Role of peak current in conversion of patients with ventricular fibrillation.

INTRODUCTION: Peak currents are the final arbiter of defibrillation in patients with ventricular fibrillation (VF). However, biphasic defibrillators continue to use energy in joules for electrical conversion in hopes that their impedance compensation properties will address transthoracic impedance (TTI), which must be overcome when a fixed amount of energy is delivered. However, optimal peak currents for conversion of VF remain unclear. We aimed to determine the role of peak current and optimal peak levels for conversion in collapsed VF patients. METHODS: Adult, non-pregnant patients presenting with non-traumatic VF were included in the study. All defibrillations that occurred were included. Impedance values during defibrillation were used to calculate peak current values. The endpoint was return of spontaneous circulation (ROSC). RESULTS: Of the 197 patients analysed, 105 had ROSC. Characteristics of patients with and without ROSC were comparable. Short duration of collapse < 10 minutes correlated positively with ROSC. Generally, patients with average or high TTI converted at lower peak currents. 25% of patients with high TTI converted at 13.3 ± 2.3 A, 22.7% with average TTI at 18.2 ± 2.5 A and 18.6% with low TTI at 27.0 ± 4.7 A (p = 0.729). Highest peak current conversions were at < 15 A and 15-20 A. Of the 44 patients who achieved first-shock ROSC, 33 (75.0%) received < 20 A peak current vs. > 20 A for the remaining 11 (25%) patients (p = 0.002). CONCLUSION: For best effect, priming biphasic defibrillators to deliver specific peak currents should be considered.

A multicenter prospective randomized study comparing the efficacy of escalating higher biphasic versus low biphasic energy defibrillations in patients presenting with cardiac arrest in the in-hospital environment.

BACKGROUND: Biphasic defibrillation has been practiced worldwide for >15 years. Yet, consensus does not exist on the best energy levels for optimal outcomes when used in patients with ventricular fibrillation (VF)/pulseless ventricular tachycardia (VT). METHODS: This prospective, randomized, controlled trial of 235 adult cardiac arrest patients with VF/VT was conducted in the emergency and cardiology departments. One group received low-energy (LE) shocks at 150-150-150 J and the other escalating higher-energy (HE) shocks at 200-300-360 J. If return of spontaneous circulation (ROSC) was not achieved by the third shock, LE patients crossed over to the HE arm and HE patients continued at 360 J. Primary end point was ROSC. Secondary end points were 24-hour, 7-day, and 30-day survival. RESULTS: Both groups were comparable for age, sex, cardiac risk factors, and duration of collapse and VF/VT. Of the 118 patients randomized to the LE group, 48 crossed over to the HE protocol, 24 for persistent VF, and 24 for recurrent VF. First-shock termination rates for HE and LE patients were 66.67% and 64.41%, respectively (P=0.78, confidence interval: 0.65-1.89). First-shock ROSC rates were 25.64% and 29.66%, respectively (P=0.56, confidence interval: 0.46-1.45). The 24-hour, 7-day, and 30-day survival rates were 85.71%, 74.29%, and 62.86% for first-shock ROSC LE patients and 70.00%, 50.00%, and 46.67% for first-shock ROSC HE patients, respectively. Conversion rates for further shocks at 200 J and 300 J were low, but increased to 38.95% at 360 J. CONCLUSION: First-shock termination and ROSC rates were not significantly different between LE and HE biphasic defibrillation for cardiac arrest patients. Patients responded best at 150/200 J and at 360 J energy levels. For patients with VF/pulseless VT, consideration is needed to escalate quickly to HE shocks at 360 J if not successfully defibrillated with 150 or 200 J initially.

Unplanned 3-day re-attendance rate at Emergency Department (ED) and hospital's bed occupancy rate (BOR).

BACKGROUND: Unplanned re-attendance at the Emergency Department (ED) is often monitored as a quality indicator of the care accorded to patients during their index ED visit. High bed occupancy rate (BOR) has been considered as a matter of reduced patient comfort and privacy. Most hospitals in Singapore operate under BORs above 85 %. This study aims to explore factors associated with the unplanned 3-day ED re-attendance rate and, in particular, if higher BOR is associated with higher 3-day unplanned ED re-attendance rate. METHODS: This was a multicenter retrospective study using time series data. Three acute tertiary hospitals were selected from all six adult public hospitals in Singapore based on data availability. Daily data from year 2008 to 2013 were collected from the study hospitals' information systems. These included: ED visit date, day of week, month, year, public holiday, daily hospital BOR, daily bed waiting time (BWT) at ED (both median and 95th percentile), daily ED admission rate, and 3-day ED re-attendance rate. The primary outcome of the study was unplanned 3-day ED re-attendance rate from all reasons. Both univariate analysis and generalized linear regression were respectively applied to study the crude and adjusted association between the unplanned 3-day ED re-attendance rate and its potential associated factors. All analyses were conducted using SPSS 18 (PASW 18, IBM). RESULTS: The average age of patients who visited ED was 35 years old (SD = 2), 37 years old (SD = 2), and 40 years old (SD = 2) in hospitals A, B, and C respectively. The average 3-day unplanned ED re-attendance rate was 4.9 % (SE = 0.47 %) in hospital A, 3.9 % (SE = 0.35 %) in hospital B, and 4.4 % (SE = 0.30 %) in hospital C. After controlling for other covariates, the unplanned 3-day ED re-attendance rates were significantly associated with hospital, time trend, day of week, daily average BOR, and ED admission rate. Strong day-of-week effect on early ED re-attendance rate was first explored in this study. Thursday had the lowest re-attendance rate, while Sunday has the highest re-attendance rate. The patients who visited at ED on the dates with higher BOR were more likely to re-attend the ED within 3 days for hospitals A and B. There was no significant association between BOR and ED re-attendance rate in hospital C. CONCLUSIONS: A study using time series data has been conducted to explore the factors associated with the unplanned 3-day ED re-attendance rate. Strong day-of-week effect was first reported. The association between BOR and the ED re-attendance rate varied with hospital.

Analysis of patient waiting time governed by a generic maximum waiting time policy with general phase-type approximations.

Waiting time can affect patient satisfaction and quality of care in the emergency department (ED). Studies have shown that waiting time accounted for more than 50 % of total patient turnaround time at ED. The objective of this study is to examine a maximum waiting time policy such that patients who would experience a long wait are assumed to be processed in a threshold period. In particular, we are interested to investigate the associated factors of the policy such as new mean waiting time and the threshold period and their interaction. Under the policy, original patient waiting distribution is transformed to a piecewise distribution where one piecewise discontinuous and one piecewise continuous distributions are further investigated. Under the phase-type (PH) distribution assumption on the original waiting time, we establish closed-form expressions concerning new mean waiting time and time points of the threshold period. By fitting PH distributions to patient waiting data of an emergency department in Singapore, the factors are then estimated under various scenarios using the obtained analytical expressions. Specifically, for a given target mean waiting time, the threshold period needed in the policy is estimated. New mean waiting time is assessed with different choices of the threshold period. Analytical expressions in terms of the variance of the transformed waiting time and the threshold period are also presented.

Real-time prediction of waiting time in the emergency department, using quantile regression.

STUDY OBJECTIVE: Emergency department (ED) waiting times can affect patient satisfaction and quality of care. We develop and validate a model that predicts an individual patient's median and 95th percentile waiting time by using only data available at triage. METHODS: From the existing ED information system, we extracted date and time of triage completion, start time of emergency physician consultation, and patient acuity category (1=most urgent, 3=least urgent). Quantile regression was applied for model development and parameter estimation by using visits from January 2011. We assessed absolute prediction error, defined as the median difference between the 50th percentile (median) predicted waiting time and actual waiting time, and the proportion of underestimated prediction, defined as the percentage of patients whose actual waiting time exceeded the 95th percentile prediction. The model was validated retrospectively with June 2010 data and prospectively with data from April to June 2011 after integration with the existing ED information system. RESULTS: The derivation set included 13,200 ED visits; 903 (6.8%) were patient acuity category 1, 5,530 (41.9%) were patient acuity category 2, and 6,767 (51.3%) were patient acuity category 3. The median and 95th percentile waiting times were 17 and 57 minutes for patient acuity category 2 and 21 and 89 minutes for patient acuity category 3, respectively. The final model used predictors of patient acuity category, patient queue sizes, and flow rates only. In the retrospective validation, 5.9% of patient acuity category 2 and 5.4% of category 3 waiting times were underestimated. The median absolute prediction error was 11.9 minutes (interquantile range [IQR] 5.9 to 22.1 minutes) for patient acuity category 2 and 15.7 minutes (IQR 7.5 to 30.1 minutes) for category 3. In prospective validation, 4.3% of patient acuity category 2 and 5.8% of category 3 waiting times were underestimated. The median absolute prediction error was 9.2 minutes (IQR 4.4 to 15.1 minutes) for patient acuity category 2 and 12.9 minutes (IQR 6.5 to 22.5 minutes) for category 3. CONCLUSION: Using only a few data elements available at triage, the model predicts individual patients' waiting time with good accuracy.

Predicting hospital admissions at emergency department triage using routine administrative data.

OBJECTIVES: To be able to predict, at the time of triage, whether a need for hospital admission exists for emergency department (ED) patients may constitute useful information that could contribute to systemwide hospital changes designed to improve ED throughput. The objective of this study was to develop and validate a predictive model to assess whether a patient is likely to require inpatient admission at the time of ED triage, using routine hospital administrative data. METHODS: Data collected at the time of triage by nurses from patients who visited the ED in 2007 and 2008 were extracted from hospital administrative databases. Variables included were demographics (age, sex, and ethnic group), ED visit or hospital admission in the preceding 3 months, arrival mode, patient acuity category (PAC) of the ED visit, and coexisting chronic diseases (diabetes, hypertension, and dyslipidemia). Chi-square tests were used to study the association between the selected possible risk factors and the need for hospital admission. Logistic regression was applied to develop the prediction model. Data were split for derivation (60%) and validation (40%). Receiver operating characteristic curves and goodness-of-fit tests were applied to the validation data set to evaluate the model. RESULTS: Of 317,581 ED patient visits, 30.2% resulted in immediate hospital admission. In the developed predictive model, age, PAC status, and arrival mode were most predictive of the need for immediate hospital inpatient admission. The c-statistic of the receiver operating characteristic (ROC) curve was 0.849 (95% confidence interval [CI] = 0.847 to 0.851). The goodness-of-fit test showed that the predicted patients' admission risks fit the patients' actual admission status well. CONCLUSIONS: A model for predicting the risk of immediate hospital admission at triage for all-cause ED patients was developed and validated using routinely collected hospital data. Early prediction of the need for hospital admission at the time of triage may help identify patients deserving of early admission planning and resource allocation and thus potentially reduce ED overcrowding.

Improving the clinical diagnosis of influenza--a comparative analysis of new influenza A (H1N1) cases.

BACKGROUND: The presentation of new influenza A(H1N1) is broad and evolving as it continues to affect different geographic locations and populations. To improve the accuracy of predicting influenza infection in an outpatient setting, we undertook a comparative analysis of H1N1(2009), seasonal influenza, and persons with acute respiratory illness (ARI) in an outpatient setting. METHODOLOGY/PRINCIPAL FINDINGS: Comparative analyses of one hundred non-matched cases each of PCR confirmed H1N1(2009), seasonal influenza, and ARI cases. Multivariate analysis was performed to look for predictors of influenza infection. Receiver operating characteristic curves were constructed for various combinations of clinical and laboratory case definitions. The initial clinical and laboratory features of H1N1(2009) and seasonal influenza were similar. Among ARI cases, fever, cough, headache, rhinorrhea, the absence of leukocytosis, and a normal chest radiograph positively predict for both PCR-confirmed H1N1-2009 and seasonal influenza infection. The sensitivity and specificity of current WHO and CDC influenza-like illness (ILI) criteria were modest in predicting influenza infection. However, the combination of WHO ILI criteria with the absence of leukocytosis greatly improved the accuracy of diagnosing H1N1(2009) and seasonal influenza (positive LR of 7.8 (95%CI 3.5-17.5) and 9.2 (95%CI 4.1-20.3) respectively). CONCLUSIONS/SIGNIFICANCE: The clinical presentation of H1N1(2009) infection is largely indistinguishable from that of seasonal influenza. Among patients with acute respiratory illness, features such as a temperature greater than 38 degrees C, rhinorrhea, a normal chest radiograph, and the absence of leukocytosis or significant gastrointestinal symptoms were all positively associated with H1N1(2009) and seasonal influenza infection. An enhanced ILI criteria that combines both a symptom complex with the absence of leukocytosis on testing can improve the accuracy of predicting both seasonal and H1N1-2009 influenza infection.

Comparison of the New Injury Severity Score and the Injury Severity Score.

BACKGROUND: The New Injury Severity Score (NISS) was proposed in 1997 to replace the Injury Severity Score (ISS) because it is more sensitive for mortality. We aim to test whether this is true in our patients. METHODS: This study was a retrospective review of data from 6,231 consecutive patients over 3 years in the trauma registry of a Level I trauma center studying outcome, ISS, and NISS. RESULTS: Misclassification rates were 3.97% for the NISS and 4.35% for the ISS. The receiver operating characteristic curve areas were 0.936 and 0.94, respectively. Neither the ISS nor the NISS were well calibrated (Hosmer-Lemeshow statistic, 36.11 and 49.28, respectively; p < 0.001). CONCLUSION: The NISS should not replace the ISS, as they share similar accuracy and calibration.