Citations

ABSTRACT Aims Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine learning (ML)-based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. Methods and results A ridge logistic regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo (InterTAK) Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). Thirty-one clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the curve (AUC), sensitivity and specificity. As secondary endpoint, a K-medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the 10 most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85–0.92), a sensitivity of 0.85 (0.78–0.95) and a specificity of 0.76 (0.74–0.79) in the internal validation cohort and an AUC of 0.82 (0.73–0.91), a sensitivity of 0.74 (0.61–0.87) and a specificity of 0.79 (0.77–0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs. 15.5% vs. 5.4% vs. 1.0.8% vs. 0.5%) which were consistent also in the external cohort. Conclusion A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.

Despite advancements in the early-stage detection and expansion of treatments for prostate cancer (PCa), patient mortality rates remain high in patients with aggressive disease and the overtreatment of indolent disease remains a major issue. Prostate-specific antigen (PSA), a standard PCa blood biomarker, is limited in its ability to differentiate disease subtypes resulting in the overtreatment of non-aggressive indolent disease. Here we assess engorged cancer-associated macrophage-like cells (CAMLs), a ≥50 µm, cancer-specific, polynucleated circulating cell type found in the blood of patients with PCa as a potential companion biomarker to PSA for patient risk stratification. We found that rising PSA is positively correlated with increasing CAML size (r = 0.307, p = 0.004) and number of CAMLs in circulation (r = 0.399, p < 0.001). Over a 2-year period, the presence of a single engorged CAML was associated with 20.9 times increased likelihood of progression (p = 0.016) in non-metastatic PCa, and 2.4 times likelihood of progression (p = 0.031) with 5.4 times likelihood of death (p < 0.001) in metastatic PCa. These preliminary data suggest that CAML cell monitoring, in combination with PSA, may aid in differentiating non-aggressive from aggressive PCas by adding biological information that complements traditional clinical biomarkers, thereby helping guide treatment strategies.

Targeted delivery of magnetic nanoparticles (MNPs) to an area of a blood vessel with fluidic flow is hampered by the lack of a suitable real‐time imaging modality for MNPs, the control system complexity, and low targeting performance. Herein, an offline programming guidance (OLPG) scheme for aggregated MNPs is proposed based on a real‐time aggregation volume estimator. The proposed aggregation volume estimator based on a magnetic drug‐targeting simulator reflects volume changes of aggregated MNPs; hence, it can model a magnetic force acting on aggregated MNPs in real time while enhancing targeting performance. The proposed guidance system is evaluated using a simulation testbed and in vitro model of the rat brain, which yields comparable results at different fluid viscosities, flow velocities, target areas, and flow types. The OLPG with the aggregation volume estimator improves targeting performance by 116%–409% compared with the default mode, and by 111%–180% compared to the performance without the aggregation volume estimator. Furthermore, a guidance margin predicts enhanced targeting performance (root‐mean‐square error < 5%) irrespective of the flow environment. The proposed guidance strategy has the potential to overcome the problems caused by the lack of an imaging modality, control‐system complexity, and low targeting performance.

6551 Background: Despite recent advances in lung cancer therapeutics leading to considerable improvement in survival, disparities continue to persist among socially disadvantaged populations. Although the reasons underlying this phenomenon are uncertain, we posit that neighborhood-level factors could play a role. Furthermore, neighborhood disadvantage may lead to epigenetic changes which promote malignant progression. This study aimed to determine the effects of neighborhood-deprivation index (NDI), a validated 13-factor-based measure, that combines income, education, employment, and housing in a neighborhood, on lung cancer mortality. Methods: A retrospective cohort study assessed the relationship of NDI with survival weighted by age, stage, and DNA methylation among biopsy-proven lung cancer from two U.S. medical centers, Johns Hopkins University and the University of Illinois Chicago, between 2008-2020. In our analysis, U.S.-standardized NDI values for each year of sample collection were computed at the U.S. census tract level and categorized into quartiles. Participants’ residential addresses were geocoded to U.S. census tracts and linked to NDI quartiles. DNA extraction from liquid biopsy samples was performed for quantification of DNA methylation. Results: The study included 184 lung cancer patients, with 89 in the low-deprivation group and 95 in the high-deprivation group. The high-deprivation group had a higher percentage of females (57% vs. 48%), a higher percentage of African Americans (AA) (50% vs. 36%), a lower pack-year smoking history (30 vs. 39 median pack-years), and a higher percentage with stage IV disease (28% vs. 20%) than the low-deprivation group. Overall, NDI was significantly higher among AA when compared with Whites (p=0.003) and associated with lung cancer stage at diagnosis with an odds ratio of 1.17 (p=0.02). There was a significant correlation between DNA methylation and stage for HOXA7, SOX17, ZPF42, TAC1 and HOXA9. Only HOXA7 DNA methylation correlated with NDI (p=0.003).The high-deprivation group had a significantly shorter survival duration compared to the low-deprivation group, with a 1-year survival rate of 79% vs. 90%, a 2-year survival rate of 70% vs. 84%, and a 5-year-survival rate of 51% vs. 73% (p=0.02). After adjusting for age at diagnosis, stage, and DNA methylation status, belonging to the high-deprivation group was associated with a higher mortality risk with a hazard ratio of 2.02 (p=0.04). Conclusions: Neighborhood-level socioeconomic status and DNA methylation may be associated with late stage lung cancer at diagnosis; whereas NDI shows a potential association with shorter survival and increased mortality risk. Higher neighborhood deprivation among AA suggests that changes in health care policy that account for neighborhood-level indices of socioeconomic deprivation may enable more equitable improvement of lung cancer mortality.

Abstract This article presents a comprehensive review of current Magnetic Imaging Techniques (MIT), specifically Magnetic Particle Imaging (MPI) and Magnetic Resonance Imaging (MRI) methods as well as their impacts and potential in biomedical applications. The paper particularly focuses on several perspectives of MPI and MRI techniques including technical considerations, advantages, limitations, applications, future trends. Surprisingly, there are not many review articles that focused on MPI and MRI. This review will give a complete overview of present technology, perspectives, and potential future developments for MPI and MRI. MIT has become an essential and integral part of medical diagnosis in many large medical clinics and hospitals. This area is rapidly developing and evolving to meet the huge demands in medical diagnosis and prevention. MPI and MRI are promising technologies that provide reliable and effective diagnosis for many diseases including cancer. MIT uses non‐invasive scanning to obtain a detailed image of the body's tissues or organs without the necessity for surgery. These techniques allow us to detect symptoms of serious diseases at an early stage. Early detection also gives patients the chance to get the right care before their illnesses advance to a late, incurable stage, potentially saving many lives.

Abstract Introduction: Chimeric antigen receptor T-cell (CAR-T) therapy is an effective treatment that has transformed hematologic malignancy management. However, its significant short-term toxicities, mainly cytokine release syndrome (CRS) and neurotoxicity, require frequent monitoring almost universally achieved via prolonged inpatient admissions. We have reported our institution's specialized, multi-disciplinary hospital-based outpatient (HBO) CAR-T service and experience (Bansal et.al, ASCO 2021). To enhance the HBO practice, we implemented a remote patient monitoring (RPM) program, comprised of in-home, electronic health record-integrated technology to monitor vital signs and neurologic symptoms for 30 days post CAR-T infusion. The RPM program's triaging algorithm generates alerts based on predetermined parameters to guide escalation of care if needed. In this report, we describe the characteristics of RPM program patients (pts), focusing on the alerts received within 48 hours (h) of first hospitalization in the post-CAR-T setting. Methods: Pts undergoing commercial CAR-T cell therapy at Mayo Clinic Rochester were enrolled in the RPM program as part of our standard of care from 06/2020 to 05/2021. Pts with detailed RPM data available were included in this analysis. Patient characteristics, health care utilization, inpatient interventions, and RPM alert data were reviewed. Wilcoxon test was used for continuous variables, chi-squared test was used for categorical variables. Results: Baseline characteristics are summarized in Table 1. Of 20 pts in this analysis, 13 had RPM alerts in the 48h preceding the first hospitalization post CAR-T. For the remaining 7 pts without RPM alerts, 3 were hospitalized within 24h of CAR-T infusion without significant RPM data, 1 patient hospitalized with acute pancreatitis (day +8 post CAR-T infusion), 1 for doubling of CRP (day +10), 1 for acute pain crisis (day +5), and 1 with neutropenic fever (day +5) with not reporting RPM data. The median RPM program duration was 18 days (range 3-32) which accounts for interruptions in the RPM monitoring while pts are hospitalized. The median number of data points per patient was 231 (range 22-532), with a median of 20 alerts per patient (range 2-81). The median missing data points per patient were 20 (10%) (range 0-95). Of the 13 pts with RPM alerts within 48h of first hospitalization, alerts met our definition of urgent criteria in 6 pts [temperature (temp) &gt; 100F (n=3), temp &gt; 100F + tachycardia (n=1), temp &gt; 100F + hypoxia (n=1), and hypoxia (n=1)]. In the remaining 7 pts, the RPM alerts met our routine criteria definition [temp 99-100 (n=2), tachycardia + hypertension + weight loss (n=2), hypertension (n=2), new incontinence (n=1)]. CRS was reported in 12 pts [grade 1-2 (n=11), grade 3-4 (n=1)] and neurotoxicity in 8 pts [grade 1-2 (n=7), grade 3-4 (n=1)]. The median post CAR-T first hospitalization day was day +4 (range day +2-10), Figure 1. The median time from the last RPM alert to hospitalization was 5.8h for pts meeting urgent criteria vs 10.8h for those meeting routine criteria (p=0.13). Tocilizumab was used in 5 pts during the first hospitalization [3 (50%) in the urgent criteria group and 2 (28%) in the routine criteria group], with a median time from admission to tocilizumab of 12h in the urgent criteria group vs 43h in the routine criteria group (p=0.05). Early hospitalization (≤ 3 days) post CAR-T infusion was associated with later need for tocilizumab [median 51h (range 30-57) vs. 11.5h (range 6-33) from admission, p=0.03] than late hospitalization (&gt; 4 days post CAR-T). Upon admission, oxygen supplementation was started on both pts with hypoxia noted in the RPM alerts. Only 1 patient (routine criteria group) required use of vasopressors while hospitalized, initiated more than 48h after admission. Conclusion: A CAR-T RPM program can identify adverse symptoms and vital sign changes in CAR-T pts managed in the outpatient setting. In this pilot implementation, the CAR-T RPM triaging algorithm identified pts requiring expedited hospitalization and facilitated early initiation of tocilizumab. The intermittent monitoring and proportion of missing data are the main limitations of the RPM in the acute care setting post-CAR-T infusion. Incorporation of wearable devices for continuous remote monitoring is being explored as a mitigating strategy to these limitations. Figure 1 Figure 1. Disclosures Paludo: Karyopharm: Research Funding. Dingli: Apellis: Consultancy; Janssen: Consultancy; GSK: Consultancy; Novartis: Research Funding; Sanofi: Consultancy; Alexion: Consultancy. Kapoor: Glaxo SmithKline: Research Funding; Sanofi: Consultancy; Amgen: Research Funding; AbbVie: Research Funding; Sanofi: Research Funding; Karyopharm: Research Funding; Ichnos Sciences: Research Funding; Pharmacyclics: Consultancy; Takeda: Research Funding; Regeneron Pharmaceuticals: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy. Gertz: Aurora Biopharma: Other: Stock option; Ionis Pharmaceuticals: Other: Advisory Board; Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria. Wang: InnoCare: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; MorphoSys: Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; Genentech: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding. Kenderian: Humanigen, Inc.: Consultancy, Honoraria, Research Funding. Kumar: Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Oncopeptides: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche-Genentech: Consultancy, Research Funding; Carsgen: Research Funding; Antengene: Consultancy, Honoraria; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Bluebird Bio: Consultancy; Beigene: Consultancy; Tenebio: Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding. Bennani: Purdue Pharma: Other: Advisory Board; Daichii Sankyo Inc: Other: Advisory Board; Kyowa Kirin: Other: Advisory Board; Vividion: Other: Advisory Board; Kymera: Other: Advisory Board; Verastem: Other: Advisory Board. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding. Lin: Merck: Research Funding; Vineti: Consultancy; Gamida Cell: Consultancy; Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Legend: Consultancy; Celgene: Consultancy, Research Funding; Sorrento: Consultancy; Takeda: Research Funding; Janssen: Consultancy, Research Funding; Novartis: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding.

Pertussis has re-emerged in both developed and developing countries and is an ongoing public health problem, even in countries with high rates of vaccination. Pertussis encephalopathy is a known complication of the disease, but the pathophysiology of this complication and the role of the pertussis bacteria have not been elucidated. We report three confirmed cases of pertussis infant younger than 3 months of age with neurological complications including lethargy, encephalopathy, and seizures. In each case, the cerebrospinal fluid was positive for Bordetella pertussis as determined by polymerase chain reaction. One patient did not survive and two were discharged with a normal neurological exam on follow-up. The cases emphasize the importance of analyzing cerebrospinal fluid in cases of pertussis-associated encephalopathy including polymerase chain reaction.

Background Atrial fibrillation (AF) is a major risk factor for mortality. The prevalence, clinical correlates, and prognostic impact of AF in Takotsubo syndrome (TTS) have not yet been investigated in a large patient cohort. This study aimed to investigate the prevalence, clinical correlates, and prognostic impact of AF in patients with TTS. Methods and Results Patients with TTS were enrolled from the International Takotsubo Registry, which is a multinational network with 26 participating centers in Europe and the United States. Patients were dichotomized according to the presence or absence of AF at the time of admission. Of 1584 patients with TTS, 112 (7.1%) had AF. The mean age was higher ( P <0.001), and there were fewer women ( P =0.046) in the AF than in the non‐AF group. Left ventricular ejection fraction was significantly lower ( P =0.001), and cardiogenic shock was more often observed ( P <0.001) in the AF group. Both in‐hospital ( P <0.001) and long‐term mortality ( P <0.001) were higher in the AF group. Multivariable Cox regression analysis revealed that AF was independently associated with higher long‐term mortality (hazard ratio, 2.31; 95% CI, 1.50–3.55; P <0.001). Among patients with AF on admission, 42% had no known history of AF before the acute TTS event, and such patients had comparable in‐hospital and long‐term outcomes compared with those with a history of AF. Conclusions In patients presenting with TTS, AF on admission is significantly associated with increased in‐hospital and long‐term mortality rates. Whether antiarrhythmics and/or cardioversion are beneficial in TTS with AF should thus be tested in a future trial. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01947621.