The association of COVID-19 with executive functioning raises key questions regarding patients' longterm treatment. Future studies are needed to identify the risk factors and mechanisms underlying cognitive dysfunction as well as options for rehabilitation.
Background: There is insufficient evidence about the ability of pretransplant psychosocial evaluations to predict posttransplant outcomes. While standardized assessments were developed to increase predictive validity, it is unclear whether the risk scores they yield predict outcomes. We investigated if the Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT), a scaling approach to those assessments, would have been a superior predictor than the standard psychosocial evaluation. Methods: In this retrospective study, medical records of 182 adult liver transplant recipients who were at least 1 year posttransplant and prescribed tacrolimus for immunosuppression were analyzed. Regression analyses predicted outcomes of interest, including immunosuppressant nonadherence and biopsy-proven rejection, obtained 1-year posttransplant to time of data collection. Nonadherence was determined using the medication level variability index (MLVI). Results: Approximately 49% of patients had MLVI > 2.5, suggestive of nonadherence, and 15% experienced rejection. SIPAT total score did not predict adherence either using the continuous (P = .70), or dichotimized score, above or below > 2.5 (P = .14), or rejection (P = 0.87). Using a SIPAT threshold (total score > 69) did not predict adherence (p = .16) nor was a superior predictor of the continuous adherence score (P = .45), MLVI > 2.5 (P = .42), or rejection (P = 0.49), than the standard evaluation. Conclusion: Our findings suggest that the SIPAT is unable to predict 2 of the most important outcomes in this population, immunosuppressant adherence and rejection. Research efforts should attempt to evaluate the best manner to use psychosocial evaluations.
While COVID-19 is primarily considered a respiratory disease, it has been shown to affect the central nervous system. Mounting evidence shows that COVID-19 is associated with neurological complications as well as effects thought to be related to neuroinflammatory processes. Due to the novelty of COVID-19, there is a need to better understand the possible long-term effects it may have on patients, particularly linkage to neuroinflammatory processes. Perivascular spaces (PVS) are small fluid-filled spaces in the brain that appear on MRI scans near blood vessels and are believed to play a role in modulation of the immune response, leukocyte trafficking, and glymphatic drainage. Some studies have suggested that increased number or presence of PVS could be considered a marker of increased blood-brain barrier permeability or dysfunction and may be involved in or precede cascades leading to neuroinflammatory processes. Due to their size, PVS are better detected on MRI at ultrahigh magnetic field strengths such as 7 Tesla, with improved sensitivity and resolution to quantify both concentration and size. As such, the objective of this prospective study was to leverage a semi-automated detection tool to identify and quantify differences in perivascular spaces between a group of 10 COVID-19 patients and a similar subset of controls to determine whether PVS might be biomarkers of COVID-19-mediated neuroinflammation. Results demonstrate a detectable difference in neuroinflammatory measures in the patient group compared to controls. PVS count and white matter volume were significantly different in the patient group compared to controls, yet there was no significant association between PVS count and symptom measures. Our findings suggest that the PVS count may be a viable marker for neuroinflammation in COVID-19, and other diseases which may be linked to neuroinflammatory processes.
Audio and speech have several implicit characteristics that have the potential for the identification and quantification of clinical disorders. This PRISMA-guided review is designed to provide an overview of the landscape of automated clinical audio processing to build data-driven predictive models and infer phenotypes of a variety of neuropsychiatric, cardiac, respiratory and other disorders. We detail the important components of this processing workflow, specifically data acquisition and processing, algorithms used and their customization for clinical applications, commonly used tools and software, and benchmarking and evaluation methodologies. Finally, we discuss important open challenges for the field, and potential strategies for addressing them.
Remote interventions are increasingly used in transplant medicine but have rarely been rigorously evaluated. We investigated a remote intervention targeting immunosuppressant management in pediatric lung transplant recipients. Patients were recruited from a larger multisite trial if they had a Medication Level Variability Index (MLVI) ≥2.0, indicating worrisome tacrolimus level fluctuation. The manualized intervention included three weekly phone calls and regular follow-up calls. A comparison group included patients who met enrollment criteria after the subprotocol ended.Outcomes were defined before the intent-to-treat analysis. Feasibility was defined as ≥50% of participants completing the weekly calls. MLVI was compared pre-and 180 days postenrollment and between intervention and comparison groups. Of 18 eligible patients, 15 enrolled. Seven additional patients served as the comparison.Seventy-five percent of participants completed ≥3 weekly calls; average time on protocol was 257.7 days. Average intervention group MLVI was significantly lower (indicating improved blood level stability) at 180 days postenrollment (2.9 ± 1.29) compared with pre-enrollment (4.6 ± 2.10), p = .02. At 180 days, MLVI decreased by 1.6 points in the intervention group but increased by 0.6 in the comparison group DUNCAN-PARK et Al. | 3113 AJT 1 | INTRODUC TI ON Remote communications (e.g., telephone, video, or text messaging) can improve access to medical services. 1 More recently, clinicians and institutions have turned to remote communication to minimize contagion during the COVID-19 pandemic. 2Remote contacts may have particular appeal in the long-term management of transplant recipients. First, reducing exposure to pathogens is especially important in immunosuppressed individuals, 3 and indeed, transplant programs have rapidly turned to telehealth in the context of COVID-19. [4][5][6] Second, many transplant recipients travel extensively for their care, 7,8 and remote technology may reduce this burden. Third, immunosuppressant nonadherence remains the leading cause of preventable graft failure, [9][10][11][12][13][14][15] and frequent remote encounters could facilitate adherence. But, there are challenges. Remote interventions may be less effective at communicating with patients than in-person encounters, confidentiality must be maintained, and some patients may not have the means or comfort with technology to participate. 16,17 Key components of a robust evaluation of telehealth interventions include assessing predefined outcomes in prospective multisite trials. 18 Yet, despite its promise, there is a dearth of such investigations into telehealth in pediatric populations. [19][20][21]
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