Examining lack of referrals to heart valve specialists as mechanisms of potential underutilization of aortic valve replacement.

BACKGROUND: Recent studies suggest that aortic valve replacement (AVR) remains underutilized. AIMS: Investigate the potential role of non-referral to heart valve specialists (HVS) on AVR utilization. METHODS: Patients with severe aortic stenosis (AS) between 2015 and 2018, who met class I indication for intervention, were identified. Baseline data and process-related parameters were collected to analyze referral predictors and evaluate outcomes. RESULTS: Among 981 patients meeting criteria AVR, 790 patients (80.5%) were assessed by HVS within six months of index TTE. Factors linked to reduced referral included increasing age (OR: 0.95; 95% CI: 0.94-0.97; P < .001), unmarried status (OR: 0.59; 95% CI: 0.43-0.83; P = .002) and inpatient TTE (OR: 0.27; 95% CI: 0.19-0.38; P < .001). Conversely, higher hematocrit (OR: 1.13; 95% CI: 1.09-1.16; P < .001) and eGFR (OR: 1.01; 95% CI: 1.00-1.02; P = .003), mean aortic valve gradient (OR: 1.03; 95% CI: 1.01-1.04; P < .001) and preserved LVEF (OR: 1.59; 95% CI: 1.02-2.48; P = .04), were associated with increased referral likelihood. Moreover, patients assessed by HVS referral as a time-dependent covariate had a significantly lower two-year mortality risk than those who were not (aHR: 0.30; 95% CI: 0.23-0.39; P < .001). CONCLUSION: A substantial proportion of severe AS patients meeting indications for AVR are not evaluated by HVS and experience markedly increased mortality. Further research is warranted to assess the efficacy of care delivery mechanisms, such as e-consults, and telemedicine, to improve access to HVS expertise.

Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.

BACKGROUND: Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability. OBJECTIVE: We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF. METHODS: We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF). RESULTS: Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001). CONCLUSIONS: Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843.

A simple scoring rule to predict survival to discharge after out of hospital cardiac arrest at the time of ED arrival.

BACKGROUND: It is important to be able to predict the chance of survival to hospital discharge upon ED arrival in order to determine whether to continue or terminate resuscitation efforts after out of hospital cardiac arrest. This study was conducted to develop and validate a simple scoring rule that could predict survival to hospital discharge at the time of ED arrival. METHODS: This was a multicenter retrospective cohort study based on a nationwide registry (Korean Cardiac Arrest Research Consortium) of out of hospital cardiac arrest (OHCA). The study included adult OHCA patients older than 18 years old, who visited one of 33 tertiary hospitals in South Korea from September 1st, 2015 to June 30th, 2020. Among 12,321 screened, 5471 patients were deemed suitable for analysis after exclusion. Pre-hospital ROSC, pre-hospital witness, shockable rhythm, initial pH, and age were selected as the independent variables. The dependent variable was set to be the survival to hospital discharge. Multivariable logistic regression (LR) was performed, and the beta-coefficients were rounded to the nearest integer to formulate the scoring rule. Several machine learning algorithms including the random forest classifier (RF), support vector machine (SVM), and K-nearest neighbor classifier (K-NN) were also trained via 5-fold cross-validation over a pre-specified grid, and validated on the test data. The prediction performances and the calibration curves of each model were obtained. Pre-processing of the registry was done using R, model training & optimization using Python. RESULTS: A total of 5471 patients were included in the analysis. The AUROC of the scoring rule over the test data was 0.7620 (0.7311-0.7929). The AUROCs of the machine learning classifiers (LR, SVM, k-NN, RF) were 0.8126 (0.7748-0.8505), 0.7920 (0.7512-0.8329), 0.6783 (0.6236-0.7329), and 0.7879 (0.7465-0.8294), respectively. CONCLUSION: A simple scoring rule consisting of five, binary variables could aid in the prediction of the survival to hospital discharge at the time of ED arrival, showing comparable results to conventional machine learning classifiers.

Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians.

BACKGROUND: Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that accurately assesses hyperkalemia risk from ECGs could revolutionize patient screening and treatment. We aimed to evaluate the efficacy and reliability of a smartphone application, which utilizes camera-captured ECG images, in quantifying hyperkalemia risk compared to human experts. METHODS: We performed a retrospective analysis of ED hyperkalemic patients (serum potassium ≥ 6 mmol/L) and their age- and sex-matched non-hyperkalemic controls. The application was tested by five users and its performance was compared to five board-certified emergency physicians (EPs). RESULTS: Our study included 125 patients. The area under the curve (AUC)-receiver operating characteristic of the application's output was nearly identical among the users, ranging from 0.898 to 0.904 (median: 0.902), indicating almost perfect interrater agreement (Fleiss' kappa 0.948). The application demonstrated high sensitivity (0.797), specificity (0.934), negative predictive value (NPV) (0.815), and positive predictive value (PPV) (0.927). In contrast, the EPs showed moderate interrater agreement (Fleiss' kappa 0.551), and their consensus score had a significantly lower AUC of 0.662. The physicians' consensus demonstrated a sensitivity of 0.203, specificity of 0.934, NPV of 0.527, and PPV of 0.765. Notably, this performance difference remained significant regardless of patients' sex and age (P < 0.001 for both). CONCLUSION: Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED.

Impact of an Emergency Department Isolation Policy for Patients With Suspected COVID-19 on Door-to-Electrocardiography Time and Clinical Outcomes in Patients With Acute Myocardial Infarction.

BACKGROUND: Rapid electrocardiography diagnosis within 10 minutes of presentation is critical for acute myocardial infarction (AMI) patients in the emergency department (ED). However, the coronavirus disease 2019 (COVID-19) pandemic has significantly impacted the emergency care system. Screening for COVID-19 symptoms and implementing isolation policies in EDs may delay the door-to-electrocardiography (DTE) time. METHODS: We conducted a cross-sectional study of 1,458 AMI patients who presented to a single ED in South Korea from January 2019 to December 2021. We used multivariate logistic regression analysis to assess the impact of COVID-19 pandemic and ED isolation policies on DTE time and clinical outcomes. RESULTS: We found that the mean DTE time increased significantly from 5.5 to 11.9 minutes (P < 0.01) in ST segment elevation myocardial infarction (STEMI) patients and 22.3 to 26.7 minutes (P < 0.01) in non-ST segment elevation myocardial infarction (NSTEMI) patients. Isolated patients had a longer mean DTE time compared to non-isolated patients in both STEMI (9.2 vs. 24.4 minutes) and NSTEMI (22.4 vs. 61.7 minutes) groups (P < 0.01). The adjusted odds ratio (aOR) for the effect of COVID-19 duration on DTE ≥ 10 minutes was 1.93 (95% confidence interval [CI], 1.51-2.47), and the aOR for isolation status was 5.62 (95% CI, 3.54-8.93) in all patients. We did not find a significant association between in-hospital mortality and the duration of COVID-19 (aOR, 0.9; 95% CI, 0.52-1.56) or isolation status (aOR, 1.62; 95% CI, 0.71-3.68). CONCLUSION: Our study showed that ED screening or isolation policies in response to the COVID-19 pandemic could lead to delays in DTE time. Timely evaluation and treatment of emergency patients during pandemics are essential to prevent potential delays that may impact their clinical outcomes.

A Retrospective Clinical Evaluation of an Artificial Intelligence Screening Method for Early Detection of STEMI in the Emergency Department.

BACKGROUND: Rapid revascularization is the key to better patient outcomes in ST-elevation myocardial infarction (STEMI). Direct activation of cardiac catheterization laboratory (CCL) using artificial intelligence (AI) interpretation of initial electrocardiography (ECG) might help reduce door-to-balloon (D2B) time. To prove that this approach is feasible and beneficial, we assessed the non-inferiority of such a process over conventional evaluation and estimated its clinical benefits, including a reduction in D2B time, medical cost, and 1-year mortality. METHODS: This is a single-center retrospective study of emergency department (ED) patients suspected of having STEMI from January 2021 to June 2021. Quantitative ECG (QCG™), a comprehensive cardiovascular evaluation system, was used for screening. The non-inferiority of the AI-driven CCL activation over joint clinical evaluation by emergency physicians and cardiologists was tested using a 5% non-inferiority margin. RESULTS: Eighty patients (STEMI, 54 patients [67.5%]) were analyzed. The area under the curve of QCG score was 0.947. Binned at 50 (binary QCG), the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 98.1% (95% confidence interval [CI], 94.6%, 100.0%), 76.9% (95% CI, 60.7%, 93.1%), 89.8% (95% CI, 82.1%, 97.5%) and 95.2% (95% CI, 86.1%, 100.0%), respectively. The difference in sensitivity and specificity between binary QCG and the joint clinical decision was 3.7% (95% CI, -3.5%, 10.9%) and 19.2% (95% CI, -4.7%, 43.1%), respectively, confirming the non-inferiority. The estimated median reduction in D2B time, evaluation cost, and the relative risk of 1-year mortality were 11.0 minutes (interquartile range [IQR], 7.3-20.0 minutes), 26,902.2 KRW (22.78 USD) per STEMI patient, and 12.39% (IQR, 7.51-22.54%), respectively. CONCLUSION: AI-assisted CCL activation using initial ECG is feasible. If such a policy is implemented, it would be reasonable to expect some reduction in D2B time, medical cost, and 1-year mortality.

The index of oxygenation to respiratory rate as a prognostic factor for mortality in Sepsis.

OBJECTIVES: An index combining respiratory rate and oxygenation (ROX) has been introduced, and the ROX index is defined as the ratio of oxygen saturation by pulse oximetry/fraction of inspired oxygen to respiratory rate. In sepsis, hypoxemia and tachypnea are commonly observed. We performed this study to investigate the association between the ROX index and 28-day mortality in patients with sepsis or septic shock. METHODS: This retrospective study included 2862 patients. The patients were divided into three groups according to the ROX index: Group I (ROX index >20), Group II (ROX index >10 and ≤ 20), and Group III (ROX index ≤10). RESULTS: The median ROX index was significantly lower in the nonsurvivors than in the survivors (12.8 and 18.2, respectively) (p < 0.001). The 28-day mortality rates in Groups I, II and III were 14.5%, 21.3% and 34.4%, respectively (p < 0.001). In the multivariable Cox regression analysis, Group III had an approximately 40% higher risk of death than Group I during the 28-day period (hazard ratio = 1.41, 95% confidence interval 1.13-1.76). The area under the curve of the ROX index was significantly higher than that of the quick Sequential Organ Failure Assessment score (p < 0.001). CONCLUSIONS: The ROX index was lower in nonsurvivors than in survivors, and a ROX index less than or equal to 10 was an independent prognostic factor for 28-day mortality in patients with sepsis or septic shock. Therefore, the ROX index could be used as a prognostic marker in sepsis.

Prediction of Neurological Outcomes in Out-of-hospital Cardiac Arrest Survivors Immediately after Return of Spontaneous Circulation: Ensemble Technique with Four Machine Learning Models.

BACKGROUND: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods. METHODS: We performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome. RESULTS: A total of 1,207 patients were included in the study. Among them, 631, 139, and 153 were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI], 0.9352-0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612-0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860-1.0000); sensitivity, 0.9594 (95% CI, 0.9245-0.9943); specificity, 0.9714 (95% CI, 0.9162-1.0000); PPV, 0.9916 (95% CI, 0.9752-1.0000); NPV, 0.8718 (95% CI, 0.7669-0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825-0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845-0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087-0.9867); sensitivity, 0.9595 (95% CI, 0.9145-1.0000); specificity, 0.6500 (95% CI, 0.5022-0.7978); PPV, 0.8353 (95% CI, 0.7564-0.9142); NPV, 0.8966 (95% CI, 0.7857-1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets. CONCLUSION: We established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

Factors associated with first-pass success of emergency endotracheal intubation.

STUDY OBJECTIVE: Endotracheal intubation is frequently performed in emergency departments (EDs). First-pass success is important because repeated attempts are associated with poor outcomes. We sought to identify factors associated with first-pass success in emergency endotracheal intubation. METHODS: We analyzed emergency orotracheal intubations on adult patients in an ED located in South Korea from Jan. 2013 to Dec. 2016. Various operator-, procedure- and patient-related factors were screened with univariable logistic regression. Using variables with P-values less than 0.2, a multiple logistic regression model was constructed to identify independent predictors. RESULTS: There were 1154 eligible cases. First-pass success was achieved in 974 (84.4%) cases. Among operator-related factors, clinical experience (OR: 2.93, 5.26, 3.80 and 5.71; 95% CI: 1.62-5.26, 2.80-9.84, 1.81-8.13 and 2.07-18.67 for PGY 3, 4 and 5 residents and EM specialists, respectively, relative to PGY 2 residents) and physician based outside the ED (OR: 0.10; 95% CI: 0.04-0.25) were independently associated with first-pass success. There was no statistically or clinically significant difference for first-pass success rate as determined by operator's gender (83.6% for female vs. 84.8% for male; 95% CI for difference: -3.1% to 5.8%). Among patient-related factors, restricted mouth opening (OR: 0.47; 95% CI: 0.31-0.72), restricted neck extension (OR: 0.57; 95% CI: 0.39-0.85) and swollen tongue (OR: 0.46; 95% CI: 0.28-0.77) were independent predictors of first-pass success. CONCLUSIONS: Operator characteristics, including clinical experience and working department, and patient characteristics, including restricted mouth opening, restricted neck extension and swollen tongue, were independent predictors of first-pass success in emergency endotracheal intubation.

Developing neural network models for early detection of cardiac arrest in emergency department.

BACKGROUND: Automated surveillance for cardiac arrests would be useful in overcrowded emergency departments. The purpose of this study is to develop and test artificial neural network (ANN) classifiers for early detection of patients at risk of cardiac arrest in emergency departments. METHODS: This is a single-center electronic health record (EHR)-based study. The primary outcome was the development of cardiac arrest within 24 h after prediction. Three ANN models were trained: multilayer perceptron (MLP), long-short-term memory (LSTM), and hybrid. These were compared to other classifiers including the modified early warning score (MEWS), logistic regression, and random forest. We used AUROC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the comparison. RESULTS: During the study period, there were a total of 374,605 ED visits and 2,910,321 patient status updates. The ANN models (MLP, LSTM, and hybrid) achieved higher AUROC (AUROC: 0.929, 0.933, and 0.936; 95% confidential interval: 0.926-0.932, 0.930-0.936, and 0.933-0.939, respectively) compared to the non-ANN models, and the hybrid model exhibited the best performance. The ANN classifiers displayed higher performance in most of the test characteristics when the threshold levels of the classifiers were fixed to display the same positive result as those at the three MEWS thresholds (score ≥ 3, ≥4, and ≥5), and when compared with each other. CONCLUSIONS: The ANN improves upon MEWS and conventional machine learning algorithms for the prediction of cardiac arrests in emergency departments. The hybrid ANN model utilizing both baseline and sequence information achieved the best performance.

Moderate to severe hyperphosphataemia as an independent prognostic factor for 28-day mortality in adult patients with sepsis.

BACKGROUND: Ischaemic tissue injury caused by tissue hypoperfusion is one of the major consequences of sepsis. Phosphate concentrations are elevated in ischaemic tissue injury. This study was performed to investigate the association of phosphate concentrations with mortality in patients with sepsis. METHODS: This was a retrospective cohort study of patients with sepsis conducted at an urban, tertiary care emergency department (ED) in Korea. Patients with sepsis arriving between March 2010 and April 2017 were stratified into four groups according to the initial phosphate concentration at presentation to the ED: group I (hypophosphataemia, phosphate <2 mg/dL), group II (normophosphataemia, phosphate 2-4 mg/dL), group III (mild hyperphosphataemia, phosphate 4-6 mg/dL), group IV (moderate to severe hyperphosphataemia, phosphate ≥6 mg/dL). Multivariable Cox proportional hazard regression analyses were performed to evaluate the independent association of initial phosphate concentration with 28-day mortality. RESULTS: Of the 3034 participants in the study, the overall mortality rate was 21.9%. The 28-day mortality rates were group I (hypophosphataemia) 14.6%, group II 17.4% (normophosphataemia), group III (mild hyperphosphataemia) 29.2% and group IV (moderate to severe hyperphosphataemia) 51.4%, respectively (p<0.001). In the multivariable analyses, patients with severe hyperphosphataemia had a significantly higher risk of death than those with normal phosphate levels (HR 1.59; 95% CI 1.23 to 2.05). Mortality in the other groups was not significantly different from mortality in patients with normophosphataemia. CONCLUSIONS: Moderate to severe hyperphosphataemia was associated with 28-day mortality in patients with sepsis. Phosphate level could be used as a prognostic indicator in sepsis.

Prolonged stay in the emergency department is an independent risk factor for hospital-acquired pressure ulcer.

It is not easy to ensure optimal prevention of hospital-acquired pressure ulcer (HAPU) in crowded emergency departments (EDs). We hypothesised that a prolonged ED length of stay (LOS) is associated with an increased risk of HAPU. This is a single-centre observational study. Prospectively collected HAPU surveillance data were analysed. Adult (aged ≥20 years) patients admitted through the ED from April 1, 2013 to December 31, 2016 were included. The primary outcome was the development of HAPU within a month. Covariates included demographics, comorbidities, conditions at triage, initial laboratory results, primary ED diagnosis, critical ED interventions, and ED dispositions. The association between ED LOS and HAPU was modelled using logistic and extended Cox regression. A total of 48 641 admissions were analysed. The crude odds ratio (OR) and hazard ratio (HR) for HAPU were increased to 1.44 (95% CI, 1.20-1.72) and 1.21 (95% CI, 1.02-1.45), respectively, in ED LOS ≥24 hours relative to ED LOS <6 hours. In multivariable logistic regression, ED LOS ≥12 and ≥24 hours were associated with higher risk of HAPU, with ORs of 1.30 (95% CI, 1.05-1.60) and 1.80 (95% CI, 1.45-2.23) relative to ED LOS <6 hours, respectively. The extended Cox regression showed that the risk lasted up to a week, with HRs of 1.42 (95% CI, 1.07-1.88) and 1.92 (95% CI, 1.44-2.57) relative to ED LOS <6 hours, respectively. In conclusion, Prolonged ED LOS is independently associated with HAPU. Shorter ED LOS should be pursued as a goal in a multifaceted solution for HAPU.

A Novel Infant Chest Compression Assist Device Using a Palm Rather Than Fingers: A Randomized Crossover Trial.

Objective: We developed a novel compression assist device (palm presser) to perform chest compressions using a palm in infant cardiopulmonary resuscitation (CPR). We hypothesized that the palm presser will increase compression depth without increasing hands-off time and will reduce rescuer fatigue compared with the two-finger technique (TFT).Methods: In this randomized crossover manikin trial, participants performed two minutes of CPR with a 30:2 compression:ventilation ratio using the palm presser and the TFT in randomized sequence on an infant manikin. CPR parameters, including compression depth and hands-off time, were collected to compare CPR quality between the palm presser and the TFT. The linear mixed-effect model was used to control the carryover effect of a crossover design in the analysis of CPR parameters. To evaluate rescuer fatigue, we compared changes in compression depth over time and calculated the odds of sufficiently deep compressions over time between the two groups.Results: The palm presser resulted in greater mean compression depth (41.5 ± 1.6 mm vs. 36.8 ± 5.5 mm, p < 0.001), greater sufficiently deep compressions (80.9 ± 27.8% vs. 42.4 ± 35.4%, p < 0.001), and better correct hand position (99.9 ± 0.5% vs. 83.9 ± 25.3%, p = 0.013) than the TFT. Total compressions, compression rate, total ventilations, volume of ventilations, and hands-off time were not significantly different between the two groups. The mean change in compression depth over time was greater with the TFT than with the palm presser (regression coefficient: -0.024 [95% CI -0.030 to -0.018] vs. -0.004 [95% CI -0.006 to -0.002]). The odds of a compression depth greater than 40 mm increased 2.8 times (95% CI 2.2 to 3.4) with the TFT during the first minute compared with the last minute, whereas the corresponding odds ratio when using the palm presser was not significantly different in the first and last minutes (OR: 1.2 [95% CI 0.9 to 1.5]).Conclusions: Compression with palm pressers resulted in greater compression depth without increasing hands-off time and reduced rescuer fatigue compared with compression with the TFT in simulated infant CPR with manikins.

The Korean Triage and Acuity Scale: associations with admission, disposition, mortality and length of stay in the emergency department.

OBJECTIVE: The Korean Triage and Acuity Scale (KTAS) was implemented in our emergency department (ED) in May 2016 and is fully integrated into the electronic medical record (EMR) system. Our objective was to determine whether the KTAS is associated with changes in admissions to the hospital, admission disposition, inpatient mortality and length of stay (LOS). DESIGN: Quasi-experimental, uncontrolled before-and-after study. SETTING: The urban tertiary teaching hospital with 1100 beds and receives approximately annual 90 000 ED visits. PARTICIPANTS: 122 370 patients who visited the ED during the before-and-the after period. INTERVENTIONS: ED staff were educated on the KTAS for 1 month, after which the KTAS evaluation period began. Admission, disposition, mortality and LOS were compared between the 'before' period (1 June 2015 to 30 April 2016) and the 'after' period (1 June 2016 to 30 April 2017). MAIN OUTCOME MEASURES: Admissions to the hospital, admission disposition, inpatient mortality and LOS. RESULTS: A total of 59 220 and 63 150 patients were included in the before-and-after periods of KTAS implementation, respectively. The pattern of admission and disposition changed significantly after implementation of the KTAS. The mean LOS was 343 min (standard deviation [SD] = 432 min) during the before period, which significantly decreased to 289 min (SD = 333 min) after implementation (P < 0.001). The total mortality rate was significantly reduced after implementation of the KTAS (213 (0.36%) vs. 179 (0.28%), P = 0.020). CONCLUSION: Implementation of the KTAS changed admission and disposition patterns and reduced the LOS and mortality in the ED.

Impact of bacteremia prediction rule in CAP: Before and after study.

OBJECTIVE: In cases of community acquired pneumonia (CAP), it has been known that blood cultures have low yields and rarely affect clinical outcomes. Despite many studies predicting the likelihood of bacteremia in CAP patients, those results have been rarely implemented in clinical practice, and use of blood culture in CAP is still increasing. This study evaluated impact of implementing a previously derived and validated bacteremia prediction rule. METHODS: In this registry-based before and after study, we used piecewise regression analysis to compare the blood culture rate before and after implementation of the prediction rule. We also compared 30-day mortality, emergency department (ED) length of stay, time-interval to initial antibiotics after ED arrival, and any changes to the antibiotics regimen as results of the blood cultures. In subgroup analysis, we compared two groups (with or without the use of the prediction rule) after implementation period, using propensity score matching. RESULTS: Following the implementation, the blood culture rate declined from 85.5% to 78.1% (P=0.003) without significant changes in 30-day mortality and antibiotics regimen. The interval to initial antibiotics (231min vs. 221min, P=0.362) and length of stay (1019min vs. 954min, P=0.354) were not significantly changed. In subgroup analysis, the group that use the prediction rule showed 25min faster antibiotics initiation (P=0.002) and 48min shorter length of stay (P=0.007) than the group that did not use the rule. CONCLUSION: Implementation of the bacteremia prediction rule in CAP patients reduced the blood culture rate without affecting the 30-day mortality and antibiotics regimen.

Incidence and risk factors of delayed intracranial hemorrhage in the emergency department.

OBJECTIVES: This study was performed to identify the risk factors for delayed intracranial hemorrhage and develop a risk stratification system for disposition of head trauma patients with negative initial brain imaging. METHODS: The data source was National Health Insurance Service-National Sample Cohort of Korea. We analyzed adult patients presenting to the ER from January 2004 to September 2012, who underwent brain imaging and discharged with or without short-term observation no longer than two days. The primary outcome was defined as any intracranial bleeding within a month defined by a new appearance of any of the diagnostic codes for intracranial hemorrhage accompanied by a new claim for brain imaging(s) within a month of the index visit. We performed a multivariable logistic regression analysis and built a parsimonious model for variable selection to develop a simple scoring system for risk stratification. RESULTS: During the study period, a total of 19,723 head injury cases were identified from the cohort and a total of 149 cases were identified as having delayed intracranial hemorrhage within 30days. In multivariable logistic regression model, old age, craniofacial fracture, neck injury, diabetes mellitus and hypertension were independent risk factors for delayed intracranial hemorrhage. We constructed the parsimonious model included age, craniofacial fracture and diabetes mellitus. The score showed area under the curve of 0.704 and positive predictive value of the score system was 0.014 when the score≥2. CONCLUSIONS: We found old age, associated craniofacial fracture, any neck injury, diabetes mellitus and hypertension are the independent risk factors of delayed intracranial hemorrhage.

Initial blood pH during cardiopulmonary resuscitation in out-of-hospital cardiac arrest patients: a multicenter observational registry-based study.

BACKGROUND: When an out-of-hospital cardiac arrest (OHCA) patient receives cardiopulmonary resuscitation (CPR) in the emergency department (ED), blood laboratory test results can be obtained by using point-of-care testing during CPR. In the present study, the relationship between blood laboratory test results during CPR and outcomes of OHCA patients was investigated. METHODS: This study was a multicenter retrospective analysis of prospective registered data that included 2716 OHCA patients. Data from the EDs of three university hospitals in different areas were collected from January 2009 to December 2014. Univariate and multivariable analyses were conducted to elucidate the factors associated with survival to discharge and neurological outcomes. A final analysis was conducted by including patients who had no prehospital return of spontaneous circulation and those who underwent rapid blood laboratory examination during CPR. RESULTS: Overall, 2229 OHCA patients were included in the final analysis. Among them, the rate of survival to discharge and a good Cerebral Performance Categories Scale score were 14% and 4.4%, respectively. The pH level was independently related to survival to hospital discharge (adjusted OR 6.287, 95% CI 2.601-15.197; p < 0.001) and good neurological recovery (adjusted OR 15.395, 95% CI 3.439-68.911; p < 0.001). None of the neurologically intact patients had low pH levels (< 6.8) or excessive potassium levels (> 8.5 mEq/L) during CPR. CONCLUSIONS: Among the blood laboratory test results during CPR of OHCA patients, pH and potassium levels were observed as independent factors associated with survival to hospital discharge, and pH level was considered as an independent factor related to neurological recovery.

Blood pressure-targeted stepwise resuscitation of hemorrhagic shock in a swine model.

BACKGROUND: Prolonged hemorrhagic shock and subsequent resuscitation frequently results in hemodynamic compromise. This study was designed to investigate the hemodynamic effect of the stepwise increase of blood pressure during initial resuscitation. MATERIALS AND METHODS: Fifteen anesthetized male pigs (35 ± 5 kg) were used. Hemorrhagic shock was induced by losing 40% of estimated blood volume over 40 min and 10% of estimated blood volume over 20 min through the femoral artery and was maintained at a mean arterial pressure (MAP) of 30 ± 3 mm Hg for 2.5 h. The resuscitation of the rapid resuscitation (RR) group was targeted to reach MAP of 70 ± 5 mm Hg immediately by transfusion of shed blood via the femoral artery. The resuscitation of the pressure-targeted stepwise resuscitation (PSR) group was targeted to increase MAP by 10 mm Hg every 10 min up to 70 mm Hg, and then, the MAP was maintained at 70 ± 5 mm Hg until transfusion of the entire shed blood. RESULTS: During the initial resuscitation period of 30 min, the heart rate was significantly lower in the RR group than in the PSR group (P < 0.05), and mixed venous oxygen saturation was significantly higher in the RR group than in the PSR group during the 30 min of initial resuscitation (P < 0.05). After 2 h of resuscitation, cardiac output and stroke volume were significantly higher in the PSR group than in the RR group (P < 0.05), and the systemic vascular resistance was significantly lower in the PSR group than in the RR group (P < 0.05). CONCLUSIONS: A stepwise increase of blood pressure compared with rapid normalization improves hemodynamic parameters in the swine hemorrhagic shock model.

Limiting scan range of cardiac CT and the chance of missed acute aortic syndrome.

PURPOSE: Recently, dedicated cardiac computed tomography (CT) has been performed to rule out acute coronary syndrome in patients with chest pain equivalents. However, acute aortic syndrome (AAS) could mimic acute coronary syndrome. We investigated the reliability of CT with a limited scan range for the detection of AAS. METHODS: Patients older than 18 years with a diagnosis of AAS were included for a 10-year period. We reviewed all patients' electronic medical record and cardiac CT scan images. The AAS lesions outside of the upper or lower margin of the cardiac CT scan range were measured. Other abnormalities defined as indirect evidence of AAS such as pericardial effusion were also collected. RESULTS: Of a total of 309 cases, 6 (1.9%; 95% confidence interval, 0.71-4.17) patients had aortic lesions outside of the cardiac CT scan range. One patient had an aortic lesion above the cardiac CT scan range, and 5 patients had aortic lesions below the cardiac CT scan range. CONCLUSIONS: Aortic lesions outside of the cardiac CT scan range were not rare. Therefore, using a cardiac CT might not guarantee ruling out AAS completely.

External validation of the STONE score and derivation of the modified STONE score.

OBJECTIVE: The STONE score is a clinical prediction rule for the presence of uncomplicated ureter stones with a low probability of acutely important alternative findings. This study performed an external validation of the STONE score, focusing on the Korean population, and a derivation of the modified STONE score for better specificity and sensitivity. METHODS: We retrospectively reviewed medical records of patients complaining of flank pain at a single emergency department from January 2013 to December 2014. Patients were categorized into 3 groups according to their STONE score. The prevalence of ureter stones and other alternative findings were calculated in each group. We derived a modified STONE score based on a multivariable analysis and performed an interval validation. RESULTS: From the 700 patients included in the analysis, 555 patients (79%) had a ureter stone. The area under the receiver operating characteristic curve of the STONE score was 0.92. The sensitivity of the high stone score was 0.56. In the modified STONE score, nausea, vomiting, and racial predictors were substituted by C-reactive protein and previous stone history. The area under the receiver operating characteristic curve and sensitivity of the modified STONE score in the internal validation group significantly increased to 0.94 and 0.80, respectively. CONCLUSION: The STONE score can be used to predict a ureter stone with a low probability of other alternative findings. The modified STONE score might increase the diagnostic performance in suspicious urinary stone cases. KEY POINTS: We performed external validation of the STONE score and derivation of the modified STONE score. This scoring system could help the clinicians with radiation reducing decision making.