Citations

Chest CTs done for unrelated reasons have the potential to reveal unsuspected abnormalities and provide complementary information to help direct appropriate management. By thoroughly understanding the diagnostic role and limitations of CT in assessing ventricular size and morphology as well as myocardial attenuation, enhancement, and thickness, radiologists will be better equipped to differentiate between normal and abnormal findings and provide precise, clinically appropriate recommendations.

Abstract Objective The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled “Developing a Clinical Genomic Informatics Research Agenda”. The meeting’s goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. Materials and Methods Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. Results Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. Discussion Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

ABSTRACT Objectives The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity, and other estimates of diagnostic performance compared to the gold standard of electronic medical records (EMRs) review. Design We performed a single-center diagnostic performance study. Patients and setting A prospective cohort consisted of consecutive patients aged 18 years and older who were admitted from May 1 through September 30, 2020 to six Mayo Clinic intensive care units (ICUs) and five progressive care units. Measurements and main results During the study time, 5,384 out of 6,630 hospital admissions were eligible. During the same period, the 4TDS shock model sent 825 alerts and 632 were eligible. Among 632 hospital admissions with alerts, 287 were screened positive and 345 were negative. Among 4,752 hospital admissions without alerts, 78 were screened positive and 4,674 were negative. The area under the receiver operating characteristics curve for the 4TDS shock model was 0.86 (95% CI 0.85-0.87%). The 4TDS shock model demonstrated a sensitivity of 78.6% (95% CI 74.1-82.7%) and a specificity of 93.1% (95% CI 92.4-93.8%). The model showed a positive predictive value of 45.4% (95% CI 42.6-48.3%) and a negative predictive value of 98.4% (95% CI 98-98.6%). Conclusions We successfully validated an ML model to detect circulatory shock in a prospective observational study. The model used only vital signs and showed moderate performance compared to the gold standard of clinician EMR review when applied to an ICU patient cohort.

ABSTRACT Introduction The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers’ ability to treat patients create an urgent need for effective tools to support care. We report on a project to develop a phone-/tablet-based decision support system for prehospital tactical combat casualty care that collects physiologic and other clinical data and uses machine learning to detect and differentiate shock manifestation. Materials and Methods Software interface development methods included literature review, rapid prototyping, and subject matter expert design requirements reviews. Machine learning algorithm methods included development of a model trained on publicly available Medical Information Mart for Intensive Care data, then on de-identified data from Mayo Clinic Intensive Care Unit. Results The project team interviewed 17 Army, Air Force, and Navy medical subject matter experts during design requirements review sessions. They had an average of 17 years of service in military medicine and an average of 4 deployments apiece and all had performed tactical combat casualty care on live patients during deployment. Comments provided requirements for shock identification and management in prehospital settings, including support for indication of shock probability and shock differentiation. The machine learning algorithm based on logistic regression performed best among other algorithms we tested and was able to predict shock onset 90 minutes before it occurred with better than 75% accuracy in the test dataset. Conclusions We expect the Trauma Triage, Treatment, and Training Decision Support system will augment a medic’s ability to make informed decisions based on salient patient data and to diagnose multiple types of shock through remotely trained, field deployed ML models.

This report presents the American Society of Clinical Oncology’s (ASCO’s) evaluation of the adaptations in care delivery, research operations, and regulatory oversight made in response to the coronavirus pandemic and presents recommendations for moving forward as the pandemic recedes. ASCO organized its recommendations for clinical research around five goals to ensure lessons learned from the COVID-19 experience are used to craft a more equitable, accessible, and efficient clinical research system that protects patient safety, ensures scientific integrity, and maintains data quality. The specific goals are: (1) ensure that clinical research is accessible, affordable, and equitable; (2) design more pragmatic and efficient clinical trials; (3) minimize administrative and regulatory burdens on research sites; (4) recruit, retain, and support a well-trained clinical research workforce; and (5) promote appropriate oversight and review of clinical trial conduct and results. Similarly, ASCO also organized its recommendations regarding cancer care delivery around five goals: (1) promote and protect equitable access to high-quality cancer care; (2) support safe delivery of high-quality cancer care; (3) advance policies to ensure oncology providers have sufficient resources to provide high-quality patient care; (4) recognize and address threats to clinician, provider, and patient well-being; and (5) improve patient access to high-quality cancer care via telemedicine. ASCO will work at all levels to advance the recommendations made in this report.

Abstract Two death receptors, TRAIL-R1 (DR4) and TRAIL-R2 (DR5), expressed in a wide variety of cancer cell types including lymphoma, enable TRAIL (TNF related apoptosis inducing ligand), a member of the TNF ligand superfamily, to induce apoptosis. HGS-ETR1 (TRM-1, mapatumumab) is a fully human monoclonal antibody agonistic to TRAIL-R1 with preclinical evidence of anti-tumor activity in vitro and in vivo. We conducted a phase 2 multicenter study of HGS-ETR1 in 40 subjects with relapsed or refractory NHL. Patients were included on the study if they had relapsed/refractory NHL of any histologic subtype, irrespective of the number of previous treatment regimens. HGS-ETR1 was administered at 2 dose levels: 3 mg/kg (8 subjects) or 10 mg/kg (32 subjects) every 21 days in the absence of disease progression or prohibitive toxicity for up to 6 cycles. The primary endpoint was tumor response evaluated with the International Working Group Criteria after every third treatment. Responses were confirmed between 4 and 8 weeks after initial documentation of response. Secondary endpoints included safety and tolerability. The median age of subjects was 62 years (range 32–81) (24M:16F) with an ECOG performance status 0–2. Subjects had received up to 12 previous therapeutic regimens (69% received 3 or more prior regimens). Original histologic classification is presently available for 34 subjects (14 follicular, 7 diffuse large B-cell, 6 mantle cell, 1 small lymphocytic, 2 peripheral T-cell, 1 anaplastic large cell, 3 lymphomas not classified). Preliminary response data are available for all 40 subjects. 3 subjects (8%), all with follicular lymphoma, had clinical responses (1CR, 2PR). Additionally, 12/40 subjects (30%) had stable disease, and the remainder had progressive disease at the time of first evaluation. 8 subjects (7 follicular) remain on study without disease progression for >5 to >13 months. One subject with a PR (special permission) has continued to receive HGS-ETR1 (10 cycles to date). Subjects tolerated therapy well with no subjects discontinuing therapy due to toxicity related to drug. There have been 10 reported SAEs, 2 of which were considered possibly/probably related to HGS-ETR1 administration (shingles, fever). In conclusion, HGS-ETR1 can be safely administered to heavily treated NHL patients. Clinical responses were seen in 3/14 follicular lymphoma subjects to date. Final data for all subjects will be reported at the meeting. These data suggest that HGS-ETR1 has activity in heavily pretreated lymphoma patients and further studies in combination with agents active in lymphoma are justified.

Abstract Introduction: Although HER2+ breast cancer (BC) is considered a moderately immunogenic tumor, several studies have shown a role of pre-existing immunity associated with favorable long-term prognosis and better response to treatment. In this study, we performed exploratory analyses to assess whether the efficacy of HER2 targeted treatment in the MARIANNE trial correlated with immune gene expression. Methods: MARIANNE (NCT01120184) is a phase 3 study in patients (pts) with centrally confirmed HER2+ local advanced/metastatic BC naïve to prior treatments in the advanced disease. Pts were randomized (1:1:1) to trastuzumab+taxane (HT), T-DM1, or T-DM1+Petuzumab (P) and the trial showed noninferior PFS of T-DM1 and T-DM1+P vs HT. Gene expression (RNA) analysis was performed on tumor samples by a custom 800-gene codeset on the nCounter platform. PD-L1, CD8 expressions and immune gene signatures (sign) analyses were assessed by multivariate Cox regression models using median (cut-off) as categorical variable and adjusted by prior HT, presence of visceral disease, world region, baseline ECOG, measureable disease at baseline, therapy setting, HER2 mRNA expression, PIK3CA mutation status. Results: MARIANNE randomized 1095 pts (HT, n=365; T-DM1, n=367; T-DM1+P, n=363). Gene expression results were available for 671 pts (61.3% of the intent-to-treat [ITT] population) which was representative of ITT. In ITT, HR below 1 was observed when comparing pts with high (>median) vs low (≤median) immune gene expression by clinical outcome suggesting a potential association of high immune marker expression with improved PFS (Table 1) and to some extent with OS (data not shown). This association was primarily observed in the T-DM1 arm where the HR suggested a risk reduction of disease progression(PD)/death especially in the high Teff, high PD-L1 and high CD8 subgroups, and to some extent in the HT arm (Table 1). When assessing the predictive impact on PFS by comparing T-DM1 vs HT, HR below 1 was observed especially in pts with high Teff signature, high PD-L1 and high CD8 expressions (HR 0.67 (95% CI (0.47-0.95)), HR 0.68 (95% CI (0.48-0.97), and HR 0.64 (95%CI 0.44-0.93), respectively). When comparing T-DM1+P vs. HT, HR below 1 was observed especially in pts with low Teff signature and low PD-L1 expression (HR 0.70 (95% CI (0.50-0.99), and HR 0.68 (95% CI (0.48-0.96) respectively). No clear differences between immune gene expression subgroups was observed when comparing treatment arms in regards to OS (data not shown). Conclusions: In the exploratory analysis from the MARIANNE study, high immune gene expression, especially in the high PD-L1, CD8 and Teff subgroups, showed an association with improved clinical benefit with HRs reflecting for a risk reduction of PD/death for PFS and partially for OS. This association was less obvious in the T-DM1+P arm. When comparing the treatments effect, the data showed a potential impact of high Teff signature, and high CD8 and PD-L1 expressions on T-DM1 and less on HT. The potential opposite association of low Teff signature and low PD-L1 expression with improved benefit in the T-DM1+P arm was unexpected and needs further investigation. Table 1: Prognostic biomarker effect on PFSBiomarker by categories (>Median vs ≤Median)HR (95% CI) ITT n=671HR (95% CI) HT n=220HR (95% CI) T-DM1 n=227HR (95% CI) T-DM1+P n=224Teff sign0.89 (0.73-1.09)0.97 (0.68-1.38)0.64 (0.45-0.91)1.09 (0.75-1.58)Th1 cytokine sign0.91 (0.74-1.11)0.92 (0.64-1.31)0.78 (0.55-1.11)0.96 (0.67-1.36)Checkpoint inhibitor sign0.95 (0.78-1.15)0.91 (0.64-1.29)0.90 (0.64-1.26)1.02 (0.71-1.47)PD-L10.80 (0.66-0.98)0.79 (0.55-1.13)0.62 (0.44-0.87)1.07 (0.74-1.55)CD80.91 (0.75-1.11)1.10 (0.77-1.57)0.66 (0.46-0.93)0.98 (0.68-1.41) Citation Format: Edith A Perez, Sanne Lysbet de Haas, Carlos H Barrios, Wolfgang Eiermann, Masakazu Toi, Young-Hyuck Im, Pier Franco Conte, Miguel Martin, Tadeusz Pienkowski, Xavier B Pivot, Howard A Burris III, Chiara Lambertini, Silke Hoersch, Monika Patre, Paul Anthony Ellis. Association of immune gene expression with outcome in the MARIANNE phase 3 clinical trial in HER2-positive metastatic breast cancer (MBC) [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD5-11.