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

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.

Multi-modal fusion model for predicting adverse cardiovascular outcome post percutaneous coronary intervention

Background.Clinical medicine relies heavily on the synthesis of information and data from multiple sources. However, often simple feature concatenation is used as a strategy for developing a multimodal machine learning model in the cardiovascular domain, and thus the models are often limited by pre-selected features and moderate accuracy.Method.We proposed a two-branched joint fusion model for fusing the 12-lead electrocardiogram (ECG) signal data with clinical variables from the electronic medical record (EMR) in an end-to-end deep learning architecture. The model follows the joint fusion scheme and learns complementary information from ECG and EMR. Retrospective data from the Mayo Clinic Health Systems across four sites for patients that underwent percutaneous coronary intervention (PCI) were obtained. Model performance was assessed by area under the receiver-operating characteristics (AUROC) and Delong's test.Results.The final cohort included 17,356 unique patients with a mean age of 67.2 ± 12.6 year (mean ± std) and 9,163 (52.7%) were male. The joint fusion model outperformed the ECG time-domain model with statistical margin. The model with clinical data obtained the highest AUROC for all-cause mortality (0.91 at 6 months) but the joint fusion model outperformed for cardiovascular outcomes - heart failure hospitalization and ischemic stroke with a significant margin (Delong's p < 0.05).Conclusion.To the best of our knowledge, this is the first study that developed a deep learning model with joint fusion architecture for the prediction of post-PCI prognosis and outperformed machine learning models developed using traditional single-source features (clinical variables or ECG features). Adding ECG data with clinical variables did not improve prediction of all-cause mortality as may be expected, but the improved performance of related cardiac outcomes shows that the fusion of ECG generates additional value.

Prediction of major adverse cardiovascular events in patients with acute coronary syndrome: Development and validation of a non-invasive nomogram model based on autonomic nervous system assessment

BACKGROUND

Disruption of the autonomic nervous system (ANS) can lead to acute coronary syndrome (ACS). We developed a nomogram model using heart rate variability (HRV) and other data to predict major adverse cardiovascular events (MACEs) following emergency coronary angiography in patients with ACS.

METHODS

ACS patients admitted from January 2018 to June 2020 were examined. Holter monitors were used to collect HRV data for 24 h. Coronary angiograms, clinical data, and MACEs were recorded. A nomogram was developed using the results of Cox regression analysis.

RESULTS

There were 439 patients in a development cohort and 241 in a validation cohort, and the mean follow-up time was 22.80 months. The nomogram considered low-frequency/high-frequency ratio, age, diabetes, previous myocardial infarction, and current smoking. The area-under-the-curve (AUC) values for 1-year MACE-free survival were 0.790 (95% CI: 0.702-0.877) in the development cohort and 0.894 (95% CI: 0.820-0.967) in the external validation cohort. The AUCs for 2-year MACE-free survival were 0.802 (95% CI: 0.739-0.866) in the development cohort and 0.798 (95% CI: 0.693-0.902) in the external validation cohort. Development and validation were adequately calibrated and their predictions correlated with the observed outcome. Decision curve analysis (DCA) showed the model had good discriminative ability in predicting MACEs.

CONCLUSION

Our validated nomogram was based on non-invasive ANS assessment and traditional risk factors, and indicated reliable prediction of MACEs in patients with ACS. This approach has potential for use as a method for non-invasive monitoring of health that enables provision of individualized treatment strategies.

Impact of Short-Term Heart Rate Variability in Patients with STEMI Treated by Delayed versus Immediate Stent in Primary Percutaneous Coronary Intervention: A Prospective Cohort Study

Objective

Patients with ST-segment elevated myocardial infarction (STEMI) have been treated with the delayed stent strategy to reduce the occurrence of postoperative no-reflow and improve the recovery of postoperative cardiac function. However, the effects of electrocardiac activity and autonomic nerve function after primary percutaneous coronary intervention (pPCI) have been rarely reported. The purpose of this study was to investigate the effects of short-term heart rate variability (HRV) in patients with STEMI treated by immediate stent (IS) and delayed stent (DS) strategy.

Methods

A total of 178 patients with STEMI were divided into 124 cases (69.66%) in the IS group and 54 cases (30.34%) in the DS group from July 2019 to September 2021. The mean heart rate, premature ventricular contraction (PVC), left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVED), and HRV indexes were compared between the two groups.

Results

In terms of cardiac electrical stability, the number of PVCs, the percentage of PVCs, and the number of paired PVCs in the DS group were lower than those in the IS group. In terms of HRV, high frequency (HF) and standard deviation of all NN (SDNN) intervals were higher in the patients with DS strategy than IS strategy. There were no significant differences in the LVED and LVEF between the two groups.

Conclusion

Compared to the IS strategy, the DS strategy in pPCI in patients with STEMI has advantages in postoperative cardiac electrical stability and short-term cardiac autonomic nerve function, with no difference in postoperative short-term cardiac function.

Development and Validation of a Risk Prediction Model for Ventricular Arrhythmia in Elderly Patients with Coronary Heart Disease

BACKGROUND

Sudden cardiac death is a leading cause of death from coronary heart disease (CHD). The risk of sudden cardiac death (SCD) increases with age, and sudden arrhythmic death remains a major cause of mortality in elderly individuals, especially ventricular arrhythmias (VA). We developed a risk prediction model by combining ECG and other clinical noninvasive indexes including biomarkers and echocardiology for VA in elderly patients with CHD.

METHOD

In the retrospective study, a total of 2231 consecutive elderly patients (≥60 years old) with CHD hospitalized were investigated, and finally 1983 patients were enrolled as the model group. The occurrence of VA within 12 months was mainly collected. Study parameters included clinical characteristics (age, gender, height, weight, BMI, and past medical history), ECG indexes (QTcd, Tp-e/QT, and HRV indexes), biomarker indexes (NT-proBNP, Myo, cTnT, CK-MB, CRP, K+, and Ca2+), and echocardiology indexes. In the respective study, 406 elderly patients (≥60 years old) with CHD were included as the verification group to verify the model in terms of differentiation and calibration.

RESULTS

In the multiparameter model, seven independent predictors were selected: LVEF, LAV, HLP, QTcd, sex, Tp-e/QT, and age. Increased HLP, Tp-e/QT, QTcd, age, and LAV were risk factors (RR > 1), while female and increased LVEF were protective factors (RR < 1). This model can well predict the occurrence of VA in elderly patients with CHD (for model group, AUC: 0.721, 95% CI: 0.669∼0.772; for verification group, AUC: 0.73, 95% CI: 0.648∼0.818; Hosmer-Lemeshow χ 2 = 13.541, P=0.095). After adjusting the predictors, it was found that the combination of clinical indexes and ECG indexes could predict VA more efficiently than using clinical indexes alone.

CONCLUSIONS

LVEF, LAV, QTcd, Tp-e/QT, gender, age, and HLP were independent predictors of VA risk in elderly patients with CHD. Among these factors, the echocardiology indexes LVEF and LAV had the greatest influence on the predictive efficiency of the model, followed by ECG indexes, QTcd and Tp-e/QT. After verification, the model had a good degree of differentiation and calibration, which can provide a certain reference for clinical prediction of the VA occurrence in elderly patients with CHD.

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.

Effects of coronary revascularization by elective percutaneous coronary intervention on cardiac autonomic modulation assessed by heart rate variability: a single-center prospective cohort study

OBJECTIVE

To study the effects of coronary revascularization using elective percutaneous coronary intervention (PCI) on autonomic modulation assessed by heart rate variability measurement (HRV) in coronary artery disease (CAD) patients.

METHODS

A single-center prospective cohort study included 100 patients were included undergoing elective PCI excluding those with contraindication to contrast or dual antiplatelet therapy, atrial fibrillation or multiple premature beats, receiving anti-arrhythmic drugs and those who underwent previous PCI or coronary artery bypass graft (CABG). Short-term measurement of time domain parameters (mean, SDNN, RMSSD) and frequency domain parameters (LF component, HF component, LF/HF ratio) of HRV was performed at the same time of the day, pre-PCI, 24 hours and 6 months post-PCI by CheckMyheart™ handheld HRV device. 5-min HRV analysis software was used to interpret the data using standard methods of HRV measurement of the Task Force of The European Society of Cardiology (ESC) and The North American Society of Pacing and Electrophysiology. SYNTAX (SX) score was calculated before PCI and residual SYNTAX (rSS) score was calculated after PCI using SYNTAX score calculator software.

RESULTS

The mean age of the studied population was 56.89±10.75 years with 85% males. HRV time and frequency domain parameters showed a statistically significant improvement at different time intervals (before PCI, 24 hours and 6 months after PCI) (p-value <0.001). HRV time and frequency domain measures showed a statistically significant difference between time and frequency domain HRV parameters 24 hours and 6 months after PCI in patients who had complete revascularization (CR) with those who had incomplete revascularization (IR). (p-value <0.001).

CONCLUSION

Autonomic modulation in CAD patients was improved by coronary revascularization using PCI assessed by serial HRV measurement. Patients with CR had better autonomic modulation than those with IR assessed by HRV 24 and 6 months after PCI.

Continuous In-Bed Monitoring of Vital Signs Using a Multi Radar Setup for Freely Moving Patients

In hospitals, continuous monitoring of vital parameters can provide valuable information about the course of a patient’s illness and allows early warning of emergencies. To enable such monitoring without restricting the patient’s freedom of movement and comfort, a radar system is attached under the mattress which consists of four individual radar modules to cover the entire width of the bed. Using radar, heartbeat and respiration can be measured without contact and through clothing. By processing the raw radar data, the presence of a patient can be determined and movements are categorized into the classes “bed exit”, “bed entry”, and “on bed movement”. Using this information, the vital parameters can be assessed in sections where the patient lies calmly in bed. In the first step, the presence and movement classification is demonstrated using recorded training and test data. Next, the radar was modified to perform vital sign measurements synchronized to a gold standard device. The evaluation of the individual radar modules shows that, regardless of the lying position of the test person, at least one of the radar modules delivers accurate results for continuous monitoring.

A narrative review of heart rate and variability in sepsis

Clinicians face challenges in the timely diagnosis and management of pediatric sepsis. Pediatric heart rate has been incorporated into early warning systems and studied as a predictor for critical illness. We aim to review: (I) the role of heart rate in pediatric warning systems and (II) the role of heart rate variability (HRV) in adult and neonatal sepsis, with a focus on its potential applications in pediatrics. We conducted a literature search for papers published up to December 2019 on the utility of heart rate and HRV analysis in the diagnosis and management of sepsis, using four medical databases: PubMed, Google Scholar, EMBASE and Web of Science. This review demonstrates that the clinical utility of pediatric heart rate in predicting clinical deterioration is limited by the lack of consensus among warning systems, consensus-based guidelines, and evidence-based studies as to what constitutes abnormal heart rate in the pediatric age group. Current studies demonstrate that abnormal heart rate itself does not adequately discriminate children with sepsis from those without. HRV analysis provides a quick and non-invasive method of assessment and can provide more information than traditional heart rate. HRV analysis has the potential to add value in identification and prognostication of adult and neonatal sepsis. With further studies to explore its role, HRV analysis has the potential to add to current tools in the diagnosis and prognosis of pediatric sepsis.

[Construction of multi-parameter emergency database and preliminary application research]

The analysis of big data in medical field cannot be isolated from the high quality clinical database, and the construction of first aid database in our country is still in the early stage of exploration. This paper introduces the idea and key technology of the construction of multi-parameter first aid database. By combining emergency business flow with information flow, an emergency data integration model was designed with reference to the architecture of the Medical Information Mart for Intensive Care III (MIMIC-III), created by Computational Physiology Laboratory of Massachusetts Institute of Technology (MIT), and a high-quality first-aid database was built. The database currently covers 22 941 medical records for 19 814 different patients from May 2015 to October 2017, including relatively complete information on physiology, biochemistry, treatment, examination, nursing, etc. And based on the database, the first First-Aid Big Data Datathon event, which 13 teams from all over the country participated in, was launched. The First-Aid database provides a reference for the construction and application of clinical database in China. And it could provide powerful data support for scientific research, clinical decision making and the improvement of medical quality, which will further promote secondary analysis of clinical data in our country.

A Clinically Evaluated Interferometric Continuous-Wave Radar System for the Contactless Measurement of Human Vital Parameters

Vital parameters are key indicators for the assessment of health. Conventional methods rely on direct contact with the patients’ skin and can hence cause discomfort and reduce autonomy. This article presents a bistatic 24 GHz radar system based on an interferometric six-port architecture and features a precision of 1 µm in distance measurements. Placed at a distance of 40 cm in front of the human chest, it detects vibrations containing respiratory movements, pulse waves and heart sounds. For the extraction of the respiration rate, time-domain approaches like autocorrelation, peaksearch and zero crossing rate are compared to the Fourier transform, while template matching and a hidden semi-Markov model are utilized for the detection of the heart rate from sphygmograms and heart sounds. A medical study with 30 healthy volunteers was conducted to collect 5.5 h of data, where impedance cardiogram and electrocardiogram were used as gold standard for synchronously recording respiration and heart rate, respectively. A low root mean square error for the breathing rate (0.828 BrPM) and a high overall F1 score for heartbeat detection (93.14%) could be achieved using the proposed radar system and signal processing.

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.

Towards an automated multimodal clinical decision support system at the post anesthesia care unit

BACKGROUND

The aim of this study was to develop a predictive algorithm detecting early signs of deterioration (ESODs) in the post anesthesia care unit (PACU), thus being able to intervene earlier in the future to avoid serious adverse events. The algorithm must utilize continuously collected cardiopulmonary vital signs and may serve as an alternative to current practice, in which an alarm is activated by single parameters.

METHODS

The study was a single center, prospective cohort study including 178 patients admitted to the PACU after major surgical procedures. Peripheral blood oxygenation, arterial blood pressure, perfusion index, heart rate and respiratory rate were monitored continuously. Potential ESODs were automatically detected and scored by two independent experts with regards to the severity of the observation. Based on features extracted from the obtained measurements, a random forest classifier was trained, classifying each event being either an ESOD or not an ESOD. The algorithm was evaluated and compared to the automated single modality alarm system at the PACU.

RESULTS

The algorithm detected ESODs with an accuracy of 92.2% (99% CI: 89.6%-94.8%), sensitivity of 90.6% (99% CI: 85.7%-95.5%), specificity of 93.0% (99% CI: 89.9%-96.2%) and area under the receiver operating characteristic curve of 96.9% (99% CI: 95.3%-98.5%). The number of false alarms decreased by 85% (99% CI: 77%-93%) and the number of missed ESODs decreased by 73% (99% CI: 61%-85%) as compared to the currently used alarm system in the hospital. The algorithm was able to detect an ESOD in average 26.4 (99% CI: 1.1-51.7) minutes before the current single parameter system used in the PACU.

CONCLUSION

In conclusion, the proposed biomedical classification algorithm, when compared to the currently used single parameter alarm system of the hospital, showed significantly increased performance in both detecting ESODs fast and classifying these correctly. The clinical effect of the predictive system must be evaluated in future trials.

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.

FAM-FACE-SG: a score for risk stratification of frequent hospital admitters

Background

An accurate risk stratification tool is critical in identifying patients who are at high risk of frequent hospital readmissions. While 30-day hospital readmissions have been widely studied, there is increasing interest in identifying potential high-cost users or frequent hospital admitters. In this study, we aimed to derive and validate a risk stratification tool to predict frequent hospital admitters.

Methods

We conducted a retrospective cohort study using the readily available clinical and administrative data from the electronic health records of a tertiary hospital in Singapore. The primary outcome was chosen as three or more inpatient readmissions within 12 months of index discharge. We used univariable and multivariable logistic regression models to build a frequent hospital admission risk score (FAM-FACE-SG) by incorporating demographics, indicators of socioeconomic status, prior healthcare utilization, markers of acute illness burden and markers of chronic illness burden. We further validated the risk score on a separate dataset and compared its performance with the LACE index using the receiver operating characteristic analysis.

Results

Our study included 25,244 patients, with 70% randomly selected patients for risk score derivation and the remaining 30% for validation. Overall, 4,322 patients (17.1%) met the outcome. The final FAM-FACE-SG score consisted of nine components: Furosemide (Intravenous 40 mg and above during index admission); Admissions in past one year; Medifund (Required financial assistance); Frequent emergency department (ED) use (≥3 ED visits in 6 month before index admission); Anti-depressants in past one year; Charlson comorbidity index; End Stage Renal Failure on Dialysis; Subsidized ward stay; and Geriatric patient or not. In the experiments, the FAM-FACE-SG score had good discriminative ability with an area under the curve (AUC) of 0.839 (95% confidence interval [CI]: 0.825–0.853) for risk prediction of frequent hospital admission. In comparison, the LACE index only achieved an AUC of 0.761 (0.745–0.777).

Conclusions

The FAM-FACE-SG score shows strong potential for implementation to provide near real-time prediction of frequent admissions. It may serve as the first step to identify high risk patients to receive resource intensive interventions.