FLAVOUR Study: FLow profiles And postoperative VasOplegia after continUous-flow left ventriculaR assist device implantation.

This study aims to associate the incidence of postoperative vasoplegia and short-term survival to the implantation of various left ventricular assist devices differing in hemocompatibility and flow profiles. The overall incidence of vasoplegia was 25.3% (73/289 patients) and 30.3% (37/122), 25.0% (18/72), and 18.9% (18/95) in the axial flow (AXF), centrifugal flow (CF), and centrifugal flow with artificial pulse (CFAP) group, respectively. Vasoplegia was associated with longer intensive care (ICU) and hospital length of stay (LOS) and mortality. ICU and in-hospital LOS and 1-year mortality were the lowest in the CFAP group. Post hoc analysis resulted in a p-value of 0.43 between AXF and CF; 0.35 between CF and CFAP; and 0.06 between AXF and CFAP. Although there is a trend in diminished incidence of vasoplegia, pooled logistic regression using flow profile and variables that remained after feature selection showed that flow profile was not an independent predictor for postoperative vasoplegia.

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.

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.

Monitoring left ventricular assist device parameters to detect flow- and power-impacting complications: a proof of concept.

Aims: 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 arrhythmia 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 arrhythmia or major bleeding. Methods and results: The source code of the algorithm is published in an open repository. The algorithm was optimized and tested retrospectively using HeartMate 3 (HM3) power and flow data of 120 patients, including 29 admissions due to cardiac arrhythmia 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 arrhythmia and major bleeding, respectively, with a false alarm rate of 2%. Conclusion: The proposed algorithm showed that the personalized algorithm is a viable approach to early identify cardiac arrhythmia and major bleeding by monitoring HM3 pump parameters. External validation is needed and integration with other clinical parameters could potentially improve the predictive value. In addition, the algorithm can be further enhanced using continuous data.

Incidence and risk factors of late right heart failure in chronic mechanical circulatory support.

BACKGROUND: Late right heart failure (LRHF) is a common complication during long-term left ventricular assist device (LVAD) support. We aimed to identify risk factors for LRHF after LVAD implantation. METHODS: Patients undergoing primary LVAD implantation between 2006 and 2019 and surviving the perioperative period were included for this study (n = 261). Univariate Cox proportional hazards analysis was used to assess the association of clinical covariates and LRHF, stratified for device type. Variables with p < 0.10 entered the multivariable model. In a subset of patients with complete echocardiography or right catheterization data, this multivariable model was extended. Postoperative cardiopulmonary exercise test data were compared in patients with and without LRHF. RESULTS: Nineteen percentage of patients suffered from LRHF after a median of 12 months, of which 67% required hospitalization. A history of atrial fibrillation (AF) (HR: 2.06 [1.08-3.93], p = 0.029), a higher preoperative body mass index (BMI) (HR: 1.07 [1.01-1.13], p = 0.023), and intensive care unit (ICU) duration (HR: 1.03 [1.00-1.06], p = 0.025) were independent predictors of LHRF in the multivariable model. A significant relation between the severity of tricuspid regurgitation (TR) and LRHF (HR: 1.91 [1.13-3.21], p = 0.016) was found in patients with echocardiographic data. Patients with LRHF demonstrated a lower maximal workload and peak VO2 at 6 months postoperatively. CONCLUSION: A history of AF, BMI, and longer ICU stay may help identify patients at high risk for LRHF. Severity of TR was significantly related to LRHF in a subset of patients.

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 < 0.01) in 458 matched patients, with a median follow-up of 23 months. Within the matched cohort, HM3 patients had a median age of 58 years, and 83% were male, 80% of the HVAD patients were male, with a median age of 59 years. PS-adjusted Cox regression confirmed a significantly better survival for HM3 patients when compared with HVAD, with a HR of 1.46 (95% confidence interval 1.14-1.85, P < 0.01). Pump thrombosis (P < 0.01) and ischaemic stroke (P < 0.01) occurred less in HM3 patients. No difference was found for haemorrhagic stroke, right heart failure, driveline infection, and major bleeding. Landmark-analysis confirmed a significant difference in conditional survival >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 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.

Hyperpolypharmacy is a predictor of mortality after left ventricular assist device (LVAD) implantation.

Background: The prevalence of (hyper)polypharmacy in patients on left ventricular assist device (LVAD) support and its effect on clinical outcomeis unknown. Therefore, we aimed to determine the prevalence of (hyper)polypharmacy in LVAD patients and evaluate its association with mortality and complications. Materials and methods: 210 patients aged ≥40 years who received a primary LVAD implantation between 2011 and 2019 were included for analysis. Polypharmacy and hyperpolypharmacy were defined as the concomitant use of 5-9 and ≥10 medications at discharge after LVAD implantation, respectively. Cause specific cox regression was used to assess the association of ≥10 medications with mortality, cardiac arrhythmia, driveline infection and major bleeding. Results: The median age of the patients was 57.5 years, and 35.7 % were female. The average number of discharge medications was 8.8 ± 2.3 per patient. The prevalence of patients with 5-9 medications and ≥10 medications was 62.9 % and 34.8 %, respectively. The median follow-up duration was 948 days (interquartile range 874 days). The prescription of ≥10 medications was significantly associated with a higher risk of mortality (HR 2.03; 95 % CI 1.15-3.6, p-value 0.02) adjusted for sex, age, comorbidity and stratified for device type. The prescription of ≥10 medications was not associated with a higher risk of major bleeding, cardiac arrhythmia or driveline infection. Conclusions: (Hyper)polypharmacy is highly prevalent in LVAD patients and is independently associated with a higher risk of mortality. Future research is needed to assess the efficacy of individual risk-benefit profiling of (cardiovascular) medication to ensure appropriate polypharmacy and to decrease negative health outcomes.

Propensity score-based analysis of long-term outcome of patients on HeartWare and HeartMate 3 left ventricular assist device support.

AIMS: Left ventricular assist device therapy has become the cornerstone in the treatment of end-stage heart failure and is increasingly used as destination therapy next to bridge to transplant or recovery. HeartMate 3 (HM3) and HeartWare (HVAD) are centrifugal continuous flow devices implanted intrapericardially and most commonly used worldwide. No randomized controlled trials have been performed yet. Analysis based on large registries may be considered as the best alternative but has the disadvantage of different standard of care between centres and missing data. Bias is introduced, because the decision which device to use was not random, even more so because many centres use only one type of left ventricular assist device. Therefore, we performed a propensity score (PS)-based analysis of long-term clinical outcome of patients that received HM3 or HVAD in a single centre. METHODS AND RESULTS: Between December 2010 and December 2019, 100 patients received HVAD and 81 patients HM3 as primary implantation at the University Medical Centre Utrecht. We performed PS matching with an extensive set of covariates, resulting in 112 matched patients with a median follow-up of 28 months. After PS matching, survival was not significantly different (P = 0.21) but was better for HM3. The cumulative incidences for haemorrhagic stroke (P = 0.01) and pump thrombosis (P = 0.02) were significantly higher for HVAD patients. The cumulative incidences for major bleeding, ischaemic stroke, right heart failure, and driveline infection were not different between the groups. We found no interaction between the surgeon who performed the implantation and survival (P = 0.59, P = 0.78, and P = 0.89). Sensitivity analysis was performed, by PS matching without patients on preoperative temporary support resulting in 74 matched patients. This also resulted in a non-significant difference in survival (P = 0.07). The PS-adjusted Cox regression showed a worse but non-significant (P = 0.10) survival for HVAD patients with hazard ratio 1.71 (95% confidence interval 0.91-3.24). CONCLUSIONS: Survival was not significantly different between both groups after PS matching, but was better for HM3, with a significantly lower incidence of haemorrhagic stroke and pump thrombosis for HM3. These results need to be interpreted carefully, because matching may have introduced greater imbalance on unmeasured covariates. A multicentre approach of carefully selected centres is recommended to enlarge the number of matched patients.

Wireless Remote Home Monitoring of Vital Signs in Patients Discharged Early After Esophagectomy: Observational Feasibility Study.

BACKGROUND: Hospital stays after major surgery are shorter than ever before. Although enhanced recovery and early discharge have many benefits, some complications will now first manifest themselves in home settings. Remote patient monitoring with wearable sensors in the first days after hospital discharge may capture clinical deterioration earlier but is largely uncharted territory. OBJECTIVE: This study aimed to assess the technical feasibility of patients, discharged after esophagectomy, being remotely monitored at home with a wireless patch sensor and the experiences of these patients. In addition, we determined whether observing vital signs with a wireless patch sensor influences clinical decision making. METHODS: In an observational feasibility study, vital signs of patients were monitored with a wearable patch sensor (VitalPatch, VitalConnect Inc) during the first 7 days at home after esophagectomy and discharge from hospital. Vital signs trends were shared with the surgical team once a day, and they were asked to check the patient's condition by phone each morning. Patient experiences were evaluated with a questionnaire, and technical feasibility was analyzed on a daily basis as the percentage of data loss and gap durations. In addition, the number of patients for whom a change in clinical decision was made based on the results of remote vital signs monitoring at home was assessed. RESULTS: Patients (N=20) completed 7 days each of home monitoring with the wearable patch sensor. Each of the patients had good recovery at home, and remotely observed vital signs trends did not alter clinical decision making. Patients appreciated that surgeons checked their vital signs daily (mean 4.4/5) and were happy to be called by the surgical team each day (mean 4.5/5). Wearability of the patch was high (mean 4.4/5), and no reports of skin irritation were mentioned. Overall data loss of vital signs measurements at home was 25%; both data loss and gap duration varied considerably among patients. CONCLUSIONS: Remote monitoring of vital signs combined with telephone support from the surgical team was feasible and well perceived by all patients. Future studies need to evaluate the impact of home monitoring on patient outcome as well as the cost-effectiveness of this new approach.

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 (SpO2) 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 SpO2, 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.