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.

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.

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.

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.

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.

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.

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.

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.

Validation of the mortality in emergency department sepsis (MEDS) score in a Singaporean cohort.

The emergency department (ED) serves as the first point of hospital contact for most septic patients. Early mortality risk stratification using a quick and accurate triage tool would have great value in guiding management. The mortality in emergency department sepsis (MEDS) score was developed to risk stratify patients presenting to the ED with suspected sepsis, and its performance in the literature has been promising. We report in this study the first utilization of the MEDS score in a Singaporean cohort.In this retrospective observational cohort study, adult patients presenting to the ED with suspected sepsis and fulfilling systemic inflammatory response syndrome (SIRS) criteria were recruited. Primary outcome was 30-day in-hospital mortality (IHM) and secondary outcome was 72-hour mortality. MEDS, acute physiology and chronic health evaluation II (APACHE II), and sequential organ failure assessment (SOFA) scores were compared for prediction of primary and secondary outcomes. Receiver operating characteristic (ROC) analysis was conducted to compare predictive performance.Of the 249 patients included in the study, 46 patients (18.5%) met 30-day IHM. MEDS score achieved an area under the ROC curve (AUC) of 0.87 (95% confidence interval [CI], 0.82-0.93), outperforming the APACHE II score (0.77, 95% CI 0.69-0.85) and SOFA score (0.78, 95% CI 0.71-0.85). On secondary analysis, MEDS score was superior to both APACHE II and SOFA scores in predicting 72-hour mortality, with AUC of 0.88 (95% CI 0.82-0.95), 0.81 (95% CI 0.72-0.89), and 0.79 (95% CI 0.71-0.87), respectively. In predicting 30-day IHM, MEDS score ≥12, APACHE II score ≥23, and SOFA score ≥5 performed at sensitivities of 76.1%, 67.4%, and 76.1%, and specificities of 83.3%, 73.9%, and 65.0%, respectively.The MEDS score performed well in its ability for mortality risk stratification in a Singaporean ED cohort.

Combining Heart Rate Variability with Disease Severity Score Variables for Mortality Risk Stratification in Septic Patients Presenting at the Emergency Department.

The emergency department (ED) serves as the first point of hospital contact for many septic patients, where risk-stratification would be invaluable. We devised a combination model incorporating demographic, clinical, and heart rate variability (HRV) parameters, alongside individual variables of the Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation II (APACHE II), and Mortality in Emergency Department Sepsis (MEDS) scores for mortality risk-stratification. ED patients fulfilling systemic inflammatory response syndrome criteria were recruited. National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), quick SOFA (qSOFA), SOFA, APACHE II, and MEDS scores were calculated. For the prediction of 30-day in-hospital mortality, combination model performed with an area under the receiver operating characteristic curve of 0.91 (95% confidence interval (CI): 0.88-0.95), outperforming NEWS (0.70, 95% CI: 0.63-0.77), MEWS (0.61, 95% CI 0.53-0.69), qSOFA (0.70, 95% CI 0.63-0.77), SOFA (0.74, 95% CI: 0.67-0.80), APACHE II (0.76, 95% CI: 0.69-0.82), and MEDS scores (0.86, 95% CI: 0.81-0.90). The combination model had an optimal sensitivity and specificity of 91.4% (95% CI: 81.6-96.5%) and 77.9% (95% CI: 72.6-82.4%), respectively. A combination model incorporating clinical, HRV, and disease severity score variables showed superior predictive ability for the mortality risk-stratification of septic patients presenting at the ED.

Development of a heart rate variability and complexity model in predicting the need for life-saving interventions amongst trauma patients.

BACKGROUND: Triage trauma scores are utilised to determine patient disposition, interventions and prognostication in the care of trauma patients. Heart rate variability (HRV) and heart rate complexity (HRC) reflect the autonomic nervous system and are derived from electrocardiogram (ECG) analysis. In this study, we aimed to develop a model incorporating HRV and HRC, to predict the need for life-saving interventions (LSI) in trauma patients, within 24 h of emergency department presentation. METHODS: We included adult trauma patients (≥ 18 years of age) presenting at the emergency department of Singapore General Hospital between October 2014 and October 2015. We excluded patients who had non-sinus rhythms and larger proportions of artefacts and/or ectopics in ECG analysis. We obtained patient demographics, laboratory results, vital signs and outcomes from electronic health records. We conducted univariate and multivariate analyses for predictive model building. RESULTS: Two hundred and twenty-five patients met inclusion criteria, in which 49 patients required LSIs. The LSI group had a higher proportion of deaths (10, 20.41% vs 1, 0.57%, p < 0.001). In the LSI group, the mean of detrended fluctuation analysis (DFA)-α1 (1.24 vs 1.12, p = 0.045) and the median of DFA-α2 (1.09 vs 1.00, p = 0.027) were significantly higher. Multivariate stepwise logistic regression analysis determined that a lower Glasgow Coma Scale, a higher DFA-α1 and higher DFA-α2 were independent predictors of requiring LSIs. The area under the curve (AUC) for our model (0.75, 95% confidence interval, 0.66-0.83) was higher than other scoring systems and selected vital signs. CONCLUSIONS: An HRV/HRC model outperforms other triage trauma scores and selected vital signs in predicting the need for LSIs but needs to be validated in larger patient populations.

Combining quick sequential organ failure assessment score with heart rate variability may improve predictive ability for mortality in septic patients at the emergency department.

BACKGROUND: Although the quick Sequential Organ Failure Assessment (qSOFA) score was recently introduced to identify patients with suspected infection/sepsis, it has limitations as a predictive tool for adverse outcomes. We hypothesized that combining qSOFA score with heart rate variability (HRV) variables improves predictive ability for mortality in septic patients at the emergency department (ED). METHODS: This was a retrospective study using the electronic medical record of a tertiary care hospital in Singapore between September 2014 and February 2017. All patients aged 21 years or older who were suspected with infection/sepsis in the ED and received electrocardiography monitoring with ZOLL X Series Monitor (ZOLL Medical Corporation, Chelmsford, MA) were included. We fitted a logistic regression model to predict the 30-day mortality using one of the HRV variables selected from one of each three domains those previously reported as strong association with mortality (i.e. standard deviation of NN [SDNN], ratio of low frequency to high frequency power [LF/HF], detrended fluctuation analysis α-2 [DFA α-2]) in addition to the qSOFA score. The predictive accuracy was assessed with other scoring systems (i.e. qSOFA alone, National Early Warning Score, and Modified Early Warning Score) using the area under the receiver operating characteristic curve. RESULTS: A total of 343 septic patients were included. Non-survivors were significantly older (survivors vs. non-survivors, 65.7 vs. 72.9, p <0.01) and had higher qSOFA (0.8 vs. 1.4, p <0.01) as compared to survivors. There were significant differences in HRV variables between survivors and non-survivors including SDNN (23.7s vs. 31.8s, p = 0.02), LF/HF (2.8 vs. 1.5, p = 0.02), DFA α-2 (1.0 vs. 0.7, P < 0.01). Our prediction model using DFA-α-2 had the highest c-statistic of 0.76 (95% CI, 0.70 to 0.82), followed by qSOFA of 0.68 (95% CI, 0.62 to 0.75), National Early Warning Score at 0.67 (95% CI, 0.61 to 0.74), and Modified Early Warning Score at 0.59 (95% CI, 0.53 to 0.67). CONCLUSIONS: Adding DFA-α-2 to the qSOFA score may improve the accuracy of predicting in-hospital mortality in septic patients who present to the ED. Further multicenter prospective studies are required to confirm our results.

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratification tools, namely the Quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), and our previously described Singapore ED Sepsis (SEDS) model, in the prediction of 30-day in-hospital mortality (IHM) among suspected sepsis patients in the ED.Adult patients who presented to Singapore General Hospital (SGH) ED between September 2014 and April 2016, and who met ≥2 of the 4 Systemic Inflammatory Response Syndrome (SIRS) criteria were included. Patient demographics, vital signs and heart rate variability (HRV) measures obtained at triage were used as predictors. Baseline models were created using qSOFA, NEWS, MEWS, and SEDS scores. Candidate models were trained using k-nearest neighbors, random forest, adaptive boosting, gradient boosting and support vector machine. Models were evaluated on F1 score and area under the precision-recall curve (AUPRC).A total of 214 patients were included, of whom 40 (18.7%) met the outcome. Gradient boosting was the best model with a F1 score of 0.50 and AUPRC of 0.35, and performed better than all the baseline comparators (SEDS, F1 0.40, AUPRC 0.22; qSOFA, F1 0.32, AUPRC 0.21; NEWS, F1 0.38, AUPRC 0.28; MEWS, F1 0.30, AUPRC 0.25).A machine learning model can be used to improve prediction of 30-day IHM among suspected sepsis patients in the ED compared to traditional risk stratification tools.

Serial Heart Rate Variability Measures for Risk Prediction of Septic Patients in the Emergency Department.

In this study, we used serial heart rate variability (HRV) measures over 2 hours to improve the prediction of 30-day in-hospital mortality among septic patients in the emergency department (ED). We presented a generalizable methodology for processing and analysing HRV time series (HRVTS) data which may be noisy and incomplete. Feature sets were created from the HRVTS data of 162 patients with suspected sepsis using aggregation-based, deltabased and regression-based series-to-point transformations, and modelled over 100 random stratified splits. An optimized feature set comprising 12 selected HRVTS features performed better than baseline feature sets which only included patient demographics, vital signs and single time-point HRV measures taken at triage. This improved risk stratification approach could be used in the ED to identify high-risk septic patients for appropriate management and disposition.

A novel heart rate variability based risk prediction model for septic patients presenting to the emergency department.

A quick, objective, non-invasive means of identifying high-risk septic patients in the emergency department (ED) can improve hospital outcomes through early, appropriate management. Heart rate variability (HRV) analysis has been correlated with mortality in critically ill patients. We aimed to develop a Singapore ED sepsis (SEDS) predictive model to assess the risk of 30-day in-hospital mortality in septic patients presenting to the ED. We used demographics, vital signs, and HRV parameters in model building and compared it with the modified early warning score (MEWS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA) score.Adult patients clinically suspected to have sepsis in the ED and who met the systemic inflammatory response syndrome (SIRS) criteria were included. Routine triage electrocardiogram segments were used to obtain HRV variables. The primary endpoint was 30-day in-hospital mortality. Multivariate logistic regression was used to derive the SEDS model. MEWS, NEWS, and qSOFA (initial and worst measurements) scores were computed. Receiver operating characteristic (ROC) analysis was used to evaluate their predictive performances.Of the 214 patients included in this study, 40 (18.7%) met the primary endpoint. The SEDS model comprises of 5 components (age, respiratory rate, systolic blood pressure, mean RR interval, and detrended fluctuation analysis α2) and performed with an area under the ROC curve (AUC) of 0.78 (95% confidence interval [CI]: 0.72-0.86), compared with 0.65 (95% CI: 0.56-0.74), 0.70 (95% CI: 0.61-0.79), 0.70 (95% CI: 0.62-0.79), 0.56 (95% CI: 0.46-0.66) by qSOFA (initial), qSOFA (worst), NEWS, and MEWS, respectively.HRV analysis is a useful component in mortality risk prediction for septic patients presenting to 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.