Dynamic prediction of left ventricular assist device pump thrombosis based on lactate dehydrogenase trends

Prediction model for leaflet thrombosis in patients undergoing transcatheter aortic valve implantation: the EFFORT study

BACKGROUND

Leaflet thrombosis (LT) is a multifaceted and underexplored condition that can manifest following transcatheter aortic valve implantation (TAVI). The objective of this study was to formulate a prediction model based on laboratory assessments and clinical parameters, providing additional guidance and insight into this relatively unexplored aspect of post-TAVI complications.

METHODS

The present study was an observational prospective hypothesis-generating study, including 101 patients who underwent TAVI and a screening for LT (the primary endpoint) by multidetector computed tomography (MDCT). All images were acquired on a third-generation dual-source CT system. Levels of von Willebrand factor (vWF) activity, hemoglobin (Hb), and lactate dehydrogenase (LDH) were measured among other parameters. A predictive score utilizing binary logistic regression, Kaplan-Meier time-to-event analysis, and receiver operating characteristics (ROC) analysis was established.

RESULTS

LT (11 subclinical and 2 clinical) was detected in 13 of 101 patients (13%) after a median time to screening by MDCT of 105 days (IQR, 98-129 days). Elevated levels of vWF activity (> 188%) pre-TAVI, decreased Hb values (< 11.9 g/dL), as well as increased levels of LDH (> 312 U/L) post-TAVI and absence of oral anticoagulation (OAC) were found in patients with subsequent LT formation as compared to patients without LT. The established EFFORT score ranged from - 1 to 3 points, with an increased probability for LT development in patients with ≥ 2 points (85.7% of LT cases) vs < 2 points (14.3% of LT cases; p < 0.001). Achieving an EFFORT score of ≥ 2 points was found to be significantly associated with a 10.8 times higher likelihood of developing an LT (p = 0.001). The EFFORT score has an excellent c-statistic (area under the curve (AUC) = 0.89; 95% CI 0.74-1.00; p = 0.001) and a high negative predictive value (98%).

CONCLUSION

An EFFORT score might be a helpful tool to predict LT development and could be used in risk assessment, if validated in confirmatory studies. Therefore, the score has the potential to guide the stratification of individuals for the planning of subsequent MDCT screenings.

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.

Joint modelling of longitudinal processes and time-to-event outcomes in heart failure: systematic review and exemplar examining the relationship between serum digoxin levels and mortality

BACKGROUND

Joint modelling combines two or more statistical models to reduce bias and increase efficiency. As the use of joint modelling increases it is important to understand how and why it is being applied to heart failure research.

METHODS

A systematic review of major medical databases of studies which used joint modelling within heart failure alongside an exemplar; joint modelling repeat measurements of serum digoxin with all-cause mortality using data from the Effect of Digoxin on Mortality and Morbidity in Patients with Heart Failure (DIG) trial.

RESULTS

Overall, 28 studies were included that used joint models, 25 (89%) used data from cohort studies, the remaining 3 (11%) using data from clinical trials. 21 (75%) of the studies used biomarkers and the remaining studies used imaging parameters and functional parameters. The exemplar findings show that a per unit increase of square root serum digoxin is associated with the hazard of all-cause mortality increasing by 1.77 (1.34-2.33) times when adjusting for clinically relevant covariates.

CONCLUSION

Recently, there has been a rise in publications of joint modelling being applied to heart failure. Where appropriate, joint models should be preferred over traditional models allowing for the inclusion of repeated measures while accounting for the biological nature of biomarkers and measurement error.

Durable Left Ventricular Assist Device Outflow Graft Obstructions: Clinical Characteristics and Outcomes

PURPOSE

We report on the clinical course and management of patients supported with durable implantable LVADs who developed outflow graft obstructions at a large academic center.

METHODS

We performed a retrospective review of patients receiving LVAD support from 2012 through 2020. Patients who developed an outflow graft obstruction diagnosed by computed tomography angiography (CTA) or angiogram were identified, and patient characteristics and outcomes were reported.

RESULTS

Of the 324 patients supported by LVAD at our institution, 11 patients (3.4%) were diagnosed with outflow graft obstructions. The most common presentation was low flow alarms, which was present in 10/11 patients, and the remaining patient presented with lightheadedness. Patients had minimal LDH elevation with 8/11 presenting with less than 2-fold the upper limit of normal. Transthoracic echocardiograms were not diagnostic, but CTA enabled non-invasive diagnoses in 8/11 of the patients. Three patients with extrinsic compression of the outflow graft successfully underwent endovascular stent placement, and three patients with outflow cannula kinks received supportive care. Of the five patients diagnosed with intraluminal thromboses, one received a heart transplant, one underwent an outflow graft revision, and three received supportive care due to comorbidities.

CONCLUSION

Outflow graft obstructions remain a rare, but serious complication. The true prevalence of this entity is likely underestimated due to the non-specific clinical presentation. CTA is a pivotal non-invasive diagnostic step. Patients with external compression were successfully treated with endovascular stenting.

Coronavirus disease 2019 and mechanical circulatory support devices: A comprehensive review

Coronavirus disease (COVID-19) can cause circulatory shock refractory to medical therapy. Such patients can be managed with mechanical circulatory support (MCS) devices like IABP, Impella, VA ECMO, and Left Ventricular Assist Devices (LVADs). Moreover, patients on long-term durable LVADs are a special population having increased susceptibility and mortality to COVID-19 infection. In this narrative review, we searched PubMed and Medline for studies on COVID-19 patients on short-term MCS devices. We found 36 papers with 110 patients who met our review criteria, including 89 LVAD patients and 21 COVID-19 patients who needed MCS device therapy. These studies were used to extract patient demographics, clinical presentation, MCS device details, management, and outcomes. Mean age of patients with COVID-19 infection on LVADs was 60, 73% were male, and HeartMate 3 was the most common device (53%). Most patients (77.5%) needed hospitalization, and mortality was 23.6%. Among the 21 reported cases of critically ill COVID-19 patients who required MCS, the mean age was 49.8 years, 52% were women, and the most common MCS device used was VA ECMO (62%) in conjunction with an Impella for LV venting. Comorbidities were not present in 43%, but 71% had abnormal ventricular function on echocardiography. MCS is a viable option for managing severe COVID-19 infection with shock, with many reported cases of favorable outcomes.

How Are We Monitoring Brain Injuries in Patients With Left Ventricular Assist Device? A Systematic Review of Literature

Despite the common occurrence of brain injury in patients with left ventricular assist device (LVAD), optimal neuromonitoring methods are unknown. A systematic review of PubMed and six electronic databases from inception was conducted until June 5, 2019. Studies reporting methods of neuromonitoring while on LVAD were extracted. Of 5,190 records screened, 37 studies met the inclusion criteria. The neuromonitoring methods include Transcranial Doppler ultrasound for emboli monitoring (TCD-e) (n = 13) and cerebral autoregulation (n = 3), computed tomography and magnetic resonance imaging (n = 9), serum biomarkers (n = 7), carotid ultrasound (n = 3), and near-infrared spectroscopy (n = 2). Of 421 patients with TCD-e, thromboembolic events (TEs) were reported in 79 patients (20%) and microembolic signals (MES) were detected in 105 patients (27%). Ischemic stroke was more prevalent in patients with MES compared to patients without MES (43% vs.13%, p < 0.001). Carotid ultrasound for assessing carotid stenosis was unreliable after LVAD implantation. Elevated lactate dehydrogenase (LDH) levels were associated with TEs. Significant heterogeneity exists in timing, frequency, and types of neuromonitoring tools. TCD-e and serial LDH levels appeared to have potential for assessing the risk of ischemic stroke. Future prospective research incorporating protocolized TCD-e and LDH may assist in monitoring adverse events in patients with LVAD.

Prothrombin Complex Concentrate for Emergent Reversal of Intracranial Hemorrhage in Patients with Ventricular Assist Devices

BACKGROUND

Intracranial hemorrhage (ICH) is a devastating complication for patients with ventricular assist devices (VADs). The safety of emergent anticoagulation reversal with four-factor prothrombin complex concentrate (PCC) and optimal timing of anticoagulation resumption are not clear. In addition, lactate dehydrogenase (LDH) is used as a biomarker for thromboembolic risk, but its utility in guiding anticoagulation management after reversal with PCC has not be described.

METHODS

We retrospectively reviewed a consecutive series of patients with VADs presenting with ICH between 2014 and 2020 who received four-factor PCC for rapid anticoagulation reversal. We collected the timing of PCC administration, timing of resumption of anticoagulation, survival, occurrence of thromboembolic events, and LDH levels throughout hospitalization.

RESULTS

We identified 16 ICH events in 14 patients with VADs treated with rapid anticoagulation reversal using four-factor PCC (11 intraparenchymal, 4 subdural, 1 subarachnoid hemorrhage). PCC was administered at a mean of 3.3 ± 0.3 h after imaging diagnosis of ICH. Overall mortality was 63%. Survivors had higher presenting Glasgow Coma Scale (median 15, interquartile range [IQR] 15-15 versus 14, IQR 8-14.7, P = 0.041). In all six instances where the patient survived, anticoagulation was resumed on average 9.16 ± 1.62 days after reversal. There were no thromboembolic events prior to resumption of anticoagulation. Three events occurred after anticoagulation resumption and within 3 months of reversal: VAD thrombosis in a patient with thrombosis at the time of reversal, ischemic stroke, and readmission for elevated LDH in the setting of subtherapeutic international normalized ratio.

CONCLUSIONS

Our limited series found no thromboembolic complications immediately following anticoagulation reversal with PCC prior to resumption of anticoagulation. LDH trends may be useful to monitor thromboembolic risk after reversal.

Prediction of aortic valve regurgitation after continuous-flow left ventricular assist device implantation using artificial intelligence trained on acoustic spectra

Significant aortic regurgitation (AR) is a common complication after continuous-flow left ventricular assist device (LVAD) implantation. Using machine-learning algorithms, this study was designed to examine valuable predictors obtained from LVAD sound and to provide models for identifying AR. During a 2-year follow-up period of 13 patients with Jarvik2000 LVAD, sound signals were serially obtained from the chest wall above the LVAD using an electronic stethoscope for 1 min at 40,000 Hz, and echocardiography was simultaneously performed to confirm the presence of AR. Among the 245 echocardiographic and acoustic data collected, we found 26 episodes of significant AR, which we categorized as “present”; the other 219 episodes were characterized as “none”. Wavelet (time–frequency) analysis was applied to the LVAD sound and 19 feature vectors of instantaneous spectral components were extracted. Important variables for predicting AR were searched using an iterative forward selection method. Seventy-five percent of 245 episodes were randomly assigned as training data and the remaining as test data. Supervised machine learning for predicting concomitant AR involved an ensemble classifier and tenfold stratified cross-validation. Of the 19 features, the most useful variables for predicting concomitant AR were the amplitude of the first harmonic, LVAD rotational speed during intermittent low speed (ILS), and the variation in the amplitude during normal rotation and ILS. The predictive accuracy and area under the curve were 91% and 0.73, respectively. Machine learning, trained on the time–frequency acoustic spectra, provides a novel modality for detecting concomitant AR during follow-up after LVAD.

Left Ventricular Assist Device-Related Complications

Left ventricular assist device (LVAD) has emerged as a safe, durable, and revolutionary therapy for end-stage heart failure patients. Despite the appearance of newer-generation devices that have improved patient outcomes, the burden of adverse events remains significant. Although the survival rate for patients with LVAD is appreciated to be 81% at 1 year and 70% at 2 years, the incidence of adverse events is also high. Over time, both early and late postimplant complications have diminished in terms of prevalence and impact; however, complications, such as infections, bleeding, right heart failure, pump thrombosis, aortic insufficiency, or stroke, continue to represent a challenge for the practitioner. Therefore, the aim of this review is to highlight the most recent data regarding the current use of LVAD in the treatment of end-stage heart failure, with a specific focus on LVAD-related complications, in order to improve device-related outcomes. It will also revise how to mitigate the risk and how to approach specific adverse events. Withal, understanding the predisposing risk factors associated with postimplant complications, early recognition and appropriate treatment help to significantly improve the prognosis for patients with end-stage heart failure.

Dynamic prediction of left ventricular assist device pump thrombosis based on lactate dehydrogenase trends

Aims

The risk of HeartMate II (HMII) left ventricular assist device (LVAD) thrombosis has been reported, and serum lactate dehydrogenase (LDH), a biomarker of haemolysis, increases secondary to LVAD thrombosis. This study evaluated longitudinal measurements of LDH post‐LVAD implantation, hypothesizing that LDH trends could timely predict future LVAD thrombosis.

Methods and results

From October 2004 to October 2014, 350 HMIIs were implanted in 323 patients at Cleveland Clinic. Of these, patients on 339 HMIIs had at least one post‐implant LDH value (7996 total measurements). A two‐step joint model combining longitudinal biomarker data and pump thrombosis events was generated to assess the effect of changing LDH on thrombosis risk. Device‐specific LDH trends were first smoothed using multivariate boosted trees, and then used as a time‐varying covariate function in a multiphase hazard model to analyse time to thrombosis. Pre‐implant variables associated with time‐varying LDH values post‐implant using boostmtree were also investigated. Standardized variable importance for each variable was estimated as the difference between model‐based prediction error of LDH when the variable was randomly permuted and prediction error without permuting the values. The larger this difference, the more important a variable is for predicting the trajectory of post‐implant LDH. Thirty‐five HMIIs (10%) had either confirmed (18) or suspected (17) thrombosis, with 15 (43%) occurring within 3 months of implant. LDH was associated with thrombosis occurring both early and late after implant (P < 0.0001 for both hazard phases). The model demonstrated increased probability of HMII thrombosis as LDH trended upward, with steep changes in LDH trajectory paralleling trajectories in probability of pump thrombosis. The most important baseline variables predictive of the longitudinal pattern of LDH were higher bilirubin, higher pre‐implant LDH, and older age. The effect of some pre‐implant variables such as sodium on the post‐implant LDH longitudinal pattern differed across time.

Conclusions

Longitudinal trends in surveillance LDH for patients on HMII support are useful for dynamic prediction of pump thrombosis, both early after implant and late. Incorporating upward and downward trends in LDH that dynamically update a model of LVAD thrombosis risk provides a useful tool for clinical management and decisions.