Impact of Back-to-Base Normothermic Machine Perfusion on Complications and Costs: A Multicenter, Real-World Risk-Matched Analysis.

OBJECTIVE: Assess cost and complication outcomes after liver transplantation (LT) using normothermic machine perfusion (NMP). BACKGROUND: End-ischemic NMP is often used to aid logistics, yet its impact on outcomes after LT remains unclear, as does its true impact on costs associated with transplantation. METHODS: Deceased donor liver recipients at 2 centers (January 1, 2019, to June 30, 2023) were included. Retransplants, splits, and combined grafts were excluded. End-ischemic NMP (OrganOx-Metra) was implemented in October 2022 for extended-criteria donation after brain death (DBDs), all donations after circulatory deaths (DCDs), and logistics. NMP cases were matched 1:2 with static cold storage controls (SCS) using the Balance-of-Risk [donation after brain death (DBD)-grafts] and UK-DCD Score (DCD-grafts). RESULTS: Overall, 803 transplantations were included, 174 (21.7%) receiving NMP. Matching was achieved between 118 NMP-DBDs with 236 SCS; and 37 NMP-DCD with 74 corresponding SCS. For both graft types, median inpatient comprehensive complications index values were comparable between groups. DCD-NMP grafts experienced reduced cumulative 90-day comprehensive complications index (27.6 vs 41.9, P =0.028). NMP also reduced the need for early relaparotomy and renal replacement therapy, with subsequently less frequent major complications (Clavien-Dindo ≥IVa). This effect was more pronounced in DCD transplants. NMP had no protective effect on early biliary complications. Organ acquisition/preservation costs were higher with NMP, yet NMP-treated grafts had lower 90-day pretransplant costs in the context of shorter waiting list times. Overall costs were comparable for both cohorts. CONCLUSIONS: This is the first risk-adjusted outcome and cost analysis comparing NMP and SCS. In addition to logistical benefits, NMP was associated with a reduction in relaparotomy and bleeding in DBD grafts, and overall complications and post-LT renal replacement for DCDs. While organ acquisition/preservation was more costly with NMP, overall 90-day health care costs-per-transplantation were comparable.

The impact of normothermic machine perfusion and acuity circles on waitlist time, mortality, and cost in liver transplantation: A multicenter experience.

Ex situ normothermic machine perfusion (NMP) helps increase the use of extended criteria donor livers. However, the impact of an NMP program on waitlist times and mortality has not been evaluated. Adult patients listed for liver transplant (LT) at 2 academic centers from January 1, 2015, to September 1, 2023, were included (n=2773) to allow all patients ≥6 months follow-up from listing. Routine NMP was implemented on October 14, 2022. Waitlist outcomes were compared from pre-NMP pre-acuity circles (n=1460), pre-NMP with acuity circles (n=842), and with NMP (n=381). Median waitlist time was 79 days (IQR: 20-232 d) at baseline, 49 days (7-182) with acuity circles, and 14 days (5-56) with NMP ( p <0.001). The rate of transplant-per-100-person-years improved from 61-per-100-person-years to 99-per-100-person-years with acuity circles and 194-per-100-person-years with NMP ( p <0.001). Crude mortality without transplant decreased from 18.3% (n=268/1460) to 13.3% (n=112/843), to 6.3% (n=24/381) ( p <0.001) with NMP. The incidence of mortality without LT was 15-per-100-person-years before acuity circles, 19-per-100 with acuity circles, and 9-per-100-person-years after NMP ( p <0.001). Median Model for End-Stage Liver Disease at LT was lowest with NMP, but Model for End-Stage Liver Disease at listing was highest in this era ( p <0.0001). The median donor risk index of transplanted livers at baseline was 1.54 (1.27-1.82), 1.66 (1.42-2.16) with acuity circles, and 2.06 (1.63-2.46) with NMP ( p <0.001). Six-month post-LT survival was not different between eras ( p =0.322). The total cost of health care while waitlisted was lowest in the NMP era ($53,683 vs. $32,687 vs. $23,688, p <0.001); cost-per-day did not differ between eras ( p =0.152). The implementation of a routine NMP program was associated with reduced waitlist time and mortality without compromising short-term survival after liver transplant despite increased use of riskier grafts. Routine NMP use enables better waitlist management with reduced health care costs.

A case with temporal spikes on electroencephalography induced by over 80 sessions of electroconvulsive therapy.

Background: Electroconvulsive therapy (ECT) is widely recognized as one of the most effective treatments for various psychiatric disorders and is generally considered safe. However, a few reports have mentioned that multiple ECT sessions could induce electroencephalography (EEG) abnormalities and epileptic seizures, a serious side effect of ECT. We experienced a case with EEG abnormalities after multiple ECT sessions and aimed to share our insights on conducting ECT safely. Case Presentation: We present the case of a 73-year-old female diagnosed with major depressive disorder. She underwent regular ECT sessions to alleviate her psychiatric symptoms. However, after more than 80 sessions, previously undetected EEG abnormalities were observed. Since the patient did not have clinical seizures, we were able to continue ECT at longer intervals without the use of antiepileptic drugs. Conclusion: Our case suggests the importance of routine EEG testing in patients undergoing prolonged ECT. While careful monitoring is necessary, continuing ECT without antiepileptic medication in patients with EEG abnormalities could be permissible.

Utilizing portable electroencephalography to screen for pathology of Alzheimer's disease: a methodological advancement in diagnosis of neurodegenerative diseases.

Background: The current biomarker-supported diagnosis of Alzheimer's disease (AD) is hindered by invasiveness and cost issues. This study aimed to address these challenges by utilizing portable electroencephalography (EEG). We propose a novel, non-invasive, and cost-effective method for identifying AD, using a sample of patients with biomarker-verified AD, to facilitate early and accessible disease screening. Methods: This study included 35 patients with biomarker-verified AD, confirmed via cerebrospinal fluid sampling, and 35 age- and sex-balanced healthy volunteers (HVs). All participants underwent portable EEG recordings, focusing on 2-minute resting-state EEG epochs with closed eyes state. EEG recordings were transformed into scalogram images, which were analyzed using "vision Transformer(ViT)," a cutting-edge deep learning model, to differentiate patients from HVs. Results: The application of ViT to the scalogram images derived from portable EEG data demonstrated a significant capability to distinguish between patients with biomarker-verified AD and HVs. The method achieved an accuracy of 73%, with an area under the receiver operating characteristic curve of 0.80, indicating robust performance in identifying AD pathology using neurophysiological measures. Conclusions: Our findings highlight the potential of portable EEG combined with advanced deep learning techniques as a transformative tool for screening of biomarker-verified AD. This study not only contributes to the neurophysiological understanding of AD but also opens new avenues for the development of accessible and non-invasive diagnostic methods. The proposed approach paves the way for future clinical applications, offering a promising solution to the limitations of advanced diagnostic practices for dementia.

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (iNPH). Addressing this gap, our study evaluates the generalizability of a DCNN trained on EEG data from a single hospital (Hospital #1). For data from Hospital #1, the DCNN achieved a balanced accuracy (bACC) of 0.927 in classifying individuals as healthy (n = 69) or as having AD, DLB, or iNPH (n = 188). The model demonstrated robustness across institutions, maintaining bACCs of 0.805 for data from Hospital #2 (n = 73) and 0.920 at Hospital #3 (n = 139). Additionally, the model could differentiate AD, DLB, and iNPH cases with bACCs of 0.572 for data from Hospital #1 (n = 188), 0.619 for Hospital #2 (n = 70), and 0.508 for Hospital #3 (n = 139). Notably, it also identified MCI pathologies with a bACC of 0.715 for Hospital #1 (n = 83), despite being trained on overt dementia cases instead of MCI cases. These outcomes confirm the DCNN's adaptability and scalability, representing a significant stride toward its clinical application. Additionally, our findings suggest a potential for identifying shared EEG signatures between MCI and dementia, contributing to the field's understanding of their common pathophysiological mechanisms.