Telemonitoring in Heart Failure Management

Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study

BACKGROUND

Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients.

OBJECTIVE

This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone.

METHODS

This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness.

RESULTS

Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion.

CONCLUSIONS

This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist.

Auditing the Audits: A Systematic Review on Different Procedures in Telemedicine

Telemedicine is a process of delivering health care using information and communication technologies. Audit and feedback (A&F) constitute a systematic intervention that is aimed at collecting data, which are subsequently compared with reference standards and then returned to health care operators through feedback meetings. The aim of this review is to analyse different audit procedures on and by mean of telemedicine services and to identify a practice that is more effective than the others. Systematic searches were performed in three databases evaluating studies focusing on clinical audits performed on and by means of telemedicine systems. Twenty-five studies were included in the review. Most of them focused on telecounselling services with an audit and a maximum duration of one year. Recipients of the audit were telemedicine systems and service users (general practitioners, referring doctors, and patients). Data resulting from the audit were inherent to the telemedicine service. The overall data collected concerned the number of teleconsultations, service activity, reasons for referral, response times, follow-up, reasons why treatment was not completed, technical issues, and other information specific to each telemedicine service. Only two of the considered studies dealt with organizational aspects, and of these, only one analysed communicative aspects. The complexity and heterogeneity of the treatments and services provided meant that no index of uniformity could be identified. Certainly, some audits were performed in an overlapping manner in the different studies, and these show that although attention is often paid to workers' opinions, needs, and issues, little interest was shown in communicative/organizational and team dynamics. Given the importance and influence that communication has in teamwork and care settings, an audit protocol that takes into account intra- and extra-team communication processes could be essential to improving the well-being of operators and the quality of the service provided.

Telemedicine application in patients with chronic disease: a systematic review and meta-analysis

Background

Telemedicine has been widely used for long-term care and self-management in patients with chronic disease, but there is no consensus regarding the effect of telemedicine on chronic disease management. The aim of this study is to review and analyse the effect of telemedicine on the management of chronic diseases such as hypertension, diabetes, and rheumatoid arthritis using a systematic review and meta-analysis.

Methods

We performed a comprehensive literature search of the Web of Science, PubMed, MEDLINE, EMBASE, CNKI (Chinese database), VIP (Chinese database), WanFang (Chinese database), and SinoMed (Chinese database) databases from their inception until December 31, 2021. The retrieved literature was screened and assessed independently by two authors. We used the risk-of-bias assessment tool recommended by the Cochrane Handbook for Systematic Reviews of Interventions 5.0.2 for assessing literature quality and Revman 5.3 software to conduct the meta-analysis.

Results

Fifteen articles were included in this study. The results of the systematic review indicated that telemedicine consultation and telemonitoring are the most commonly used intervention methods. Telemedicine is helpful for improving self-management in patients with rheumatoid arthritis. The results of the meta-analysis showed patients’ index of glycosylated hemoglobin (HbA1c) improved after 12 months of intervention (MD =  − 0.84; 95% CI =  − 1.53, − 0.16; Z = 2.42; P = 0.02), and no significant differences in fasting blood glucose (FBG) levels were observed after 6 months of intervention (MD =  − 0.35; 95% CI =  − 0.75,0.06; Z = 1.69; P = 0.09). The results also showed that systolic blood pressure (MD =  − 6.71; 95% CI =  − 11.40, − 2.02; Z = 2.81; P = 0.005) was reduced after 6 months of intervention.

Conclusion

Telemedicine had a positive effect on the management of diabetes, hypertension, and rheumatoid arthritis, especially when telemedicine consultation and telemonitoring method were used. When telemedicine was used as a disease management tool for patients with diabetes, the optimal intervention time is 12 months. Telemedicine improved the systolic blood pressure in hypertensive patients while also reducing negative emotions and enhancing medication adherence in rheumatoid arthritis patients.

Nocturnal Respiratory Rate Dynamics Enable Early Recognition of Impending Hospitalizations

The days and weeks preceding hospitalization are poorly understood because they transpire before patients are seen in conventional clinical care settings. Home health sensors offer opportunities to learn signatures of impending hospitalizations and facilitate early interventions, however the relevant biomarkers are unknown. Nocturnal respiratory rate (NRR) is an activity-independent biomarker that can be measured by adherence-independent sensors in the home bed. Here, we report automated longitudinal monitoring of NRR dynamics in a cohort of high-risk recently hospitalized patients using non-contact mechanical sensors under patients' home beds. Since the distribution of nocturnal respiratory rates in populations is not well defined, we first quantified it in 2,000 overnight sleep studies from the NHLBI Sleep Heart Health Study. This revealed that interpatient variability was significantly greater than intrapatient variability (NRR variances of 11.7 brpm2 and 5.2 brpm2 respectively, n=1,844,110 epochs), which motivated the use of patient-specific references when monitoring longitudinally. We then performed adherence-independent longitudinal monitoring in the home beds of 34 high-risk patients and collected raw waveforms (sampled at 80 Hz) and derived quantitative NRR statistics and dynamics across 3,403 patient-nights (n= 4,326,167 epochs). We observed 23 hospitalizations for diverse causes (a 30-day hospitalization rate of 20%). Hospitalized patients had significantly greater NRR deviations from baseline compared to those who were not hospitalized (NRR variances of 3.78 brpm2 and 0.84 brpm2 respectively, n= 2,920 nights). These deviations were concentrated prior to the clinical event, suggesting that NRR can identify impending hospitalizations. We analyzed alarm threshold tradeoffs and demonstrated that nominal values would detect 11 of the 23 clinical events while only alarming 2 times in non-hospitalized patients. Taken together, our data demonstrate that NRR dynamics change days to weeks in advance of hospitalizations, with longer prodromes associating with volume overload and heart failure, and shorter prodromes associating with acute infections (pneumonia, septic shock, and covid-19), inflammation (diverticulitis), and GI bleeding. In summary, adherence-independent longitudinal NRR monitoring has potential to facilitate early recognition and management of pre-symptomatic disease.

The Future of Telemedicine in the Management of Heart Failure Patients

Telemedicine (TM) is potentially a way of escalating heart failure (HF) multidisciplinary integrated care. Despite the initial efforts to implement TM in HF management, we are still at an early stage of its implementation. The coronavirus disease 2019 pandemic led to an increased utilisation of TM. This tendency will probably remain after the resolution of this threat. Face-to-face medical interventions are gradually transitioning to the virtual setting by using TM. TM can improve healthcare accessibility and overcome geographic inequalities. It promotes healthcare system efficiency gains, and improves patient self-management and empowerment. In cooperation with human intervention, artificial intelligence can enhance TM by helping to deal with the complexities of multicomorbidity management in HF, and will play a relevant role towards a personalised HF patient approach. Artificial intelligence-powered/telemedical/heart team/multidisciplinary integrated care may be the next step of HF management. In this review, the authors analyse TM trends in the management of HF patients and foresee its future challenges within the scope of HF multidisciplinary integrated care.

Novel findings of respiratory rate increases using the multisensor HeartLogic heart failure monitoring algorithm in COVID-19-positive patients: a case series

Background

With the ongoing coronavirus disease 2019 (COVID-19) epidemic, remote monitoring of patients with implanted cardiac devices has become more important than ever, as physical distancing measures have placed limits on in-clinic device monitoring. Remote monitoring alerts, particularly those associated with heart failure trends, have proved useful in guiding care in regard to monitoring fluid status and adjusting heart failure medications.

Case summary

This report describes use of Boston Scientific’s HeartLogic algorithm, which is a multisensor device algorithm in implantable cardioverter-defibrillator devices that is proven to be an early predictor of heart failure decompensation by measuring several variables, including respiratory rate, nighttime heart rate, and heart sounds. We present three cases of patients who were actively surveilled by the various HeartLogic device algorithm sensors and were identified to have increasing respiratory rates high enough to trigger a HeartLogic alert prior to a positive COVID-19 diagnosis.

Discussion

We propose that the HeartLogic algorithm and its accompanying individual physiologic sensors demonstrate potential for use in identifying non-heart failure-related decompensation, such as COVID-19-positive diagnoses.

Applications of the Multisensor HeartLogic Heart Failure Monitoring Algorithm During the COVID-19 Global Pandemic

In the COVID-19 era, the heart failure community has witnessed an unprecedented reduction in heart failure–related patient visits and hospitalizations. Social distancing measures present a dilemma for patients with heart failure who require frequent surveillance of volume status and vital signs to minimize heart failure–related symptoms and hospitalizations. With the rise of telemedicine comes an increased focus on remote monitoring technologies. This report describes use of a multisensor device algorithm in implantable cardioverter defibrillator devices by Boston Scientific, called HeartLogic. We present 2 cases of patients with advanced heart failure who were actively surveilled by the HeartLogic device algorithm to guide care. (Level of Difficulty: Beginner.)