Citations

Abstract Introduction: Blood-based tests represent a compelling option for early detection and management of cancers and other chronic diseases. While blood-based tests may offer potential to increase patient engagement, the assumption of greater adherence in clinical practice requires further evaluation. This systematic review aimed to evaluate the real-world adherence to established blood-based tests for commonly recommended screening indications, to better inform expectations for proposed application to average-risk colorectal cancer (CRC) screening. Methods: A systematic search of PubMed, Embase, and citations was performed to identify literature published from 2010 to 2023. Studies examined adherence to routine blood-based laboratory tests with a Grade A or B recommendation per the United States Preventive Services Task Force (USPSTF) for diabetes, dyslipidemia, human immunodeficiency virus (HIV), and hepatitis C virus (HCV) in an average-risk patient population were included. In addition, prostate-specific antigen (PSA) testing adherence for prostate cancer screening (Grade-C USPSTF recommendation), the only commonly accessible blood-based single-cancer screening test for average-risk adults, was also included. Studies without venipuncture-derived blood were excluded. Results: Of 53,067 articles screened, 69 were included in the analysis. Overall, adherence rates of blood-based tests were highly variable, with median values (interquartile range, IQR) of 66.3% (11.8%), 67.8% (18.0%), 34% (28.7%), and 36.8% (30%) for diabetes, dyslipidemia, HCV, and HIV, respectively. PSA testing adherence was 37.2% (18.4%). Beyond disease type, adherence varied by study design, data source, patient characteristics, clinical setting, and study country. Information and selection bias were the most common risks of bias. Conclusions: Based on data from this comprehensive systematic review, real-world adherence to currently recommended, blood-based screening strategies is suboptimal. Future research is needed to identify the extent to which these data are generalizable to blood-based CRC screening, and to determine if/how this candidate strategy would impact clinical, economic, and health equity outcomes. Citation Format: Quang A. Le, Takako Kiener, Heather A. Johnson, Rukaiyya S. Khan, Joyce Kong, Kevin H. Li, Gina Thompson, A. Burak Ozbay, Mallik Greene, Paul J. Limburg, A. Mark Fendrick, John B. Kisiel, Derek W. Ebner. Systematic review of real-world adherence to blood-based laboratory tests for average-risk disease screening: potential implications for emerging colorectal cancer screening modalities [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A027.

AbstractBackgroundIdiopathic rapid eye movement sleep behavior disorder (RBD) is a strong known predictor of a final clinicopathological diagnosis of a Lewy type α‐synucleinopathy (LTS). Olfactory dysfunction is an early symptom of synucleinopathies and has been repeatedly associated with the presence of post‐mortem LTS.ObjectiveTo assess the combined value of a clinician diagnosis of probable RBD (PRBD) and hyposmia in predicting the post‐mortem presence of LTS in a broader, less‐selected, volunteer elderly population.MethodsWe studied 652 autopsied subjects from the Arizona Study of Aging and Neurodegenerative Disorders, which were evaluated for PRBD, had completed annual movement and cognitive assessments, and had at least one the University of Pennsylvania Smell Identification Test (UPSIT) olfactory test.ResultsHistological evidence of LTS was significantly more frequent in those who had PRBD (112/152: 73.7%) than those without (177/494: 35.8%) (P < 0.001). LTS was more frequent in cases with PRBD and a low UPSIT score (90.8%) compared to cases with PRBD only (73.7%) (P < 0.001) or cases with a low UPSIT score only (69.4%) (P < 0.001). Sensitivity of PRBD diagnosis for predicting LTS was 38.8% and specificity 88.8%, whereas sensitivity of a low UPSIT score was 74.4% and specificity 73.4% (Youden's index = 0.276 for PRBD, 0.478 for UPSIT). When combining both measures, sensitivity was 34.3% and specificity increased to 97.2%.ConclusionPRBD, diagnosed without sleep study confirmation, combined with a reduced olfactory performance is highly specific for predicting post‐mortem presence of LTS. The combination of both measures may provide a cost‐effective means of predicting LTS in a broader community.

Abstract Background Incorporation of CT-derived fractional flow reserve (CT-FFR) enhances the precision of Coronary CT Angiography (CCTA) to detect hemodynamically significant coronary artery disease (CAD). However, the financial consequences remain unclear. Purpose Our objective is to assess the economic implications of CT-FFR in the Dutch Healthcare system. Methods This retrospective, single-center cost effectiveness analysis includes patient data of a previously published study assessing the impact of routine CT-FFR availability on clinical management. We collected medical resource use for both diagnostic and therapeutic cardiovascular care with a follow-up duration of 1 year. Costs of CT-FFR analysis were set at €1000, its current price for Dutch hospitals. Median total costs at were compared using the Wilcoxon rank sum test, including a sensitivity analysis in which cost estimations (without registered maximum tariffs) were increased or decreased with 10%. Results A total of 360 patients, classified as having low to intermediate risk of CAD, were enrolled in the study – 224 in the CCTA group and 136 in the CT-FFR group. In addition to undergoing more (invasive) diagnostic testing and revascularization procedures over the studied period, patients in the CCTA-group had more outpatient clinic consultations (p&lt;0.001), presented more frequently to the emergency department (p=0.035) and spend more nights in the hospital (p=0.11). Median costs were €3.347 (IQR €1.066 – €4.862) in the CCTA-group and €1.436 (IQR €1.362 - €3.943) in the CT-FFR group (p=0.091). Increasing cost estimation with 10% revealed a median of €3.431 in the CCTA-group versus €1.479 in the CT-FFR group (p=0.078), decreasing with 10% showed median costs of €3.263 in the CCTA-group and €1.392 in the CT-FFR group (p=0.118). In our sample, CT-FFR guided care would have been significantly cheaper than CCTA-guided care at or below a market price of €770. Conclusion Despite the significant reduction in additional diagnostic testing and hospital visits in the CT-FFR group, CT-FFR guided care was not cheaper than CCTA-guided care at its current market price of €1.000. Economic cost-effectiveness would have occurred from a price of €770 per analysis. The large difference between medians suggests that the lack of significance might partially be attributed to insufficient power in this relatively small sample size. Additionally, this study did not account for costs associated with loss of productivity and quality of life. However, less additional diagnostic testing, less revascularization procedures and less hospital consultations and admissions leads to reduction in hospital resource consumption and patient burden, which is an important advantage of CT-FFR as the number of referred patients increases yearly.

Abstract Objectives To perform a systematic review and meta-analysis on diagnostic accuracy and other outcomes of coronary computed tomography angiography (CCTA) and CT-derived fractional flow reserve (CT-FFR) in candidates for trans-catheter aortic valve implantation (TAVI). Background Screening for obstructive coronary artery disease (CAD) with CCTA during TAVI work-up could prevent unnecessary invasive coronary angiography (ICA). This becomes increasingly relevant as the TAVI-population grows. CT-FFR improves CCTA accuracy in chest pain patients, but its reliability is unknown in the TAVI-population. Methods Pubmed, Embase and Web of Science were searched for studies regarding CCTA and/or CT-FFR in TAVI-candidates. Primary endpoint was correct identification and rule-out of obstructive CAD. Results were pooled in a meta-analysis. Results Twenty-four of the 29 included articles were part of the meta-analysis, reporting results for CCTA and CT-FFR in 3.494 and 797 patients respectively. Reference standard was anatomical severity of CAD in most studies. At patient-level, pooled CCTA sensitivity was 94.3% and specificity 74.8%. CT-FFR sensitivity was 91.5% and specificity 60.3% with strong variation between studies. CCTA correctly ruled out CAD in 52% of all patients, but 42% of positive CCTAs were false positives. CT-FFR correctly ruled out CAD in 47% of all patients included in this meta-analysis, but showed better accuracy than CCTA in studies that compared both. In these studies, sensitivity of CCTA versus CT-FFR was 70.7% versus 79.0% and specificity was 64.4% versus 92.9%. Conclusions Negative CCTA effectively rules-out CAD in the TAVI-population. Adding CCTA to TAVI work-up could lead to significant reduction in pre-TAVI ICA procedures, but the proportion of false-positives remains high. In studies performing a direct comparison, CT-FFR performed better than CCTA. This is not reflected in the CT-FFR meta-analysis. Therefore, diagnostic accuracy of CT-FFR and added value over CCTA in the TAVI-population remains unclear. Further research regarding validation and reliability is warranted.Meta-analysis of CCTA and CT-FFR

Joyce, Kelsey E., Catherine A. Campbell, Arthur R. Bradwell, Samuel J.E. Lucas, Christopher T. Lewis, Rebekah A.I. Lucas, and Mark Edsell; for the Birmingham Medical Research Expeditionary Society (BMRES). The environmental impact of a high-altitude medical research expedition. High Alt Med Biol. 26:140–147, 2025. Introduction: Scientists must begin examining the environmental cost(s) of their research. The purpose of this study was to evaluate a component of the environmental impact of a high-altitude medical research expedition by totaling the carbon dioxide (CO 2 ) emissions calculated from as many direct and indirect sources as possible. Methods: Eighteen individuals flew from London to Bagdogra (via Delhi), and then drove onward to Lachung (via Gangtok) where they began their ascent on foot to 4,800 m (Kanchenjunga National Park, Sikkim). Several research experiments were conducted throughout the expedition, which required use of a laboratory centrifuge, solid CO 2 (specimen storage), rechargeable laptop computers and battery-powered oximeters. International Civil Aviation Organization calculators estimated aviation CO 2 production. Land emissions were calculated for Mahindra vehicles. Solid waste was weighed and CO 2 emissions estimated for its incineration. Results : Total CO 2 emissions equated to ∼16.7 tonnes from the following sources: air and land transportation of expedition team (87.3%); sublimation and transportation of solid CO 2 (7.7%), waste incineration (0.58%), generator transportation and gasoline (12 l) combustion (0.48%), and battery transportation (3.3%). Conclusions : Air travel contributed the most to the overall environmental cost of the research expedition. Further investigation is required to contextualize these findings in relation to lab-based alternative(s).

The NCCN Guidelines for Ovarian Cancer/Fallopian Tube Cancer/Primary Peritoneal Cancer provide multidisciplinary diagnostic workup, staging, and treatment recommendations for this disease. These NCCN Guidelines Insights detail how the evolution of the use of PARP inhibitors as maintenance and single-agent regimens for the treatment of ovarian cancer informed panel recommendations in the guidelines.

Abstract Pharmacogenomic (PGx) biomarkers integrated using machine learning can be embedded within the electronic health record (EHR) to provide clinicians with individualized predictions of drug treatment outcomes. Currently, however, drug alerts in the EHR are largely generic (not patient‐specific) and contribute to increased clinician stress and burnout. Improving the usability of PGx alerts is an urgent need. Therefore, this work aimed to identify principles for optimal PGx alert design through a health‐system‐wide, mixed‐methods study. Clinicians representing multiple practices and care settings ( N = 1062) in urban, rural, and underserved regions were invited to complete an electronic survey comparing the usability of three drug alerts for citalopram, as a case study. Alert 1 contained a generic warning of pharmacogenomic effects on citalopram metabolism. Alerts 2 and 3 provided patient‐specific predictions of citalopram efficacy with varying depth of information. Primary outcomes included the System's Usability Scale score (0–100 points) of each alert, the perceived impact of each alert on stress and decision‐making, and clinicians' suggestions for alert improvement. Secondary outcomes included the assessment of alert preference by clinician age, practice type, and geographic setting. Qualitative information was captured to provide context to quantitative information. The final cohort comprised 305 geographically and clinically diverse clinicians. A simplified, individualized alert (Alert 2) was perceived as beneficial for decision‐making and stress compared with a more detailed version (Alert 3) and the generic alert (Alert 1) regardless of age, practice type, or geographic setting. Findings emphasize the need for clinician‐guided design of PGx alerts in the era of digital medicine.

Background Transplantation using hearts obtained through donation after circulatory death (DCD) is increasing, but data on recipient renal outcomes are limited. Methods and Results Patients at a single institution who underwent heart transplantation using organs procured through DCD or donation after brain death (DBD) from April 2016 to August 2022 were included in this retrospective cohort study. Hemodynamic measures were collected via right heart catheterization performed 1 week after transplantation. Posttransplantation renal outcomes included estimated glomerular filtration rate at 1 week, 4 weeks, and 16 weeks, and the incidence of acute kidney injury (AKI) and renal replacement therapy within 1 week. The analysis included 225 patients (55 recipients of DCD). Baseline characteristics were comparable between recipients of DCD and DBD. Renal outcomes within 1 week posttransplantation in recipients of DCD were similar to recipients of DBD, including percent change in estimated glomerular filtration rate (−37.9% [−58.6 to −6.2] versus −31.9% [−52.4 to −9.9]; P =0.91), incidence of AKI (47.3% versus 46.5%; P >0.99) and incidence of renal replacement therapy (3.6% versus 4.7%; P >0.99). Recipients of DCD with AKI within 1 week (“early AKI”) did not recover to baseline estimated glomerular filtration rate (75.8 [60.2–91.3] mL/min per 1.73 m 2 ) by week 16 (59.3 [46.9–73.6] mL/min per 1.73 m 2 ; P =0.002), whereas recipients without early AKI exhibited comparable estimated glomerular filtration rate to baseline by week 4 (84.5 [70.8–98.5] mL/min per 1.73 m 2 ; P =0.084). Similar trends were observed in recipients of DBD. Conclusions Recipients of DCD demonstrated similar renal outcomes compared with recipients of DBD, supporting the ongoing use of DCD transplantation. Early AKI was associated with persistent renal dysfunction for recipients of both DCD and DBD.

AbstractObjectivesInterpatient variability in bipolar I depression (BP‐D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score of ≤8 on the Montgomery Åsberg Depression Rating Scale [MADRS]).MethodsSupervised machine learning models were trained on data from BP‐D patients treated with olanzapine (N = 168) and were externally validated on patients treated with olanzapine/fluoxetine combination (OFC; N = 131) and lamotrigine (LTG; N = 126). Top predictors were used to develop a prognosis rule informing how many symptoms should change and by how much within 4 weeks to increase the odds of achieving remission.ResultsAn AUC of 0.76 (NIR:0.59; p = 0.17) was established to predict remission in olanzapine‐treated subjects. These trained models achieved AUCs of 0.70 with OFC (NIR:0.52; p < 0.03) and 0.73 with LTG (NIR:0.52; p < 0.003), demonstrating external replication of prediction performance. Week‐4 changes in four MADRS symptoms (reported sadness, reduced sleep, reduced appetite, and concentration difficulties) were top predictors of remission. Across all pharmacotherapies, three or more of these symptoms needed to improve by ≥2 points at Week‐4 to have a 65% chance of achieving remission at 8 weeks (OR: 3.74, 95% CI: 2.45–5.76; p < 9.3E‐11).ConclusionMachine learning strategies achieved cross‐trial and cross‐drug replication in predicting remission after 8 weeks of pharmacotherapy for BP‐D. Interpretable prognoses rules required only a limited number of depressive symptoms, providing a promising foundation for developing simple quantitative decision aids that may, in the future, serve as companions to clinical judgment at the point of care.