Citations

ABSTRACT Objective We sought to characterize the sixth most common finding in our neuroimmunological laboratory practice (tissue assay‐observed unclassified neural antibodies [UNAs]), combining protein microarray and phage immunoprecipitation sequencing (PhIP‐Seq). Methods Patient specimens (258; 133 serums; 125 CSF) meeting UNA criteria were profiled; October 2022–September 2023. Top‐ranking candidate antigens were validated in silico, by dual‐staining confocal microscopy, and ≥ 1 protein‐specific assay. Clinical data were reviewed. Results Among 21 patients, 11 autoantibodies were characterized (serum, 19; CSF, all 9 available). Autoantigens were CACNA1I, 1; CAMK2B, 2; CLIP2, 1; FMN2, 2; MAP1A, 2; MAP2, 5; NECAB1, 1; SNAP91, 3; SRCIN1, 1; SYNJ1, 1; SYT3, 2. Analytical validation was by confocal TIIFA (all), western blot (10/10 available), and cell‐based assay (5/5 performed). Clinical accompaniments were: encephalitis, 6; brainstem encephalitis, 2; encephalomyelitis, 2; cerebellar ataxia, 2; longitudinally extensive transverse myelitis (LETM), 2; sensory neuronopathy, 1; peripheral neuropathy, 4, and movement disorders, 2. Inflammatory MRI abnormalities were identified in 5/16 patients (31%) with CNS disorders: T2 signal change (2), LETM (2), leptomeningeal enhancement (1). Seven of 8 (88%) had inflammatory CSF (pleocytosis, 5 [median 25.5 cells, range 7–294]; elevated IgG index/synthesis rate, 4; CSF‐exclusive oligoclonal bands, 4). Six had paraneoplastic causation (lung cancer, 2; other, 4); 3 were postinfectious (1 each of COVID‐19, HSV‐1, and post‐Group A streptococcal infection). Of 9 immunotherapy‐treated patients, 5 improved. Interpretation UNAs are partly accounted for by a repertoire of diverse mostly intracellular synaptic antigens. Their characterization is expedited by protein arrays and PhIPSeq. Further individual studies are needed to assess them as disease biomarkers.

Abstract The addition of pembrolizumab to preoperative radiotherapy (RT) improved disease-free survival (DFS) for patients with stage III undifferentiated pleomorphic sarcoma (UPS) and dedifferentiated/pleomorphic liposarcoma (LPS) in the randomized SU2C-SARC032 trial. To precisely identify patients who benefit from pembrolizumab and RT, we performed comprehensive multi-omics profiling of pre- and post-treatment tumor and blood samples, including bulk RNA-seq, flow cytometry, and cytometry by time of flight. Additionally, we built a single-cell RNA-seq atlas spanning 65,786 cells from UPS and LPS to recover single-cell states in bulk tumor samples using digital cytometry. Two opposing tumor microenvironments (TMEs), immune-cold sarcoma ecotype 1 (SE1) and immune-hot sarcoma immune class E (SIC E), benefited from pembrolizumab. Pembrolizumab combined with RT caused an overall increase in activated CD8 + T cells, CD56 low NK cells, and T cell receptor diversity, while diminishing matrix-remodeling stromal cells and sarcoma cells. Our findings identify different mechanisms of response to pembrolizumab in localized, high-risk UPS/LPS and suggest that sarcoma TME signatures may identify patients most likely to benefit from adding pembrolizumab to preoperative RT.

Background: Breast cancer polygenic risk scores (PRS) and traditional risk models (e.g., the Gail model [Gail]) are known to contribute largely independent information, but it is unclear how the overlap varies by ancestry, age, disease type (invasive breast cancer, DCIS), and risk threshold. Methods: In a retrospective case–control study, we evaluated risk prediction performance in 180,398 women (161,849 of European ancestry; 18,549 of Asian ancestry). Odds ratios (ORs) from logistic regression models and the area under the receiver operating characteristic curve (AUC) were estimated. Results: PRS for invasive disease showed a stronger association in younger (<50 years) women (OR = 2.51, AUC = 0.622) than in women ≥ 50 years (OR = 2.06, AUC = 0.653) of European ancestry. PRS performance in Asians was lower (OR range = 1.62–1.64, AUC = 0.551–0.600). Gail performance was modest across groups and poor in younger Asian women (OR = 0.94–0.99, AUC = 0.523–0.533). Age interactions were observed for both PRS (p < 0.001) and Gail (p < 0.001) in Europeans, whereas in Asians, age interaction was observed only for Gail (invasive: p < 0.001; DCIS: p = 0.002). PRS identified more high-risk individuals than Gail in Asian populations, especially ≥50 years, while Gail identified more in Europeans. Overlap between PRS, Gail, and family history was limited at higher thresholds. Calibration analysis, comparing empirical and model-based ROC curves, showed divergence for both PRS and Gail (p < 0.001), which indicates miscalibration. In Europeans, family history and prior biopsies drove Gail discrimination. In younger Asians, age at first live birth was influential. Conclusions: PRS adds value to risk stratification beyond traditional tools, especially in younger women and Asian ancestry populations.

Introduction: The use of intravascular ultrasound (IVUS) to guide lower extremity deep venous stent placement is associated with improved identification of the degree of maximal vessel stenosis, vessel obstruction, and thrombus burden, compared to venography alone. As the US medical system moves towards value-based care, the financial impact of interventional tools requires close consideration. Currently, there are limited evaluations of the cost effectiveness of IVUS during venous procedures. Research Question: This study seeks to examine the cost-effectiveness of IVUS in lower extremity deep venous interventions. Methods: Medicare fee-for-service claims data from 2016 through 2021 for beneficiaries aged ≥ 65 years undergoing lower extremity venous interventions with stent placement, thrombolysis, and/or thrombectomy were analyzed. Cost ratios between IVUS-guided and non-IVUS-guided procedures were assessed using gamma regression, adjusting for demographic and clinical variables. Results: A total of 52,610 patients were included, with 42.6% undergoing IVUS-guided procedures. IVUS utilization varied by setting, with higher usage in hospital-based outpatient centers (75.3%) and ambulatory surgical centers/outpatient-based laboratories (ASC/OBL; 86.4%) compared to inpatient hospitalizations (10.4%). Among inpatient procedures, IVUS-guided venous intervention was associated with greater cost than non-IVUS procedures at 90 days, with an adjusted cost ratio of 1.04 (95% CI 1.01-1.07), yet was associated with an average 0.6-day shorter hospitalization (p < 0.001; Figure 1, Figure 2 ). There were marginal increased costs associated with use of IVUS in inpatient procedures persisting through one- and three-years follow-up, with adjusted cost ratios of 1.05 (95% CI 1.02-1.08) and 1.05 (95% CI 1.02-1.09), respectively. In contrast, IVUS use was associated with significant cost savings in outpatient settings, with adjusted cost ratios of 0.71 (95% CI 0.69-0.73) in hospital-based outpatient settings and 0.75 (95% CI 0.73-0.78) in ASC/OBL at 90 days. These cost savings persisted at one- and three-year follow-up. Conclusions: IVUS-guided venous intervention was associated with cost savings in outpatient and ASC/OBL settings, while maintaining near cost equivalence in inpatient settings. These findings suggest that in addition to the clinical benefit of IVUS for venous intervention, IVUS is a cost-effective strategy, particularly in the outpatient setting.

Background: Pulmonary hypertension (PH) is a life-threatening disease affecting up to 1% of the global population. Diagnosis can be challenging and is often delayed due to the need for advanced imaging and invasive procedures. The use of artificial intelligence applied to ECGs (ECG-AI) has been shown to detect subtle patterns in voltage-time data and may be a valuable tool for the early detection of PH. Hypothesis and Purpose: To evaluate the performance of a previously trained, ECG-AI algorithm to detect PH (ECG-AI PH) using real-world data (RWD) collected in a multicenter, validation study. A joint primary hypothesis required sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) to exceed the null values of 76%, 70%, 10% and 90%. Study Design and Methods: This retrospective validation study was conducted at 5 geographically diverse U.S. health systems. Adult subjects were eligible for inclusion if they had a 12-lead ECG paired with an echocardiogram (Echo) in which tricuspid regurgitation velocity (TRV) was recorded, following presentation with dyspnea. Patients were classified according to echocardiographic criteria as either PH (PH+, TRV >3.4 m/s) or controls (PH-, TRV ≤2.8 m/s) to simulate the real-world use of ECG-AI, where a positive result could lead to a follow-up Echo. The study database was locked before processing the digital ECGs with ECG-AI PH. Performance was also estimated in a subset of subjects that later had a right heart catheterization using mPAP ≥ 20mmHg as the definition of PH. Results: A total of 14281 subjects (53% female, 63% aged 50+ years) met the inclusion criteria, including 3019 PH+ cases and 11262 PH- controls (Figure). Sn and Sp were 84.0% (95% CI: 82.6%, 85.3%) and 71.7% (95% CI: 70.9%, 72.6%), respectively. The positive and negative predictive values were 18.9% (95% CI: 18.4%, 19.4%) and 98.3% (95% CI: 98.1%, 98.4%), respectively, at 7.3% prevalence. Each endpoint met pre-defined performance criteria. In the subset of 1683 subjects with a follow up RHC, performance remained robust (Sn 85% (1155/1358); Sp 46% (151/325)). Conclusion: While ECG-AI PH was first developed as an investigational tool to detect PH, continued development as software as a medical device for clinical use demonstrated that the algorithm retained strong performance to detect PH in diverse, non-overlapping clinical settings and patient populations.

Abstract The incidence of venous thromboembolism (VTE) in patients with acute lymphoblastic leukemia (ALL) receiving asparaginase-based induction is high despite primary thromboprophylaxis. Our aim was to derive and externally validate a VTE risk prediction model in patients with ALL receiving asparaginase-based induction. We conducted a multicenter retrospective cohort study of patients (aged ≥18 years) with newly diagnosed ALL receiving asparaginase-based induction. The derivation and external validation cohorts included 306 and 94 patients, respectively. Primary outcome was VTE at any site. A cause-specific Cox proportional hazards model stratified by thromboprophylaxis and center was performed to identify VTE risk factors in the derivation cohort. A risk prediction model for VTE at 30 days was derived using variables with P value &lt; .05 in the multivariable model and was tested in the validation cohort. VTE risk factors on multivariable analysis in the derivation cohort included D-dimer ≥1 μg fibrinogen equivalent unit per mL (hazard ratio [HR], 2.64; 95% confidence interval [CI], 1.07-6.5) and hemoglobin (HR for each 1 g/dL increment, 1.19; 95% CI, 1.06-1.34) at ALL diagnosis. A VTE risk score based on these variables distinguished between a 4% (95% CI, 0.72-12) and 20% (95% CI, 14-27) 30-day cumulative incidence of VTE in the derivation cohort, with similar findings in the validation cohort (area under the curve, 0.56). The negative predictive value for VTE at 30 days was 96% and 93% in the derivation and validation cohorts, respectively, and the positive predictive value was 20% in both. We derived and validated a model using D-dimer and hemoglobin, which stratifies VTE risk in patients with ALL receiving asparaginase-based induction.

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.

Objective: The specialty of critical care in the United States currently lacks a single, broad, unified database. We conducted a scoping review of existing established national ICU databases, describing national and international patterns of critical care delivery. Data Sources: A systematic literature search was undertaken using MEDLINE, Embase, and Web of Science search engines. Study Selection: Projects describing national critical care delivery (including any subspecialty) published in any language were included. Titles, abstracts, and full-text manuscripts were reviewed in duplicate for inclusion. Data Extraction: National database characteristics were collected, including the number and subspecialty of ICUs, the inaugural year, data entry methodology, the number of episodes of care included, and captured clinical data elements. Data Synthesis: Of 24,003 abstracts screened, 185 manuscripts were eligible for inclusion. Thirty countries were identified as having established national ICU registries: Argentina, Australia/New Zealand, Austria, Belgium, Brazil, Canada, Denmark, Ecuador, Finland, Germany, Iceland, India, Ireland, Italy, Japan, Kenya, Malaysia, Mexico, Nepal, Netherlands, Norway, Pakistan, Paraguay, Spain, Sri Lanka, Sweden, Switzerland, United Kingdom, United States and Uruguay. Data entry commonly incorporates a combination of automated data abstraction from electronic healthcare systems and manual data entry, followed by independent validation. Frequently recorded variables include patient demographics; admission vital signs and laboratory data; comorbidities; admission source and diagnoses; ICU diagnoses, treatments, and complications; illness severity scores; and clinically relevant outcomes including discharge disposition, functional status, lengths of stay, and mortality. Conclusions: Insights and experience gained from the study of mature national ICU registries may be used to guide an equivalent U.S. multidisciplinary program aimed at benchmarking, needs assessment, quality improvement, and research facilitation