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OBJECTIVES To characterize patterns of surgery among pediatric patients during terminal hospitalizations in children’s hospitals. METHODS We reviewed patients ≤20 years of age who died among 4 424 886 hospitalizations from January 2013–December 2019 within 49 US children’s hospitals in the Pediatric Health Information System database. Surgical procedures, identified by International Classification of Diseases procedure codes, were classified by type and purpose. Descriptive statistics characterized procedures, and hypothesis testing determined if undergoing surgery varied by patient age, race and ethnicity, or the presence of chronic complex conditions (CCCs). RESULTS Among 33 693 terminal hospitalizations, the majority (n = 30 440, 90.3%) of children were admitted for nontraumatic causes. Of these children, 15 142 (49.7%) underwent surgery during the hospitalization, with the percentage declining over time (P < .001). When surgical procedures were classified according to likely purpose, the most common were to insert or address hardware or catheters (31%), explore or aid in diagnosis (14%), attempt to rescue patient from mortality (13%), or obtain a biopsy (13%). Specific CCC types were associated with undergoing surgery. Surgery during terminal hospitalization was less likely among Hispanic children (47.8%; P < .001), increasingly less likely as patient age increased, and more so for Black, Asian American, and Hispanic patients compared with white patients (P < .001). CONCLUSIONS Nearly half of children undergo surgery during their terminal hospitalization, and accordingly, pediatric surgical care is an important aspect of end-of-life care in hospital settings. Differences observed across race and ethnicity categories of patients may reflect different preferences for and access to nonhospital-based palliative, hospice, and end-of-life care.

PurposeTo improve the estimation of coil sensitivity functions from limited auto‐calibration signals (ACS) in SENSE‐based reconstruction for brain imaging.MethodsWe propose to use deep learning to estimate coil sensitivity functions by leveraging information from previous scans obtained using the same RF receiver system. Specifically, deep convolutional neural networks were designed to learn an end‐to‐end mapping from the initial sensitivity to the high‐resolution counterpart. Sensitivity alignment was further proposed to reduce the geometric variation caused by different subject positions and imaging FOVs. Cross‐validation with a small set of datasets was performed to validate the learned neural network. Iterative SENSE reconstruction was adopted to evaluate the utility of the sensitivity functions from the proposed and conventional methods.ResultsThe proposed method produced improved sensitivity estimates and SENSE reconstructions compared to the conventional methods in terms of aliasing and noise suppression with very limited ACS data. Cross‐validation with a small set of data demonstrated the feasibility of learning coil sensitivity functions for brain imaging. The network learned on the spoiled GRE data can be applied to predict sensitivity functions for spin‐echo and MPRAGE datasets.ConclusionA deep learning‐based method has been proposed for improving the estimation of coil sensitivity functions. Experimental results have demonstrated the feasibility and potential of the proposed method for improving SENSE‐based reconstructions especially when the ACS data are limited.

Abstract Introduction: Despite recent treatment (tx) advances that have improved overall survival (OS), multiple myeloma (MM) remains a mostly incurable malignancy. Txs become less effective with each successive line of therapy (LOT). The rapidly evolving tx landscape has raised questions about the optimal tx sequence; recent trial data may be useful to develop and test hypotheses about tx sequencing. However, real-world (RW) data have shown that while most patients (pts) with newly diagnosed MM (NDMM) receive first-line (1L) tx, attrition rates are high in later LOT, with up to 50% of transplant ineligible (TIE) pts receiving no tx beyond 1L. As many pts will not have an opportunity to be salvaged with later LOT, achieving the longest possible progression-free survival (PFS) upfront is critical. In the MAIA trial, daratumumab (DARA), lenalidomide, and dexamethasone (D-Rd) demonstrated significant PFS (hazard ratio [HR]=0.53, P<0.0001) and OS (HR=0.68, P=0.0013) benefit vs Rd. In a subgroup of pts ≥65 years old in the SWOG S0777 study, bortezomib, lenalidomide, and dexamethasone (VRd) had no significant OS benefit vs Rd. In the absence of head-to-head studies comparing different tx sequences, clinical trial data can be used to explore different scenarios. Published models suggest that delaying DARA until later LOT is a viable tx strategy. Limitations of these models include basing conclusions on tx sequences other than those recommended in clinical guidelines, not accounting for the attrition rates from 1L to subsequent LOT and relying on inappropriate clinical inputs for second-line (2L) tx and beyond. While addressing those limitations, we examined different clinical scenarios to explore the clinical value of using DARA first vs saving it until a later LOT. Methods: We examined 3 potential clinical tx sequences using a lifetime three-health state clinical simulation to estimate survival outcomes in pts with TIE NDMM. The 3 health states were: 1L (time on tx + any subsequent tx-free interval), 2L (time on 2L + any subsequent LOT), and death. Tx sequences were reflective of clinical guidelines: D-Rd in 1L followed by pomalidomide (POM)- or carfilzomib (CAR)-based regimens in 2L and VRd or Rd in 1L followed by DARA-based regimens in 2L (Figure). Pt characteristics were assumed to be similar to those of pts in MAIA. Time spent in the 1L health state with D-Rd and Rd was based on time to next tx or death (TTNT) curves derived from MAIA; TTNT curves with VRd were derived by applying PFS HRs estimated in the PEGASUS study. The attrition rate for the base case analysis (58.8%) was based on MAIA data. Although similar to published studies, it includes pts who were lost to follow-up and may be an overestimate. A sensitivity analysis evaluated a more conservative estimate (27.2%) excluding censored pts. Attrition rates were assumed to be the same across tx arms. Time spent in the 2L health state with DARA-based and POM- or CAR-based regimens was based on RW OS curves derived from the nationwide EHR-derived deidentified Flatiron Health database. RW data were used as pts in published 2L trials are younger than in MAIA and many have had prior ASCT. We evaluated median OS and 5- and 10-year survival rates. Results: Using D-Rd in 1L improved median OS by 2.5 or 3.5 years compared with delaying DARA-based regimens until 2L after VRd or Rd, respectively (Table). The probability of being alive at 5 and 10 years was higher with D-Rd than with VRd or Rd as initial therapy (Table). Changes in attrition rates impacted the expected median OS, but the absolute difference in median OS was maintained, with an incremental OS benefit consistently >2 years. Conclusions: Unlike previous attempts to examine outcomes with different tx sequences, we incorporated clinically representative sequences for pts with NDMM, included attrition rates, and explored their impact on OS with different tx sequences. Our approach shows that achieving the longest possible PFS in 1L drives OS outcomes. Saving agents until later LOT does not take into account that many pts do not reach 2L, as evidenced by published data on attrition rates in MM. Our findings show that in pts with TIE NDMM, starting with D-Rd may provide up to 3.5 years of additional OS gain with the currently available 2L tx. This gain could increase with new 2L agents currently in development, reinforcing the importance of using the best agents first, to increase the probability of pts benefitting from tx currently in development. Figure 1 Figure 1. Disclosures Fonseca: Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Janssen: Consultancy; GSK: Consultancy; OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Aduro: Consultancy; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; AbbVie: Consultancy; Mayo Clinic in Arizona: Current Employment; BMS: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Bayer: Consultancy; Novartis: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Juno: Consultancy; Takeda: Consultancy; Merck: Consultancy. Hashim: Johnson and Johnson: Current Employment. Nair: Janssen Research & Development: Current Employment. He: Janssen: Current Employment, Current equity holder in publicly-traded company. Ammann: Janssen: Current Employment, Current equity holder in publicly-traded company. Lam: Janssen: Current Employment, Current equity holder in publicly-traded company. Wildgust: Janssen: Current Employment, Current equity holder in publicly-traded company. Kumar: Beigene: Consultancy; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Amgen: Consultancy, Research Funding; Antengene: Consultancy, Honoraria; Roche-Genentech: Consultancy, Research Funding; Tenebio: Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Consultancy; Bluebird Bio: Consultancy; BMS: Consultancy, Research Funding; Carsgen: Research Funding; Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Abstract Background: Sickle cell disease (SCD) is a group of genetic disorders in which sickle hemoglobin polymerizes under deoxygenation, altering red blood cell (RBC) morphology and behavior. The properties of sickle RBCs contribute to increased viscosity of blood and occurrence of vaso-occlusions, a major aspect of SCD pathophysiology. Voxelotor is a novel FDA-approved treatment for SCD which modulates hemoglobin O 2 affinity, and while its known mechanism inhibits sickle polymerization, the impact on other aspects of SCD pathophysiology remain unknown. Thus, despite the new treatment option, highly variable clinical manifestation continues to be a hallmark of sickle cell and there is consequently a need to optimize the use of current therapies based on patient-specific factors. In this work, we leverage datasets generated from a unique microfluidic assay that measures blood flow behavior under varying oxygen tension in conjunction with novel statistical approaches to model and assess sources of variability in sickle blood flow response to voxelotor. Methods: RBCs from patients with SCD (n=28) were treated with voxelotor at 500 uM concentration. Treated samples and untreated controls were perfused through a microfluidic platform that dynamically modulates oxygen tension and measures flow velocity (Wood et al, 2012; Valdez et al, 2019). The area between curves (ABC) of the normalized velocity across the range of oxygen tension between treated and control conditions was calculated to quantify the effect of voxelotor for each sample (figure 1). A paired t-test was used to assess the difference in response between treated and untreated samples. Where available, clinical data including the hemoglobin fractions and complete blood count (CBC) were collected for each sample as predictor variables, and partial least squares regression (PLSR) modeling was used to assess the correlation of predictors and responses. Results: Voxelotor increased the velocity ABC from untreated to treated conditions (p<.0001). We observed that there were differences in response for velocity ABC between sickle cell genotypes (figure 2). Thus, generating separate PLSR models for distinct SCD genotypes revealed differences in sets of clinical factors that explained the most variance in response to voxelotor treatment. A 2-component model was constructed for the HbSC samples (n=6) that best explained variance in the data and had good predictive abilities (R 2X=.69, R 2Y=.97, Q 2=.74). Within this subset, clustering of variables related to hemolysis and inflammation were observed (figure 3). An equivalent model constructed for the HbSS samples (n=15) characterized the predictor variables but lacked predictive power of the response (R 2X=.74, R 2Y=.25, Q 2=.-0.21). Response to voxelotor for this model was most strongly correlated with HbA. Due to low sample size (n=2 samples with full set of predictors), predictive modeling was not performed for HbSβ 0 samples, however, these samples responded the least to voxelotor treatment. Conclusions: Our analysis quantified patient-specific differences in the blood flow response to voxelotor, showing a wide variability in response despite treatment by the same drug concentration. Genotype-specific multivariable models that take into account easily measurable clinical variables such as the CBC have the potential to explain the variability in patient response to voxelotor treatment. In HbSC samples, the WBC, platelet, and reticulocyte counts were highly correlated and strong predictors of response to voxelotor, which may point to markers of hemolysis and inflammation being useful in determining patients that can be optimally treated with this drug. In HbSS, response to voxelotor was mainly inversely correlated with HbA levels, which is a surrogate marker for blood transfusions, indicating that the effect of voxelotor is lessened for patients who are receiving transfusions. However, the low R2Y of this model highlights the clinical variability in this SCD genotype and consequent need for additional biomarkers of disease severity. In conclusion, our hybrid experimental-computational approach is able to identify clinical factors that highly impact the response of patient blood samples to treatment with voxelotor for HbSC patients, and highlights the need for precision therapy recommendations in SCD. Figure 1 Figure 1. Disclosures Lam: Sanguina, Inc.: Current holder of individual stocks in a privately-held company. Kemp: Parthenon Therapeutics: Membership on an entity's Board of Directors or advisory committees.

Abstract Purpose: To evaluate the association between neutrophil-to-lymphocyte ratio (NLR) and efficacy of avelumab plus axitinib or sunitinib. Experimental Design: Adult patients with untreated advanced renal cell carcinoma (RCC) with a clear-cell component, ≥1 measurable lesions, Eastern Cooperative Oncology Group performance status of 0 or 1, fresh or archival tumor specimen, and adequate renal, cardiac, and hepatic function were included. Retrospective analyses of the association between baseline NLR and progression-free survival (PFS) and overall survival (OS) in the avelumab plus axitinib or sunitinib arms were performed using the first interim analysis of the phase 3 JAVELIN Renal 101 trial (NCT02684006). Multivariate Cox regression analyses of PFS and OS were conducted. Translational data were assessed to elucidate the underlying biology associated with differences in NLR. Results: Patients with below-median NLR had longer observed PFS with avelumab plus axitinib [stratified HR, 0.85; 95% confidence interval (CI), 0.634–1.153] or sunitinib (HR, 0.56; 95% CI, 0.415–0.745). In the avelumab plus axitinib or sunitinib arms, respectively, median PFS was 13.8 and 11.2 months in patients with below-median NLR, and 13.3 and 5.6 months in patients with median-or-higher NLR. Below-median NLR was also associated with longer observed OS in the avelumab plus axitinib (HR, 0.51; 95% CI, 0.300–0.871) and sunitinib arms (HR, 0.30; 95% CI, 0.174–0.511). Tumor analyses showed an association between NLR and key biological characteristics, suggesting a role of NLR in underlying mechanisms influencing clinical outcome. Conclusions: Current data support NLR as a prognostic biomarker in patients with advanced RCC receiving avelumab plus axitinib or sunitinib.

ObjectiveEpalrestat, an aldose reductase inhibitor increases phosphomannomutase (PMM) enzyme activity in a PMM2‐congenital disorders of glycosylation (CDG) worm model. Epalrestat also decreases sorbitol level in diabetic neuropathy. We evaluated the genetic, biochemical, and clinical characteristics, including the Nijmegen Progression CDG Rating Scale (NPCRS), urine polyol levels and fibroblast glycoproteomics in patients with PMM2‐CDG.MethodsWe performed PMM enzyme measurements, multiplexed proteomics, and glycoproteomics in PMM2‐deficient fibroblasts before and after epalrestat treatment. Safety and efficacy of 0.8 mg/kg/day oral epalrestat were studied in a child with PMM2‐CDG for 12 months.ResultsPMM enzyme activity increased post‐epalrestat treatment. Compared with controls, 24% of glycopeptides had reduced abundance in PMM2‐deficient fibroblasts, 46% of which improved upon treatment. Total protein N‐glycosylation improved upon epalrestat treatment bringing overall glycosylation toward the control fibroblasts’ glycosylation profile. Sorbitol levels were increased in the urine of 74% of patients with PMM2‐CDG and correlated with the presence of peripheral neuropathy, and CDG severity rating scale. In the child with PMM2‐CDG on epalrestat treatment, ataxia scores improved together with significant growth improvement. Urinary sorbitol levels nearly normalized in 3 months and blood transferrin glycosylation normalized in 6 months.InterpretationEpalrestat improved PMM enzyme activity, N‐glycosylation, and glycosylation biomarkers in vitro. Leveraging cellular glycoproteome assessment, we provided a systems‐level view of treatment efficacy and discovered potential novel biosignatures of therapy response. Epalrestat was well‐tolerated and led to significant clinical improvements in the first pediatric patient with PMM2‐CDG treated with epalrestat. We also propose urinary sorbitol as a novel biomarker for disease severity and treatment response in future clinical trials in PMM2‐CDG. ANN NEUROL 20219999:n/a–n/a

Transmission electron microscopy (TEM) is widely used as an imaging modality to provide high-resolution details of subcellular components within cells and tissues. Mitochondria and endoplasmic reticulum (ER) are organelles of particular interest to those investigating metabolic disorders. A straightforward method for quantifying and characterizing particular aspects of these organelles would be a useful tool. In this protocol, we outline how to accurately assess the morphology of these important subcellular structures using open source software ImageJ, originally developed by the National Institutes of Health (NIH). Specifically, we detail how to obtain mitochondrial length, width, area, and circularity, in addition to assessing cristae morphology and measuring mito/endoplasmic reticulum (ER) interactions. These procedures provide useful tools for quantifying and characterizing key features of sub-cellular morphology, leading to accurate and reproducible measurements and visualizations of mitochondria and ER.