Search

Accurate estimation of physiological biomarkers using raw waveform data from non-invasive wearable devices requires extensive data preprocessing. An automatic noise detection method would offer significant utility for various domains. As data labeling is onerous, having a minimally supervised abnormality detection method for input data, as well as an estimation of the severity of the signal corruptness, is essential. We propose a model-free, time-series biomedical waveform noise detection framework using a Variational Autoencoder coupled with Gaussian Mixture Models, which can detect a range of waveform abnormalities without annotation, providing a confidence metric for each segment. Our technique operates on biomedical signals that exhibit periodicity of heart activities. This framework can be applied to any machine learning or deep learning model as an initial signal validator component. Moreover, the confidence score generated by the proposed framework can be incorporated into different models' optimization to construct confidence-aware modeling. The result confirms that our approach removes noisy cardiac cycles and the remaining signals, classified as clean, exhibit a 59.92% reduction in the standard deviation of DTW distances. Using a dataset of bio-impedance data of 97885 cardiac cycles, we further demonstrate a significant improvement in the downstream task of cuffless blood pressure estimation, with an average reduction of 2.67 mmHg root mean square error (RMSE) of Diastolic Blood pressure and 2.13 mmHg RMSE of systolic blood pressure, with increases of average Pearson correlation of 0.28 and 0.08, with a statistically significant improvement of signal-to-noise ratio respectively in the presence of different synthetic noise sources.

We propose our Confidence-Aware Particle Filter (CAPF) framework that analyzes a series of estimated changes in blood pressure (BP) to provide several true state hypotheses for a given instance. Particularly, our novel confidence-awareness mechanism assigns likelihood scores to each hypothesis in an effort to discard potentially erroneous measurements – based on the agreement amongst a series of estimated changes and the physiological plausibility when considering DBP/SBP pairs. The particle filter formulation (or sequential Monte Carlo method) can jointly consider the hypotheses and their probabilities over time to provide a stable trend of estimated BP measurements. In this study, we evaluate BP trend estimation from an emerging bio-impedance (Bio-Z) prototype wearable modality although it is applicable to all types of physiological modalities. Each subject in the evaluation cohort underwent a hand-gripper exercise, a cold pressor test, and a recovery state to increase the variation to the captured BP ranges. Experiments show that CAPF yields superior continuous pulse pressure (PP), diastolic blood pressure (DBP), and systolic blood pressure (SBP) estimation performance compared to ten baseline approaches. Furthermore, CAPF performs on track to comply with AAMI and BHS standards for achieving a performance classification of Grade A, with mean error accuracies of −0.16 ± 3.75 mmHg for PP (r = 0.81), 0.42 ± 4.39 mmHg for DBP (r = 0.92), and −0.09 ± 6.51 mmHg for SBP (r = 0.92) from more than test 3500 data points.

IMPORTANCE: Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights. OBJECTIVE: To evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020. MAIN OUTCOMES AND MEASURES: Three machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation characteristics, and initial laboratory values. Models were developed based on extreme gradient descent boosting (XGBoost, an interpretable model), a neural network, and a meta-classifier model. Their accuracy was compared against the current standard developed using a logistic regression model in a validation sample. RESULTS: A total of 755 402 patients (mean [SD] age, 65 [13] years; 495 202 [65.5%] male) were identified during the study period. In independent validation, 2 machine learning models, gradient descent boosting and meta-classifier (combination including inputs from gradient descent boosting and a neural network), marginally improved discrimination compared with logistic regression (C statistic, 0.90 for best performing machine learning model vs 0.89 for logistic regression). Nearly perfect calibration in independent validation data was found in the XGBoost (slope of predicted to observed events, 1.01; 95% CI, 0.99-1.04) and the meta-classifier model (slope of predicted-to-observed events, 1.01; 95% CI, 0.99-1.02), with more precise classification across the risk spectrum. The XGBoost model reclassified 32 393 of 121 839 individuals (27%) and the meta-classifier model reclassified 30 836 of 121 839 individuals (25%) deemed at moderate to high risk for death in logistic regression as low risk, which were more consistent with the observed event rates. CONCLUSIONS AND RELEVANCE: In this cohort study using a large national registry, none of the tested machine learning models were associated with substantive improvement in the discrimination of in-hospital mortality after AMI, limiting their clinical utility. However, compared with logistic regression, XGBoost and meta-classifier models, but not the neural network, offered improved resolution of risk for high-risk individuals.

This study sought to develop models for predicting mortality and heart failure (HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) trial.

Supplemental Digital Content is available in the text.

Introduction:Maximal oxygen uptake (VO2max), an indicator of cardiorespiratory fitness (CRF) requires exercise testing and, as a result, is rarely ascertained in large-scale population-based studies. There are many non-exercise algorithms that can estimate VO2max, but they are limited by their non-representative study population, lack of predictors, and the insufficient predictive power for the type of model chosen. In this study, we aim to improve the non-exercise algorithms using machine learning (ML) methods and data from U.S. national population surveys.Methods:We used the 1999-2004 data from the National Health and Nutrition Examination Survey (NHANES) because it included exercise testing to measure VO2max. Based on the literature review, predictors were identified from demographic, interview, examination, and laboratory data. The study population was split into a training set (80%) and a testing set (20%) for model development and validation. We built the improved model using Light Gradient Boosting Machine (LightGBM) for its outstanding performance and built-in functionality for handling missingness. The model was trained by optimizing Root-Mean Squared Error (RMSE) and using 5-fold cross-validation. Existing non-exercise algorithms for comparison were applied to the testing set.Results:Among the 5,668 participants included, the mean age was 32.5 and 49.9% were women. 40 predictors in total were selected for the final feature set. The fitted LightGBM model achieved an RMSE of 8.43 ml/kg/min (95% CI: 7.64 -9.19) on the testing set, significantly reducing the error by 14% (P <0.05) compared with the best existing non-exercise algorithms that could be cross-validated in NHANES (Table1).Conclusion:In conclusion, the extended non-exercise ML model in this study can provide a more accurate prediction of VO2max for NHANES participants, and those with similar data, than the existing non-exercise algorithms.

Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The triage process is critical in the appropriate allocation of resources and in anticipating eventual patient disposition, typically admission to the hospital or discharge home. EHR data are high-dimensional and complex, but offer the opportunity to discover and characterize underlying data-driven patient phenotypes. Data-driven phenotypes are intended to relieve reliance on weak labels like diagnosis codes and to aid in identifying populations of existing patients that are most similar to a specific patient. These phenotypes will enable improved, personalized therapeutic decision making and prognostication. In this work, we focus on the challenge of two-dimensional patient projections. A low dimensional embedding offers visual interpretability lost in higher dimensions. While linear dimensionality reduction techniques such as principal component analysis are often used towards this aim, they are insufficient to describe the variance of patient data. This linear reduction does not account for higher order, non-linear interactions of variables. In this work, we employ the newly-described non-linear embedding technique called uniform manifold approximation and projection (UMAP). UMAP seeks to capture both local and global structures in high-dimensional data. We then use Gaussian mixture models to identify clusters in the embedded data and use the adjusted Rand index (ARI) to establish stability in the discovery of these clusters. This technique is applied to five common clinical chief complaints from a real-world ED EHR dataset, describing the emergent properties of discovered clusters. We observe clinically-relevant cluster attributes, suggesting that visual embeddings of EHR data using non-linear dimensionality reduction is a promising approach to reveal data-driven patient phenotypes. In the five chief complaints, we find between 2 and 6 clusters, with the peak mean pairwise ARI between subsequent training iterations to range from 0.35 to 0.74.

IMPORTANCE: Mechanical circulatory support (MCS) devices, including intravascular microaxial left ventricular assist devices (LVADs) and intra-aortic balloon pumps (IABPs), are used in patients who undergo percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI) complicated by cardiogenic shock despite limited evidence of their clinical benefit. OBJECTIVE: To examine trends in the use of MCS devices among patients who underwent PCI for AMI with cardiogenic shock, hospital-level use variation, and factors associated with use. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used the CathPCI and Chest Pain-MI Registries of the American College of Cardiology National Cardiovascular Data Registry. Patients who underwent PCI for AMI complicated by cardiogenic shock between October 1, 2015, and December 31, 2017, were identified from both registries. Data were analyzed from October 2018 to August 2020. EXPOSURES: Therapies to provide hemodynamic support were categorized as intravascular microaxial LVAD, IABP, TandemHeart, extracorporeal membrane oxygenation, LVAD, other devices, combined IABP and intravascular microaxial LVAD, combined IABP and other device (defined as TandemHeart, extracorporeal membrane oxygenation, LVAD, or another MCS device), or medical therapy only. MAIN OUTCOMES AND MEASURES: Use of MCS devices overall and specific MCS devices, including intravascular microaxial LVAD, at both patient and hospital levels and variables associated with use. RESULTS: Among the 28?304 patients included in the study, the mean (SD) age was 65.4 (12.6) years and 18 968 were men (67.0%). The overall MCS device use was constant from the fourth quarter of 2015 to the fourth quarter of 2017, although use of intravascular microaxial LVADs significantly increased (from 4.1% to 9.8%; P?&lt;?.001), whereas use of IABPs significantly decreased (from 34.8% to 30.0%; P?&lt;?.001). A significant hospital-level variation in MCS device use was found. The median (interquartile range [IQR]) proportion of patients who received MCS devices was 42% (30%-54%), and the median proportion of patients who received intravascular microaxial LVADs was 1% (0%-10%). In multivariable analyses, cardiac arrest at first medical contact or during hospitalization (odds ratio [OR], 1.82; 95% CI, 1.58-2.09) and severe left main and/or proximal left anterior descending coronary artery stenosis (OR, 1.36; 95% CI, 1.20-1.54) were patient characteristics that were associated with higher odds of receiving intravascular microaxial LVADs only compared with IABPs only. CONCLUSIONS AND RELEVANCE: This study found that, among patients who underwent PCI for AMI complicated by cardiogenic shock, overall use of MCS devices was constant, and a 2.5-fold increase in intravascular microaxial LVAD use was found along with a corresponding decrease in IABP use and a significant hospital-level variation in MCS device use. These trends were observed despite limited clinical trial evidence of improved outcomes associated with device use.

Supplemental Digital Content is available in the text.Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.