Decreased systemic oxygen delivery derived from gas exchange abnormalities in severe hemolysis complicates patients requiring mechanical circulatory support devices. Severe hemolysis releases free hemoglobin in plasma causing elevation of carboxyhemoglobin and methemoglobin levels. Hemolysis-induced decline in hemoglobin and oxyhemoglobin saturation significantly reduces the arterial oxygen content in blood, reducing systemic oxygen delivery. These patients develop hypoxemia with misleadingly normal oxygen saturation measured by standard pulse oximetry. Retrospective review of 2 clinical cases reaching carboxyhemoglobin and methemoglobin levels > 2% while supported with an Impella device. Case 1. Patient with cardiogenic shock refractory to maximal medical therapy required insertion of Impella device achieving improvement in cardiac output, pulse oximetry, arterial oxygen saturation and systemic oxygen delivery. The device caused significant hemolytic anemia with severe decline in hemoglobin and arterial oxygen saturation with elevation of carboxyhemoglobin and methemoglobin levels, causing drastic reduction in systemic oxygen delivery despite adequate cardiac output. Device removal reversed severe hemolytic anemia, causing increased arterial oxygen saturation and systemic oxygen delivery despite borderline cardiac output. Case 2. Patient with refractory cardiogenic shock improved after insertion of Impella device. Initial improvement cardiac output and systemic oxygen delivery was negated by hemolytic anemia associated with elevation of carboxyhemoglobin and methemoglobin levels. Hemolysis decreased by reducing the Impella power output. Carboxyhemoglobin and methemoglobin levels correlated precisely with degree of hemolysis allowing to titrate therapy to best systemic oxygen delivery. Monitoring carboxyhemoglobin and methemoglobin levels readily identifies patients with ongoing hemolysis secondary to invasive supportive devices.

Background: Progressive heart failure cardiogenic shock (HFCS) often requires escalation to temporary or durable mechanical circulatory support (MCS) as a bridge to transplant (BTT). Following the 2018 UNOS allocation changes, our center revised its BTT strategy to optimize support and shorten wait times. At our institution, the Impella 5.5 with SmartAssist via the axillary approach was selectively used for patients who remained refractory to guideline-directed medical therapy, failed single-inotrope therapy, and were not considered suitable durable LVAD candidates by our multidisciplinary heart team. We compared transplant-related outcomes of BTT patients supported with Impella 5.5 versus durable LVAD. Methods: We performed a single-center retrospective review of all heart and heart/kidney transplant candidates at Mayo Clinic Florida from October 2018 to February 2021. INTERMACS profile, baseline characteristics, and perioperative data were collected at the time of device implantation and throughout the transplant hospitalization. Results: A total of 87 heart and 4 heart–kidney transplants were completed. Forty-five patients (49%) required MCS as BTT: 27 (60%) with a durable LVAD and 18 (40%) with an Impella 5.5. All eighteen patients with Impella 5.5 as BTT (100%) were transplanted compared to nineteen patients with durable LVAD (70%), p = 0.001. The median time from listing to transplant was substantially shorter with Impella (32 vs. 696 days, p < 0.001), and this difference persisted across INTERMACS profiles. UNOS status at transplant was more urgent for Impella than LVAD (p < 0.001). Transplant surgery following Impella support required shorter cardiopulmonary bypass time (181 vs. 219 min, p < 0.001) and resulted in lower postoperative vasoactive-inotropic requirements (7.9 vs. 13, p = 0.003). No patients in the Impella group died or were delisted while awaiting transplant, whereas 5 LVAD patients (26%) died or were removed due to LVAD complications (p < 0.001). Conclusions: Our data demonstrates that the use of the Impella 5.5 as BTT was associated with significantly shorter waitlist time, higher transplantation rates, reduced perioperative morbidity, and lower postoperative vasoactive support compared with durable LVAD as BTT. These benefits were achieved despite a higher severity of illness at transplantation in the Impella cohort.

Patients undergoing heart‐kidney transplants who have primary graft dysfunction (PGD) of the heart are at risk of losing both organs, which may cause reluctance on the part of the transplant team to proceed with transplanting the kidney while the transplanted heart is being supported by mechanical device. We describe a case series in which 2 patients received kidney transplants while on veno‐arterial ECMO support for PGD after heart transplant. Both patients are alive more than 1 year following transplant, with good cardiac and renal function and no signs of cardiac rejection. Kidney transplant surgery is safe for patients on veno‐arterial ECMO support for cardiac PGD. It allows the heart recipient to receive a kidney from the same donor with both immunologic and survival advantages.

Background.: Acute cellular rejection (ACR) remains a major cause of morbidity after heart transplantation despite advances in immunosuppression. Whole genome transcriptomic profiling offers a systems-based, unbiased approach to elucidate the molecular mechanisms underlying ACR. However, noninvasive, longitudinal biomarker assessments capable of capturing the temporal dynamics of rejection biology remain scarce. Methods.: RNA sequencing of peripheral blood from heart transplant recipients before, during, and after ACR was compared with nonrejection controls. Pathway analysis was conducted using differentially expressed genes (DEGs), and a machine learning approach was applied to assess gene-based prediction of ACR. Results.: A total of 235 rejection-specific significant DEGs and 863 postrejection DEGs (false discovery rate < 0.05) were identified. During ACR, DEGs were enriched for T-cell activation/differentiation, apoptosis, and B-cell receptor signaling pathways. By combining the 2 sets of DEGs, a panel of 71 common genes was identified that reflected the significant, longitudinal transcriptomic dynamics of ACR. In an elastic net machine learning–based classifier, DYNLL1 and SERF2 were identified as ACR predictive genes, and achieved a cross-validated area under the receiver operating characteristic curve of 0.63. Conclusions.: Peripheral blood transcriptomics identify dynamic temporal responses in ACR including T- and B-cell pathways with potential ACR predictive genes that warrant further investigation.