Lopez, Camden

Background and Purpose: Less is known about the risk factors and outcomes associated with stroke in the current era of increasing heart transplantation (HT) being performed in older patients. The impact of immunosuppression on risk of stroke has not yet been previously studied. We aimed to determine the incidence, risk factors and outcomes of stroke after HT. Methods: We retrospectively analyzed the incidence of ischemic and hemorrhagic strokes and associated outcomes in all consecutive HT recipients transplanted between 1994 and 2016 at a single institution. Results: Of 529 patients who underwent HT, 57 (10.7%) developed stroke, 8.1% had an ischemic events and (2.6%) had a hemorrhagic stroke. Age at HT (adjusted hazard ratio [HR] 1.33; P =0.03) and diabetes (HR, 2.60; P =0.02) were associated with increased risk of ischemic events. Patients with stroke (any type) were more likely to have worse kidney function (HR, 1.81; P =0.02) whereas patients with ischemic events were more likely to undergo combined organ transplantation (HR, 2.01; P =0.05). Cytomegalovirus infection was found to be associated with increased risk of any stroke (HR, 2.09; P =0.02).Conversion from calcineurin inhibitor to sirolimus-based immunosuppression was not found to be associated with a significant change in stroke risk (HR, 1.39; P =0. 45) compared with calcineurin inhibitor maintenance therapy. Stroke of any type and ischemic events were independently associated with increased risk of death (HR, 1.90; P =0.001 and HR, 2.14; P <0.001, respectively). Conclusions: Stroke after HT is associated with increased mortality. Older age at HT, diabetes, renal dysfunction, and CMV infection were associated with greater risk of stroke.

Introduction: An artificial intelligence (AI) algorithm applied to electrocardiography (ECG) during normal sinus rhythm (NSR) has been shown to predict concomitant atrial fibrillation (AF). We sought to characterize the value of AI-ECG as a predictor of future AF and assess its performance compared to other clinical prediction scores in a population-based sample. Methods: We calculated the AI-ECG probability during NSR in patients who enrolled in the population-based Mayo Clinic Study of Aging with at least one ECG in NSR within two years prior and no history of AF at the time of the baseline study visit. The cumulative incidence of AF was estimated for strata defined by AI-ECG probability and CHARGE-AF score. Cox proportional hazards were fit to assess the independent prognostic value and interaction of AI-ECG probability and CHARGE-AF score. Concordance (c) statistics were calculated for AI-ECG probability, CHARGE-AF score and combined AI-ECG and CHARGE-AF score. Results: A total of 1,936 patients with a median age 75.8 (quartile range [QR] 70.4, 81.8) years, median CHARGE-AF score 14.0 (QR 13.2, 14.7) and median CHADS2VASC score 3 (QR 2, 4) were included in the analysis. The cumulative incidence of AF increased in a stepwise fashion across quartiles of AI-ECG probability and CHARGE-AF score (Figure 1). When compared in the same model, both AI-ECG probability (hazard ratio [HR] 1.76, 95% confidence interval [CI] 1.51-2.04) and CHARGE-AF score (HR 1.90, 95% CI 1.58-2.28) independently predicted AF without significant interaction (p=0.54). C statistics were 0.69 (95% CI 0.66-0.72) for AI-ECG probability, 0.69 (95% CI 0.66-0.71) for CHARGE-AF and 0.72 (95% CI 0.69-0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the Mayo Clinic Study of Aging, both the AI-ECG probability and CHARGE-AF score independently predicted time to AF. The AI-ECG may offer a means to assess risk with a single test without requiring manual or automated clinical data abstraction.

Background: Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG AF score is associated with presence of cerebral infarcts. Methods: Participants from a population-based study ages 30 to 95 years with T2 fluid attenuation inversion recovery (FLAIR) MRI obtained between October 10, 2011, and November 2, 2017 were considered for inclusion. Participants without ECG were excluded. AI-ECG score was calculated using most recent ECG with normal sinus rhythm at the time of MRI. Presence of infarcts was determined on FLAIR MRI scans. Logistic regression was run to evaluate the relationship between AI-ECG AF score and presence of cerebral infarcts. Similar analyses were performed using history of AF rather than AI-ECG AF score as predictor. Age and sex were included as covariates. We also examined whether a high-threshold AI-ECG score was associated with infarcts. In a prior study, an AI-ECG AF score > 0.5 was associated with a cumulative incidence of AF of 21.5% at 2 years and 52.2% at 10 years. Results: This study included 1,373 individuals. Average age was 69.6 years and 53% of participants were male. There were 136 (10%) individuals with ECG-confirmed AF; 1237 (90%) participants had no AF history. Of participants with AF, 23% (n=31) were on anticoagulation, 47% (n=64) were on antiplatelet and 18% (n=24) were on dual therapy. Only 1.3% (n=16) of patients without AF were on anticoagulation and 47% (n=578) were on antiplatelet therapy. Ischemic infarcts were detected in 214 (15.6%) patients. As a continuous measure AI-ECG was associated with infarcts but not after adjusting for age and sex (p=0.46). AI-ECG AF score > 0.5 was associated with infarcts ( p < 0.001); even after adjusting for age and sex ( p = 0.03). History of AF was also associated with infarcts after adjusting for age and sex ( p = 0.018). Conclusion: AI-ECG AF score and history of AF were associated with presence of cerebral infarcts after adjusting for age and sex. This tool could be useful in select patients with cryptogenic stroke but further investigation would be required.

Chemotherapy-induced peripheral neuropathy (CIPN) is a common and potentially permanent adverse effect of chemotherapeutic agents including taxanes such as paclitaxel and platinum-based compounds such as oxaliplatin and carboplatin. Previous studies have suggested that genetics may impact the risk of CIPN. We conducted genome-wide association studies (GWASs) for CIPN in two independent populations who had completed European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ)-CIPN20 assessments (a CIPN-specific 20-item questionnaire which includes three scales that evaluate sensory, autonomic, and motor symptoms). The study population N08Cx included 692 participants from three clinical trials (North Central Cancer Treatment Group (NCCTG) N08C1, N08CA, and N08CB) who had been treated with paclitaxel, paclitaxel plus carboplatin, or oxaliplatin. The primary endpoint for the GWAS was the change from pre-chemotherapy CIPN20 sensory score to the worse score over the following 18 weeks. Study population The Mayo Clinic Breast Disease Registry (MCBDR) consisted of 381 Mayo Clinic Breast Disease Registry enrollees who had been treated with taxane or platinum-based chemotherapy. The primary endpoint for the GWAS assessed was the earliest CIPN20 sensory score available after the completion of chemotherapy. In multivariate model analyses, chemotherapy regimen (p = 3.0 × 10−8) and genetic ancestry (p = 0.007) were significantly associated with CIPN in the N08Cx population. Only age (p = 0.0004) was significantly associated with CIPN in the MCBDR population. The SNP most associated with CIPN was rs56360211 near PDE6C (p =7.92 × 10−8) in N08Cx and rs113807868 near TMEM150C in the MCBDR (p = 1.27 × 10−8). Due to a lack of replication, we cannot conclude that we identified any genetic predictors of CIPN.

Health-related quality of life (HRQOL) is an important prognostic patient-reported outcome in oncology. Because prior studies suggest that HRQOL is, in part, heritable, we performed a GWAS to elucidate genetic factors associated with HRQOL in breast cancer survivors. Physical and mental HRQOL were measured via paper surveys that included the PROMIS-10 physical and mental health domain scales in 1442 breast cancer survivors participating in the Mayo Clinic Breast Disease Registry (MCBDR). In multivariable regression analyses, age and financial concerns were significantly associated with global physical health (age: p = 1.6 × 10−23; financial concerns: p = 4.8 × 10−40) and mental health (age: p = 3.5 × 10−7; financial concerns: p = 2.0 × 10−69). Chemotherapy was associated with worse global mental health (p = 0.01). In the GWAS, none of the SNPs reached the genome-wide association significance threshold of 5 × 10−8 for associations with either global physical or global mental health, however, a cluster of SNPs in SCN10A, particularly rs112718371, appeared to be linked to worse global physical health (p = 5.21 × 10−8). Additionally, SNPs in LMX1B, SGCD, PARP12 and SEMA5A were also moderately associated with worse physical and mental health (p < 10−6). These biologically plausible candidate SNPs warrant further study as possible predictors of HRQOL.

AbstractThree sets of criteria (International Society of Amyloidosis [ISA], Palladini and Kastritis) were independently developed for staging, progression and response criteria to predict renal survival in patients with AL amyloidosis. We evaluated these criteria using a cohort of 495 newly diagnosed AL amyloidosis patients with renal involvement using time to event competing risk analysis at baseline, 3, 6 and 12 months after treatment. Only Palladini and Kastritis had a staging system and both predicted a higher risk of end stage renal disease (ESRD) in the stage III vs stage I patients but only the Palladini model was predictive for stage II patients. At 3 months, risk of ESRD was significantly higher for Palladini and ISA renal progression (hazard ratio [HR] 2.8 [95% CI: 1.5–5.3, p = .001] and 2.5 [CI: 1.4–4.6, p = .004, respectively]), but renal response was not significantly protective; conversely, the risk of ESRD was not significantly higher for the Kastritis renal progression, but was significantly protective for the Kastritis renal responders (HR 0.38 [95% CI: 0.17–0.84], p = .017). Both progression and response with ISA, Palladini and Kastritis criteria were predictive of ESRD at 6 months and 12 months. While the Palladini staging criteria at baseline, and the ISA and Palladini criteria for progression at 3 months performed better than the Kastritis criteria at baseline and 3 months post‐treatment, the Kastritis criteria performed better for response 3 months after treatment. All three sets of criteria performed well at and after 6 months post‐treatment. These differences are important when choosing endpoints for clinical trials.

Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.

Magnesium is an essential element that is involved in the regulation of cell growth, division and differentiation. Epidemiologic studies have demonstrated that a diet poor in magnesium is associated with an increased risk of developing cancer. There are few studies investigating whether magnesium deficiency is associated with a poor prognosis in lymphomas. The recent discovery of X-linked immunodeficiency with magnesium defect, Epstein-Barr virus, and neoplasia (XMEN) disease, suggests a role of magnesium in the development of hematologic malignancies and lymphocyte function. Furthermore, a recently published study found that hypomagnesemia prior to chemotherapy is associated with inferior overall survival (OS) in patients with Burkitt Lymphoma. We hypothesized that low magnesium levels prior to autologous hematopoietic stem cell transplant (AHSCT) may lead to inferior outcomes via impaired lymphocyte recovery. We evaluated whether low serum magnesium levels were associated with inferior survival in patients with relapsed or refractory diffuse large B cell lymphoma (DLBCL) who underwent autologous hematopoietic stem cell transplant (AHSCT) between January 1998 and May 2020 using a retrospective study design. We cross-referenced our search with patients who had a magnesium level as part of their pre-transplant workup between 7 and 28 days prior to their first transplant. Two-sided Wilcoxon rank sum test and Chi square/Fischer's exact test were used to compare the continuous and categorical variables, respectively. Kaplan-Meier and log-rank tests were used to perform all time to event analysis. Hazard ratios (HR) with confidence intervals (CI) were calculated using Cox-proportional hazards. Our study included 581 patients of which 82 (14.1%) had hypomagnesemia (magnesium &lt; 1.7 mg/dL) prior to AHSCT. Our study found that hypomagnesemia was associated with inferior OS and event-free survival (EFS) defined as re-transplant, relapse or death, compared to those with a normal magnesium level; median OS: 7.3 years (95% CI: 2.91 - upper limit not estimable) versus 9.7 years (95% CI: 6.92 - 12.3 years), p=0.0041; and median EFS: 3.9 years (95% CI: 1.63 - 8.98 years) versus 6.29 years (95% CI: 4.73 - 8.95 years), p = 0.016 respectively(B). Higher serum magnesium (HR 0.199 per doubling, 95% CI 0.078 - 0.503; p&lt;0.001) and higher albumin (HR 0.090 per doubling, 95% CI 0.021 - 0.394; p=0.001) were associated with lower mortality rates, and higher creatinine (HR 1.669 per doubling, 95% CI 1.03 - 2.52; p=0.04) was associated with greater mortality up to 1 year post-transplantation. In a multivariate Cox regression excluding albumin, hypomagnesemia (p &lt; 0.001) and elevated creatinine (p = 0.038) were independent predictors of poor overall survival(A). There was no significant difference between day 15 absolute lymphocyte count between patients with normal magnesium levels and low serum magnesium levels prior to their conditioning regimen. Although we hypothesized that we would find a significant difference between 15 day absolute lymphocyte count recovery and hypomagnesemia, this was not apparent. However, magnesium is an important nutrient for immune function and immune recovery is important after the bone marrow is restored post ablative therapy. Thus, low serum magnesium levels may lead to impaired OS and EFS via impaired lymphocyte function. In vitro studies will be necessary to understand this mechanism further. Magnesium may simply be a marker of illness and replacement of magnesium may not improve outcomes. However, further investigations are needed to determine whether outcomes would improve with replacement of magnesium in patients with hypomagnesemia, which our study did not address. Overall, our preliminary data suggests that hypomagnesemia is an independent prognostic factor and predictor of inferior OS and EFS in this cohort of patients. Figure Disclosures Ansell: ADC Therapeutics:Research Funding;AI Therapeutics:Research Funding;Trillium:Research Funding;Affimed:Research Funding;Regeneron:Research Funding;Seattle Genetics:Research Funding;Bristol Myers Squibb:Research Funding;Takeda:Research Funding.Witzig:Spectrum:Consultancy;Celgene:Consultancy, Research Funding;Immune Design:Research Funding;Karyopharm Therapeutics:Research Funding;Acerta:Research Funding;Incyte:Consultancy;AbbVie:Consultancy;MorphSys:Consultancy.

AbstractBackgroundEligibility criteria and endpoints for cancer cachexia trials—and whether weight loss should be included—remain controversial. Although most cachexia trials enrol patients after initial cancer diagnosis, few studies have addressed whether weight loss well after a cancer diagnosis is prognostic.MethodsWe pooled data from non‐small cell lung cancer patients from prospectively conducted trials within the Alliance for Clinical Trials in Oncology (1998–2008), a nationally funded infrastructure. We examined (i) weight data availability and weight changes and (ii) survival.ResultsA total of 822 patients were examined. Of these, 659 (80%) were on treatment at the beginning of Cycle 2 of chemotherapy; weight was available for 656 (80%). By Cycles 3 and 4, weight was available for 448 (55%) and 384 (47%), respectively. From baseline to immediately prior to Cycle 2, 208 (32%) gained weight; 225 (34%) lost <2% of baseline weight; and 223 (34% of 656) lost 2% or more. Median survival from the beginning of Cycle 2 was 13.0, 10.9, and 6.9 months for patients with weight gain, weight loss of <2%, and weight loss of 2% or more, respectively. In multivariate analyses, adjusted for age, sex, performance score, type of treatment, and body mass index, weight loss of 2% or more was associated with poor overall survival compared with weight gain [hazard ratio (HR) = 1.66; 95% confidence interval (CI): 1.33–2.07; P < 0.001] and compared with weight loss of <2% (HR = 1.57; 95% CI: 1.27–1.95; P < 0.001). Although weight loss of <2% was not associated with poorer overall survival compared with weight gain, it was associated with poorer progression‐free survival (HR = 1.24; 95% CI: 1.01–1.51; P = 0.036). Similar findings were observed in a separate 255‐patient validation cohort.ConclusionsWeight should be integrated into cancer cachexia trials because of its ease of frequent measurement and sustained prognostic association.

Significance Statement The quality of a kidney obtained from a living donor is often inferred from the donor’s age, risk factors, and kidney function. Little is known about the influence of a donated kidney’s structural features on the risk of death-censored graft failure in the recipient. In an analysis of 2293 kidney donor-recipient pairs, the authors identified subclinical nephrosclerosis, larger nephron size (but not nephron number), and smaller medullary volume as structural predictors of death-censored graft failure that were independent of both donor and recipient clinical characteristics. These findings provide important insights into the factors that define the “intrinsic quality” of the living kidney donor graft at the time of donation, and support use of intraoperative biopsies to identify donor kidneys that are at higher risk for failure. Background Nephrosclerosis, nephron size, and nephron number vary among kidneys transplanted from living donors. However, whether these structural features predict kidney transplant recipient outcomes is unclear. Methods Our study used computed tomography (CT) and implantation biopsy to investigate donated kidney features as predictors of death-censored graft failure at three transplant centers participating in the Aging Kidney Anatomy study. We used global glomerulosclerosis, interstitial fibrosis/tubular atrophy, artery luminal stenosis, and arteriolar hyalinosis to measure nephrosclerosis; mean glomerular volume, cortex volume per glomerulus, and mean cross-sectional tubular area to measure nephron size; and calculations from CT cortical volume and glomerular density on biopsy to assess nephron number. We also determined the death-censored risk of graft failure with each structural feature after adjusting for the predictive clinical characteristics of donor and recipient. Results The analysis involved 2293 donor-recipient pairs. Mean recipient follow-up was 6.3 years, during which 287 death-censored graft failures and 424 deaths occurred. Factors that predicted death-censored graft failure independent of both donor and recipient clinical characteristics included interstitial fibrosis/tubular atrophy, larger cortical nephron size (but not nephron number), and smaller medullary volume. In a subset with 12 biopsy section slides, arteriolar hyalinosis also predicted death-censored graft failure. Conclusions Subclinical nephrosclerosis, larger cortical nephron size, and smaller medullary volume in healthy donors modestly predict death-censored graft failure in the recipient, independent of donor or recipient clinical characteristics. These findings provide insights into a graft’s “intrinsic quality” at the time of donation, and further support the use of intraoperative biopsies to identify kidney grafts that are at higher risk for failure.