Abstract
Funding Acknowledgements
Type of funding sources: None.
Background
Utilization of remote monitoring platforms was recommended amidst the COVID-19 pandemic. The HeartLogic algorithm combines ...data from multiple implantable cardioverter defibrillator (ICD) sensors (first and third heart sounds, intrathoracic impedance, respirations, night heart rate, and patient activity) to provide integrated data that may allow for detection of early signs of worsening HF.
Purpose
We examined whether the HeartLogic platform may elucidate behavioral changes that impact HF decompensation, and the possible consequences of home confinement caused by the COVID-19 pandemic.
Methods
The Italian lockdown was imposed from March 8th to May 18th. On March 8th 2020, the HeartLogic feature was active in 349 ICD and cardiac resynchronization therapy ICD patients at 20 Italian centers. The period from January 1st to July 19th was divided in 3 phases: Pre-Lockdown (weeks 1-11), Lockdown (weeks 12-20), Post-Lockdown (weeks 21-29).
Results
Immediately after the implementation of stay at home orders (week 12) we observed a significant drop in median activity level (65min 36-103 in week 12 vs. 101min 61-140 in Pre-Lockdown; p < 0.001), while there was no difference in the other contributing sensors. The median composite HeartLogic index increased at the end of Lockdown (4.7 1.3-10.2 in week 20 vs. 2.5 0.7-7.0 in Pre-Lockdown; p = 0.019). The weekly rate of HeartLogic alerts was significantly higher during Lockdown (1.56 alerts/week/100pts, 95%CI:1.15-2.06; IRR = 1.71, p = 0.014) and Post-Lockdown (1.37 alerts/week/100pts, 95%CI:0.99-1.84; IRR = 1.50, p = 0.072) than that reported in Pre-Lockdown (0.91 alerts/week/100pts, 95%CI:0.64-1.27). However, the median duration of alert state and the maximum index value did not change among phases, as well as the proportion of alerts followed by clinical actions at the centers (Pre-Lockdown: 31%, Lockdown: 22%, Post-Lockdown: 28%), and the proportion of alerts fully managed remotely (i.e. no in-clinic visits) (Pre-Lockdown: 89%, Lockdown: 90%, Post-Lockdown: 88%).
Conclusions
The system was sensitive to the behavioral changes occurred during the lockdown, i.e. decrease in activity. However, the home confinement had no impact on the other sensors. The higher rate of HeartLogic alerts during lockdown and the increase in the median index after 8 weeks of home confinement suggest the worsening of the HF status, possibly explained by the behavioral changes. Nonetheless, the management of the HF detected events (actions performed and management strategy) was not impacted by the restrictions.
Abstract
Funding Acknowledgements
Type of funding sources: None.
Introduction
Decompensation of heart failure leading (HF) to hospitalisation is the single most important drain on healthcare ...resources when managing patients with left ventricular systolic dysfunction. Cardiac resynchronisation therapy with/without defibrillators (CRT-P/D) decreases hospitalisation due to HF and improves survival while implantable cardiac defibrillators (ICD"s) have a favourable effect on the former. Proprietary software algorithms embedded in these complex devices give an early warning to clinicians when decompensation of HF is imminent allowing preventative action to be undertaken.
HeartLogic (HL) is one such new algorithm in Boston Scientific CRT-D/ICD devices using multiple sensors to track 5 physiological parameters, combining them into one composite Index, with an Alert being triggered if the Index is >16. The COVID-19 pandemic, due to multiple reasons, resulted in a significant decrease in availability of routine HF services in the United Kingdom, especially during the initial lockdown period from 23rd March to 1st July 2020.
Aim
To assess the impact of the COVID-19 pandemic, using HL, in patients with HF and complex devices.
Materials and Methods
Retrospective analysis of patients in a tertiary care cardiac centre in whom the HL software had been activated in March/April 2019 (n = 49) and comparison of those with (Group A n = 21) and without (Group B n = 28) an Alert (HLA) during the COVID-19 pandemic.
Results (Table): Whole cohort n = 49. Age: 72 ± 12 years, Median: 75, Range: 36-95. 36/49 (73.5%) males. Type of device implanted: Resonate X4 CRT-D: 28/49 (57.1%); Momentum CRT-D: 8/49 (16.3%); Resonate ICD: 13/49 (26.5%). Ischaemic aetiology of HF: 35/49 (71.4%), Total duration of HL monitoring: 632 ± 7 days (median: 632; range: 626-672). There was no difference in the age, gender, and type of device implanted between Group A and Group B. Over nearly ∼1 year of monitoring in each of the groups, Group A had more unstable HF with 10/21 (47.6%) having their first HLA during the pandemic. Multiple HLA"s, longer period in HLA and those with ischaemic aetiology of HF were higher in Group A. 17/40 (42.5%) HLA"s in Group A were within the first lockdown period (March - July). 24/28 (85.7%) patients in Group B had no HLA"s either before or during the pandemic. There was no difference in the HLA score between Groups A and B.
Conclusion
In this limited group of patients with a medium term follow-up, using the HeartLogic software, patients with ischaemic aetiology of HF and those with more HLA"s prior to the pandemic did worse than those who no HLA"s. First HLA"s, multiple alerts and longer duration of alerts in this group of patients suggests a lack of access to adequate HF services during the pandemic. It has implications with regard to how HF services are configured in future whenever resources are constrained. Abstract Figure.
Abstract
Funding Acknowledgements
Type of funding sources: Private company. Main funding source(s): BIOTRONIK SE & Co. KG
OnBehalf
BIO|STREAM.HF
Background
At the beginning of the Covid-19 pandemic ...in spring 2020, governments around the world issued curfews and other stay at home orders (‘lockdown’) to limit the spread of the SARS-CoV19 virus. This may have forced people to decrease their physical activity. Physical inactivity as well as social stress is known to be especially deleterious for heart failure (HF) patients. The BIO|STREAM.HF study enrolled such HF patients into a prospective registry with Home Monitoring.
Purpose
We aimed to evaluate the impact of the lockdown during the first Covid-19 pandemic wave on physical activity and arrhythmia burden of heart failure patients.
Methods
We analysed daily transmitted data of patients enrolled into a large international registry (BIO|STREAM.HF) being implanted with a cardiac resynchronization therapy (CRT) devices. Patients with NYHA ≥ II and LVEF ≤ 40% before CRT implantation were selected.
Intra-individual weekly mean and median values were calculated for the following daily transmitted parameters: physical activity (measured as % of the day during which the patient moves), atrial arrhythmia burden, mean heart rate (at rest), PP variability, PVC burden, and rate of biventricular pacing. Values were calculated for 12 weeks before and 12 weeks after the country-specific effective date of most rigorous restrictions in spring 2020 to visualize the general trend of parameter changes. Moreover, values for intra-individual changes between three 28-days periods (before, during, and after the lockdown) were calculated.
Results
Of 444 patients, 76% were male. They had a mean age of 69 ± 10 years and LVEF of 28.2 ± 6.7%. HF was of ischemic etiology in 42% of cases and they were in NYHA class II (47.5%), III (50.0%) or IV (2.5%).
On average, patients were active for 9% of the day (2 h 10 min). The physical activity decreased by approx. 10% with the onset of the lockdown (figure 1) and recovered within the following eight weeks.
Comparison of the 28-days periods before, during and after the lockdown showed a statistically significant intra-individual decrease in physical activity (mean decrease 9 min per day) during the lockdown compared to pre- and post-lockdown values and a trend toward reduced mean heart rates. In parallel, a significant increase in device detected atrial arrhythmia burden (mean increase 17 min per day) was observed. All other parameters did not change significantly.
Conclusion
Our results show that patients reduced their physical activity during the Covid-19 related lockdown in spring 2020. This was associated with an increase in atrial arrhythmia burden and a reduction of the mean heart rate. Prognostic implications of these results will further be analysed. Abstract Figure.
Abstract
Funding Acknowledgements
Type of funding sources: None.
Background
In Italy, a strict lockdown was imposed from 8 March 2020 to stop the spread of the Coronavirus Disease 2019 (COVID-19).
...Purpose
To explore the effect of this lockdown on data transmitted by remote monitoring (RM) of implantable cardioverter and cardiac resynchronization therapy defibrillators (ICDs/CRT-Ds).
Methods
RM daily transmissions from ICDs and CRT-Ds were analyzed and compared in two consecutive 1-month frames pre- and post-lockdown: Period I (7 February–7 March 2020) and Period II (8 March–7 April 2020).
Results
The study cohort included 180 patients (81.1% male, 63.3% ICDs and 36.7% CRT-Ds) with a median age of 70 (interquartile range 62-78) years. The median value of physical activity provided by accelerometric sensors showed a significant reduction between Period I and II (13.1% 8.2-18.1% versus 9.4% 6.3-13.8%, p < 0.001. Eighty-nine percent of patients decreased their activity, for 43.3% the relative reduction was ≥25%. The mean heart rate decreased significantly (69.2 63.8-75.6 bpm vs 67.9 62.7- 75.3 bpm, p < 0.001), but with greater reduction (≈3 beats/minute) in patients aged < 70 years. Resting heart rate and thoracic impedance showed minor variations. No differences were observed in device pacing percentages and arrhythmias.
Conclusions
In cardiac patients, the lockdown imposed to contain COVID-19 outbreak significantly reduced the amount of physical activity and the mean heart rate. These side effects of in-home confinement quarantine should be taken in consideration for frail patients. Abstract Figure. Activity and mean heart rate trends
Recent technologic advances capable of measuring outcomes after total knee arthroplasty (TKA) are critical in quantifying value-based care. Traditionally accomplished through office assessments and ...surveys with variable follow-up, this strategy lacks continuous and complete data. The primary objective of this study was to validate the feasibility of a remote patient monitoring (RPM) system in terms of the frequency of data interruptions and patient acceptance. Second, we report pilot data for (1) mobility; (2) knee range of motion, (3) patient-reported outcome measures (PROMs); (4) opioid use; and (5) home exercise program (HEP) compliance.
A pilot cohort of 25 patients undergoing primary TKA for osteoarthritis was enrolled. Patients downloaded the RPM mobile application preoperatively to collect baseline activity and PROMs data, and the wearable knee sleeve was paired to the smartphone during admission. The following was collected up to 3 months postoperatively: mobility (step count), range of motion, PROMs, opioid consumption, and HEP compliance. Validation was determined by acquisition of continuous data and patient tolerance at semistructured interviews 3 months after operation.
Of the 25 enrolled patients, 100% had uninterrupted passive data collection. Of the 22 available for follow-up interviews, all found the system motivating and engaging. Mean mobility returned to baseline within 6 weeks and exceeded preoperative baseline by 30% at 3 months. Mean knee flexion achieved was 119°, which did not differ from clinic measurements (P = .31). Mean KOOS improvement was 39.3 after 3 months (range: 3-60). Opioid use typically stopped by postoperative day 5. HEP compliance was 62% (range: 0%-99%).
In this pilot study, we established the ability to remotely acquire continuous data for patients undergoing TKA, who found the application to be engaging. RPM offers the newfound ability to more completely evaluate the patients undergoing TKA in terms of mobility and rehabilitation compliance. Study with more patients is required to establish clinical significance.
Patients with adult congenital heart disease (ACHD) are a rapidly growing cardiovascular population with increasing health needs and co-morbidities. Furthermore, their management requires frequent ...and ongoing hospital visits which can be burdensome. Digital health and remote monitoring have been shown to have a vast potential to enhance delivery of healthcare for patients, reducing their need for travel to clinic appointments therefore reducing costs to the patient and the healthcare service.
Patients over the age of 16 with a diagnosis of ACHD were invited to use the tailored digital application too. They were monitored for a period of 6 months. Information on patient demographics, time using the application, flagged events that prompted clinical reviews and their feedback through patient surveys were collected.
A total of 103 patients were enrolled and registered to use the digital application tool. There were 57 (56%) males, median age at the time of enrolment was 39 (16–73) years. The majority (96%) had a moderate or complex ACHD according to the ACC/AHA classification. There was a total of 7 modules that were completed on a weekly basis. The median length of a participant session was 2.2 min and the mean time to complete a module was 21 s. In total, 35 (67%) felt that the application helped them better manage their cardiac condition. Almost all (94%) of patients expressed that they would like to continue using the application beyond the pilot. There were 18 flagged events during the 6 month observation period, and 50% of received early clinical intervention.
Application based remote monitoring in this select group was well received and potentially holds large benefit to patients both clinically and economically. There were no safety concerns in our pilot feasibility study. Our data may inform much needed and timely investment in digital health.
•Digital technology and remote monitoring are an increasing resource for patients and healthcare professionals.•The use of remote monitoring applications is cost effective for both patients and health care services.•Remote monitoring may be incorporated into standard medical care for patients.
Driven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal ...medicine.
In this review, we discuss the 2 core objectives of the MLAL as they relate to the practice and progress of orthopedic surgery: (1) patient-specific, value-based care and (2) human movement.
We developed and validated several machine learning-based models for primary lower extremity arthroplasty that preoperatively predict patient-specific, risk-adjusted value metrics, including cost, length of stay, and discharge disposition, to provide improved expectation management, preoperative planning, and potential financial arbitration. Additionally, we leveraged passive, ubiquitous mobile technologies to build a small data registry of human movement surrounding TKA that permits remote patient monitoring to evaluate therapy compliance, outcomes, opioid intake, mobility, and joint range of motion.
The rapid rate with which we in arthroplasty are acquiring and storing continuous data, whether passively or actively, demands an advanced processing approach: AI. By carefully studying AI techniques with the MLAL, we have applied this evolving technique as a first step that may directly improve patient outcomes and practice of orthopedics.
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