Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure

Aims Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD). Methods and results In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods. Conclusion The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement

Data-driven monitoring in patients on left ventricular assist device support

Introduction: Despite an increasing population of patients supported with a left ventricular assist device (LVAD), it remains a complex therapy, and patients are frequently admitted. Therefore, a strict follow-up including frequent hospital visits, patient self-management and telemonitoring is needed. Areas covered: The current review describes the principles of LVADs, the possibilities of (tele)monitoring using noninvasive and invasive devices. Furthermore, possibilities, challenges, and future perspectives in this emerging field are discussed. Expert Opinion: Several studies described initial experiences on telemonitoring in LVAD patients, using mobile phone applications to collect clinical data and pump data. This may replace frequent hospital visits in near future. In addition, algorithms were developed aiming to early detect pump thrombosis or driveline infections. Since not all complications are reflected by pump parameters, data from different sources should be combined to detect a broader spectrum of complications in an early stage. We need to focus on the development of sophisticated but understandable algorithms and infrastructure combining different data sources, while addressing essential aspects such as data safety, privacy, and cost-effectiveness

Identifying patients at risk: multi-centre comparison of HeartMate 3 and HeartWare left ventricular assist devices

Aims: Since the withdrawal of HeartWare (HVAD) from the global market, there is an ongoing discussion if and which patients require prophylactically exchange for a HeartMate 3 (HM3). Therefore, it is important to study outcome differences between HVAD and HM3 patients. Because centres differ in patient selection and standard of care, we performed a propensity score (PS)-based study including centres that implanted both devices and aimed to identify which HVAD patients are at highest risk. Methods and results: We performed an international multi-centre study (n = 1021) including centres that implanted HVAD and HM3. PS-matching was performed using clinical variables and the implanting centre. Survival and complications were compared. As a sensitivity analysis, PS-adjusted Cox regression was performed. Landmark analysis with conditional survival >2 years was conducted to evaluate long-term survival differences. To identify which HVAD patients may benefit from a HM3 upgrade, Cox regression using pre-operative variables and their interaction with device type was performed. Survival was significantly better for HM3 patients (P 2 years after implantation (P = 0.03). None of the pre-operative variable interactions in the Cox regression were significant. Conclusions: HM3 patients have a significantly better survival and a lower incidence of ischaemic strokes and pump thrombosis than HVAD patients. This survival difference persisted after 2 years of implantation. Additional research using post-operative variables is warranted to identify which HVAD patients need an upgrade to HM3 or expedited transplantation

Data-driven innovation of left ventricular assist device therapy

Heart failure is a global and growing problem that affects 1-2% of people. For patients with end-stage heart failure, a heart transplant is the “gold standard”. Due to the permanent shortage of donor hearts and a growing number of patients on the transplant waiting list, treatment with a left ventricular assist device (LVAD) has become increasingly important. Survival of patients after LVAD implantation has improved significantly over the past two decades. Nevertheless, patients frequently suffer from important complications such as strokes, driveline infections, right heart failure or pump thrombosis. To further improve outcome after LVAD implantation, early detection of deterioration is key to enable early treatment. Therefore, the aim of this thesis was to identify LVAD patients at risk and to explore possibilities of remote monitoring possibilities. For example, we have demonstrated that outcomes after a HeartMate3 LVAD implantation are better compared to HeartWare LVAD implantation. In addition, we showed that patients with a higher BMI, a longer ICU duration and a history of atrial fibrillation prior to implantation are more likely to develop late right ventricular failure. In addition, it was demonstrated that hyperpolypharmacy (the use of more than 10 medications) occurs regularly in this patient group. There are various ways to monitor LVAD patients (remotely) in order to recognize and treat complications at an early stage. We demonstrated that the biomarker sST2 can play an important role for monitoring purposes. We also developed an algorithm to monitor LVAD parameters which helps in the early recognition of certain complications. In this way, outcomes LVAD implantation can be further improved

Data-driven monitoring in patients on left ventricular assist device support

INTRODUCTION: Despite an increasing population of patients supported with a left ventricular assist device (LVAD), it remains a complex therapy, and patients are frequently admitted. Therefore, a strict follow-up including frequent hospital visits, patient self-management and telemonitoring is needed. AREAS COVERED: The current review describes the principles of LVADs, the possibilities of (tele)monitoring using noninvasive and invasive devices. Furthermore, possibilities, challenges, and future perspectives in this emerging field are discussed. EXPERT OPINION: Several studies described initial experiences on telemonitoring in LVAD patients, using mobile phone applications to collect clinical data and pump data. This may replace frequent hospital visits in near future. In addition, algorithms were developed aiming to early detect pump thrombosis or driveline infections. Since not all complications are reflected by pump parameters, data from different sources should be combined to detect a broader spectrum of complications in an early stage. We need to focus on the development of sophisticated but understandable algorithms and infrastructure combining different data sources, while addressing essential aspects such as data safety, privacy, and cost-effectiveness

Accompanying code for "Monitoring left ventricular assist device parameters to detect flow- and power-impacting complications: a proof of concept"

<p>The number of patients on left ventricular assist device (LVAD) support increases due to the growing number of patients with end-stage heart failure and the limited number of donor hearts. Despite improving survival rates, patients frequently suffer from adverse events such as cardiac arrythmia and major bleeding. Telemonitoring is a potentially powerful tool to early detect deteriorations and may further improve outcome after LVAD implantation. Hence, we developed a personalized algorithm to remotely monitor HeartMate3 (HM3) pump parameters aiming to early detect unscheduled admissions due to. cardiac arrythmia and major bleeding. The source code of the algorithm made publicly available. The algorithm was optimized and tested retrospectively using HM3 power and flow data of 120 patients, including 29 admissions due to cardiac arrythmia and 14 admissions due to major bleeding. Using a true alarm window of 14 days prior to the admission date, the algorithm detected 59% and 79% of unscheduled admissions due to cardiac arrythmia and major bleeding, respectively, with a false alarm rate of 2%. </p> <p>Within this repository, you will discover the R code essential for implementing the personalized algorithm presented in the paper. It also encompasses simulated data, which serves as a means to assess and validate the algorithm's performance. Furthermore, a simple tutorial has been prepared to guide users on effectively utilizing the algorithm.</p> <p>We suggest users to submit either their original or simulated data for testing the algorithm.</p&gt

Are current wireless monitoring systems capable of detecting adverse events in high-risk surgical patients?:A descriptive study

Background: Adverse events are common in high-risk surgical patients, but early detection is difficult. Recent innovations have resulted in wireless and ‘wearable’ sensors, which may capture patient deterioration at an early stage, but little is known regarding their ability to timely detect events. The objective of this study is to describe the ability of currently available wireless sensors to detect adverse events in high-risk patients. Methods: A descriptive analysis was performed of all vital signs trend data obtained during an observational comparison study of wearable sensors for vital signs monitoring in high-risk surgical patients during the initial days of recovery at a surgical step-down unit (SDU) and subsequent traumatology or surgical oncology ward. Heart rate (HR), respiratory rate (RR) and oxygen saturation (SpO 2) were continuously recorded. Vital sign trend patterns of patients that developed adverse events were described and compared to vital sign recordings of patients without occurrence of adverse events. Two wearable patch sensors were used (SensiumVitals and HealthPatch), a bed-based mattress sensor (EarlySense) and a patient-worn monitor (Masimo Radius-7). Results: Twenty adverse events occurred in 11 of the 31 patients included. Atrial fibrillation (AF) was most common (20%). The onset of AF was recognizable as a sudden increase in HR in all recordings, and all patients with new-onset AF after esophagectomy developed other postoperative complications. Patients who developed respiratory insufficiency showed an increase in RR and a decrease in SpO 2, but an increase in HR was not always visible. In patients without adverse events, temporary periods of high HR and RR are observed as well, but these were transient and less frequent. Conclusions: Current systems for remote wireless patient monitoring on the ward are capable of detecting abnormalities in vital sign patterns in patients who develop adverse events. Remote patient monitoring may have potential to improve patient safety by generating early warnings for deterioration to nursing staff

Soluble Suppression of Tumorigenicity‐2 Predicts Mortality and Right Heart Failure in Patients With a Left Ventricular Assist Device

Background Soluble suppression of tumorigenicity‐2 (sST2) predicts mortality in patients with heart failure. The predictive value of sST2 in patients with a left ventricular assist device remains unknown. Therefore, we studied the relationship between sST2 and outcome after left ventricular assist device implantation. Methods and Results sST2 levels of patients with a left ventricular assist device implanted between January 2015 and December 2022 were included in this observational study. The median follow‐up was 25 months, during which 1573 postoperative sST2 levels were measured in 199 patients, with a median of 29 ng/mL. Survival of patients with normal and elevated preoperative levels was compared using Kaplan‐Meier analysis, which did not differ significantly (P=0.22) between both groups. The relationship between postoperative sST2, survival, and right heart failure was evaluated using a joint model, which showed a significant relationship between the absolute sST2 level and mortality, with a hazard ratio (HR) of 1.20 (95% CI, 1.10–1.130; P<0.01) and an HR of 1.22 (95% CI, 1.07–1.39; P=0.01) for right heart failure, both per 10‐unit sST2 increase. The sST2 instantaneous change was not predictive for survival or right heart failure (P=0.99 and P=0.94, respectively). Multivariate joint model analysis showed a significant relationship between sST2 with mortality adjusted for NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide), with an HR of 1.19 (95% CI, 1.00–1.42; P=0.05), whereas the HR of right heart failure was not significant (1.22 [95% CI, 0.94–1.59]; P=0.14), both per 10‐unit sST2 increase. Conclusions Time‐dependent postoperative sST2 predicts all‐cause mortality after left ventricular assist device implantation after adjustment for NT‐proBNP. Future research is warranted into possible target interventions and the optimal monitoring frequency