Risk factors for delirium among patients with advanced cancer in palliative care: a multicenter, patient-based registry cohort in South Korea

OBJECTIVE: Previous studies have comprehensively investigated the preva-lence and various potential risk factors for de-lirium among patients with advanced cancer ad-mitted to the acute palliative care unit (APCU). Our objective was to evaluate the comprehen-sive association between delirium and various risk factors among patients with advanced can-cer in an acute palliative care setting using a pa-tient-based multicenter registry cohort.PATIENTS AND METHODS: We performed a multicenter, patient-based registry cohort study collected in South Korea between January 1, 2019, and December 31, 2020. Delirium was identified using a medical record review based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.RESULTS: In total, 2,124 eligible patients with advanced cancer in the APCU met the inclu-sion criteria. There were 127 out of 2,124 pa-tients (prevalence, 6.0%;95% CI, 5.0 to 7.1) with delirium during admission. Delirium in patients with advanced cancer was associated with age >70 years (OR, 1.793;95% CI, 1.246 to 2.581), male sex (OR, 1.675;95% CI, 1.131 to 2.479), no chemotherapy during hospitalization (OR, 2.019;95% CI, 1.236 to 3.298), hearing impairment (OR, 3.566;95% CI, 1.176 to 10.810), underweight (OR, 1.826;95% CI, 1.067 to 3.124), current use of opioid medication (OR, 1.942;95% CI, 1.264 to 2.982), previous history of delirium (OR, 12.497;95% CI, 6.920 to 22.568), and mental illness (OR, 2.333;95% CI, 1.251 to 4.352).CONCLUSIONS: In a large-scale multicenter patient-based registry cohort, delirium was asso-ciated with old age, male sex, no chemotherapy during hospitalization, hearing impairment, un-derweight, current use of opioid medication, and a history of delirium and mental illness. Our find-ings suggest physicians should pay attention to delirium in patients with advanced cancer admit-ted to the APCU with the above risk factors.

17-year trends of body mass index, overweight, and obesity among adolescents from 2005 to 2021, including the COVID-19 pandemic: a Korean national representative study.

OBJECTIVE: There is a lack of pediatric studies that have analyzed trends in mean body mass index (BMI) and the prevalence of obesity and overweight over a period that includes the mid-stage of the COVID-19 pandemic. Thus, we aimed to investigate trends in BMI, overweight, and obesity among Korean adolescents from 2005 to 2021, including the COVID-19 pandemic. SUBJECTS AND METHODS: We used data from the Korea Youth Risk Behavior Web-based Survey (KYRBS), which is nationally representative of South Korea. The study included middle- and high-school students between the ages of 12 and 18. We examined trends in mean BMI and prevalence of obesity and/or overweight during the COVID-19 pandemic and compared these to those of pre-pandemic trends in each subgroup by gender, grade, and residential region. RESULTS: Data from 1,111,300 adolescents (mean age: 15.04 years) were analyzed. The estimated weighted mean BMI was 20.48 kg/m2 (95% CI, 20.46-20.51) between 2005 and 2007, and this was 21.61 kg/m2 (95% CI, 21.54-21.68) in 2021. The prevalence of overweight and obesity was 13.1% (95% CI, 12.9-13.3%) between 2005 and 2007 and 23.4% (95% CI, 22.8-24.0%) in 2021. The mean BMI and prevalence of obesity and overweight have gradually increased over the past 17 years; however, the extent of change in mean BMI and in the prevalence of obesity and overweight during the pandemic was distinctly less than before. The 17-year trends in the mean BMI, obesity, and overweight exhibited a considerable rise from 2005 to 2021; however, the slope during the COVID-19 pandemic (2020-2021) was significantly less prominent than in the pre-pandemic (2005-2019). CONCLUSIONS: These findings enable us to comprehend long-term trends in the mean BMI of Korean adolescents and further emphasize the need for practical prevention measures against youth obesity and overweight.

Intelligent Posture Monitoring System

With the shift to at-home work due to the Covid-19 pandemic, longer hours are spent sitting in front of a computer without proper ergonomic seating available in most home-office settings. Most home office arrangements often lack the necessary back support needed for prolonged periods of sedentary work. The goal of the proposed system is to automatically track a user's postural positions throughout the day through the use of a non-invasive, wearable system and automatically provide feedback from an algorithm to warn the user to correct or change their poor posture. This is done by placing magnets in the form of a rectangular grid on a shirt as well as an MMR sensor on the chest of the body. The onboard magnetic sensor records the data values from the grid of magnetics, which is then, along with data recorded from the onboard accelerometer, analyzed to determine the position of the user. A trained algorithm recognizes and automatically detects the spinal position of the user from the recorded data points and provides direction to alter their posture. These recommendations act as a warning system and allow the user to self-monitor and correct their own behavior to prevent back and neck pain and reduce the chance of long-lasting damage that can result from poor posture. © 2022 IEEE.

DREAMM-9: PHASE I STUDY OF BELANTAMAB MAFODOTIN PLUS STANDARD OF CARE IN PATIENTS WITH TRANSPLANT-INELIGIBLE NEWLY DIAGNOSED MULTIPLE MYELOMA

Background: The bortezomib, lenalidomide, and dexamethasone (VRd) regimen is a standard of care for newly diagnosed multiple myeloma (NDMM). Belantamab mafodotin (belamaf) is a B-cell maturation antigen-binding antibody-drug conjugate that eliminates myeloma cells by a multimodal mechanism: direct cell kill and anti-myeloma tumor immune response. Belamaf has demonstrated deep and durable responses as a monotherapy in the DREAMM-2 study of patients (pts) with relapsed/refractory multiple myeloma (RRMM). Preclinical evidence of belamaf in combination with bortezomib or lenalidomide suggests enhanced anti-myeloma activity, providing rationale for this treatment combination. Aims: To evaluate the safety and tolerability of this combination in adult pts with transplant-ineligible (TI) NDMM and establish the recommended Phase III dose. Methods: DREAMM-9 (NCT04091126) is an ongoing Phase I, open-label, randomized study of belamaf + VRd. The belamaf dose cohorts currently being evaluated are Cohort 1 (1.9 mg/kg Q3/4W), Cohort 2 (1.4 mg/kg Q6/8W), Cohort 3 (1.9 mg/kg Q6/8W), Cohort 4 (1.0 mg/kg Q3/4W), and Cohort 5 (1.4 mg/kg Q3/4W). Belamaf is given with VRd Q3W until Cycle 8, and with Rd Q4W thereafter. After evaluation of safety data for Cohort 1, Cohorts 2-5 were opened in parallel and enrolled pts were randomized 1:1:1:1. Primary endpoint is safety. Secondary endpoints include efficacy, tolerability, and pharmacokinetics (PK). Results: As of data cutoff (07 Dec 2021), 64 pts were analyzed across all cohorts. Median age (range) was 73.0 (51- 88) years, 55% were male, 80% were white, 8% had extramedullary disease, 59% were International Staging System stage II or III, 20% had amp1q, and 17% had high-risk cytogenetics (≥1 of: t(4;14), t(14;16), del17p). The median duration of follow-up varied: Cohort 1 (17.4 months [mo]), Cohort 2 (5.9 mo), Cohort 3 (6.1 mo), Cohort 4 (4.7 mo), Cohort 5 (5.8 mo). Median number of belamaf cycles were: Cohort 1 (6), Cohort 2 (3), Cohort 3 (3.5), Cohort 4 (4.5), and Cohort 5 (5). Most common adverse events (AEs) across cohorts included thrombocytopenia (49%), constipation (43%), diarrhea (32%), and peripheral sensory neuropathy (30%). AEs related to study treatment were experienced by 61 (97%) pts. Belamaf-related grade 3/4 AEs occurred in 24 (38%) pts. Belamaf dose reductions occurred in 11 (18%) pts, with dose delays in 10 (16%) pts. Three pts experienced a fatal severe AE (unrelated to study treatment);2 due to COVID-19 infection, 1 due to pancreatic adenocarcinoma. Early deep responses were observed;67-92% pts achieved ≥very good partial response (VGPR) (Table), with median time to VGPR of 2.1-2.9 months across cohorts. Of pts with ≥VGPR, 17 were minimal residual disease (MRD) negative, 10 in Cohort 1. As of data cutoff, 8-75% of pts achieved best response of complete response (CR) or stringent CR (sCR). Grade 3 corneal exam findings were reported in 25-58% of pts;grade 3 visual acuity changes were reported in 21-75% of pts. No grade 4 corneal exam findings or visual acuity changes were reported in pts receiving belamaf Q6/8W, compared with 0-17% and 0-8%, respectively, in the Q3/4W cohorts. Belamaf PK profile was similar to that in pts with RRMM, accounting for baseline characteristics. Image: Summary/Conclusion: Belamaf + VRd demonstrated high response rates in pts with TI NDMM, with a high rate of MRD negativity indicating deep responses. No new safety signals were observed relative to DREAMM-2. Study is ongoing to evaluate the safety and efficacy of variable dose intensities of belamaf in combination with VRd.

DREAMM-9: PHASE I STUDY OF BELANTAMAB MAFODOTIN PLUS STANDARD OF CARE IN PATIENTS WITH TRANSPLANT-INELIGIBLE NEWLY DIAGNOSED MULTIPLE MYELOMA

Background: The bortezomib, lenalidomide, and dexamethasone (VRd) regimen is a SoC for NDMM. Belamaf, a B-cell maturation antigen (BCMA)-targeting antibody-drug conjugate, demonstrated durable responses in patients with relapsed/refractory multiple myeloma. Preclinical studies of belamaf in combination with bortezomib/ lenalidomide suggest enhanced antimyeloma activity. We report preliminary findings of belamaf + VRd for patients with TI NDMM. Materials and Methods: DREAMM-9 (NCT04091126) is an ongoing Phase I, open label, randomized, dose and schedule evaluation trial. Adults with TI NDMM and ECOG status 0-2 are eligible. VRd is administered Q3W until Cycle 8, followed by lenalidomide + dexamethasone (Rd) Q4W. Belamaf + VRd is administered until Cycle 8, and with Rd thereafter. The currently evaluated belamaf dose cohorts are: Cohort 1 (1.9 mg/kg Q3/4W), Cohort 2 (1.4 mg/kg Q6/8W), Cohort 3 (1.9 mg/ kg Q6/8W), Cohort 4 (1.0 mg/kg Q3/4W), and Cohort 5 (1.4 mg/kg Q3/4W). Primary endpoint is safety. Secondary endpoints include efficacy, tolerability, and pharmacokinetics (PK). Results: Overall 36 patients were treated across the 5 cohorts. The median (range) age was 74.0 (63-80) years;56% patients were male, 17 (47%) had stage 2 disease, 3 (8%) had extramedullary disease, 6 (17%) patients had high risk cytogenetic abnormalities;the median number of belamaf cycles ranged from 1-9. No new safety signals were observed. Across Cohorts 1-5, all patients experienced AEs related to study treatment;1 patient in Cohort 1 died due to COVID-19 infection. The most common AEs leading to dose modification were thrombocytopenia, neutropenia, and corneal events. Patients in Cohort 2 and 3 had the lowest number of Grade ≥3 corneal events (3 and 2 events, respectively). All 12 patients in Cohort 1, all 6 in Cohorts 3 and 5, and 5/6 patients in Cohorts 2 and 4 have responded to the treatment;≥half of patients in each cohort achieved very good partial response or better. As of data cut-off, 3/12 patients in Cohort 1, 2/6 in Cohort 4, and 1/6 patients each in Cohorts 3 and 5 remained in complete response. Belamaf PK profile was similar to that observed in patients with RRMM taking into consideration baseline patients characteristics. Conclusions: Preliminary data suggest addition of belamaf to VRd did not reveal new safety signals and demonstrates high response rates, albeit with short follow-up. The trial is ongoing to confirm safety and evaluate the efficacy of belamaf + VRd. .

Re-configurable, expandable, and cost-effective heterogeneous FPGA cluster approach for resource-constrained data analysis

Field programmable gate arrays (FPGAs) have become widely prevalent in recent years as a great alternative to application-specific integrated circuits (ASIC) and as a potentially cheap alternative to expensive graphics processing units (GPUs). Introduced as a prototyping solution for ASIC, FPGAs are now widely popular in applications such as artificial intelligence (AI) and machine learning (ML) models that require processing data rapidly. As a relatively low-cost option to GPUs, FPGAs have the advantage of being reprogrammed to be used in almost any data-driven application. In this work, we propose an easily scalable and cost-effective cluster-based co-processing system using FPGAs for ML and AI applications that is easily reconfigured to the requirements of each user application. The aim is to introduce a clustering system of FPGA boards to improve the efficiency of the training component of machine learning algorithms. Our proposed configuration provides an opportunity to utilise relatively inexpensive FPGA development boards to produce a cluster without expert knowledge in VHDL, Verilog, or the system designs related to FPGA development. Consisting of two parts–a computer-based host application to control the cluster and an FPGA cluster connected through a high-speed Ethernet switch, allows the users to customise and adapt the system without much effort. The methods proposed in this paper provide the ability to utilise any FPGA board with an Ethernet port to be used as a part of the cluster and unboundedly scaled. To demonstrate the effectiveness of the proposed work, a two-part experiment to demonstrate the flexibility and portability of the proposed work–a homogeneous and heterogeneous cluster, was conducted with results compared against a desktop computer and combinations of FPGAs in two clusters. Data sets ranging from 60,000 to 14 million, including stroke prediction and covid-19, were used in conducting the experiments. Results suggest that the proposed system in this work performs close to 70% faster than a traditional computer with similar accuracy rates. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

DREAMM-9: Phase I Study of Belantamab Mafodotin Plus Standard of Care in Patients with Transplant-Ineligible Newly Diagnosed Multiple Myeloma

Introduction: The bortezomib, lenalidomide, and dexamethasone (VRd) regimen is an acceptable standard of care (SoC) for both transplant-eligible and transplant-ineligible newly diagnosed multiple myeloma (TI NDMM). Ongoing development of novel therapies and combinations strive to improve survival outcomes beyond what is expected from SoC. Belantamab mafodotin (belamaf) is a B-cell maturation antigen-binding antibody-drug conjugate that eliminates myeloma cells by a multimodal mechanism and has demonstrated durable responses in patients with relapsed/refractory multiple myeloma (RRMM). Preclinical evidence of belamaf in combination with bortezomib or lenalidomide suggests enhanced anti-myeloma activity, providing rationale for this treatment combination. We report the preliminary findings of belamaf + VRd for TI NDMM patients. Methods: DREAMM-9 (NCT04091126) is an ongoing Phase I, open-label, randomized, dose and schedule evaluation study of belamaf + VRd in patients with TI NDMM. Eligible patients include those ≥18 years old with ECOG status 0-2 and adequate organ system functions. The study evaluates safety and tolerability of belamaf + VRd in up to 8 cohorts, up to 144 patients, to establish the recommended phase 3 dose (RP3D). VRd is administered Q3W until cycle 8, followed by lenalidomide + dexamethasone (Rd) Q4W. Belamaf + VRd is administered until cycle 8, and then in combination with Rd thereafter. The belamaf dose cohorts currently being evaluated are: cohort 1 (1.9 mg/kg Q3/4W), cohort 2 (1.4 mg/kg Q6/8W), cohort 3 (1.9 mg/kg Q6/8W), cohort 4 (1.0 mg/kg Q3/4W), and cohort 5 (1.4 mg/kg Q3/4W). After evaluation of safety data for cohort 1, cohorts 2-5 were opened in parallel and enrolled patients were randomized 1:1:1:1. Safety data, clinical activity, and drug concentrations will be assessed, and used to determine the belamaf RP3D. This analysis reports the preliminary results from cohort 1. Primary endpoints include number of patients with adverse events (AEs). Secondary endpoints include establishing relative dose intensity of lenalidomide and bortezomib in combination with belamaf, cumulative dose of belamaf, pharmacokinetics (PK) profile of belamaf when combined with VRd, overall response rate (ORR), complete response (CR), stringent complete response (sCR), complete response rate ([CRR];% of patients with a confirmed CR or better), and rate of very good partial response or better (≥VGPR). Exploratory endpoints include assessing minimal residual disease (MRD) in patients who achieve ≥VGPR, and safety and efficacy exposure-response relationships. Results: Twelve patients in cohort 1 were included in this preliminary analysis. Eight patients (67%) were male;median age (range) was 72.5 years (63-77). Ten patients (83%) were white and 2 (17%) were Asian. Nine patients (75%), were ISS stage II or III, and 4 (33%) patients had high-risk cytogenetics (consisting of one or more of the following: t(4;14), t(14;16), del17p, 17p13del). AEs related to study treatment were experienced by all 12 patients. Dose reductions occurred in 12 (100%) patients, all of whom also experienced a dose delay. Most common AEs leading to dose modification were thrombocytopenia, neutropenia, and corneal events. Grade 3 or 4 AEs related to belamaf occurred in 9 (75%) patients. During the trial, one patient experienced a fatal severe AE due to COVID-19 infection (unrelated to study treatment;Table). All patients, 100% (n=12;95% CI: 73.5-100) achieved ≥VGPR. Early deep responses were observed;2 (17%) patients achieved VGPR as early as 4 weeks. As of data cut-off, 5 (42%) remain in CR and 3 (25%) in sCR. Based on real-time data captured in the clinical database, 7 out of 9 patients achieved MRD-negative status at the first test after VGPR. Belamaf PK profile was similar to that observed in patients with RRMM taking into consideration baseline patients characteristics. Conclusion: Preliminary data suggest addition of belamaf to VRd did not reveal new safety signals and demonstrates high response rates, albeit with short follow up. The study is ongoing to confirm safety and evaluate the efficacy of belamaf + VRd. Updated data for cohort 1 will be reported at the congress. Funding: GSK (Study 209664);belamaf drug linker technology licensed from Seagen;belamaf monoclonal antibody produced using POTELLIGENT Technology licensed from BioWa. [Formula presented] Disclosures: Usmani: Pharmacyclics: Consultancy, Research Funding;Seattle Genetics: Consultancy, Research Funding;Takeda: Consultancy, Research Funding, Speakers Bureau;Merck: Consultancy, Research Funding;SkylineDX: Consultancy, Research Funding;Sanofi: Consultancy, Research Funding, Speakers Bureau;Janssen: Consultancy, Research Funding, Speakers Bureau;Janssen Oncology: Consultancy, Research Funding;Bristol-Myers Squibb: Research Funding;EdoPharma: Consultancy;GSK: Consultancy, Research Funding;Celgene/BMS: Consultancy, Research Funding, Speakers Bureau;Array BioPharma: Consultancy, Research Funding;Abbvie: Consultancy;Amgen: Consultancy, Research Funding, Speakers Bureau. Quach: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding;Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding;GlaxoSmithKline: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding;Janssen/Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees;Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding;Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding;Bristol Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding;Antengene: Consultancy, Membership on an entity's Board of Directors or advisory committees;Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees;CSL: Consultancy, Membership on an entity's Board of Directors or advisory committees. Koh: Pfizer: Consultancy;Jassen: Honoraria;AstraZeneca: Honoraria;Novartis: Honoraria;GSK: Honoraria;Roche: Honoraria;Takeda: Honoraria. Guenther: Novartis: Consultancy;Celgene: Consultancy, Honoraria;Roche: Consultancy;Takeda: Consultancy, Honoraria;Amgen: Consultancy, Honoraria;AbbVie: Consultancy;Jazz Pharmaceuticals: Honoraria;Janssen Pharmaceuticals: Consultancy, Honoraria. Zhou: GlaxoSmithKline: Current Employment. Kaisermann: GlaxoSmithKline: Current Employment, Current equity holder in publicly-traded company. Mis: GlaxoSmithKline: Current Employment. Williams: GlaxoSmithKline: Current Employment. Yeakey: GlaxoSmithKline: Current Employment, Current equity holder in publicly-traded company. Ferron-Brady: GlaxoSmithKline: Current Employment, Current equity holder in publicly-traded company. Figueroa: GlaxoSmithKline: Current Employment. Kremer: GlaxoSmithKline: Current Employment. Gupta: Novartis: Current equity holder in publicly-traded company;GlaxoSmithKline: Current Employment, Current equity holder in publicly-traded company. Janowski: Celgene: Consultancy;AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees;Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees;Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees;BMS: Membership on an entity's Board of Directors or advisory committees.

Towards Automatic Recognition of Perceived Level of Understanding on Online Lectures using Earables

The COVID-19 pandemic has seriously impacted education and forced the whole education system to shift to online learning. Such a transition has been readily made by virtue of today's Internet technology and infrastructure, but online learning also has limitations compared to traditional face-to-face lectures. One of the biggest hurdles is that it is challenging for teachers to instantly keep track of students' learning status. In this paper, we envision earables as an opportunity to automatically estimate learner's understanding of learning material for effective learning and teaching, e.g., to pinpoint the part for which learners need to put more effort to understand. To this end, we conduct a small-scale exploratory study with 8 participants for 24 lectures in total and investigate learner's behavioral characteristics that indicate the level of understanding. We demonstrate that those behaviors can be captured from a motion signal on earables. We discuss challenges that need to be further addressed to realize our vision. © 2021 ACM.

Detection and Monitoring of High Germ Spreading Activities Using Wearable Sensors

This paper discusses our preliminary study and results on data collection, data processing, feature extraction and classification in developing a machine learning model for detecting daily activities of living, especially the germ spreading activities during the COVID-19 pandemic. In this research, a Mbient Lab MetaWear wearable sensor system is used to collect arm and hand motion data from subjects performing various activities. After data was collected from these different activities, the data was processed. Important statistical time-domain features and frequency domain features, such as the total energy in different frequency bands, were extracted with respect to these different activities to differentiate between them. Various features were collected to create a feature matrix and were used to train different Machine Learning algorithms to determine the germ spreading activity classification accuracy. Using the ensemble bagged tree model, a classification accuracy of 97.0% was obtained. © 2021 IEEE.

Magnet Integrated Shirt for Upper Body Posture Detection Using Wearable Magnetic Sensors

COVID-19 is a global pandemic that has caused an increase in remote work. Sitting in various positions at home without the proper back support is undesirable and can cause chronic back pain and other undesired side effects. Therefore, a new non-intrusive method to continuously monitor back postures in homes is proposed. A shirt is designed with integrated magnets. A magnetic sensor would be placed above the body's sternum, and magnets will be implemented on a shirt. The sensor will help ascertain the back posture position (straight or curved) and help provide feedback to mend the posture if deformed. In this paper, the initial results using the proposed system are presented using a wearable sensor system with a magnet integrated garment that can continuously monitor the varying sitting postures throughout daily lives. In addition, we discuss how the lower body posture affects the magnetic recording otherwise not detectable using the accelerometer-based systems currently available on the market.

Detection of High Germ Spreading Activities

This paper discusses the preliminary process of data collection, data processing, and feature extraction and selection in applications of developing a machine learning model for activity detection, especially the germ spreading activities during the COVID-19 pandemic. In this research, a MetaWear wearable device is used to collect arm and hand motion data from a subject performing various activities. After data was collected from these different activities, the data was processed, and important time-domain features as well as frequency domain features, such as the total energy contained in different frequency bands, were extracted in respect to these different activities with the objective of differentiating between these various activities. Various features were collected to create a feature matrix and input to different Machine Learning algorithms to determine the classification accuracy of the germ spreading activities. Using the ensemble bagged tree model, a classification accuracy of 99.4% was obtained.