Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients With Heart Failure: A Feasibility Study.

OBJECTIVE: Tracking changes in hemodynamic congestion and the consequent proactive readjustment of treatment has shown efficacy in reducing hospitalizations for patients with heart failure (HF). However, the cost-prohibitive nature of these invasive sensing systems precludes their usage in the large patient population affected by HF. The objective of this research is to estimate the changes in pulmonary artery mean pressure (PAM) and pulmonary capillary wedge pressure (PCWP) following vasodilator infusion during right heart catheterization (RHC), using changes in simultaneously recorded wearable seismocardiogram (SCG) signals captured with a small wearable patch. METHODS: A total of 20 patients with HF (20% women, median age 55 (interquartile range (IQR), 44-64) years, ejection fraction 24 (IQR, 16-43)) were fitted with a wearable sensing patch and underwent RHC with vasodilator challenge. We divided the dataset randomly into a training-testing set (n = 15) and a separate validation set (n = 5). We developed globalized (population) regression models to estimate changes in PAM and PCWP from the changes in simultaneously recorded SCG. RESULTS: The regression model estimated both pressures with good accuracies: root-mean-square-error (RMSE) of 2.5 mmHg and R2 of 0.83 for estimating changes in PAM, and RMSE of 1.9 mmHg and R2 of 0.93 for estimating changes in PCWP for the training-testing set, and RMSE of 2.7 mmHg and R2 of 0.81 for estimating changes in PAM, and RMSE of 2.9 mmHg and R2 of 0.95 for estimating changes in PCWP for the validation set respectively. CONCLUSION: Changes in wearable SCG signals may be used to track acute changes in intracardiac hemodynamics in patients with HF. SIGNIFICANCE: This method holds promise in tracking longitudinal changes in hemodynamic congestion in hemodynamically-guided remote home monitoring and treatment for patients with HF.

Cardiac Function Monitoring for Patients Undergoing Cancer Treatments Using Wearable Seismocardiography: A Proof-of-Concept Study.

Advances in cancer therapeutics have dramatically improved the survival rate and quality of life in patients affected by various cancers, but have been accompanied by treatment-related cardiotoxicity, e.g. left ventricular (LV) dysfunction and/or overt heart failure (HF). Cardiologists thus need to assess cancer treatment-related cardiotoxic risks and have close followups for cancer survivors and patients undergoing cancer treatments using serial echocardiography exams and cardiovascular biomarkers testing. Unfortunately, the cost-prohibitive nature of echocardiography has made these routine follow-ups difficult and not accessible to the growing number of cancer survivors and patients undergoing cancer treatments. There is thus a need to develop a wearable system that can yield similar information at a minimal cost and can be used for remote monitoring of these patients. In this proof-of-concept study, we have investigated the use of wearable seismocardiography (SCG) to monitor LV function non-invasively for patients undergoing cancer treatment. A total of 12 subjects (six with normal LV relaxation, five with impaired relaxation and one with pseudo-normal relaxation) underwent routine echocardiography followed by a standard six-minute walk test. Wearable SCG and electrocardiogram signals were collected during the six-minute walk test and, later, the signal features were compared between subjects with normal and impaired LV relaxation. Pre-ejection period (PEP) from SCG decreased significantly (p < 0.05) during exercise for the subjects with impaired relaxation compared to the subjects with normal relaxation, and changes in PEP/LV ejection time (LVET) were also significantly different between these two groups (p < 0.05). These results suggest that wearable SCG may enable monitoring of patients undergoing cancer treatments by assessing cardiotoxicity.

Wearable Patch-Based Estimation of Oxygen Uptake and Assessment of Clinical Status during Cardiopulmonary Exercise Testing in Patients With Heart Failure.

BACKGROUND: To estimate oxygen uptake (VO2) from cardiopulmonary exercise testing (CPX) using simultaneously recorded seismocardiogram (SCG) and electrocardiogram (ECG) signals captured with a small wearable patch. CPX is an important risk stratification tool for patients with heart failure (HF) owing to the prognostic value of the features derived from the gas exchange variables such as VO2. However, CPX requires specialized equipment, as well as trained professionals to conduct the study. METHODS AND RESULTS: We have conducted a total of 68 CPX tests on 59 patients with HF with reduced ejection fraction (31% women, mean age 55 ± 13 years, ejection fraction 0.27 ± 0.11, 79% stage C). The patients were fitted with a wearable sensing patch and underwent treadmill CPX. We divided the dataset into a training-testing set (n = 44) and a separate validation set (n = 24). We developed globalized (population) regression models to estimate VO2 from the SCG and ECG signals measured continuously with the patch. We further classified the patients as stage D or C using the SCG and ECG features to assess the ability to detect clinical state from the wearable patch measurements alone. We developed the regression and classification model with cross-validation on the training-testing set and validated the models on the validation set. The regression model to estimate VO2 from the wearable features yielded a moderate correlation (R2 of 0.64) with a root mean square error of 2.51 ± 1.12 mL · kg-1 · min-1 on the training-testing set, whereas R2 and root mean square error on the validation set were 0.76 and 2.28 ± 0.93 mL · kg-1 · min-1, respectively. Furthermore, the classification of clinical state yielded accuracy, sensitivity, specificity, and an area under the receiver operating characteristic curve values of 0.84, 0.91, 0.64, and 0.74, respectively, for the training-testing set, and 0.83, 0.86, 0.67, and 0.92, respectively, for the validation set. CONCLUSIONS: Wearable SCG and ECG can assess CPX VO2 and thereby classify clinical status for patients with HF. These methods may provide value in the risk stratification of patients with HF by tracking cardiopulmonary parameters and clinical status outside of specialized settings, potentially allowing for more frequent assessments to be performed during longitudinal monitoring and treatment.

Classification of Decompensated Heart Failure From Clinical and Home Ballistocardiography.

OBJECTIVE: To improve home monitoring of heart failure patients so as to reduce emergency room visits and hospital readmissions. We aim to do this by analyzing the ballistocardiogram (BCG) to evaluate the clinical state of the patient. METHODS: 1) High quality BCG signals were collected at home from HF patients after discharge. 2) The BCG recordings were preprocessed to exclude outliers and artifacts. 3) Parameters of the BCG that contain information about the cardiovascular system were extracted. These features were used for the task of classification of the BCG recording based on the status of HF. RESULTS: The best AUC score for the task of classification obtained was 0.78 using slight variant of the leave one subject out validation method. CONCLUSION: This work demonstrates that high quality BCG signals can be collected in a home environment and used to detect the clinical state of HF patients. SIGNIFICANCE: In future work, a clinician/caregiver can be introduced into the system so that appropriate interventions can be performed based on the clinical state monitored at home.

The frequency of missed delirium in patients referred to palliative care in a comprehensive cancer center.

BACKGROUND: Delirium is one of the most common neuropsychiatric complications in advanced cancer patients with a frequency of up to 85 % before death. It is associated with adverse clinical outcomes such as increased morbidity and mortality as well as significant family and patient distress. The aim of our study is to determine at the frequency of missed delirium (MD) and identify factors associated with MD. METHODS: Seven hundred seventy-one consecutive palliative care inpatient consults from August 1, 2009 to January 31, 2010 were reviewed. Demographics, Memorial Delirium Assessment Scale (MDAS), Edmonton Symptom Assessment Scale (ESAS), primary referral symptom, Eastern Cooperative Oncology Group (ECOG), and physician diagnosis of delirium were collected along with delirium etiology, subtype, and reversibility. Delirium was diagnosed with a MDAS score of ≥ 7 or by a palliative medicine specialist using Diagnostic and Statistical Manual of Mental Disorders, 4th Edition Text Revision (DSM-IV TR) Criteria. MD was reported in those patients found to have delirium by the palliative medicine specialists but were referred by the primary team for other reasons besides delirium. Chi-squared test and Wilcoxon-Mann-Whitney test were used to examine the difference on measurements among or between different groups. Univariate logistic regression model was applied to assess for associations for MD. RESULTS: Two hundred fifty-two (33 %) had a diagnosis of delirium by the palliative medicine specialist. One hundred fifty-three (61 %) were missed by the primary referring team. Females comprised 53 % (n = 81), white 62 % (n = 95), and pain was the most common referral symptom (n = 77, 50 %). Hypoactive delirium was the most common subtype of delirium in MD (n = 47, 63 %). Opioid-related delirium was the most common etiology of MD (n = 47, 31 %). Patients referred for pain were more likely to have MD (odds ratio (OR) = 2.57, p = 0.0109). Of the 82 patients with delirium that was reversed, 67 % (n = 55) had a diagnosis of MD. CONCLUSION: Sixty-one percent of patients with a diagnosis of delirium by a palliative care specialist were missed by the primary referring team. Patients with MD were frequently referred for pain. Universal screening of cancer patients for delirium is recommended.