First Experience With Augmented Reality Guidance for Cerebral Embolic Protection During TAVR.

Background: Augmented reality (AR) guidance holds potential to improve transcatheter interventions by enabling visualization of and interaction with patient-specific 3-dimensional virtual content. Positioning of cerebral embolic protection devices (CEP) during transcatheter aortic valve replacement (TAVR) increases patient exposure to radiation and iodinated contrast, and increases procedure time. AR may enhance procedural guidance and facilitate a safer intervention. Objectives: The purpose of this study was to develop and test a novel AR guidance system with a custom user interface that displays virtual, patient-specific 3-dimensional anatomic models, and assess its intraprocedural impact during CEP placement in TAVR. Methods: Patients undergoing CEP during TAVR were prospectively enrolled and assigned to either AR guidance or control groups. Primary endpoints were contrast volume used prior to filter placement, times to filter placement, and fluoroscopy time. Postprocedure questionnaires were administered to assess intraprocedural physician experience with AR guidance. Results: A total of 24 patients presenting for TAVR were enrolled in the study (12 with AR guidance and 12 controls). AR guidance eliminated the need for aortic arch angiograms prior to device placement thus reducing contrast volume (0 mL vs 15 mL, P < 0.0001). There was no significant difference in the time required for filter placement or fluoroscopy time. Postprocedure questionnaires indicated that AR guidance increased confidence in wiring of the aortic arch and facilitated easier device placement. Conclusions: We developed a novel AR guidance system that eliminated the need for additional intraprocedural angiograms prior to device placement without any significant difference in time to intervention and offered a subjective improvement in performance of the intervention.

OptimICE-RD: sacituzumab govitecan + pembrolizumab vs pembrolizumab (± capecitabine) for residual triple-negative breast cancer.

Patients with early-stage triple-negative breast cancer (TNBC) with residual invasive disease after neoadjuvant therapy have a high risk of recurrence even with neoadjuvant and adjuvant treatment with pembrolizumab. Sacituzumab govitecan, a Trop-2-directed antibody-drug conjugate with a topoisomerase I inhibitor payload, improved progression-free survival (PFS) and overall survival (OS) versus chemotherapy in patients with pre-treated metastatic TNBC. Moreover, preclinical data suggest that topoisomerase I inhibitors may enhance the effects of immune checkpoint inhibitors through activation of the cGAS-STING pathway. Here we describe the international randomized phase III AFT-65/ASCENT-05/OptimICE-RD trial, which evaluates the efficacy and safety of sacituzumab govitecan plus pembrolizumab versus treatment of physician's choice (pembrolizumab ± capecitabine) among patients with early-stage TNBC with residual invasive disease after neoadjuvant therapy.Clinical Trial Registration: NCT05633654 (ClinicalTrials.gov)Other Study ID Number(s): Gilead Study ID: GS-US-595-6184Registration date: 1 December 2022Study start date: 12 December 2022Recruitment status: Recruiting.

AFT-65/ASCENT-05/OptimICE-RD is an ongoing clinical trial that is testing a new treatment combination for patients with stage II or III triple-negative breast cancer (TNBC). Stage II­III means the cancer is confined to the breast and/or nearby lymph nodes and can be surgically removed. However, there remains a risk that the cancer could recur after surgery. To reduce this risk, patients with stage II­III TNBC receive anti-cancer medication before and after surgery. For some patients, receipt of anti-cancer medication before surgery produces a pathologic complete response (pCR), meaning there is no observable cancer left behind at surgery. Patients with a pCR have a lower risk of recurrence than patients with residual disease.The AFT-65/ASCENT-05/OptimICE-RD trial includes people with stage II-III TNBC who have residual cancer after completing their course of pre-surgery anti-cancer medication. All participants have any remaining cancer in their breast and/or lymph nodes removed surgically, after which they are randomly assigned to receive one of two treatments. The experimental therapy consists of pembrolizumab along with a medication called sacituzumab govitecan, which kills cancer cells directly and may strengthen the anti-cancer immune response. Pembrolizumab strengthens the anti-cancer immune response, so the hypothesis of this trial is that the two medications will be more effective together. The control therapy consists of pembrolizumab, alone or in combination with a chemotherapy medication called capecitabine, which is the current standard of care. To study the effectiveness of each treatment, the researchers are following up with all participants to learn if and when their breast cancer returns.

Outcomes in Patients With von Willebrand Disease Receiving Recombinant von Willebrand Factor on Demand and in Surgical Settings: Chart Review.

This European observational chart review assessed the efficacy/safety of recombinant von Willebrand factor (rVWF) for on-demand treatment of spontaneous/traumatic bleeds and prevention and/or treatment of surgery-related bleeding in adults with von Willebrand disease (VWD). Patients (n = 91) were enrolled at first rVWF administration (index). Data were collected for the 12 months before index and until death, loss to follow-up, or end of study (3-12 months after index). Fifteen patients reported an rVWF-treated spontaneous/traumatic bleed at index. Bleed resolution was obtained for 14 patients (unknown status, n = 1), and investigators assessed treatment satisfaction for 13 rVWF prescriptions (2 moderate, 5 good, and 6 excellent). rVWF was used to prevent/treat surgery-related bleeds at index in 76 patients. Bleed resolution was achieved in 25/58 rVWF-treated surgeries; bleed resolution was not applicable for 33 surgeries. In both groups, there were no reports of treatment-emergent adverse events after initiating rVWF, including hypersensitivity reactions, thrombotic events, and VWF inhibitor development. rVWF was shown to be effective for the on-demand treatment of spontaneous/traumatic bleeds, and for the prevention and treatment of surgical bleeds in this real-world VWD population.

Emerging and Evolving Concepts in Cancer Immunotherapy Imaging.

Criteria based on measurements of lesion diameter at CT have guided treatment with historical therapies due to the strong association between tumor size and survival. Clinical experience with immune checkpoint modulators shows that editing immune system function can be effective in various solid tumors. Equally, novel immune-related phenomena accompany this novel therapeutic paradigm. These effects of immunotherapy challenge the association of tumor size with response or progression and include risks and adverse events that present new demands for imaging to guide treatment decisions. Emerging and evolving approaches to immunotherapy highlight further key issues for imaging evaluation, such as dissociated response following local administration of immune checkpoint modulators, pseudoprogression due to immune infiltration in the tumor environment, and premature death due to hyperprogression. Research that may offer tools for radiologists to meet these challenges is reviewed. Different modalities are discussed, including immuno-PET, as well as new applications of CT, MRI, and fluorodeoxyglucose PET, such as radiomics and imaging of hematopoietic tissues or anthropometric characteristics. Multilevel integration of imaging and other biomarkers may improve clinical guidance for immunotherapies and provide theranostic opportunities.

Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.

Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology's role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57-180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10-16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.

Assessing Pathologic Response in Resected Lung Cancers: Current Standards, Proposal for a Novel Pathologic Response Calculator Tool, and Challenges in Practice.

The efficacy of neoadjuvant treatment for NSCLC can be pathologically assessed in resected tissue. Major pathologic response (MPR) and pathologic complete response (pCR), defined as less than or equal to 10% and 0% viable tumor cells, respectively, are increasingly being used in NSCLC clinical trials to establish them as surrogate end points for efficacy to shorten time to outcome. Nevertheless, sampling and MPR calculation methods vary between studies. The International Association for the Study of Lung Cancer recently published detailed recommendations for pathologic assessment of NSCLC after neoadjuvant treatment, with methodology being critical. To increase methodological rigor further, we developed a novel MPR calculator tool (MPRCT) for standardized, comprehensive collection of percentages of viable tumor, necrosis, and stroma in the tumor bed. In addition, tumor width and length in the tumor bed are measured and unweighted and weighted MPR averages are calculated, the latter to account for the varying proportions of tumor beds on slides. We propose sampling the entire visible tumor bed for tumors having pCR regardless of size, 100% of tumors less than or equal to 3 cm in diameter, and at least 50% of tumors more than 3 cm. We describe the uses of this tool, including potential formal analyses of MPRCT data to determine the optimum sampling strategy that balances sensitivity against excessive use of resources. Solutions to challenging scenarios in pathologic assessment are proposed. This MPRCT will facilitate standardized, systematic, comprehensive collection of pathologic response data with a standardized methodology to validate studies designed to establish MPR and pCR as surrogate end points of neoadjuvant treatment efficacy.

Deep learning prediction of axillary lymph node status using ultrasound images.

OBJECTIVE: To investigate the ability of our convolutional neural network (CNN) to predict axillary lymph node metastasis using primary breast cancer ultrasound (US) images. METHODS: In this IRB-approved study, 338 US images (two orthogonal images) from 169 patients from 1/2014-12/2016 were used. Suspicious lymph nodes were seen on US and patients subsequently underwent core-biopsy. 64 patients had metastatic lymph nodes. A custom CNN was utilized on 248 US images from 124 patients in the training dataset and tested on 90 US images from 45 patients. The CNN was implemented entirely of 3 × 3 convolutional kernels and linear layers. The 9 convolutional kernels consisted of 6 residual layers, totaling 12 convolutional layers. Feature maps were down-sampled using strided convolutions. Dropout with a 0.5 keep probability and L2 normalization was utilized. Training was implemented by using the Adam optimizer and a final SoftMax score threshold of 0.5 from the average of raw logits from each pixel was used for two class classification (metastasis or not). RESULTS: Our CNN achieved an AUC of 0.72 (SD ± 0.08) in predicting axillary lymph node metastasis from US images in the testing dataset. The model had an accuracy of 72.6% (SD ± 8.4) with a sensitivity and specificity of 65.5% (SD ± 28.6) and 78.9% (SD ± 15.1) respectively. Our algorithm is available to be shared for research use. (https://github.com/stmutasa/MetUS). CONCLUSION: It's feasible to predict axillary lymph node metastasis from US images using a deep learning technique. This can potentially aid nodal staging in patients with breast cancer.

Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes.

OBJECTIVES: To investigate the effect of CT image acquisition parameters on the performance of radiomics in classifying benign and malignant pulmonary nodules (PNs) with respect to nodule size. METHODS: We retrospectively collected CT images of 696 patients with PNs from March 2015 to March 2018. PNs were grouped by nodule diameter: T1a (diameter ≤ 1.0 cm), T1b (1.0 cm < diameter ≤ 2.0 cm), and T1c (2.0 cm < diameter ≤ 3.0 cm). CT images were divided into four settings according to slice-thickness-convolution-kernels: setting 1 (slice thickness/reconstruction type: 1.25 mm sharp), setting 2 (5 mm sharp), setting 3 (5 mm smooth), and random setting. We created twelve groups from two interacting conditions. Each PN was segmented and had 1160 radiomics features extracted. Non-redundant features with high predictive ability in training were selected to build a distinct model under each of the twelve subsets. RESULTS: The performance (AUCs) on predicting PN malignancy were as follows: T1a group: 0.84, 0.64, 0.68, and 0.68; T1b group: 0.68, 0.74, 0.76, and 0.70; T1c group: 0.66, 0.64, 0.63, and 0.70, for the setting 1, setting 2, setting 3, and random setting, respectively. In the T1a group, the AUC of radiomics model in setting 1 was statistically significantly higher than all others; In the T1b group, AUCs of radiomics models in setting 3 were statistically significantly higher than some; and in the T1c group, there were no statistically significant differences among models. CONCLUSIONS: For PNs less than 1 cm, CT image acquisition parameters have a significant influence on diagnostic performance of radiomics in predicting malignancy, and a model created using images reconstructed with thin section and a sharp kernel algorithm achieved the best performance. For PNs larger than 1 cm, CT reconstruction parameters did not affect diagnostic performance substantially. KEY POINTS: • CT image acquisition parameters have a significant influence on the diagnostic performance of radiomics in pulmonary nodules less than 1 cm. • In pulmonary nodules less than 1 cm, a radiomics model created by using images reconstructed with thin section and a sharp kernel algorithm achieved the best diagnostic performance. • For PNs larger than 1 cm, CT image acquisition parameters do not affect diagnostic performance substantially.

Weakly Supervised Deep Learning Approach to Breast MRI Assessment.

RATIONALE AND OBJECTIVES: To evaluate a weakly supervised deep learning approach to breast Magnetic Resonance Imaging (MRI) assessment without pixel level segmentation in order to improve the specificity of breast MRI lesion classification. MATERIALS AND METHODS: In this IRB approved study, the dataset consisted of 278,685 image slices from 438 patients. The weakly supervised network was based on the Resnet-101 architecture. Training was implemented using the Adam optimizer and a final SoftMax score threshold of 0.5 was used for two class classification (malignant or benign). 278,685 image slices were combined into 92,895 3-channel images. 79,871 (85%) images were used for training and validation while 13,024 (15%) images were separated for testing. Of the testing dataset, 11,498 (88%) were benign and 1531 (12%) were malignant. Model performance was assessed. RESULTS: The weakly supervised network achieved an AUC of 0.92 (SD ± 0.03) in distinguishing malignant from benign images. The model had an accuracy of 94.2% (SD ± 3.4) with a sensitivity and specificity of 74.4% (SD ± 8.5) and 95.3% (SD ± 3.3) respectively. CONCLUSION: It is feasible to use a weakly supervised deep learning approach to assess breast MRI images without the need for pixel-by-pixel segmentation yielding a high degree of specificity in lesion classification.

Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies.

Achieving high feature reproducibility while preserving biological information is one of the main challenges for the generalizability of current radiomics studies. Non-clinical imaging variables, such as reconstruction kernels, have shown to significantly impact radiomics features. In this study, we retrain an open-source convolutional neural network (CNN) to harmonize computerized tomography (CT) images with various reconstruction kernels to improve feature reproducibility and radiomic model performance using epidermal growth factor receptor (EGFR) mutation prediction in lung cancer as a paradigm. In the training phase, the CNN was retrained and tested on 32 lung cancer patients' CT images between two different groups of reconstruction kernels (smooth and sharp). In the validation phase, the retrained CNN was validated on an external cohort of 223 lung cancer patients' CT images acquired using different CT scanners and kernels. The results showed that the retrained CNN could be successfully applied to external datasets with different CT scanner parameters, and harmonization of reconstruction kernels from sharp to smooth could significantly improve the performance of radiomics model in predicting EGFR mutation status in lung cancer. In conclusion, the CNN based method showed great potential in improving feature reproducibility and generalizability by harmonizing medical images with heterogeneous reconstruction kernels.

Important Surgical and Clinical End Points in Neoadjuvant Immunotherapy Trials in Resectable NSCLC.

Neoadjuvant immunotherapy may improve outcomes in patients with resectable NSCLC and is being evaluated in phase 2 and 3 studies. Nevertheless, preoperative treatment postpones resection; the potential for increased surgical complexity and greater intra- and postoperative morbidity and mortality is an additional consideration. In studies primarily designed to evaluate efficacy, the impact of neoadjuvant immunotherapy on surgery is based on parameters that are poorly defined and reported differently between studies. Defining and reporting common end points among trials would improve understanding and facilitate cross-comparison of different immunotherapy regimens and may facilitate wider adoption of induction therapies by surgeons and oncologists. We propose several surgical end points and related metrics for neoadjuvant immunotherapy in resectable NSCLC. These include the periods from screening to treatment initiation and from last neoadjuvant dose to surgery; reporting of the allowable window for surgery to preclude masking delays caused by induction treatment-related toxicity; complete resection (R0) rate; preoperative downstaging; a standardized list of immune-related adverse events and associated delay to surgery; preoperative attrition; postoperative attrition before adjuvant therapy; and postoperative 30- and 90-day mortality and morbidity rates. Intraoperative end points (blood loss, duration, and type of surgery) and our proposed system of grading complexity based on lymphadenopathy and fibrosis would allow quantitation of technical difficulty and quality of oncologic resection. In conclusion, the standardization, reporting, and prospective inclusion of these end points in study protocols would provide a comparative overview of the impact of different neoadjuvant immunotherapy regimens on surgery and ultimately clinical oncologic outcomes in resectable NSCLC.

Identifying Robust Radiomics Features for Lung Cancer by Using In-Vivo and Phantom Lung Lesions.

We propose a novel framework for determining radiomics feature robustness by considering the effects of both biological and noise signals. This framework is preliminarily tested in a study predicting the epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients. Pairs of CT images (baseline, 3-week post therapy) of 46 NSCLC patients with known EGFR mutation status were collected and a FDA-customized anthropomorphic thoracic phantom was scanned on two vendors' scanners at four different tube currents. Delta radiomics features were extracted from the NSCLC patient CTs and reproducible, non-redundant, and informative features were identified. The feature value differences between EGFR mutant and EGFR wildtype patients were quantitatively measured as the biological signal. Similarly, radiomics features were extracted from the phantom CTs. A pairwise comparison between settings resulted in a feature value difference that was quantitatively measured as the noise signal. Biological signals were compared to noise signals at each setting to determine if the distributions were significantly different by two-sample t-test, and thus robust. Four optimal features were selected to predict EGFR mutation status, Tumor-Mass, Sigmoid-Offset-Mean, Gabor-Energy and DWT-Energy, which quantified tumor mass, tumor-parenchyma density transition at boundary, line-like pattern inside tumor and intratumoral heterogeneity, respectively. The first three variables showed robustness across the majority of studied CT acquisition parameters. The textual feature DWT-Energy was less robust. The proposed framework was able to determine robustness of radiomics features at specific settings by comparing biological signal to noise signal. Identification of robust radiomics features may improve the generalizability of radiomics models in future studies.

Radiomics Prediction of EGFR Status in Lung Cancer-Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data.

We investigated the performance of multiple radiomics feature extractors/software on predicting epidermal growth factor receptor mutation status in 228 patients with non-small cell lung cancer from publicly available data sets in The Cancer Imaging Archive. The imaging and clinical data were split into training (n = 105) and validation cohorts (n = 123). Two of the most cited open-source feature extractors, IBEX (1563 features) and Pyradiomics (1319 features), and our in-house software, Columbia Image Feature Extractor (CIFE) (1160 features), were used to extract radiomics features. Univariate and multivariate analyses were performed sequentially to predict EGFR mutation status using each individual feature extractor. Our univariate analysis integrated an unsupervised clustering method to identify nonredundant and informative candidate features for the creation of prediction models by multivariate analyses. In training, unsupervised clustering-based univariate analysis identified 5, 6, and 4 features from IBEX, Pyradiomics, and CIFE as candidate features, respectively. Multivariate prediction models using these features from IBEX, Pyradiomics, and CIFE yielded similar areas under the receiver operating characteristic curve of 0.68, 0.67, and 0.69. However, in validation, areas under the receiver operating characteristic curve of multivariate prediction models from IBEX, Pyradiomics, and CIFE decreased to 0.54, 0.56 and 0.64, respectively. Different feature extractors select different radiomics features, which leads to prediction models with varying performance. However, correlation between those selected features from different extractors may indicate these features measure similar imaging phenotypes associated with similar biological characteristics. Overall, attention should be paid to the generalizability of individual radiomics features and radiomics prediction models.

Atezolizumab in Combination With Carboplatin and Nab-Paclitaxel in Advanced Squamous NSCLC (IMpower131): Results From a Randomized Phase III Trial.

INTRODUCTION: Cytotoxic agents have immunomodulatory effects, providing a rationale for combining atezolizumab (anti-programmed death-ligand 1 [anti-PD-L1]) with chemotherapy. The randomized phase III IMpower131 study (NCT02367794) evaluated atezolizumab with platinum-based chemotherapy in stage IV squamous NSCLC. METHODS: A total of 1021 patients were randomized 1:1:1 to receive atezolizumab+carboplatin+paclitaxel (A+CP) (n = 338), atezolizumab+carboplatin+nab-paclitaxel (A+CnP) (n = 343), or carboplatin+nab-paclitaxel (CnP) (n = 340) for four or six 21-day cycles; patients randomized to the A+CP or A+CnP arms received atezolizumab maintenance therapy until progressive disease or loss of clinical benefit. The coprimary end points were investigator-assessed progression-free survival (PFS) and overall survival (OS) in the intention-to-treat (ITT) population. The secondary end points included PFS and OS in PD-L1 subgroups and safety. The primary PFS (January 22, 2018) and final OS (October 3, 2018) for A+CnP versus CnP are reported. RESULTS: PFS improvement with A+CnP versus CnP was seen in the ITT population (median, 6.3 versus 5.6 mo; hazard ratio [HR] = 0.71, 95% confidence interval [CI]: 0.60-0.85; p = 0.0001). Median OS in the ITT population was 14.2 and 13.5 months in the A+CnP and CnP arms (HR = 0.88, 95% CI: 0.73-1.05; p = 0.16), not reaching statistical significance. OS improvement with A+CnP versus CnP was observed in the PD-L1-high subgroup (HR = 0.48, 95% CI: 0.29-0.81), despite not being formally tested. Treatment-related grade 3 and 4 adverse events and serious adverse events occurred in 68.0% and 47.9% (A+CnP) and 57.5% and 28.7% (CnP) of patients, respectively. CONCLUSIONS: Adding atezolizumab to platinum-based chemotherapy significantly improved PFS in patients with first-line squamous NSCLC; OS was similar between the arms.

Toward radiomics for assessment of response to systemic therapies in lung cancer.

This editorial comment explains recent developments in radiomics regarding the use of quantitative imaging biomarkers to predict lung cancer sensitivity to a variety of cancer therapies. Tumor response assessment has been a crucial component guiding cancer treatment. Evaluation of treatment response was standardized and classically based on measuring changes in tumor lesion size. Recent breakthroughs in artificial intelligence pave the way for the use of radiomics in tumor response assessment. Such objective techniques would bring a remarkable transformation to conventional methods, which can be inherently subjective. Successful implementation of these technologies would allow for faster and more accurate predictions of treatment efficacy, which will be critical to the advancement of personalized medicine.

Recent progress in therapeutic antibodies for cancer immunotherapy.

Therapeutic antibodies have advanced the clinical management of multiple diseases including cancer. Cancer immunotherapy has been a focal point of recent clinical research with the success of checkpoint inhibitor antibodies, particularly those that target the PD-L1/PD-1 pathway. These antibodies that target specific steps of the cancer-immunity cycle show improved anti-tumor response, progression-free survival and overall survival versus standard therapy across multiple tumor types. Despite these advancements, not all patients experience durable response from checkpoint inhibition treatment. Ongoing research is focused on identifying various biomarkers of cancer-immune biology and elucidating specific immune escape mechanisms. Additionally, current clinical development efforts have focused on checkpoint inhibitors in combination with other agents as well as developing new antibody technologies targeting different targets simultaneously.

Clinical and Economic Burden of Elevated Blood Eosinophils in Patients With and Without Uncontrolled Asthma.

BACKGROUND: The European Respiratory Society and American Thoracic Society (ERS/ATS) published guidelines in 2014 for the evaluation and treatment of asthma. These guidelines draw attention to management of patients with asthma that remains uncontrolled despite therapy. One phenotypic characteristic of therapy-resistant asthma is eosinophil elevation. It is important to better understand the burden of care gaps in this patient subgroup in order to support improved treatment strategies in the future. OBJECTIVE: To quantify the economic burden of asthma patients with and without peripheral blood eosinophil elevation. METHODS: A retrospective cohort study was conducted using data from patients aged 12 years or older with a diagnosis of asthma using electronic health records of over 2 million patients between 2004-2010. Patients with a diagnosis of chronic obstructive pulmonary disease, Churg Strauss syndrome/Wegener's granulomatosis, eosinophilia, cystic/pulmonary fibrosis, allergic bronchopulmonary aspergillosis, or lung cancer in the 12-month period before the date of asthma diagnosis were excluded. Patients with asthma were followed for 12 months after their initial asthma diagnosis to identify those with controlled versus uncontrolled asthma based on ERS/ATS criteria. Patients with at least 1 peripheral blood eosinophil test result of ≥ 400 cells/µL were classified as those with elevated eosinophils. Total annual paid-claim cost was compared by eosinophil levels within the controlled and uncontrolled asthma subgroups. Costs were adjusted to 2015 U.S. dollars. Patients were stratified by control level, and generalized linear modeling regressions were used to assess the magnitude of increase in cost of the elevated eosinophil group. RESULTS: A total of 2,701 patients were included in the study, of which 17% had uncontrolled asthma and 21% had elevated eosinophils. The mean total annual cost of patients with uncontrolled asthma was more than 2 times the cost of those with controlled asthma ($18,341 vs. $8,670, P < 0.001). Patients with uncontrolled asthma in the elevated eosinophil group had almost double the total cost ($28,644 vs. $14,188, P = 0.008) compared with those with blood eosinophil levels in a normal range. Similarly, patients classified as those with controlled asthma in the elevated eosinophil group had almost twice the average costs as those without elevated eosinophils ($14,754 vs. $7,203, P < 0.001). Uncontrolled asthma with elevated eosinophils had 4 times greater hospital admissions and over 4 times higher total costs than controlled asthma without elevated eosinophils. Among patients with uncontrolled asthma, patients with elevated eosinophils had a 53% increase in mean cost ($17,723 vs. $11,581, P < 0.001) compared with patients without elevated eosinophils. Among patients with controlled asthma, patients with elevated eosinophils had a 62% increase in mean cost ($8,897 vs. $5,486, P < 0.001) compared with patients without elevated eosinophils. CONCLUSIONS: Elevated peripheral blood eosinophil level is associated with higher cost irrespective of disease control status. DISCLOSURES: This study was funded by Teva Pharmaceuticals. Dotiwala and Casciano report consulting and writing fees from Teva Pharmaceuticals for work on this study. Sun is an employee and stockholder of Teva Pharmaceuticals. Li reports consulting fees from eMAX Health. All authors contributed to study design. Dotiwala took the lead in data collection, along with the other authors, and data interpretation was performed primarily by Krishnan, Sun, and Li, along with Casciano and Dotiwala. The manuscript was written by Casciano, Dotiwala, and Li, along with Sun and Krishnan, and revised by Casciano, Dotiwala, Sun, and Li, with assistance from Krishnan.