Citations

Abstract MAJIC is a phase II trial of Ruxolitinib (RUX) vs Best Available Therapy (BAT) in polycythemia vera (PV) patients with resistance/intolerance to Hydroxycarbamide (HC). This analysis involved a primary comparison between the RUX and BAT arms for quality of life (QOL) durability over 60 months, with secondary comparisons of best QOL response within first year in complete hematologic responders (CR) vs a group of partial or no response (NR/PR). This is a unique analysis due to cross-over nature of prior studies. Patients were stratified by treatment arm with either RUX or BAT. QOL was assessed over 0-60 months using the Myeloproliferative Neoplasms Symptom Assessment Form (MPN-SAF). MPN-SAF Total Symptom Score (TSS) was computed as the average of all completed items multiplied by 10 (scale 0-100, higher score represents higher symptom burden). Estimates of change from baseline and between arm differences in change by timepoint were made using a linear mixed model with compound symmetry covariance structure, which included covariates for categorical time point, treatment arm, and the interaction between time point and treatment arm. The difference between arms in proportion of patients with best post-baseline TSS response of 50% or greater was testing using a Chi-square test. In this study, 147 of the 190 trial patients had started treatment and completed at least their baseline assessment and were included in the analysis with 39 patients completing through 60 months. Groups were comprised of 76 patients in the RUX group (31 females, 45 males, mean age 64.2 [SD 11.4]) and 71 patients in the BAT group (29 females, 42 males, mean age 64.6 [SD 11.5]). Symptom scores at baseline were similar between arms, with MPN-SAF weight loss the only statistically significant difference (BAT 0.7 [SD 1.71] vs RUX 1.7 [SD 2.83], p=0.02). Significant, durable improvements in TSS were noted in RUX patients with symptom improvements lasting a mean of 52 months. BAT patients experienced a worsening of their symptom burden with improvements back to baseline notable at 56 months (Figure 1). MPN-SAF TSS there were significant differences between treatment arms in change from baseline to months 2-32, 44, and 48 (all p<0.05). In all months the trend was in the direction of larger improvement in the RUX arm, with the point estimate for difference ranging from 2.1 (at month 56) to 11.3 (at month 4) with most prominent changes occurring between months 2 and 32. Of the 80 patients with MPN-SAF TSS scores at baseline and at least one post-baseline timepoint, 13/41 (31.7%) BAT and 24/39 (61.5%) RUX patients had TSS reduction of 50% or greater in at least one time point, which was statistically significant (p=0.008). In regard to specific symptoms, there was a statistically significant between arm difference seen in over 5 timepoints for fatigue, early satiety, night-sweats, itching, bone pain, and weight loss. Comparing MPN-SAF TSS in Complete Response (CR n=51) vs Non-Responder/Partial Responders (NR/PR) (n=96) there was no difference at baseline in any of the measures at any time point. A possible limitation of this study is the potential for bias in which the patients who were lost to follow up could be different then the patients who remained in the study through all 60 months which would affect generalization, particularly at time points at the later years. The novel findings of this investigation demonstrate that Ruxolitinib ameliorates the PV symptom burden in patients resistant or intolerant to HC in a robust durable manner over at least four years, while patients receiving BAT have worsening of their symptom burden over this same time period. Figure 1 Figure 1. Disclosures Mead: Novartis: Consultancy, Honoraria, Speakers Bureau; Celgene/BMS: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria. Yap: Celgene: Honoraria; Faron Pharmaceuticals: Honoraria. Scherber: Incyte Corporation: Current Employment, Current holder of stock options in a privately-held company. Knapper: Novartis: Consultancy, Research Funding, Speakers Bureau; Astellas: Ended employment in the past 24 months, Speakers Bureau; Pfizer: Consultancy, Speakers Bureau; Jazz: Consultancy, Speakers Bureau. Drummond: Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; CTI: Membership on an entity's Board of Directors or advisory committees. McMullin: Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Other: clinical trial support, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AOP Orphan: Research Funding, Speakers Bureau. Mesa: CTI: Research Funding; AOP: Consultancy; Celgene: Research Funding; Novartis: Consultancy; Gilead: Research Funding; Pharma: Consultancy; Promedior: Research Funding; Constellation Pharmaceuticals: Consultancy, Research Funding; CTI: Research Funding; Samus: Research Funding; Sierra Oncology: Consultancy, Research Funding; Genentech: Research Funding; Incyte Corporation: Consultancy, Research Funding; Abbvie: Research Funding; La Jolla Pharma: Consultancy. Harrison: CTI BioPharma: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead Sciences: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Shire: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Geron: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Constellation Pharmaceuticals: Research Funding; Galacteo: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Incyte Corporation: Speakers Bureau; Sierra Oncology: Honoraria; Promedior: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AOP Orphan Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Keros: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau.

Abstract Background: Self-reported financial hardship (FH) amongst cancer patients is increasingly becoming a challenge for patients, caregivers, and healthcare providers. FH not only leads to financial struggles, significant lifestyle changes, and emotional distress, but also contributes to treatment noncompliance, affecting clinical outcomes. As treatment costs rise, it is crucial to develop efficient methods to proactively identify and alleviate FH in hematology practice. One potential approach is utilizing automated processes to identify those at highest risk of FH. At Mayo Clinic, screening for FH involves using a single financial strain question 'How hard is it for you to pay for the very basics like food, housing, medical care, and heating?' which all cancer patients answer annually as part of the institution's Social Determinants of Health (SDOH) assessment. Answers are on a five-point scale including not hard at all, not very hard, somewhat hard, hard, and very hard. In this study, we assess the prevalence and predictors for FH (denoted by a response of "Very hard" "Hard" or "Somewhat hard") amongst the Mayo Clinic hematologic malignancy patient population. Our study objective was to determine if this automated process could identify those at risk for FH. Methods: Patients who received care for hematologic malignancies (lymphoma, leukemia, plasma disorders, myelodysplastic/myeloproliferative disorders, and other heme malignancies) at any of the Mayo Clinic cancer centers (Minnesota, Arizona, and Florida) and who had completed the SDOH screen at least once were included in this study. The electronic medical record (EMR) and Mayo Clinic Cancer Registry were utilized to extract demographic and disease variables. Patient's home zip code was used to determine rural/urban residence, distance from cancer center, and the Area Deprivation Index (ADI), a measure of socioeconomic disadvantage based on home zip code (ranging from 1-100, with 100 representing the most disadvantaged). Multivariable logistic regression modeling was used to examine predictor variables for FH in this patient population. Results: The final cohort included 10,024 patients from 2018 to 2020. Median age was 64.6 years (IQR 58.1,73.7), 58% were male, and 79% married. Race/ethnicity composition was 94% White (n=9,268), 2.5% Black (n=246), 0.4% American Indian/Alaskan Native (44), and 4% Hispanic (n=360). Fifty-six percent of patients had Medicare and 41% had commercial insurance. Fifty percent were retired, 40% were working/students, and 72% were urban residents. Mean ADI was 41.2. Fifty-six percent of patients had lymphomas, 23.5% had plasma cell disorders, 8.5% had leukemias, 6.8% had other hematological malignancies, and 5.5% had myelodysplastic/myeloproliferative neoplasms. FH was reported by 12.8% (n=1286) of the patients. Table 1 shows the results of the multivariable model. A significantly higher likelihood of endorsing FH was noted in Hispanic vs non-Hispanics, Black and American Indian/Alaskan Native groups vs whites, Disabled/Unemployed vs working, Medicaid, Medicare, and Self-Pay groups vs commercial insurance, higher ADI (5 th quintile vs 1 st), and myelodysplastic/myeloproliferative disorder and other hematologic malignancy vs lymphoma patients. Older age, being retired, and living farther from the cancer center were associated with significantly less likelihood of endorsing FH. Conclusion: Our study used automated data extraction from the EMR to efficiently identify predictors of FH in hematologic cancer patients. Employing a dichotomized and automated "flag" for FH, particularly if incorporated in the EMR, could ease the identification of SDOH issues, facilitate timely connection to appropriate resources, and help provide better patient-centered care. Figure 1 Figure 1. Disclosures Ailawadhi: Sanofi: Consultancy; Cellectar: Research Funding; Karyopharm: Consultancy; Ascentage: Research Funding; Genentech: Consultancy; Janssen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Beigene: Consultancy; GSK: Consultancy, Research Funding; AbbVie: Consultancy; Medimmune: Research Funding; Pharmacyclics: Consultancy, Research Funding; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Xencor: Research Funding. Fonseca: OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy; BMS: Consultancy; Mayo Clinic in Arizona: Current Employment; Aduro: Consultancy; AbbVie: Consultancy; GSK: Consultancy; Merck: Consultancy; Juno: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Novartis: Consultancy; Bayer: Consultancy; Celgene: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy. Griffin: Exact Sciences: Research Funding.

Abstract The myeloproliferative neoplasms (MPN) polycythaemia vera (PV) and essential thrombocythaemia (ET) are associated with significant symptom burden with impaired quality of life (QoL). In the MAJIC study patients refractory/intolerant to hydroxycarbamide (HC), were randomised to treatment with ruxolitinib (Rux) or best available therapy (BAT). In this unique and comprehensive analysis we explore quality of life (QoL) outcomes in the MAJIC study using the MPN10 Self-Assessment Form (SAF) in particular using this unique dataset to explore for the first time differences between JAK2 ET vs PV, and JAK2 ET vs CALR ET at baseline and 12 months of therapy with Rux or BAT. Methods 306 patients were randomised, (190, PV and 116 ET arm) and followed for 5 years, no cross over was permitted. MPN10 SAF was assessed at baseline, 2, 4, 8 and 12 months in both arms. Equal variance two sample t-tests were used to test differences between diagnosis groups (JAK2 ET vs PV & JAK2 ET vs CALR ET) in baseline scores and 12-month change from baseline scores, here negative change from baseline indicates improvement. QoL data was also collected using MD Anderson Symptoms Inventory (MDASI) and 5 level EQ5D at the same time points as the MPN10 SAF. Results JAK2 ET vs PV 110 participants with JAK2 mutation were included in the analysis for JAK2 ET vs PV. 81 (74%) had PV and 29 (26%) had JAK2 ET; 44 patients (54.3%) with PV and 18 (62.1%) patients with ET were randomised to Rux, and 37 (45.6%) patients with PV and 11 (37.9%) with ET to BAT. Baseline mean total symptom score (TSS) was similar (JAK2 ET: 18.9. SD 18.4 and PV: 23.7, SD 17.81 (p=0.27). Mean for pruritus was 1.9 (SD 2.47) in JAK2 ET and 3.5 (SD 2.99) in PV (p=0.03). Other baseline scores were comparable: mean scores for fatigue was 3.3 (SD 3.07) for JAK2 ET vs 4.7 (SD 2.68) for PV, for early satiety was 2.6 (SD 2.78) for JAK2 ET vs 2.1 (SD 2.40) for PV, for abdominal discomfort 3.0 (SD 3.34) for JAK2 ET vs 2.0 (SD 2.78) for PV, for inactivity was 2.4 (SD 2.86) for JAK2 ET vs 3.2 (SD 2.72) for PV; for concentration problems was 2.2 (SD 2.51) for JAK2 ET vs 2.9 (SD 3.11) for PV, for night sweats was 1.6 (SD 2.20) for JAK2 ET vs 2.1 (SD 2.66) for PV, for bone pain was 1.8 (SD 2.97) for JAK2 ET vs 2.0 (SD 2.86) for PV, for fever was 0.5 (SD 1.46) for JAK2 ET vs 0.5 (SD 1.70) for PV and for weight loss was 0.3 (SD 0.73) for JAK2 ET vs 1.0 (SD 2.15) for PV (p>0.05 for all). Concerning change at 12 months, mean (SD) change in TSS was -0.5 (SD 10.86) for JAK2 ET (n=21) vs -0.8 (SD 13.32) for PV (n=66) (P=0.93). Mean for individual symptom scores at 12 months were also not significantly different between the two phenotypic disease group (P>0.05 for all parameters). (Fig 1, top panels). CALR vs JAK2 ET Exploring symptoms for CALR vs JAK2 ET: 45 patients were included; 9/16 (56.3%) CALR and 18/29 (62.1%) JAK2 patients were assigned to Rux (p=0.70). 10/16 (62.5%) CALR ET and 19/29 (65.5%) JAK2 ET. Baseline mean total symptom score (TSS) was similar (JAK2 ET: 18.9, SD 18.4 and CALR ET: 18.8, SD 16.99 (p=0. 0.98). All other baseline scores were comparable in this intolerant/ refractory population (p>0.05 for all. Concerning change at 12 months, mean (SD) change in TSS was -0.5 (SD 10.86) for JAK2 ET vs -2.8 (SD 9.96) for CALR ET (P=0.56). Mean for individual symptom scores at 12 months were also not significantly different between the two phenotypic disease group (P>0.05 for all parameters); the mean change in night sweats approached but did not reach significance similar (JAK2 ET: 0.2 (SD 1.89) and CALR ET: 1.8 (SD 3.07) (p=0. 0.06). (Fig 1, lower panels). Preliminary analysis of the MDASI and EQ5D-5L data at baseline and at 12 months shows similar results and will be presented. Conclusion This first analysis of its kind compared the symptom burden and other parameters of QoL of patients with PV and ET, resistant/intolerant to HC shows that with the exception of pruritus at baseline there were no substantial differences between JAK2 ET or PV. Equally changes in symptom burden across 12 months did not substantially differ between JAK2 ET or PV. A second analysis of JAK2 vs CALR ET also failed to show substantial differences. This data should be expanded in a larger cohort but supports a symptom continuum of JAK2 ET and PV and also JAK2 vs CALR ET. Figure 1 Figure 1. Disclosures Mead: Abbvie: Consultancy, Honoraria; Celgene/BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Speakers Bureau. Yap: Faron Pharmaceuticals: Honoraria; Celgene: Honoraria. Knapper: Novartis: Consultancy, Research Funding, Speakers Bureau; Astellas: Consultancy, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Speakers Bureau; Pfizer: Consultancy, Speakers Bureau. Drummond: CTI: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Mesa: Pharma: Consultancy; CTI: Research Funding; Abbvie: Research Funding; Genentech: Research Funding; Gilead: Research Funding; AOP: Consultancy; Incyte Corporation: Consultancy, Research Funding; Sierra Oncology: Consultancy, Research Funding; Novartis: Consultancy; La Jolla Pharma: Consultancy; Samus: Research Funding; Promedior: Research Funding; Constellation Pharmaceuticals: Consultancy, Research Funding; Celgene: Research Funding; CTI: Research Funding. Scherber: Incyte Corporation: Current Employment, Current holder of stock options in a privately-held company. McMullin: Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AOP Orphan: Research Funding, Speakers Bureau; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Other: clinical trial support, Research Funding. Harrison: CTI BioPharma: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Promedior: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead Sciences: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Keros: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AOP Orphan Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Shire: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sierra Oncology: Honoraria; Constellation Pharmaceuticals: Research Funding; Incyte Corporation: Speakers Bureau; Geron: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Galacteo: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

ObjectiveThe relative risk of SARS–CoV‐2 infection and COVID‐19 disease severity among people with rheumatic and musculoskeletal diseases (RMDs) compared to those without RMDs is unclear. This study was undertaken to quantify the risk of SARS–CoV‐2 infection in those with RMDs and describe clinical outcomes of COVID‐19 in these patients.MethodsWe conducted a systematic literature review using 14 databases from January 1, 2019 to February 13, 2021. We included observational studies and experimental trials in RMD patients that described comparative rates of SARS–CoV‐2 infection, hospitalization, oxygen supplementation/intensive care unit (ICU) admission/mechanical ventilation, or death attributed to COVID‐19. Methodologic quality was evaluated using the Joanna Briggs Institute critical appraisal tools or the Newcastle‐Ottawa scale. Risk ratios (RRs) and odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated, as applicable for each outcome, using the Mantel‐Haenszel formula with random effects models.ResultsOf the 5,799 abstracts screened, 100 studies met the criteria for inclusion in the systematic review, and 54 of 100 had a low risk of bias. Among the studies included in the meta‐analyses, we identified an increased prevalence of SARS–CoV‐2 infection in patients with an RMD (RR 1.53 [95% CI 1.16–2.01]) compared to the general population. The odds of hospitalization, ICU admission, and mechanical ventilation were similar in patients with and those without an RMD, whereas the mortality rate was increased in patients with RMDs (OR 1.74 [95% CI 1.08–2.80]). In a smaller number of studies, the adjusted risk of outcomes related to COVID‐19 was assessed, and the results varied; some studies demonstrated an increased risk while other studies showed no difference in risk in patients with an RMD compared to those without an RMD.ConclusionPatients with RMDs have higher rates of SARS–CoV‐2 infection and an increased mortality rate.

Abstract Background: There is suggestive evidence that inflammation is related to ovarian cancer survival. However, more research is needed to identify inflammation-related factors that are associated with ovarian cancer survival and to determine their combined effects. Methods: This analysis used pooled data on 8,147 women with invasive epithelial ovarian cancer from the Ovarian Cancer Association Consortium. The prediagnosis inflammation-related exposures of interest included alcohol use; aspirin use; other nonsteroidal anti-inflammatory drug use; body mass index; environmental tobacco smoke exposure; history of pelvic inflammatory disease, polycystic ovarian syndrome, and endometriosis; menopausal hormone therapy use; physical inactivity; smoking status; and talc use. Using Cox proportional hazards models, the relationship between each exposure and survival was assessed in 50% of the data. A weighted inflammation-related risk score (IRRS) was developed, and its association with survival was assessed using Cox proportional hazards models in the remaining 50% of the data. Results: There was a statistically significant trend of increasing risk of death per quartile of the IRRS [HR = 1.09; 95% confidence interval (CI), 1.03–1.14]. Women in the upper quartile of the IRRS had a 31% higher death rate compared with the lowest quartile (95% CI, 1.11–1.54). Conclusions: A higher prediagnosis IRRS was associated with an increased mortality risk after an ovarian cancer diagnosis. Further investigation is warranted to evaluate whether postdiagnosis exposures are also associated with survival. Impact: Given that pre- and postdiagnosis exposures are often correlated and many are modifiable, our study results can ultimately motivate the development of behavioral recommendations to enhance survival among patients with ovarian cancer.

AbstractWe aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting conversion from mild cognitive impairment (MCI) to dementia. In a sample of people with MCI (n = 415) we assessed predictive performance of ATN classification using empirical knowledge‐based cut‐offs for each component of ATN and compared it to two data‐driven approaches, logistic regression and RUSBoost machine learning classifiers, which used continuous clinical or biomarker scores. In data‐driven approaches, we identified ATN features that distinguish normals from individuals with dementia and used them to classify persons with MCI into dementia‐like and normal groups. Both data‐driven classification methods performed better than the empirical cut‐offs for ATN biomarkers in predicting conversion to dementia. Classifiers that used clinical features performed as well as classifiers that used ATN biomarkers for prediction of progression to dementia. We discuss that data‐driven modeling approaches can improve our ability to predict disease progression and might have implications in future clinical trials.

AbstractBackgroundA new trend includes taking a dedicated year away from medical school to complete a research fellowship. There is minimal data on the benefit of a gap year. We aimed to identify if a gap year makes a dermatology applicant more successful in The Match.MethodsDermatology applicants who applied to Mayo Clinic Arizona for the 2018–2019 application cycle and Mayo Clinic Rochester, Arizona, and Florida for the 2019–2020 application cycle were surveyed.ResultsIn total, 291 dermatology applicants completed the initial survey, and 236 completed the follow‐up survey. Ninety applicants took a gap year, 198 applicants did not. There was no significant difference in match rates. When comparing match rates at top dermatology residency programs, 40.6% of gap‐year applicants matched to these residencies versus 19.0% of no gap‐year applicants (P < 0.01).ConclusionApplicants should weigh the opportunity costs before pursuing research gap years as they may not be universally helpful. Applicants who want to match at a top dermatology program may benefit from a research gap year. This data may have limited generalizability outside of the United States.

AbstractIntroductionThe Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.MethodsWe identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.ResultsDisease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.DiscussionADNI has had a profound impact in improving clinical trials for AD.

The NCCN Guidelines for Older Adult Oncology address specific issues related to the management of cancer in older adults, including screening and comprehensive geriatric assessment (CGA), assessing the risks and benefits of treatment, preventing or decreasing complications from therapy, and managing patients deemed to be at high risk for treatment-related toxicity. CGA is a multidisciplinary, in-depth evaluation that assesses the objective health of the older adult while evaluating multiple domains, which may affect cancer prognosis and treatment choices. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines providing specific practical framework for the use of CGA when evaluating older adults with cancer.