Abstract Background Increasing attention has been paid to differences between the sexes in recent studies, but only a few have focused on such differences in aortic stenosis (AS). Purpose We sought to evaluate the differences between genders in AS outcomes and management. Methods We included 2429 patients (51.5% men; 49.5% women) with severe AS. Median follow-up was 42.0 (21–78) months. Results Women were older (p<0.001), more often symptomatic (p=0.007), and presented with smaller aortic valve area (p<0.001), and greater ejection fraction (p<0.001) than men. Despite that women have a longer life expectancy than men in the general population, estimated five-year survival of patients with severe AS was lower for women compared to men (62±2% versus 69±1% respectively, p<0.001). The 5-year survival was lower compared to expected survival, especially for women (62±2% vs. 71% for women and 69±1% vs. 71% for men). The cumulative 5-years incidence of AVR was 79±2% for men, and only 70±2% for women (p<0.001) and, being male was independently predictive of AVR (OR: 1.49 [1.18–1.97]; p=0.011). After propensity matching, when both genders were comparable in terms of characteristics and management (AVR), women, as expected due to their higher life expectancy than men, had better 5-year survival than men (69±2% vs. 62±2%; p=0.023). Conclusion Women with severe AS present at a more advance stage of the disease, at older ages with more symptoms, and incur higher mortality than men. This excess mortality is related to a combination of late diagnosis and a less frequent and later referral for AVR than in men. This justifies appropriate measures to improve the situation and to ensure that both sexes receive equivalent care. Figure 1. (A) Survival curves. (B) Surgery incidence. Funding Acknowledgement Type of funding source: None
Overall survival with oral selinexor plus low‐dose dexamethasone versus real‐world therapy in triple‐class‐refractory multiple myeloma
AbstractTriple‐class‐refractory multiple myeloma (MM) describes MM refractory to proteasome inhibitors, immunomodulatory agents, and anti‐CD38 monoclonal antibodies. In the Phase IIb STORM study (NCT02336815), oral selinexor plus low‐dose dexamethasone (Sel‐dex) demonstrated a 26.2% overall response rate in triple‐class‐refractory MM. Here, we compare overall survival (OS) of 122 patients with triple‐class‐refractory MM who received Sel‐dex in STORM Part 2 with that of 64 similar patients treated with other available therapies in a Flatiron Health Analytic Database (FHAD) cohort. OS from the date that the patients’ MM became triple‐class‐refractory was longer in STORM versus FHAD, with an unadjusted hazard ratio (HR) of 0.43 (P = .0002; adjusted HR 0.35 [P = .011]). In a subset analysis of highly resistant patients receiving further therapies after their MM first became at least triple‐class‐refractory (i.e., who received Sel‐dex in STORM,n = 64, and non‐Sel‐dex in FHAD,n = 36), the OS was significantly longer in STORM with an unadjusted HR of 0.52 (P = .0331; adjusted HR 0.33 [P = .041]). Within the limits of this analysis, the OS of patients with at least triple‐class‐refractory MM was significantly better with Sel‐dex versus available therapies, suggesting that Sel‐dex may be associated with a meaningful OS benefit in these patients.
Introduction of the Surgical Providers Assessment and Response to Climate Change (SPARC2) Tool
Objective: To assess the efficacy and safety of closed-loop control (CLC) insulin delivery system in adolescents and young adults with type 1 diabetes. Research Design and Methods: Prespecified subanalysis of outcomes in adolescents and young adults aged 14–24 years old with type 1 diabetes in a previously published 6-month multicenter randomized trial. Participants were randomly assigned 2:1 to CLC (Tandem Control-IQ) or sensor augmented pump (SAP, various pumps+Dexcom G6 CGM) and followed for 6 months. Results: Mean age of the 63 participants was 17 years, median type 1 diabetes duration was 7 years, and mean baseline HbA1c was 8.1%. All 63 completed the trial. Time in range (TIR) increased by 13% with CLC versus decreasing by 1% with SAP (adjusted treatment group difference = +13% [+3.1 h/day]; 95% confidence interval [CI] 9–16, P < 0.001), which largely reflected a reduction in time >180 mg/dL (adjusted difference −12% [−2.9 h/day], P < 0.001). Time <70 mg/dL decreased by 1.6% with CLC versus 0.3% with SAP (adjusted difference −0.7% [−10 min/day], 95% CI −1.0% to −0.2%, P = 0.002). CLC use averaged 89% of the time for 6 months. The mean adjusted difference in HbA1c after 6 months was 0.30% in CLC versus SAP (95% CI −0.67 to +0.08, P = 0.13). There was one diabetic ketoacidosis episode in the CLC group. Conclusions: CLC use for 6 months was substantial and associated with improved TIR and reduced hypoglycemia in adolescents and young adults with type 1 diabetes. Thus, CLC has the potential to improve glycemic outcomes in this challenging age group. The clinical trial was registered with ClinicalTrials.gov (NCT03563313).
Current Lung Cancer Screening Guidelines may Miss the High-risk Population in the Real-world Physical Examination Population: A Large Prospective Study
Abstract Background: Despite much research published on lung cancer screening, China has had no large-scale study on the missed diagnosis of lung cancer in physical examination population. We therefore did a real-world study using the current lung cancer screening guidelines to the physical examination population in China to determine the proportion of lung cancer cases that have been missed.Methods: A real-world prospective cohort study of screening, with the use of low-dose computed tomography, was conducted among people who took yearly health checkup in health management center of West China Hospital between 2006 and 2017. We respectively used current guidelines including lung cancer screening guidelines of the U.S. Preventive Services Task Force (USPSTF) and expert consensus on low dose spiral CT lung cancer screening in China. Results: In a total of 15996 participants with physical examination who completed the baseline screening, 6779 (42.4%) subjects had at least one positive finding, and 142 (2.1%) cases of lung cancer were screened positive. The false positive rate was 97.9%. Of 142 lung cancer cases detected in our study, only 9.2% met the lung cancer screening guidelines proposed by the USPSTF, and 24.4% met that of China. The rates of missed diagnosis were as high as 90.8% and 75.6% respectively. In addition, we did an in-depth analysis by gender. We found that among male lung cancer patients, the proportion of smokers was 75%, and the proportion of young people under 50 was 23.2%. Among female patients with lung cancer, the proportion of smokers was only 5.8%, and the proportion of young people under 50 was up to 33.3%.Conclusions: The rate of missed diagnosis was as high as 90.8% applying the current lung cancer screening guidelines to the physical examination population in China. Further study to determine screening guidelines for targeted populations, is warranted.
Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions
AbstractClinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention—the starting point for delivery of “All the right care, but only the right care,” an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.
SCN5A
Mutation Type and a Genetic Risk Score Associate Variably With Brugada Syndrome Phenotype in
SCN5A
Families
Background: Brugada syndrome (BrS) is characterized by the type 1 Brugada ECG pattern. Pathogenic rare variants in SCN5A (mutations) are identified in 20% of BrS families in whom incomplete penetrance and genotype-negative phenotype-positive individuals are observed. E1784K- SCN5A is the most common SCN5A mutation identified. We determined the association of a BrS genetic risk score (BrS-GRS) and SCN5A mutation type on BrS phenotype in BrS families with SCN5A mutations. Methods: Subjects with a spontaneous type 1 pattern or positive/negative drug challenge from cohorts harboring SCN5A mutations were recruited from 16 centers (n=312). Single nucleotide polymorphisms previously associated with BrS at genome-wide significance were studied in both cohorts: rs11708996, rs10428132, and rs9388451. An additive linear genetic model for the BrS-GRS was assumed (6 single nucleotide polymorphism risk alleles). Results: In the total population (n=312), BrS-GRS ≥4 risk alleles yielded an odds ratio of 4.15 for BrS phenotype ([95% CI, 1.45–11.85]; P =0.0078). Among SCN5A -positive individuals (n=258), BrS-GRS ≥4 risk alleles yielded an odds ratio of 2.35 ([95% CI, 0.89–6.22]; P =0.0846). In SCN5A -negative relatives (n=54), BrS-GRS ≥4 alleles yielded an odds ratio of 22.29 ([95% CI, 1.84–269.30]; P =0.0146). Among E1784K- SCN5A positive family members (n=79), hosting ≥4 risk alleles gave an odds ratio=5.12 ([95% CI, 1.93–13.62]; P =0.0011). Conclusions: Common genetic variation is associated with variable expressivity of BrS phenotype in SCN5A families, explaining in part incomplete penetrance and genotype-negative phenotype-positive individuals. SCN5A mutation genotype and a BrS-GRS associate with BrS phenotype, but the strength of association varies according to presence of a SCN5A mutation and severity of loss of function.
Clinical Management and Pump Parameter Adjustment of the Control-IQ Closed-Loop Control System: Results from a 6-Month, Multicenter, Randomized Clinical Trial
Background: Data are limited on the need for and benefits of pump setting optimization with automated insulin delivery. We examined clinical management of a closed-loop control (CLC) system and its relationship to glycemic outcomes. Materials and Methods: We analyzed personal parameter adjustments in 168 participants in a 6-month multicenter trial of CLC with Control-IQ versus sensor-augmented pump (SAP) therapy. Preset parameters (BR = basal rates, CF = correction factors, CR = carbohydrate ratios) were optimized at randomization, 2 and 13 weeks, for safety issues, participant concerns, or initiation by participants' usual diabetes care team. Time in range (TIR 70–180 mg/dL) was compared in the week before and after parameter changes. Results: In 607 encounters for parameter changes, there were fewer adjustments for CLC than SAP (3.4 vs. 4.1/participant). Adjustments involved BR (CLC 69%, SAP 80%), CR (CLC 68%, SAP 50%), CF (CLC 44%, SAP 41%), and overnight parameters (CLC 62%, SAP 75%). TIR before and after adjustments was 71.2% and 71.3% for CLC and 61.0% and 62.9% for SAP. The highest baseline HbA 1c CLC subgroup had the largest TIR improvement (51.2% vs. 57.7%). When a CR was made more aggressive in the CLC group, postprandial time >180 mg/dL was 43.1% before the change and 36.0% after the change. The median postprandial time <70 mg/dL before making CR less aggressive was 1.8%, and after the change was 0.7%. Conclusions: No difference in TIR was detected with parameter changes overall, but they may have an effect in higher HbA 1c subgroups or following user-directed boluses, suggesting that changes may matter more in suboptimal control or during discrete periods of the day. Clinical Trials Registration number: NCT03563313.
Role of Aneuploidy in Transcriptional Regulation and Clinical Prognosis in Relapsed and/or Refractory Multiple Myeloma (RRMM)
BACKGROUND Aneuploidy, defined by abnormal copy number changes of chromosomes, contributes to genome instability in multiple myeloma and has potential prognostic impact. Previous research has explored transcriptional pathways affected by aneuploidy. We aim to evaluate aneuploidy, clinical prognosis, and gene expression in RRMM patients (pts) who participated in MMRF (Multiple Myeloma Research Foundation) sequencing study at University of Michigan. We further used gene set analysis to identify enrichment and variation of genetic pathways associated with aneuploidy and overall survival (OS). This was a pilot study in view of applying similar methods to larger populations. METHODS DNA and RNA materials were obtained from 51 RRMM pts at the time of disease relapse. Targeted sequencing was performed with the Onco1700 panel and RNA sequencing was performed with a capture protocol using Agilent SureSelect All Exon V4, followed by paired end sequencing. Copy number variation (CNV) was estimated using an in-house pipeline using matched normal samples. Arm-level aneuploidy was defined as copy number status (gain, loss or neutral) with maximal proportion for each chromosomal arm. Aneuploidy score was determined by total number of arm-level CNV aberrations, with the median score defining high and low aneuploidy groups. Survival analysis was performed using Kaplan-Meier (KM) and Cox regression. RNA-seq libraries were aligned with STAR aligner to the hg38 reference and read quantification were performed with featureCounts. We used gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) to obtain genetic pathway-level summaries for 50 hallmark pathways representative of cancer from the GSEA Molecular Signatures Database (https://www.gsea-msigdb.org/). Supervised GSEA was performed, based on differential analyses comparing gene expression profiles between high versus low aneuploidy groups to identify the differences in genetic pathways in RRMM pts. Unsupervised GSVA was applied to RNAseq count data of RRMM and newly-diagnosed multiple myeloma (NDMM) pts from the MMRF CoMMpass study (797 pts). Univariate Cox proportional hazard model was used to identify pathways having significant association with OS. RESULTS Arm scale aneuploidy analysis revealed high frequency of gains and losses in multiple chromosomes (Figure 1). RRMM pts with high aneuploidy scores had worse OS (median 15.9 months since study enrollment) compared to those with low scores (median not reached, p=0.027). Multivariate analysis demonstrated that high aneuploidy score is persistently associated with poor OS (hazard ratio (HR): 3.9, p=0.006), adjusting for t(4;14) and t(11;14). Hyperdiploidy (any whole chromosome carrying >2 copies) was identified in chromosomes 3, 5, 7, 9, 11, 19, 21, with frequency >20%. Pts with hyperdiploidy had better OS via KM (p=0.04). Using RNAseq gene expression data and GSEA analysis, we found enrichment of gene markers involving the pathways for targets of E2F transcription factors (Normalized Enrichment Score (NES) = 2.07), G2-M checkpoint in the cell division cycle (NES = 1.87), MYC proto-oncogene (NES = 1.93 and 1.85), and DNA repair (NES = 1.68) in RRMM pts with high aneuploidy compared against those with low scores (Benjamini-Hochberg adjusted p-value <0.001 for all pathways, Figure 2). GSVA analysis revealed 21 genetic pathways associated with survival in NDMM and RRMM pts, with 8, 2, and 11 pathways associated with OS in NDMM only, RRMM only, and both, respectively (Figure 3). Enrichment of several biological pathways involved with cellular growth, cell cycle regulation, and stress response (E2F, G2-M, MYC, DNA repair, mTORC1 signaling, UV radiation, mitotic spindle assembly, unfolded protein response, and peroxisome) are associated with poor survival (HR>1) in both NDMM and RRMM pts. Downregulation of KRAS signaling pathway is associated with better survival (HR<1) in both groups. CONCLUSION Aneuploidy, adjusted for chromosomal translocations, is an independent adverse prognostic factor for RRMM. Aneuploidy is correlated with dysregulation of major biological pathways involving cell cycle regulation, cellular division, oncogenes, and DNA damage repair. The majority of common pathways shared between RRMM and NDMM are linked to or reflective of genomic instability, and may explain association with poor survival. Disclosures Talpaz: IMAGO: Consultancy; Takeda: Research Funding; Novartis: Research Funding; Constellation Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Ye:Janssen: Research Funding; Portola: Research Funding; Millennium: Research Funding; Celgene: Research Funding; Sanofi: Research Funding; Karyopharm: Research Funding; Nektar: Research Funding; AbbVie: Research Funding.
Estimation of Long-Term Survival with Tafasitamab + Lenalidomide in Relapsed/Refractory Diffuse Large B-Cell Lymphoma
Background L-MIND (NCT02399085) is an ongoing, open-label, single-arm, Phase II study of tafasitamab + lenalidomide (LEN) in patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who are ineligible for autologous stem cell transplant. Progression-free survival (PFS) estimates the treatment effect of the study drug only, while overall survival (OS) also includes effects of potential subsequent cancer treatments or palliative care. Given that not all OS and PFS events have occurred after a long follow-up duration, traditional survival analyses may underestimate survival benefit for tafasitamab + LEN by assuming the same mortality rate for the whole population. We used mixture cure models to estimate the proportion of long-term survivors (LTS) and survival benefit associated with tafasitamab + LEN treatment. Methods In the L-MIND study (data cut-off: Nov 30, 2019), 80/81 enrolled patients with R/R DLBCL received tafasitamab + LEN, with a median and maximum follow-up duration of 31.8 months (95% CI: 27.2-35.9) and 43.5 months, respectively. The Kaplan-Meier (KM) plots of PFS and OS were observed to plateau after 30 months, with the majority of events reported up to that time-point and very few events beyond this time-point. Patients without a PFS event or patients who did not decease after the observed plateau exhibit a high survival benefit from tafasitamab + LEN treatment and are considered LTS. Thus, the patient population can be considered to consist of two sub-populations - LTS and non-LTS. A "mixture cure model" was fitted on the PFS and OS data to estimate the proportion of LTS and associated mean survival for the two sub-populations (Lambert PC, et al. 2007). To incorporate background mortality (Bansal, et al. 2019), age- and sex-specific US mortality rates (US CDC 2017) were factored in for the entire study population (enrolled in Europe and US). The weighted average of the mean survival for the two sub-populations provided an estimate of the mean survival for the whole population. For comparative purposes, standard parametric models without considering an LTS proportion were fitted on PFS and OS data additionally. Sensitivity analyses were performed considering different survival distributions (exponential distribution, Weibull distribution and log-logistic distribution). Results By using a mixture cure model fitted to PFS and OS data separately, the estimated proportion of LTS for the tafasitamab + LEN combination was 42% (95% CI: 30-55) and 47% (95% CI: 29-66), respectively. The predicted mean survival when mixture cure model was fitted on PFS data was 16.7 years for LTS patients vs 0.5 years for non-LTS patients, yielding 7.3 years for the overall population. Under this mixture cure model, the survival rates of patients with tafasitamab + LEN treatment were 40.2% and 36.0% at 2 and 5 years. Similar results were obtained when the mixture cure model was fitted on OS data. Using a standard parametric model (Weibull distribution), the predicted mean survival in the overall population was 2.8 years fitting PFS data (Figure 1) and 4.5 years fitting OS data (Figure 2). Conclusions PFS and OS KM curves for the L-MIND study reaching a long plateau and not following a Weibull distribution suggest the presence of an LTS subgroup. Standard parametric models may lead to a biased estimation of the OS benefit in such situations. The mixture cure model suggests that the treatment of R/R DLBCL patients with tafasitamab + LEN is associated with a high LTS proportion. The survival rates of patients with tafasitamab + LEN treatment were 40.2% and 36.0% at 2 and 5 years. Disclosures Salles: Takeda:Honoraria;BMS/Celgene:Honoraria, Other: consultancy or advisory role;Debiopharm:Consultancy, Honoraria, Other: consultancy or advisory role;Genmab:Honoraria, Other;Karyopharm:Honoraria;Kite, a Gilead Company:Honoraria, Other: consultancy or advisory role ;Epizyme:Honoraria, Other: consultancy or advisory role;Janssen:Honoraria, Other: consultancy or advisory role;Abbvie:Other: consultancy or advisory role;Roche:Honoraria, Other: consultancy or advisory role;Novartis:Honoraria, Other: consultancy or advisory role;MorphoSys:Honoraria, Other: consultancy or advisory role;Autolos:Other: consultancy or advisory role.Goswami:MorphoSys AG:Ended employment in the past 24 months.Bagnardi:MorphoSys AG:Consultancy.Dey:MorphoSys AG:Current Employment.Winderlich:MorphoSys AG:Current Employment.Ambarkhane:MorphoSys AG:Current Employment.Huang:MorphoSys AG:Current Employment.Nowakowski:Curis:Consultancy;Kymera:Consultancy;Kite:Consultancy;Ryvu:Consultancy, Membership on an entity's Board of Directors or advisory committees;Celgene/BMS:Consultancy, Research Funding;Seattle Genetics:Consultancy;MorphoSys:Consultancy, Research Funding;NanoString:Research Funding.