Utilizing machine learning dimensionality reduction for risk stratification of chest pain patients in the emergency department.

BACKGROUND: Chest pain is among the most common presenting complaints in the emergency department (ED). Swift and accurate risk stratification of chest pain patients in the ED may improve patient outcomes and reduce unnecessary costs. Traditional logistic regression with stepwise variable selection has been used to build risk prediction models for ED chest pain patients. In this study, we aimed to investigate if machine learning dimensionality reduction methods can improve performance in deriving risk stratification models. METHODS: A retrospective analysis was conducted on the data of patients > 20 years old who presented to the ED of Singapore General Hospital with chest pain between September 2010 and July 2015. Variables used included demographics, medical history, laboratory findings, heart rate variability (HRV), and heart rate n-variability (HRnV) parameters calculated from five to six-minute electrocardiograms (ECGs). The primary outcome was 30-day major adverse cardiac events (MACE), which included death, acute myocardial infarction, and revascularization within 30 days of ED presentation. We used eight machine learning dimensionality reduction methods and logistic regression to create different prediction models. We further excluded cardiac troponin from candidate variables and derived a separate set of models to evaluate the performance of models without using laboratory tests. Receiver operating characteristic (ROC) and calibration analysis was used to compare model performance. RESULTS: Seven hundred ninety-five patients were included in the analysis, of which 247 (31%) met the primary outcome of 30-day MACE. Patients with MACE were older and more likely to be male. All eight dimensionality reduction methods achieved comparable performance with the traditional stepwise variable selection; The multidimensional scaling algorithm performed the best with an area under the curve of 0.901. All prediction models generated in this study outperformed several existing clinical scores in ROC analysis. CONCLUSIONS: Dimensionality reduction models showed marginal value in improving the prediction of 30-day MACE for ED chest pain patients. Moreover, they are black box models, making them difficult to explain and interpret in clinical practice.

Heart rate n-variability (HRnV) and its application to risk stratification of chest pain patients in the emergency department.

BACKGROUND: Chest pain is one of the most common complaints among patients presenting to the emergency department (ED). Causes of chest pain can be benign or life threatening, making accurate risk stratification a critical issue in the ED. In addition to the use of established clinical scores, prior studies have attempted to create predictive models with heart rate variability (HRV). In this study, we proposed heart rate n-variability (HRnV), an alternative representation of beat-to-beat variation in electrocardiogram (ECG), and investigated its association with major adverse cardiac events (MACE) in ED patients with chest pain. METHODS: We conducted a retrospective analysis of data collected from the ED of a tertiary hospital in Singapore between September 2010 and July 2015. Patients > 20 years old who presented to the ED with chief complaint of chest pain were conveniently recruited. Five to six-minute single-lead ECGs, demographics, medical history, troponin, and other required variables were collected. We developed the HRnV-Calc software to calculate HRnV parameters. The primary outcome was 30-day MACE, which included all-cause death, acute myocardial infarction, and revascularization. Univariable and multivariable logistic regression analyses were conducted to investigate the association between individual risk factors and the outcome. Receiver operating characteristic (ROC) analysis was performed to compare the HRnV model (based on leave-one-out cross-validation) against other clinical scores in predicting 30-day MACE. RESULTS: A total of 795 patients were included in the analysis, of which 247 (31%) had MACE within 30 days. The MACE group was older, with a higher proportion being male patients. Twenty-one conventional HRV and 115 HRnV parameters were calculated. In univariable analysis, eleven HRV and 48 HRnV parameters were significantly associated with 30-day MACE. The multivariable stepwise logistic regression identified 16 predictors that were strongly associated with MACE outcome; these predictors consisted of one HRV, seven HRnV parameters, troponin, ST segment changes, and several other factors. The HRnV model outperformed several clinical scores in the ROC analysis. CONCLUSIONS: The novel HRnV representation demonstrated its value of augmenting HRV and traditional risk factors in designing a robust risk stratification tool for patients with chest pain in the ED.

Risk stratification of patients with atrial fibrillation in the emergency department.

INTRODUCTION AND METHODS: Early and accurate risk stratification of patients with atrial fibrillation (AF) in the emergency department (ED) could aid the physician in determining a timely treatment strategy appropriate to the severity of disease. We conducted a retrospective review of 243 adult patients who presented to a tertiary ED with AF in 2017. Primary outcome studied was 30-day adverse event (a composite outcome of repeat visit to the ED, cardiovascular complications, and all-cause mortality). Secondary outcome studied was 90-day all-cause mortality. We compared the performance of the RED-AF, AFTER and CHA2DS2-VASc score by plotting receiver operating characteristic (ROC) curves and estimating the areas under curves (AUC), and assessed the potential to further improve the tools with their incorporation of new variables. RESULTS: Existing scoring tools had poor predictive value for 30-day adverse events, with the RED-AF score performing comparatively better, followed by the AFTER and CHA2DS2-VASc score. All scores performed collectively better to predict 90-day mortality, with the AFTER score performing the best, followed by the RED-AF and CHA2DS2-VASc score. By incorporating heart rate at initial presentation to the ED as a variable into the AFTER Score, we generated a Modified AFTER Score with superior predictive performance above existing scores for 90-day mortality. CONCLUSION: Existing scores collectively performed poorly to predict 30-day adverse outcomes, but the AFTER and Modified AFTER score showed good predictive value for 90-day mortality. Further studies should be done to validate their use in guiding clinician's disposition of patients with AF in the ED.

Integrating heart rate variability, vital signs, electrocardiogram, and troponin to triage chest pain patients in the ED.

BACKGROUND: Current triage methods for chest pain patients typically utilize symptoms, electrocardiogram (ECG), and vital sign data, requiring interpretation by dedicated triage clinicians. In contrast, we aimed to create a quickly obtainable model integrating the objective parameters of heart rate variability (HRV), troponin, ECG, and vital signs to improve accuracy and efficiency of triage for chest pain patients in the emergency department (ED). METHODS: Adult patients presenting to the ED with chest pain from September 2010 to July 2015 were conveniently recruited. The primary outcome was a composite of revascularization, death, cardiac arrest, cardiogenic shock, or lethal arrhythmia within 72-h of presentation to the ED. To create the chest pain triage (CPT) model, logistic regression was done where potential covariates comprised of vital signs, ECG parameters, troponin, and HRV measures. Current triage methods at our institution and modified early warning score (MEWS) were used as comparators. RESULTS: A total of 797 patients were included for final analysis of which 146 patients (18.3%) met the primary outcome. Patients were an average age of 60years old, 68% male, and 56% triaged to the most acute category. The model consisted of five parameters: pain score, ST-elevation, ST-depression, detrended fluctuation analysis (DFA) α1, and troponin. CPT model>0.09, CPT model>0.15, current triage methods, and MEWS≥2 had sensitivities of 86%, 74%, 75%, and 23%, respectively, and specificities of 45%, 71%, 48%, and 78%, respectively. CONCLUSION: The CPT model may improve current clinical triage protocols for chest pain patients in the ED.

Heart Rate Variability Analysis in Patients Who Have Bradycardia Presenting to the Emergency Department with Chest Pain.

BACKGROUND: Heart rate variability (HRV) is a noninvasive method to measure the function of the autonomic nervous system. It has been used to risk stratify patients with undifferentiated chest pain in the emergency department (ED). However, bradycardia can have a modifying effect on HRV. OBJECTIVE: In this study, we aimed to determine how bradycardia affected HRV analysis in patients who presented with chest pain to the ED. METHODS: Adult patients presenting to the ED at Singapore General Hospital with chest pain were included in the study. Patients with non-sinus rhythm on electrocardiogram (ECG) were excluded. HRV parameters, including time domain, frequency domain, and nonlinear variables, were analyzed from a 5-min ECG segment. Occurrence of a major adverse cardiac event ([MACE], e.g., acute myocardial infarction, percutaneous coronary intervention, coronary artery bypass graft, or mortality) within 30 days of presentation to the ED was also recorded. RESULTS: A total of 797 patients were included for analysis with 248 patients (31.1%) with 30-day MACE and 135 patients with bradycardia (16.9%). Compared to non-bradycardic patients, bradycardic patients had significant differences in all HRV parameters suggesting an increased parasympathetic component. Among non-bradycardic patients, comparing those who did and did not have 30-day MACE, there were significant differences predominantly in time domain variables, suggesting decreased HRV. In bradycardic patients, the same analysis revealed significant differences in predominantly frequency-domain variables suggesting decreased parasympathetic input. CONCLUSIONS: Chest pain patients with bradycardia have increased HRV compared to those without bradycardia. This may have important implications on HRV modeling strategies for risk stratification of bradycardic and non-bradycardic chest pain patients.

A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain.

BACKGROUND: Risk stratification models can be employed at the emergency department (ED) to evaluate patient prognosis and guide choice of treatment. We derived and validated a new cardiovascular risk stratification model comprising vital signs, heart rate variability (HRV) parameters, and demographic and electrocardiogram (ECG) variables. METHODS: We conducted a single-center, observational cohort study of patients presenting to the ED with chest pain. All patients above 21 years of age and in sinus rhythm were eligible. ECGs were collected and evaluated for 12-lead ECG abnormalities. Routine monitoring ECG data were processed to obtain HRV parameters. Vital signs and demographic data were obtained from electronic medical records. Thirty-day major adverse cardiac events (MACE) were the primary endpoint, including death, acute myocardial infarction, and revascularization. Candidate variables were identified using univariate analysis; the model for the final risk score was derived by multivariable logistic regression. We compared the performance of the new model with that of the thrombolysis in myocardial infarct (TIMI) score using receiver operating characteristic (ROC) analysis. RESULTS: In total, 763 patients were included in this study; 254 (33 %) met the primary endpoint, the mean age was 60 (σ = 13) years, and the majority was male (65 %). Nineteen candidate predictors were entered into the multivariable model for backward variable elimination. The final model contained 10 clinical variables, including age, gender, heart rate, three HRV parameters (average R-to-R interval (RR), triangular interpolation of normal-to-normal (NN) intervals, and high-frequency power), and four 12-lead ECG variables (ST elevation, ST depression, Q wave, and QT prolongation). Our proposed model outperformed the TIMI score for prediction of MACE (area under the ROC curve 0.780 versus 0.653). At the cutoff score of 9 (range 0-37), our model had sensitivity of 0.709 (95 % CI 0.653, 0.765), specificity of 0.674 (95 % CI 0.633, 0.715), positive predictive value of 0.520 (95 % CI 0.468, 0.573), and negative predictive value of 0.823 (95 % CI 0.786, 0.859). CONCLUSIONS: A non-invasive and objective ECG- and HRV-based risk stratification tool performed well against the TIMI score, but future research warrants use of an external validation cohort.

Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection.

BACKGROUND: The key aim of triage in chest pain patients is to identify those with high risk of adverse cardiac events as they require intensive monitoring and early intervention. In this study, we aim to discover the most relevant variables for risk prediction of major adverse cardiac events (MACE) using clinical signs and heart rate variability. METHODS: A total of 702 chest pain patients at the Emergency Department (ED) of a tertiary hospital in Singapore were included in this study. The recruited patients were at least 30 years of age and who presented to the ED with a primary complaint of non-traumatic chest pain. The primary outcome was a composite of MACE such as death and cardiac arrest within 72 h of arrival at the ED. For each patient, eight clinical signs such as blood pressure and temperature were measured, and a 5-min ECG was recorded to derive heart rate variability parameters. A random forest-based novel method was developed to select the most relevant variables. A geometric distance-based machine learning scoring system was then implemented to derive a risk score from 0 to 100. RESULTS: Out of 702 patients, 29 (4.1%) met the primary outcome. We selected the 3 most relevant variables for predicting MACE, which were systolic blood pressure, the mean RR interval and the mean instantaneous heart rate. The scoring system with these 3 variables produced an area under the curve (AUC) of 0.812, and a cutoff score of 43 gave a sensitivity of 82.8% and specificity of 63.4%, while the scoring system with all the 23 variables had an AUC of 0.736, and a cutoff score of 49 gave a sensitivity of 72.4% and specificity of 63.0%. Conventional thrombolysis in myocardial infarction score and the modified early warning score achieved AUC values of 0.637 and 0.622, respectively. CONCLUSIONS: It is observed that a few predictors outperformed the whole set of variables in predicting MACE within 72 h. We conclude that more predictors do not necessarily guarantee better prediction results. Furthermore, machine learning-based variable selection seems promising in discovering a few relevant and significant measures as predictors.

Heart rate variability risk score for prediction of acute cardiac complications in ED patients with chest pain.

BACKGROUND: We aimed to develop a risk score incorporating heart rate variability (HRV) and traditional vital signs for the prediction of early mortality and complications in patients during the initial presentation to the emergency department (ED) with chest pain. METHODS: We conducted a prospective observational study of patients with a primary complaint of chest pain at the ED of a tertiary hospital. The primary outcome was a composite of mortality, cardiac arrest, ventricular tachycardia, hypotension requiring inotropes or intraaortic balloon pump insertion, intubation or mechanical ventilation, complete heart block, bradycardia requiring pacing, and recurrent ischemia requiring revascularization, all within 72 hours of arrival at ED. RESULTS: Three hundred nine patients were recruited, and 25 patients met the primary outcome. Backwards stepwise logistic regression was used to derive a scoring model that included heart rate, systolic blood pressure, respiratory rate, and low frequency to high frequency ratio. For predicting complications within 72 hours, the risk score performed with an area under the curve of 0.835 (95% confidence interval [CI], 0.749-0.920); and a cutoff of 4 and higher in the risk score gave a sensitivity of 0.880 (95% CI, 0.677-0.968), specificity of 0.680 (95% CI, 0.621-0.733), positive predictive value of 0.195, and negative predictive value of 0.985. The risk score performed better than ST elevation/depression and troponin T in predicting complications within 72 hours. CONCLUSION: A risk score incorporating heart rate variability and vital signs performed well in predicting mortality and other complications within 72 hours after arrival at ED in patients with chest pain.

Febrile infants risk score at triage (FIRST) for the early identification of serious bacterial infections.

We aimed to derive the Febrile Infants Risk Score at Triage (FIRST) to quantify risk for serious bacterial infections (SBIs), defined as bacteremia, meningitis and urinary tract infections. We performed a prospective observational study on febrile infants < 3 months old at a tertiary hospital in Singapore between 2018 and 2021. We utilized machine learning and logistic regression to derive 2 models: FIRST, based on patient demographics, vital signs and history, and FIRST + , adding laboratory results to the same variables. SBIs were diagnosed in 224/1002 (22.4%) infants. Among 994 children with complete data, age (adjusted odds ratio [aOR] 1.01 95%CI 1.01-1.02, p < 0.001), high temperature (aOR 2.22 95%CI 1.69-2.91, p < 0.001), male sex (aOR 2.62 95%CI 1.86-3.70, p < 0.001) and fever of ≥ 2 days (aOR 1.79 95%CI 1.18-2.74, p = 0.007) were independently associated with SBIs. For FIRST + , abnormal urine leukocyte esterase (aOR 16.46 95%CI 10.00-27.11, p < 0.001) and procalcitonin (aOR 1.05 95%CI 1.01-1.09, p = 0.009) were further identified. A FIRST + threshold of ≥ 15% predicted risk had a sensitivity of 81.8% (95%CI 70.5-91.0%) and specificity of 65.6% (95%CI 57.8-72.7%). In the testing dataset, FIRST + had an area under receiver operating characteristic curve of 0.87 (95%CI 0.81-0.94). These scores can potentially guide triage and prioritization of febrile infants.

Adding heart rate n-variability (HRnV) to clinical assessment potentially improves prediction of serious bacterial infections in young febrile infants at the emergency department: a prospective observational study.

Background: We aim to investigate the utility of heart rate variability (HRV) and heart rate n-variability (HRnV) in addition to vital signs and blood biomarkers, among febrile young infants at risk of serious bacterial infections (SBIs). Methods: We performed a prospective observational study between December 2017 and November 2021 in a tertiary paediatric emergency department (ED). We included febrile infants <90 days old with a temperature ≥38 ℃. We obtained HRV and HRnV parameters via a single lead electrocardiogram. HRV measures beat-to-beat (R-R) oscillation and reflects autonomic nervous system (ANS) regulation. HRnV includes overlapping and non-overlapping R-R intervals and provides additional physiological information. We defined SBIs as meningitis, bacteraemia and urinary tract infections (UTIs). We performed area under curve (AUC) analysis to assess predictive performance. Results: We recruited 330 and analysed 312 infants. The median age was 35.5 days (interquartile range 13.0-61.0); 74/312 infants (23.7%) had SBIs with the most common being UTIs (66/72, 91.7%); 2 infants had co-infections. No patients died and 32/312 (10.3%) received fluid resuscitation. Adding HRV and HRnV to demographics and vital signs at ED triage successively improved the AUC from 0.765 [95% confidence interval (CI): 0.705-0.825] to 0.776 (95% CI: 0.718-0.835) and 0.807 (95% CI: 0.752-0.861) respectively. The final model including demographics, vital signs, HRV, HRnV and blood biomarkers had an AUC of 0.874 (95% CI: 0.828-0.921). Conclusions: Addition of HRV and HRnV to current assessment tools improved the prediction of SBIs among febrile infants at ED triage. We intend to validate our findings and translate them into tools for clinical care in the ED.

Improved neurologically intact survival with the use of an automated, load-distributing band chest compression device for cardiac arrest presenting to the emergency department.

INTRODUCTION: It has been unclear if mechanical cardiopulmonary resuscitation (CPR) is a viable alternative to manual CPR. We aimed to compare resuscitation outcomes before and after switching from manual CPR to load-distributing band (LDB) CPR in a multi-center emergency department (ED) trial. METHODS: We conducted a phased, prospective cohort evaluation with intention-to-treat analysis of adults with non-traumatic cardiac arrest. At these two urban EDs, systems were changed from manual CPR to LDB-CPR. Primary outcome was survival to hospital discharge, with secondary outcome measures of return of spontaneous circulation, survival to hospital admission and neurological outcome at discharge. RESULTS: A total of 1,011 patients were included in the study, with 459 in the manual CPR phase (January 01, 2004, to August 24, 2007) and 552 patients in the LDB-CPR phase (August 16, 2007, to December 31, 2009). In the LDB phase, the LDB device was applied in 454 patients (82.3%). Patients in the manual CPR and LDB-CPR phases were comparable for mean age, gender and ethnicity. The mean duration from collapse to arrival at ED (min) for manual CPR and LDB-CPR phases was 34:03 (SD16:59) and 33:18 (SD14:57) respectively. The rate of survival to hospital discharge tended to be higher in the LDB-CPR phase (LDB 3.3% vs Manual 1.3%; adjusted OR, 1.42; 95% CI, 0.47, 4.29). There were more survivors in LDB group with cerebral performance category 1 (good) (Manual 1 vs LDB 12, P = 0.01). Overall performance category 1 (good) was Manual 1 vs LDB 10, P = 0.06. CONCLUSIONS: A resuscitation strategy using LDB-CPR in an ED environment was associated with improved neurologically intact survival on discharge in adults with prolonged, non-traumatic cardiac arrest.

Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score.

INTRODUCTION: A key aim of triage is to identify those with high risk of cardiac arrest, as they require intensive monitoring, resuscitation facilities, and early intervention. We aim to validate a novel machine learning (ML) score incorporating heart rate variability (HRV) for triage of critically ill patients presenting to the emergency department by comparing the area under the curve, sensitivity and specificity with the modified early warning score (MEWS). METHODS: We conducted a prospective observational study of critically ill patients (Patient Acuity Category Scale 1 and 2) in an emergency department of a tertiary hospital. At presentation, HRV parameters generated from a 5-minute electrocardiogram recording are incorporated with age and vital signs to generate the ML score for each patient. The patients are then followed up for outcomes of cardiac arrest or death. RESULTS: From June 2006 to June 2008 we enrolled 925 patients. The area under the receiver operating characteristic curve (AUROC) for ML scores in predicting cardiac arrest within 72 hours is 0.781, compared with 0.680 for MEWS (difference in AUROC: 0.101, 95% confidence interval: 0.006 to 0.197). As for in-hospital death, the area under the curve for ML score is 0.741, compared with 0.693 for MEWS (difference in AUROC: 0.048, 95% confidence interval: -0.023 to 0.119). A cutoff ML score ≥ 60 predicted cardiac arrest with a sensitivity of 84.1%, specificity of 72.3% and negative predictive value of 98.8%. A cutoff MEWS ≥ 3 predicted cardiac arrest with a sensitivity of 74.4%, specificity of 54.2% and negative predictive value of 97.8%. CONCLUSION: We found ML scores to be more accurate than the MEWS in predicting cardiac arrest within 72 hours. There is potential to develop bedside devices for risk stratification based on cardiac arrest prediction.

EZ-IO in the ED: an observational, prospective study comparing flow rates with proximal and distal tibia intraosseous access in adults.

INTRODUCTION: Intraosseous (IO) access is an important alternative to conventional intravenous access when intravenous access is difficult. METHODS: A nonrandomized, prospective, observational study comparing flow rates with distal and proximal tibia IO access in adults using the EZ-IO-powered drill device. The proximal tibia was the first site of insertion, and a second IO was inserted in the distal tibia if clinically indicated. Intravenous saline infusion was started for all patients, initially without, then with a pressure bag device applied. RESULTS: From September 19, 2008 to November 3, 2010, 22 patients were recruited, with 20 proximal tibial and 22 distal tibia insertions. Two patients had only distal tibia IO insertions. Five distal tibia and 3 proximal tibia insertions had no flow when initiating normal saline infusion without pressure. Upon comparing the mean flow rates without pressure bag, it is significantly faster in the proximal tibia, 4.96 mL/min, compared with distal tibia, 2.07 ml/min, difference of 2.89 ml/min (95% CI 1.20-4.58). Flow rates with pressure bags also revealed a similar result. Flow rates in the proximal tibia were significantly faster, 7.70 ml/min to that of distal tibia, 3.80 ml/min, difference of 3.89 ml/min (95% CI 1.68-6.10). In both proximal and distal tibia groups, the flow rates are also significantly faster with pressure bags compared with without. CONCLUSION: Flow rates are significantly faster in the proximal tibia compared with the distal tibia. In addition, flow rates with pressure bags are significantly faster than without pressure bags in both groups.

Clinical evaluation of the use of laryngeal tube versus laryngeal mask airway for out-of-hospital cardiac arrest by paramedics in Singapore.

INTRODUCTION: It remains unclear which advanced airway device has better placement success and fewer adverse events in out-of-hospital cardiac arrests (OHCAs). This study aimed to evaluate the efficacy of the VBM laryngeal tube (LT) against the laryngeal mask airway (LMA) in OHCAs managed by emergency ambulances in Singapore. METHODS: This was a real-world, prospective, cluster-randomised crossover study. All OHCA patients above 13 years of age who were suitable for resuscitation were randomised to receive either LT or LMA. The primary outcome was placement success. Per-protocol analysis was performed, and the association between outcomes and airway device group was compared using multivariate binomial logistic regression analysis. RESULTS: Of 965 patients with OHCAs from March 2016 to January 2018, 905 met the inclusion criteria, of whom 502 (55.5%) were randomised to receive LT while 403 (44.5%) were randomised to receive LMA. Only 174 patients in the LT group actually received the device owing to noncompliance. Placement success rate for LT was lower than for LMA (adjusted odds ratio [OR] 0.52, 95% confidence interval [CI] 0.31-0.90). Complications were more likely when using LT (OR 2.82,0 95% CI 1.64-4.86). Adjusted OR for prehospital return of spontaneous circulation (ROSC) was similar in both groups. A modified intention-to-treat analysis showed similar outcomes to the per-protocol analysis between the groups. CONCLUSION: LT was associated with poorer placement success and higher complication rates than LMA. The likelihood of prehospital ROSC was similar between the two groups. Familiarity bias and a low compliance rate to LT were the main limitations of this study.

Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.

Sepsis is a potentially life-threatening condition that requires prompt recognition and treatment. Recently, heart rate variability (HRV), a measure of the cardiac autonomic regulation derived from short electrocardiogram tracings, has been found to correlate with sepsis mortality. This paper presents using novel heart rate n-variability (HRnV) measures for sepsis mortality risk prediction and comparing against current mortality prediction scores. This study was a retrospective cohort study on patients presenting to the emergency department of a tertiary hospital in Singapore between September 2014 to April 2017. Patients were included if they were above 21 years old and were suspected of having sepsis by their attending physician. The primary outcome was 30-day in-hospital mortality. Stepwise multivariable logistic regression model was built to predict the outcome, and the results based on 10-fold cross-validation were presented using receiver operating curve analysis. The final predictive model comprised 21 variables, including four vital signs, two HRV parameters, and 15 HRnV parameters. The area under the curve of the model was 0.77 (95% confidence interval 0.70-0.84), outperforming several established clinical scores. The HRnV measures may have the potential to allow for a rapid, objective, and accurate means of patient risk stratification for sepsis severity and mortality. Our exploration of the use of wealthy inherent information obtained from novel HRnV measures could also create a new perspective for data scientists to develop innovative approaches for ECG analysis and risk monitoring.

Comparison of inhalational methoxyflurane (Penthrox®) and intramuscular tramadol for prehospital analgesia.

INTRODUCTION: Treatment of pain is an important component of prehospital care. Inhalational analgesia agents have attractive strengths, but there is a paucity of studies comparing these with more conventional agents. We aimed to compare inhalational methoxyflurane and intramuscular (IM) tramadol as first-contact analgesia in the Singapore national ambulance service. METHODS: Ambulances were randomised to carry either methoxyflurane or IM tramadol for the first six months and crossed over to the other arm after six months. Patients aged ≥ 16 years, with acute pain arising from musculoskeletal trauma with Numerical Rating Scale (NRS) score ≥ 3 were enrolled. Variables included NRS reduction, time variables, adverse effects, Ramsay Sedation Scores, and patient and paramedic satisfaction scores on a Likert scale. RESULTS: A total of 369 patients were enrolled into this study, but 26 patients were excluded due to missing data. The methoxyflurane arm had a shorter median time taken from arrival at the scene to drug administration (9.0 [interquartile range 6.0-14.0] minutes vs. 11.0 [interquartile range 8.0-15.0] minutes). For patients who achieved reduction in NRS ≥ 3 within 20 minutes, those in the methoxyflurane arm took a shorter time. However, the methoxyflurane (46.7%) arm experienced lower proportion of patients not achieving NRS reduction ≥ 3 when compared to the tramadol (71.6%) arm after over 20 minutes. The methoxyflurane arm had significantly higher paramedic and patient satisfaction scores. CONCLUSION: For the doses of medication used in this implementation study, methoxyflurane was superior in efficacy, speed of onset and administration, but had more minor adverse effects when compared to IM tramadol.

Clinical evaluation of intravenous alone versus intravenous or intraosseous access for treatment of out-of-hospital cardiac arrest.

OBJECTIVE: Obtaining vascular access during out-of-hospital cardiac arrest (OHCA) is challenging. The aim of this study was to determine if using intraosseous (IO) access when intravenous (IV) access fails improves outcomes. METHODS: This was a prospective, parallel-group, cluster-randomised study that compared 'IV only' against 'IV + IO' in OHCA patients, where if 2 IV attempts failed or took more than 90 s, paramedics had 2 further attempts of IO. Primary outcome was any return of spontaneous circulation (ROSC). Secondary outcomes were insertion success rate, adrenaline administration, time to adrenaline and survival outcome. RESULTS: A total of 1007 patients were included in the analysis. An Intention To Treat analysis showed a significant difference in success rates of obtaining vascular access in the IV + IO arm compared to the IV arm (76.6% vs 61.1% p = 0.001). There were significantly more patients in the IV + IO arm than the IV arm being administered prehospital adrenaline (71.3% vs 55.4% p = 0.001). The IV + IO arm also received adrenaline faster compared to the IV arm in terms of median time from emergency call to adrenaline (23 min vs 25 min p = 0.001). There was no significant difference in ROSC (adjusted OR 0.99 95%CI: 0.75-1.29), survival to discharge or survival with CPC 2 or better in both groups. A Per Protocol analysis also showed there was higher success in obtaining vascular access in the IV + IO arm, but ROSC and survival outcomes were not statistically different. CONCLUSION: Using IO when IV failed led to a higher rate of vascular access, prehospital adrenaline administration and faster adrenaline administration. However, it was not associated with higher ROSC, survival to discharge, or good neurological outcome.

Safety and Efficacy of CarbonCool Half-Body Vest for HAZMAT Decontamination Crews Wearing Personal Protective Equipment: A Pilot Study.

BACKGROUND: Personal protective equipment (PPE) are essential for medical personnel responding to hazardous materials (HAZMAT) incidents. However, their impermeable design causes increased physiological strain and reduced thermoregulation, limiting work times and causing heat-related illnesses (HRI). Use of wearable cooling devices slow heat accumulation and have been shown to reduce thermal and cardiovascular strain in such situations. METHODS: This was a prospective clinical evaluation to determine the tolerability and effectiveness of the CarbonCool cooling system - a half-body cooling vest - in participants undergoing a HAZMAT decontamination recertification. Physiological measurements (heart rate [HR], weight, temperature, and blood pressure) and participant feedback were obtained. The main outcome of interest was participants' tolerability of the cooling vest. RESULTS: A total of 23 healthy participants were recruited, with 10 randomized to the intervention group and 13 in the control group. Mean age in the control and intervention group was 35.5 years old (SD = 7.8) and 30.0 years old (SD = 6.2), respectively. Qualitative feedback obtained from participants regarding safety, mobility, and cooling efficacy was largely positive. Difference of before-after temperature and HR was 0.3°C (SD = 0.8) and 11.5bpm (SD = 13.6) in the control group compared to 0.0°C (SD = 0.5) and 0.0bpm (SD = 6.4) for the intervention group. CONCLUSION: This clinical evaluation showed that the CarbonCool cooling vest is safe and tolerable in participants wearing PPE. Further trials with sample size powered to detect physiological outcomes are needed to assess the effect of the cooling vest on a subject's endurance to heat stress.

Prediction of ROSC After Cardiac Arrest Using Machine Learning.

Out-of-hospital cardiac arrest (OHCA) is an important public health problem, with very low survival rate. In treating OHCA patients, the return of spontaneous circulation (ROSC) represents the success of early resuscitation efforts. In this study, we developed a machine learning model to predict ROSC and compared it with the ROSC after cardiac arrest (RACA) score. Results demonstrated the usefulness of machine learning in deriving predictive models.

Validation of the ROSC after cardiac arrest (RACA) score in Pan-Asian out-of-hospital cardiac arrest patients.

AIM: Survival is the most consistently captured outcome across countries for out-of-hospital cardiac arrests (OHCA), with return of spontaneous circulation (ROSC) representing the earliest endpoint for 'unbiased' initial resuscitation success. The ROSC after cardiac arrest (RACA) score was developed to predict ROSC and has been validated in several European countries. In this study, we aimed to evaluate the performance of RACA in a Pan-Asian population. METHODS: We conducted a retrospective analysis of data collected in the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. We included OHCA cases from seven communities (Japan, South Korea, Malaysia, Singapore, Taiwan, Thailand, and United Arab Emirates) between January 2009 and December 2012. Paediatric cases, cases that were conveyed by non-emergency medical services (EMS), and cases with incomplete records were excluded from the study. RESULTS: The RACA score showed similar discrimination performance as the original German study and various European validation studies. However, it had poor calibration with the original constant regression coefficient, which was primarily due to the low ROSC rate (8.2%) in the PAROS cohort. The calibration performance of RACA significantly improved after the constant coefficient was modified to adjust for the disparity in ROSC rates between Asia and Europe. CONCLUSION: This is the largest validation study of the RACA score. RACA consistently performs well in both Pan-Asian and European communities and can thus be a valuable tool for evaluating EMS systems. However, to implement it, the constant coefficient has to be modified in the RACA formula with local historical data.