Heart rate variability and cardiovascular diseases: A Mendelian randomization study

BACKGROUND

The causal relationship between heart rate variability and cardiovascular diseases and the associated events is still unclear, and the conclusions of current studies are inconsistent. We aimed to explore the relationship between heart rate variability and cardiovascular diseases and the associated events with the Mendelian randomization study.

METHODS

We selected normal-to-normal inter-beat intervals (SDNN), root mean square of the successive differences of inter-beat intervals (RMSSD) and peak-valley respiratory sinus arrhythmia or high-frequency power (pvRSA/HF) as the three sets of instrumental variables for heart rate variability. The outcome for cardiovascular diseases included essential hypertension, heart failure, angina pectoris, myocardial infarction, nonischemic cardiomyopathy and arrhythmia. Cardiac arrest, cardiac death and major coronary heart disease event were defined as the related events of cardiovascular diseases. The data for exposures and outcomes were derived from publicly available genome-wide association studies. Inverse variance weighted was used for the main causal estimation. Analyses of heterogeneity and pleiotropy were conducted using the Cochran Q test of Inverse variance weighted and MR-Egger, leave-one-out analysis, and MR-Pleiotropy Residual Sum and Outlier methods.

RESULTS

The Inverse variance weighted method indicated that genetically predicted pvRSA/HF was associated with the increased risk of cardiac arrest (odds ratio 2.02, 95% confidence interval 1.25-3.28, p = .004). The results were free of heterogeneity and pleiotropy. There were no outliers and the leave-one-out analysis proved that the results were reliable.

CONCLUSIONS

This study provides genetic evidence that pvRSA/HF is causally related to cardiac arrest.

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Heart disease is a significant public health problem, and early detection is crucial for effective treatment and management. Conventional and noninvasive techniques are cumbersome, time-consuming, inconvenient, expensive, and unsuitable for frequent measurement or diagnosis. With the advance of artificial intelligence (AI), new invasive techniques emerging in research are detecting heart conditions using machine learning (ML) and deep learning (DL). Machine learning models have been used with the publicly available dataset from the internet about heart health; in contrast, deep learning techniques have recently been applied to analyze electrocardiograms (ECG) or similar vital data to detect heart diseases. Significant limitations of these datasets are their small size regarding the number of patients and features and the fact that many are imbalanced datasets. Furthermore, the trained models must be more reliable and accurate in medical settings. This study proposes a hybrid one-dimensional convolutional neural network (1D CNN), which uses a large dataset accumulated from online survey data and selected features using feature selection algorithms. The 1D CNN proved to show better accuracy compared to contemporary machine learning algorithms and artificial neural networks. The non-coronary heart disease (no-CHD) and CHD validation data showed an accuracy of 80.1% and 76.9%, respectively. The model was compared with an artificial neural network, random forest, AdaBoost, and a support vector machine. Overall, 1D CNN proved to show better performance in terms of accuracy, false negative rates, and false positive rates. Similar strategies were applied for four more heart conditions, and the analysis proved that using the hybrid 1D CNN produced better accuracy.

Improved inpatient deterioration detection in general wards by using time-series vital signs

Although in-hospital cardiac arrest is uncommon, it has a high mortality rate. Risk identification of at-risk patients is critical for post-cardiac arrest survival rates. Early warning scoring systems are generally used to identify hospitalized patients at risk of deterioration. However, these systems often require clinical data that are not always regularly measured. We developed a more accurate, machine learning-based model to predict clinical deterioration. The time series early warning score (TEWS) used only heart rate, systolic blood pressure, and respiratory data, which are regularly measured in general wards. We tested the performance of the TEWS in two tasks performed with data from the electronic medical records of 16,865 adult admissions and compared the results with those of other classifications. The TEWS detected more deteriorations with the same level of specificity as the different algorithms did when inputting vital signs data from 48 h before an event. Our framework improved in-hospital cardiac arrest prediction and demonstrated that previously obtained vital signs data can be used to identify at-risk patients in real-time. This model may be an alternative method for detecting patient deterioration.

An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication

Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example,take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset'sfeatures as well as a subset of them. The reduction of features has an impact on theperformance of classifiers in terms of the evaluation matrix and execution time. Experimental results of the support vector machine, K-nearest neighbor, and logistic regression are 97.5%,95 %, and 93% (accuracy) with reduced computation timesof 4.4, 7.3, and 8seconds respectively.

Machine Learning-Based Cardiac Arrest Prediction for Early Warning System

The early warning system detects early and responds quickly to emergencies in high-risk patients, such as cardiac arrest in hospitalized patients. However, traditional early warning systems have the problem of frequent false alarms due to low positive predictive value and sensitivity. We conducted early prediction research on cardiac arrest using time-series data such as biosignal and laboratory data. To derive the data attributes that affect the occurrence of cardiac arrest, we performed a correlation analysis between the occurrence of cardiac arrest and the biosignal data and laboratory data. To improve the positive predictive value and sensitivity of early cardiac arrest prediction, we evaluated the performance according to the length of the time series of measured biosignal data, laboratory data, and patient data range. We propose a machine learning and deep learning algorithm: the decision tree, random forest, logistic regression, long short-term memory (LSTM), gated recurrent unit (GRU) model, and the LSTM–GRU hybrid model. We evaluated cardiac arrest prediction models. In the case of our proposed LSTM model, the positive predictive value was 85.92% and the sensitivity was 89.70%.

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.

Design and Evaluation of Digital Filters for Non-Contact Measuring of HRV using Medical Radar and Its Application in Bedside Patient Monitoring System

A non-contact bedside monitoring system using medical radar is expected to be applied to clinical fields. Our previous studies have developed a monitoring system based on medical radar for measuring respiratory rate (RR) and heart rate (HR). Heart rate variability (HRV), which is essentially implemented in advanced monitoring system, such as prognosis prediction, is a more challenging biological information than the RR and HR. In this study, we designed a HRV measurement filter and proposed a method to evaluate the optimal cardiac signal extraction filter for HRV measurement. Because the cardiac component in the radar signal is much smaller than the respiratory component, it is necessary to extract the cardiac element from the radar output signal using digital filters. It depends on the characteristics of the filter whether the HRV information is kept in the extracted cardiac signal or not. A cardiac signal extraction filter that is not distorted in the time domain and does not miss the cardiac component must be adopted. Therefore, we focused on evaluating the interval between the R-peak of the electrocardiogram (ECG) and the radar-cardio peak of the cardiac signal measured by radar (R-radar interval). This is based on the fact that the time between heart depolarization and ventricular contraction is measured as the R-radar interval. A band-pass filter (BPF) with several bandwidths and a nonlinear filter, locally projective adaptive signal separation (LoPASS), were analyzed and compared. The optimal filter was quantitatively evaluated by analyzing the distribution and standard deviation of the R-radar intervals. The performance of this monitoring system was evaluated in elderly patient at the Yokohama Hospital, Japan.

Prediction of In-Hospital Cardiac Arrest Using Shallow and Deep Learning

Sudden cardiac arrest can leave serious brain damage or lead to death, so it is very important to predict before a cardiac arrest occurs. However, early warning score systems including the National Early Warning Score, are associated with low sensitivity and false positives. We applied shallow and deep learning to predict cardiac arrest to overcome these limitations. We evaluated the performance of the Synthetic Minority Oversampling Technique Ratio. We evaluated the performance using a Decision Tree, a Random Forest, Logistic Regression, Long Short-Term Memory model, Gated Recurrent Unit model, and LSTM–GRU hybrid models. Our proposed Logistic Regression demonstrated a higher positive predictive value and sensitivity than traditional early warning systems.

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.

Prediction of breakthrough pain during labour neuraxial analgesia: comparison of machine learning and multivariable regression approaches

INTRODUCTION

Risk-prediction models for breakthrough pain facilitate interventions to forestall inadequate labour analgesia, but limited work has used machine learning to identify predictive factors. We compared the performance of machine learning and regression techniques in identifying parturients at increased risk of breakthrough pain during labour epidural analgesia.

METHODS

A single-centre retrospective study involved parturients receiving patient-controlled epidural analgesia. The primary outcome was breakthrough pain. We randomly selected 80% of the cohort (training cohort) to develop three prediction models using random forest, XGBoost, and logistic regression, followed by validation against the remaining 20% of the cohort (validation cohort). Area-under-the-receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV and NPV) were used to assess model performance.

RESULTS

Data from 20 716 parturients were analysed. The incidence of breakthrough pain was 14.2%. Of 31 candidate variables, random forest, XGBoost and logistic regression models included 30, 23, and 15 variables, respectively. Unintended venous puncture, post-neuraxial analgesia highest pain score, number of dinoprostone suppositories, neuraxial technique, number of neuraxial attempts, depth to epidural space, body mass index, pre-neuraxial analgesia oxytocin infusion rate, maternal age, pre-neuraxial analgesia cervical dilation, anaesthesiologist rank, and multiparity, were identified in all three models. All three models performed similarly, with AUC 0.763-0.772, sensitivity 67.0-69.4%, specificity 70.9-76.2%, PPV 28.3-31.8%, and NPV 93.3-93.5%.

CONCLUSIONS

Machine learning did not improve the prediction of breakthrough pain compared with multivariable regression. Larger population-wide studies are needed to improve predictive ability.

Diagnostic options for blunt abdominal trauma

PURPOSE

Physical examination, laboratory tests, ultrasound, conventional radiography, multislice computed tomography (MSCT), and diagnostic laparoscopy are used for diagnosing blunt abdominal trauma. In this article, we investigate and evaluate the usefulness and limitations of various diagnostic modalities on the basis of a comprehensive review of the literature.

METHODS

We searched commonly used databases in order to obtain information about the aforementioned diagnostic modalities. Relevant articles were included in the literature review. On the basis of the results of our comprehensive analysis of the literature and a current case, we offer a diagnostic algorithm.

RESULTS

A total of 86 studies were included in the review. Ecchymosis of the abdominal wall (seat belt sign) is a clinical sign that has a high predictive value. Laboratory values such as those for haematocrit, haemoglobin, base excess or deficit, and international normalised ratio (INR) are prognostic parameters that are useful in guiding therapy. Extended focused assessment with sonography for trauma (eFAST) has become a well established component of the trauma room algorithm but is of limited usefulness in the diagnosis of blunt abdominal trauma. Compared with all other diagnostic modalities, MSCT has the highest sensitivity and specificity. Diagnostic laparoscopy is an invasive technique that may also serve as a therapeutic tool and is particularly suited for haemodynamically stable patients with suspected hollow viscus injuries.

CONCLUSIONS

MSCT is the gold standard diagnostic modality for blunt abdominal trauma because of its high sensitivity and specificity in detecting relevant intra-abdominal injuries. In many cases, however, clinical, laboratory and imaging findings must be interpreted jointly for an adequate evaluation of a patient's injuries and for treatment planning since these data supplement and complement one another. Patients with blunt abdominal trauma should be admitted for clinical observation over a minimum period of 24 h since there is no investigation that can reliably rule out intra-abdominal injuries.

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.

Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review

BACKGROUND

The primary objective of this review is to assess the accuracy of machine learning methods in their application of triaging the acuity of patients presenting in the Emergency Care System (ECS). The population are patients that have contacted the ambulance service or turned up at the Emergency Department. The index test is a machine-learning algorithm that aims to stratify the acuity of incoming patients at initial triage. This is in comparison to either an existing decision support tool, clinical opinion or in the absence of these, no comparator. The outcome of this review is the calibration, discrimination and classification statistics.

METHODS

Only derivation studies (with or without internal validation) were included. MEDLINE, CINAHL, PubMed and the grey literature were searched on the 14th December 2019. Risk of bias was assessed using the PROBAST tool and data was extracted using the CHARMS checklist. Discrimination (C-statistic) was a commonly reported model performance measure and therefore these statistics were represented as a range within each machine learning method. The majority of studies had poorly reported outcomes and thus a narrative synthesis of results was performed.

RESULTS

There was a total of 92 models (from 25 studies) included in the review. There were two main triage outcomes: hospitalisation (56 models), and critical care need (25 models). For hospitalisation, neural networks and tree-based methods both had a median C-statistic of 0.81 (IQR 0.80-0.84, 0.79-0.82). Logistic regression had a median C-statistic of 0.80 (0.74-0.83). For critical care need, neural networks had a median C-statistic of 0.89 (0.86-0.91), tree based 0.85 (0.84-0.88), and logistic regression 0.83 (0.79-0.84).

CONCLUSIONS

Machine-learning methods appear accurate in triaging undifferentiated patients entering the Emergency Care System. There was no clear benefit of using one technique over another; however, models derived by logistic regression were more transparent in reporting model performance. Future studies should adhere to reporting guidelines and use these at the protocol design stage.

REGISTRATION AND FUNDING

This systematic review is registered on the International prospective register of systematic reviews (PROSPERO) and can be accessed online at the following URL: https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020168696This study was funded by the NIHR as part of a Clinical Doctoral Research Fellowship.

Manifold ranking based scoring system with its application to cardiac arrest prediction: A retrospective study in emergency department patients

BACKGROUND

The recently developed geometric distance scoring system has shown the effectiveness of scoring systems in predicting cardiac arrest within 72h and the potential to predict other clinical outcomes. However, the geometric distance scoring system predicts scores based on only local structure embedded by the data, thus leaving much room for improvement in terms of prediction accuracy.

METHODS

We developed a novel scoring system for predicting cardiac arrest within 72h. The scoring system was developed based on a semi-supervised learning algorithm, manifold ranking, which explores both the local and global consistency of the data. System evaluation was conducted on emergency department patients׳ data, including both vital signs and heart rate variability (HRV) parameters. Comparison of the proposed scoring system with previous work was given in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV).

RESULTS

Out of 1025 patients, 52 (5.1%) met the primary outcome. Experimental results show that the proposed scoring system was able to achieve higher area under the curve (AUC) on both the balanced dataset (0.907 vs. 0.824) and the imbalanced dataset (0.774 vs. 0.734) compared to the geometric distance scoring system.

CONCLUSIONS

The proposed scoring system improved the prediction accuracy by utilizing the global consistency of the training data. We foresee the potential of extending this scoring system, as well as manifold ranking algorithm, to other medical decision making problems. Furthermore, we will investigate the parameter selection process and other techniques to improve performance on the imbalanced dataset.

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.