 99): return to ED 21.2%, death 4.0%. Those initially admitted experienced a much higher proportion of serious outcomes compared to those discharged (29.9% vs. 16.2%). A HEARTRISK6 Scale cut-point score of ≥ 1 would have had a sensitivity of 91.0%, specificity 24.5%, and negative likelihood ratio 0.37 for short-term serious outcomes and suggested hospital admission for 80.7% of cases. There was a large range of severity of illness of acute heart failure patients and a wide variety of treatments were administered in the ED. Both admitted and discharged patients experienced a high proportion of poor outcomes. The HEARTRISK6 Scale showed a high sensitivity for short-term serious outcomes but with the potential to increase hospital admissions. Further validation of the HEARTRISK6 Scale is required before routine clinical use.

The association between sonographic common bile duct dilatation and outcomes in emergency department patients with cholangitis.

The presentation of the patient with acute cholangitis (AC) ranges from mild illness to life-threatening shock. Therefore, prompt diagnosis and treatment are critical. Abdominal ultrasound (US) is the imaging of choice to locate bile duct dilatation. Other modalities include abdominal computed tomography (CT) or endoscopic retrograde cholangiopancreatography (ERCP). To determine whether sonographic common bile duct dilatation in emergency department (ED) patients with AC predicts outcomes including sepsis, hospital length of stay (LOS), admission to the intensive care unit (ICU), time to ERCP, and mortality. Electronic medical records of all patients hospitalized in a tertiary care medical center between July 2012-February 2021 with a discharge diagnosis of cholangitis were assessed. Patients were dichotomously classified as CBD dilated or CBD non-dilated based on ultrasound. Dilation was defined as CBD larger than 6 mm in patients younger than 60 or larger than 6 mm + 1 mm per decade in patients over 60. The study included 271 patients- 172 with CBD dilation versus 99 without. Mean LOS was 9.92 days for those with a dilated CBD versus 13.4 days without. The mean time to ERCP was 4.26 days for those with a dilated CBD versus 6.56 days without. Sepsis, mortality, and ICU admission were scarce and there was no statistically significant difference between the cohorts. Patients with a dilated CBD per the abdominal US performed during the patient's ED stay, underwent ERCP earlier, and were hospitalized fewer days than patients without CBD dilation.

Stercoral Colitis in the Emergency Department: A Retrospective Review of Presentation, Management, and Outcomes.

Stercoral colitis is inflammation of the bowel wall caused by fecal impaction. Despite reported high morbidity and risk of perforation, little research assessing outcomes is available. This study characterizes the presentation, management, and outcomes of emergency department patients with stercoral colitis. We performed a retrospective chart review of ED patients with stercoral colitis identified on computed tomography (CT) scan. Of 814, 522 visits to multiple EDs across the US, 269 met the inclusion criteria. Variables regarding patient presentation, management, and outcomes were extracted from electronic medical records. Results were analyzed with percentages and 95% confidence intervals (CIs). Of 269 patients, the median age was 76 years. The most common chief concern was abdominal pain/distension (33.8%). However, abdominal pain was documented as absent in 62.1% of cases. The most common CT findings included fecal impaction (96.7%), bowel wall inflammation (72.9%), and fat stranding (48.3%). Eighty-four (31.2%) patients were discharged home from the ED, and over half of these (45/84, 53.6%) received no enema, laxatives, or disimpaction. Overall, 9 patients (3.3%, 95% CI 1.6% to 6.5%) required surgical management of a related complication within 3 months, 27 (10.0%, 95% CI 6.8% to 14.4%) returned to the ED within 72 hours, and 9 (3.3%, 95% CI 1.6% to 6.5%) died from a cause related to stercoral colitis within 3 months. Patients with stercoral colitis often present in a nonspecific manner, and short-term mortality is substantial. In this study, most discharged patients did not receive recommended treatment. This represents the largest ED study of stercoral colitis and provides further evidence linking this diagnosis with adverse outcomes.

Benefits of MR-proADM-guided decision-making in the emergency department: clinical and economic evaluation in Italy, Germany, Spain, and the UK

AimIn a randomized control trial mid-regional proadrenomedullin (MR-proADM)-guided decision-making has been proven to safely reduce hospital admissions based on an accurate assessment of disease severity in the Emergency Department (ED). This study aimed to assess the impact of a MR-proADM-Guided Triage (MR-GT) versus standard Hospital Triage (HT) on clinical and economic outcomes in ED patients with suspected infection in Spain, Italy, Germany, and the UK, using Patient-Level Data (PLD) from two observational studies.MethodsPLD was collected from patients enrolled at a Spanish hospital during two observational studies. Logistic regression was used to identify predictors of hospitalization. A patient-level simulation model was subsequently developed to evaluate the clinical and economic impact of MR-GT versus HT using results from the statistical analysis and country-specific cost data from the published literature. Probabilistic and deterministic sensitivity analyses were carried out.ResultsFour hundred seventy-three patients were enrolled in this study. MR-proADM had the strongest association with hospital admission, followed by age and National Early Warning Score (NEWS). In the simulation model, MR-GT was associated with an overall reduction in hospitalization relative to HT, equal to 22.6 percentage points (40.9 vs. 63.5%). In addition, the use of MR-GT would reduce the total hospital cost per patient presenting to the ED with suspected infection by roughly 30%, with a mean cost saving per patient of €626, €1,484, €1,154, and £1,113 in Spain, Italy, Germany, and the UK, respectively. The robustness of these findings was confirmed by sensitivity analyses.LimitationsThe statistical analyses were not performed on the same population simulated in the model. Clinical input parameters were assumed to be the same for all countries.ConclusionsMR-proADM showed to be the main predictor of hospitalization. An MR-proADM decision algorithm provides cost savings in Spain, Italy, Germany, and the UK.

HSD13 Emergency Department Hyperkalemia Outcomes Are Improved with Newer Potassium Binders

Hyperkalemia is an expensive and potentially fatal Emergency Department (ED) presentation. This study aimed to compare outcomes in ED patients with hyperkalemia who received potassium binders, including patiromer, sodium zirconium cyclosilicate (SZC), binder sodium polystyrene sulfonate (SPS), or no binder.

Empiric anti-anaerobic antibiotics are associated with adverse clinical outcomes in emergency department patients

<b>In a retrospective cohort of 15 908 emergency department patients, empiric use of anti-anaerobic antibiotics was associated with decreased overall and infection-free survival in patients with suspected infection in the emergency department</b>https://bit.ly/3TRbE0F

Cannabis-Induced Anxiety Disorder in the Emergency Department.

In December 2018, Michigan became the 10th state to legalize marijuana for adults. Since this law took effect, increased availability and use of cannabis in Michigan have led to increased emergency department (ED) visits associated with the drug's psychiatric effects. To describe cannabis-induced anxiety disorder's prevalence, clinical features, and disposition in a community-based study. This was a retrospective cohort analysis of consecutive patients diagnosed with acute toxicity related to cannabis use (ICD-10 code F12). Patients were seen at seven EDs over a 24-month study period. Data collected included demographics, clinical features, and treatment outcomes in ED patients who met the criteria for cannabis-induced anxiety disorder. This group was compared to a cohort experiencing other forms of acute cannabis toxicity. Chi-squared and t-tests were used to compare these two groups across key demographic and outcome variables. During the study period, 1135 patients were evaluated for acute cannabis toxicity. A total of 196 patients (17.3%) had a chief complaint of anxiety, and 939 (82.7%) experienced other forms of acute cannabis toxicity, predominantly symptoms of intoxication or cannabis hyperemesis syndrome. Patients with anxiety symptoms had panic attacks (11.7%), aggression or manic behavior (9.2%), and hallucinations (6.1%). Compared to patients presenting with other forms of cannabis toxicity, those with anxiety were likelier to be younger, ingested edible cannabis, had psychiatric comorbidities, or had a history of polysubstance abuse. Cannabis-induced anxiety occurred in 17.3% of ED patients in this community-based study. Clinicians must be adept in recognizing, evaluating, managing, and counseling these patients following cannabis exposure.

Predictors of outcomes in emergency department patients with suspected infections and without fulfillment of the sepsis criteria

BackgroundData on patient characteristics and determinants of serious outcomes for acutely admitted patients with infections who do not fulfill the sepsis criteria are sparse. The study aimed to characterize acutely admitted emergency department (ED) patients with infections and a composite outcome of in-hospital mortality or transfer to the intensive care unit without fulfilling the criteria for sepsis and to examine predictors of the composite outcome. MethodsThis was a secondary analysis of data from a prospective observational study of patients with suspected bacterial infection admitted to the ED between October 1, 2017 and March 31, 2018. A National Early Warning Score 2 (NEWS2) ≥ 5 within the first 4 h in the ED was assumed to represent a sepsis-like condition with a high risk for the composite endpoint. Patients who achieved the composite outcome were grouped according to fulfillment of the NEWS2 ≥ 5 criteria. We used logistic regression analysis to estimate the unadjusted and adjusted odds ratio (OR) for the composite endpoint among patients with either NEWS2 < 5 (NEWS2-) or NEWS2 ≥ 5 (NEWS2+). ResultsA total of 2055 patients with a median age of 73 years were included. Of these, 198 (9.6%) achieved the composite endpoint, including 59 (29.8%) NEWS2- and 139 (70.2%) NEWS2+ patients, respectively. Diabetes (OR 2.23;1.23–4.0), a Sequential Organ Failure Assessment (SOFA) score ≥ 2 (OR 2.57;1.37–4.79), and a Do-not-attempt-cardiopulmonary-resuscitation order (DNACPR) on admission (OR 3.70;1.75–7.79) were independent predictive variables for the composite endpoint in NEWS2- patients (goodness-of-fit test P = 0.291; area under the receiver operating characteristic curve for the model (AUROC) = 0.72). The regression model for NEWS2+ patients revealed that a SOFA score ≥ 2 (OR 2.79; 1.59–4.91), hypothermia (OR 2.48;1.30–4.75), and DNACPR order on admission were predictive variables for the composite endpoint (goodness-of-fit test P = 0.62; AUROC for the model = 0.70). ConclusionApproximately one-third of the patients with infections and serious outcomes during hospitalization did not meet the NEWS2 threshold for likely sepsis. Our study identified factors with independent predictive values for the development of serious outcomes that should be tested in future prediction models.

Diagnostic accuracy of novel mRNA blood biomarkers of infection to predict outcomes in emergency department patients with undifferentiated abdominal pain

Abdominal pain represents greater than 20% of US Emergency Department (ED) visits due to a wide range of illnesses. There are currently no reliable blood biomarkers to predict serious outcomes in patients with abdominal pain. Our previous studies have identified three mRNA transcripts related to innate immune activation: alkaline phosphatase (ALPL), interleukin-8 receptor-β (IL8RB), and defensin-1 (DEFA1) as promising candidates to detect an intra-abdominal infection. The objective of this study was to evaluate the accuracy of these mRNA biomarkers to predict likely infection, hospitalization and surgery in Emergency Department patients with undifferentiated abdominal pain. We prospectively enrolled Emergency Department patients with undifferentiated abdominal pain who received an abdominal CT scan as part of their evaluation. Clinical outcomes were abstracted from the CT scan and medical records. mRNA biomarker levels were calculated independent of the clinical outcomes and their accuracy was assessed to predict infectious diagnoses, surgery and hospital admission. 89 patients were enrolled; 21 underwent surgery; 47 underwent hospital admission; and, no deaths were observed within 30 days. In identifying which cases were likely infectious, mRNA biomarkers’ AUC values were: ALPL, 0.83; DEFA1 0.51; IL8RB, 0.74; and ALPL + IL8RB, 0.79. In predicting which Emergency Department patients would receive surgery, the AUC values were: ALPL, 0.75; DEFA1, 0.58; IL8RB, 0.75; and ALPL + IL8RB, 0.76. In predicting hospital admission, the AUC values were: ALPL, 0.78; DEFA1, 0.52; IL8RB, 0.74; and, ALPL + IL8RB, 0.77. For predicting surgery, ALPL + IL8RB’s positive likelihood ratio (LR) was 3.97; negative LR (NLR) was 0.70. For predicting hospital admission, the same marker’s positive LR was 2.80 with an NLR of 0.45. Where the primary cause for admission was a potentially infectious disorder, 33 of 34 cases (97%) had positive RNA scores. In a pragmatic, prospective diagnostic accuracy trial in Emergency Department patients with undifferentiated abdominal pain, mRNA biomarkers showed good accuracy to identify patients with potential infection, as well as those needing surgery or hospital admission.

Role of serum lactate as prognostic marker of mortality among emergency department patients with multiple conditions: A systematic review.

In the Emergency Department, the physician's purpose is to rapidly and accurately diagnose the severity of a patient's condition so that proper treatment can be administered. Therefore, this study pays close attention to the association and prognostic value of blood lactate on different disease outcomes by systematically reviewing recently published studies. The search strategy was developed based on searches in the year 2022 for potential publications including original articles, case reports, and reviews using the following web databases google scholar, PubMed, and Science Direct. A total of 25 studies were identified and reviewed extensively to evaluate the link between mortality and serum lactate levels. This literature review presents the strong association of elevated serum lactate levels with the in-hospital mortality rate among emergency department patients. These findings suggest that even a moderately raised lactate level in serum can predict the severe outcome in emergency department patients suffering from different conditions. Therefore, early (at the time of administration of hospital) and periodic serum lactate value determination through different techniques and scores is of need and should become part of routine analyses in emergency department to predict and choose therapies that could benefit critically ill patients.