A Phase 1 Trial of Salvage Stereotactic Body Radiation Therapy for Radiorecurrent Prostate Cancer After Brachytherapy.

PURPOSE: NCT03253744 is a phase 1 trial with the primary objective to identify the maximum tolerated dose (MTD) of salvage stereotactic body radiation therapy (SBRT) in patients with local prostate cancer recurrence after brachytherapy. Additional objectives included biochemical control and imaging response. METHODS AND MATERIALS: This trial was initially designed to test 3 therapeutic dose levels (DLs): 40 Gy (DL1), 42.5 Gy (DL2), and 45 Gy (DL3) in 5 fractions. Intensity modulation was used to deliver the prescription dose to the magnetic resonance imaging and prostate-specific membrane antigen-based positron emission tomography imaging-defined gross tumor volume while simultaneously delivering 30 Gy to an elective volume defined by the prostate gland. This phase 1 trial followed a 3+3 design with a 3-patient expansion at the MTD. Toxicities were scored until trial completion at 2 years post-SBRT using Common Terminology Criteria for Adverse Events version 5.0. Escalation was halted if 2 dose limiting toxicities occurred, defined as any persistent (>4 days) grade 3 toxicity occurring within the first 3 weeks after SBRT or any grade ≥3 genitourinary (GU) or grade 4 gastrointestinal toxicity thereafter. RESULTS: Between August 2018 and January 2023, 9 patients underwent salvage SBRT and were observed for a median of 22 months (Q1-Q3, 20-43 months). No grade 3 to 5 adverse events related to study treatment were observed; thus, no dose limiting toxicities occurred during the observation period. Escalation was halted by amendment given excellent biochemical control in DL1 and DL2 in the setting of a high incidence of clinically significant late grade 2 GU toxicity. Therefore, the MTD was considered 42.5 Gy in 5 fractions (DL2). One- and 2-year biochemical progression-free survival were 100% and 86%, representing a single patient in the trial cohort with biochemical failure (prostate-specific antigen [PSA] nadir + 2.0) at 20 months posttreatment. CONCLUSIONS: The MTD of salvage SBRT for the treatment of intraprostatic radiorecurrence after brachytherapy was 42.5 Gy in 5 fractions producing an 86% 2-year biochemical progression-free survival rate, with 1 poststudy failure at 20 months. The most frequent clinically significant toxicity was late grade 2 GU toxicity.

New Approach to Equitable Intervention Planning to Improve Engagement and Outcomes in a Digital Health Program: Simulation Study.

BACKGROUND: Digital health programs provide individualized support to patients with chronic diseases and their effectiveness is measured by the extent to which patients achieve target individual clinical outcomes and the program's ability to sustain patient engagement. However, patient dropout and inequitable intervention delivery strategies, which may unintentionally penalize certain patient subgroups, represent challenges to maximizing effectiveness. Therefore, methodologies that optimize the balance between success factors (achievement of target clinical outcomes and sustained engagement) equitably would be desirable, particularly when there are resource constraints. OBJECTIVE: Our objectives were to propose a model for digital health program resource management that accounts jointly for the interaction between individual clinical outcomes and patient engagement, ensures equitable allocation as well as allows for capacity planning, and conducts extensive simulations using publicly available data on type 2 diabetes, a chronic disease. METHODS: We propose a restless multiarmed bandit (RMAB) model to plan interventions that jointly optimize long-term engagement and individual clinical outcomes (in this case measured as the achievement of target healthy glucose levels). To mitigate the tendency of RMAB to achieve good aggregate performance by exacerbating disparities between groups, we propose new equitable objectives for RMAB and apply bilevel optimization algorithms to solve them. We formulated a model for the joint evolution of patient engagement and individual clinical outcome trajectory to capture the key dynamics of interest in digital chronic disease management programs. RESULTS: In simulation exercises, our optimized intervention policies lead to up to 10% more patients reaching healthy glucose levels after 12 months, with a 10% reduction in dropout compared to standard-of-care baselines. Further, our new equitable policies reduce the mean absolute difference of engagement and health outcomes across 6 demographic groups by up to 85% compared to the state-of-the-art. CONCLUSIONS: Planning digital health interventions with individual clinical outcome objectives and long-term engagement dynamics as considerations can be both feasible and effective. We propose using an RMAB sequential decision-making framework, which may offer additional capabilities in capacity planning as well. The integration of an equitable RMAB algorithm further enhances the potential for reaching equitable solutions. This approach provides program designers with the flexibility to switch between different priorities and balance trade-offs across various objectives according to their preferences.

A Phase 1 Trial of Focal Salvage Stereotactic Body Radiation Therapy for Radiorecurrent Prostate Cancer.

PURPOSE: NCT03253744 was a phase 1 trial to identify the maximum tolerated dose (MTD) of image-guided, focal, salvage stereotactic body radiation therapy (SBRT) for patients with locally radiorecurrent prostate cancer. Additional objectives included biochemical control and imaging response. METHODS AND MATERIALS: The trial design included 3 dose levels (DLs): 40 Gy (DL1), 42.5 Gy (DL2), and 45 Gy (DL3) in 5 fractions delivered ≥48 hours apart. The prescription dose was delivered to the magnetic resonance- and prostate-specific membrane antigen imaging-defined tumor volume. Dose escalation followed a 3+3 design with a 3-patient expansion at the MTD. Toxicities were scored until 2 years after completion of SBRT using Common Terminology Criteria for Adverse Events, version 5.0, criteria. Escalation was halted if 2 dose-limiting toxicities occurred, defined as any persistent (>4 days) grade 3 toxicity occurring within the first 3 weeks after SBRT and any grade 3 genitourinary (GU) or grade 4 gastrointestinal (GI) toxicity thereafter. RESULTS: Between August 2018 and May 2022, 8 patients underwent salvage focal SBRT, with a median follow-up of 35 months. No dose-limiting toxic effects were observed on DL1. Two patients were enrolled in DL2 and experienced grade 3 GU toxicities, prompting de-escalation and expansion (n = 6) at the MTD (DL1). The most common toxicities observed were grade ≥2 GU toxicities, with only a single grade 2 GI toxicity and no grade ≥3 GI toxicities. One patient experienced biochemical failure (prostate-specific antigen nadir + 2.0) at 33 months. CONCLUSIONS: The MTD for focal salvage SBRT for isolated intraprostatic radiorecurrence was 40 Gy in 5 fractions, producing a 100% 24-month biochemical progression free survival, with 1 poststudy failure at 33 months. The most frequent clinically significant toxicity was late grade ≥2 GU toxicity.

Scalable Strategies to Increase Efficiency and Augment Public Health Activities During Epidemic Peaks.

OBJECTIVE: Scalable strategies to reduce the time burden and increase contact tracing efficiency are crucial during early waves and peaks of infectious transmission. DESIGN: We enrolled a cohort of SARS-CoV-2-positive seed cases into a peer recruitment study testing social network methodology and a novel electronic platform to increase contact tracing efficiency. SETTING: Index cases were recruited from an academic medical center and requested to recruit their local social contacts for enrollment and SARS-CoV-2 testing. PARTICIPANTS: A total of 509 adult participants enrolled over 19 months (384 seed cases and 125 social peers). INTERVENTION: Participants completed a survey and were then eligible to recruit their social contacts with unique "coupons" for enrollment. Peer participants were eligible for SARS-CoV-2 and respiratory pathogen screening. MAIN OUTCOME MEASURES: The main outcome measures were the percentage of tests administered through the study that identified new SARS-CoV-2 cases, the feasibility of deploying the platform and the peer recruitment strategy, the perceived acceptability of the platform and the peer recruitment strategy, and the scalability of both during pandemic peaks. RESULTS: After development and deployment, few human resources were needed to maintain the platform and enroll participants, regardless of peaks. Platform acceptability was high. Percent positivity tracked with other testing programs in the area. CONCLUSIONS: An electronic platform may be a suitable tool to augment public health contact tracing activities by allowing participants to select an online platform for contact tracing rather than sitting for an interview.

Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies.

Diversity, Equity, and Inclusion in Clinical Research: A Path Toward Precision Health for Everyone.

Healthcare disparities are a persistent societal problem. One of the contributing factors to this status quo is the lack of diversity and representativeness of research efforts, which result in nongeneralizable evidence that, in turn, provides suboptimal means to enable the best possible outcomes at the individual level. There are several strategies that research teams can adopt to improve the diversity, equity, and inclusion (DEI) of their efforts; these strategies span the totality of the research path, from initial design to the shepherding of clinical data through a potential regulatory process. These strategies include more intentionality and DEI-based goal-setting, more diverse research and leadership teams, better community engagement to set study goals and approaches, better tailored outreach interventions, decentralization of study procedures and incorporation of innovative technology for more flexible data collection, and self-surveillance to identify and prevent biases. Within their remit of overlooking research efforts, regulatory authorities, as stakeholders, also have the potential for a positive effect on the DEI of emerging clinical evidence. All these are implementable tools and mechanisms that can make study participation more approachable to diverse communities, and ultimately generate evidence that is more generalizable and a conduit for better outcomes. The research community has an imperative to make DEI principles key foundational aspects in study conduct in order to pursue better personalized medicine for diverse patient populations.

Criteria for the translation of radiomics into clinically useful tests.

Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially. However, despite the thousands of radiomic studies, the number of settings in which radiomics has been successfully translated into a clinically useful tool or has obtained FDA clearance is comparatively small. This relative dearth might be attributable to factors such as the varying imaging and radiomic feature extraction protocols used from study to study, the numerous potential pitfalls in the analysis of radiomic data, and the lack of studies showing that acting upon a radiomic-based tool leads to a favourable benefit-risk balance for the patient. Several guidelines on specific aspects of radiomic data acquisition and analysis are already available, although a similar roadmap for the overall process of translating radiomics into tools that can be used in clinical care is needed. Herein, we provide 16 criteria for the effective execution of this process in the hopes that they will guide the development of more clinically useful radiomic tests in the future.

Meta-Analysis of the Test-Retest Repeatability of [18F]-Fluorodeoxyglucose Standardized Uptake Values: Implications for Assessment of Tumor Response.

PURPOSE: Currently, guidelines for PET with 18F-fluorodeoxyglucose (FDG-PET) interpretation for assessment of therapy response in oncology primarily involve visual evaluation of FDG-PET/CT scans. However, quantitative measurements of the metabolic activity in tumors may be even more useful in evaluating response to treatment. Guidelines based on such measurements, including the European Organization for Research and Treatment of Cancer Criteria and PET Response Criteria in Solid Tumors, have been proposed. However, more rigorous analysis of response criteria based on FDG-PET measurements is needed to adopt regular use in practice. EXPERIMENTAL DESIGN: Well-defined boundaries of repeatability and reproducibility of quantitative measurements to discriminate noise from true signal changes are a needed initial step. An extension of the meta-analysis from de Langen and colleagues (2012) of the test-retest repeatability of quantitative FDG-PET measurements, including mean, maximum, and peak standardized uptake values (SUVmax, SUVmean, and SUVpeak, respectively), was performed. Data from 11 studies in the literature were used to estimate the relationship between the variance in test-retest measurements with uptake level and various study-level, patient-level, and lesion-level characteristics. RESULTS: Test-retest repeatability of percentage fluctuations for all three types of SUV measurement (max, mean, and peak) improved with higher FDG uptake levels. Repeatability in all three SUV measurements varied for different lesion locations. Worse repeatability in SUVmean was also associated with higher tumor volumes. CONCLUSIONS: On the basis of these results, recommendations regarding SUV measurements for assessing minimal detectable changes based on repeatability and reproducibility are proposed. These should be applied to differentiate between response categories for a future set of FDG-PET-based criteria that assess clinically significant changes in tumor response.

Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation.

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis.

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

Multiparametric Quantitative Imaging Biomarkers for Phenotype Classification: A Framework for Development and Validation.

This manuscript is the third in a five-part series related to statistical assessment methodology for technical performance of multi-parametric quantitative imaging biomarkers (mp-QIBs). We outline approaches and statistical methodologies for developing and evaluating a phenotype classification model from a set of multiparametric QIBs. We then describe validation studies of the classifier for precision, diagnostic accuracy, and interchangeability with a comparator classifier. We follow with an end-to-end real-world example of development and validation of a classifier for atherosclerotic plaque phenotypes. We consider diagnostic accuracy and interchangeability to be clinically meaningful claims for a phenotype classification model informed by mp-QIB inputs, aiming to provide tools to demonstrate agreement between imaging-derived characteristics and clinically established phenotypes. Understanding that we are working in an evolving field, we close our manuscript with an acknowledgement of existing challenges and a discussion of where additional work is needed. In particular, we discuss the challenges involved with technical performance and analytical validation of mp-QIBs. We intend for this manuscript to further advance the robust and promising science of multiparametric biomarker development.

A Framework for Evaluating the Technical Performance of Multiparameter Quantitative Imaging Biomarkers (mp-QIBs).

Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal change, or predict outcome. Multiple imaging parameters are sometimes considered separately but ideally are evaluated collectively. Often, they are transformed as Likert interpretations, ignoring the correlations of quantitative properties that may result in better reproducibility or outcome prediction. In this paper we present three use cases of multiparameter quantitative imaging: i) multidimensional descriptor, ii) phenotype classification, and iii) risk prediction. A fourth application based on data-driven markers from radiomics is also presented. We describe the technical performance characteristics and their metrics common to all use cases, and provide a structure for the development, estimation, and testing of multiparameter quantitative imaging. This paper serves as an overview for a series of individual articles on the four applications, providing the statistical framework for multiparameter imaging applications in medicine.

Telehealth Fatigue: Is It Real? What Should Be Done?

This commentary article discusses the recent trends and changes in popularity of telehealth usage as well as the most recent efforts to redefine telehealth value and usability. Six strategies to improve the patient experience and increase telehealth acceptance by overcoming simultaneous barriers are presented, which include (1) creating a new healthcare paradigm using telehealth, (2) scheduling the telehealth visit, (3) preparing for the telehealth visit, (4) conducting the telehealth visit, (5) using data and biomarkers, and (6) providing digital equity. With the application of these strategies, we believe that the recent decline in the popularity of telehealth can be reversed.

Early drug development in solid tumours: analysis of National Cancer Institute-sponsored phase 1 trials.

BACKGROUND: The low expectation of clinical benefit from phase 1 cancer therapeutics trials might negatively affect patient and physician participation, study reimbursement, and slow the progress of oncology research. Advances in cancer drug development, meanwhile, might have favourably improved treatment responses; however, little comprehensive data exist describing the response and toxicity associated with phase 1 trials across solid tumours. The aim of the study is to evaluate the trend of toxicity and response in phase 1 trials for solid tumours over time. METHODS: We analysed patient-level data from the Cancer Therapy Evaluation Program of the National Cancer Institute-sponsored investigator-initiated phase 1 trials for solid tumours, from Jan 1, 2000, to May 31, 2019. We assessed risks of treatment-related death (grade 5 toxicity ratings possibly, probably, or definitely attributable to treatment), all on-treatment deaths (deaths during protocol treatment regardless of attribution), grade 3-4 toxicity, and proportion of overall response (complete response and partial response) and complete response rate in the study periods of 2000-05, 2006-12, and 2013-2019, and evaluated their trends over time. We also analysed cancer type-specific and investigational agent-specific response, and analysed the trend of response in each cancer type over time. Univariate associations of overall response rates with patients' baseline characteristics (age, sex, performance status, BMI, albumin concentration, and haemoglobin concentration), enrolment period, investigational agents, and trial design were assessed using risk ratio based on the modified Poisson regression model. FINDINGS: We analysed 465 protocols that enrolled 13 847 patients using 261 agents. 144 (31%) trials used a monotherapy and 321 (69%) used combination therapies. The overall treatment-related death rate was 0·7% (95% CI 0·5-0·8) across all periods. Risks of treatment-related deaths did not change over time (p=0·52). All on-treatment death risk during the study period was 8·0% (95% CI 7·6-8·5). The most common grade 3-4 adverse events were haematological; grade 3-4 neutropenia occurred in 2336 (16·9%) of 13 847 patients, lymphopenia in 1230 (8·9%), anaemia in 894 (6·5%), and thrombocytopenia in 979 (7·1%). The overall response rate for all trials during the study period was 12·2% (95% CI 11·5-12·8; 1133 of 9325 patients) and complete response rate was 2·7% (2·4-3·0; 249 of 9325). Overall response increased from 9·6% (95% CI 8·7-10·6) in 2000-05 to 18·0% (15·7-20·5) in 2013-19, and complete response rates from 2·5% (2·0-3·0) to 4·3% (3·2-5·7). Overall response rates for combination therapy were substantially higher than for monotherapy (15·8% [15·0-16·8] vs 3·5% [2·8-4·2]). The overall response by class of agents differed across diseases. Anti-angiogenesis agents were associated with higher overall response rate for bladder, colon, kidney and ovarian cancer. DNA repair inhibitors were associated with higher overall response rate in ovarian and pancreatic cancer. The rates of overall response over time differed markedly by disease; there were notable improvements in bladder, breast, and kidney cancer and melanoma, but no change in the low response of pancreatic and colon cancer. INTERPRETATION: During the past 20 years, the response rate in phase 1 trials nearly doubled without an increase in the treatment-related death rate. However, there is significant heterogeneity in overall response by various factors such as cancer type, investigational agent, and trial design. Therefore, informed decision making is crucial for patients before participating in phase 1 trials. This study provides updated encouraging outcomes of modern phase 1 trials in solid tumours. FUNDING: National Cancer Institute.

The User Experience of AI.

The promise of artificial intelligence (AI) and machine learning in healthcare can be realized only when they are smoothly integrated into existing clinical workflows. Doing so requires optimizing the user experience of AI and the data on which these systems are built, enabling clinicians to deliver focused patient care.

A framework for the oversight and local deployment of safe and high-quality prediction models.

Artificial intelligence/machine learning models are being rapidly developed and used in clinical practice. However, many models are deployed without a clear understanding of clinical or operational impact and frequently lack monitoring plans that can detect potential safety signals. There is a lack of consensus in establishing governance to deploy, pilot, and monitor algorithms within operational healthcare delivery workflows. Here, we describe a governance framework that combines current regulatory best practices and lifecycle management of predictive models being used for clinical care. Since January 2021, we have successfully added models to our governance portfolio and are currently managing 52 models.

Trends in Grade 5 Toxicity and Response in Phase I Trials in Hematologic Malignancy: 20-Year Experience From the Cancer Therapy Evaluation Program at the National Cancer Institute.

PURPOSE: Cancer drug development has largely shifted from cytotoxic chemotherapy to targeted treatment in the past two decades. Although previous studies have highlighted improvement in response rates in recent phase I trials, disease-focused reporting is limited. METHODS: We integrated patient-level data for patients with hematologic malignancies who participated in phase I trials sponsored by the National Cancer Institute Cancer Therapy Evaluation Program between January 2000 and May 2019 and estimated the trend of grade 5 toxicity and response by disease subtype over time. RESULTS: We analyzed 161 trials involving 3,308 patients, all of whom were assessed for toxicity and 2,404 of whom were evaluable for response to therapy. The overall rate of grade 5 toxicities was 1.81% (95% CI, 1.36 to 2.27), with no significant change in the rate over time. Baseline characteristics associated with higher risk of grade 5 toxicity were age and performance status ≥ 2 at enrollment. Overall response rate (ORR) and complete response (CR) rate for all trials during the study period were 25.1% and 14.7%, respectively. A significant increase in both ORR and CR rate was observed over time (ORR, 18.5% in 2000-2005, 25.9% in 2006-2012, and 50.6% in 2013-2019, P < .001). ORR in phase I trials varied across disease subtypes: 20.2% in acute myeloid leukemia, 9.1% in myelodysplastic syndrome, 43.2% in lymphoma, 42.9% in chronic lymphocytic leukemia, 15.1% in acute lymphoblastic leukemia, and 16.5% in myeloma. CONCLUSION: Over time, the ORR and CR rates in phase I trials for hematologic malignancy have improved meaningfully, whereas the rate of toxicity-related death remains stable. This study provides broad experience that physicians can use when discussing the potential outcomes for patients with hematologic malignancy considering participation in phase I trials.

A Multidimensional Bioinformatic Platform for the Study of Human Response to Surgery.

OBJECTIVE: To design and establish a prospective biospecimen repository that integrates multi-omics assays with clinical data to study mechanisms of controlled injury and healing. BACKGROUND: Elective surgery is an opportunity to understand both the systemic and focal responses accompanying controlled and well-characterized injury to the human body. The overarching goal of this ongoing project is to define stereotypical responses to surgical injury, with the translational purpose of identifying targetable pathways involved in healing and resilience, and variations indicative of aberrant peri-operative outcomes. METHODS: Clinical data from the electronic medical record combined with large-scale biological data sets derived from blood, urine, fecal matter, and tissue samples are collected prospectively through the peri-operative period on patients undergoing 14 surgeries chosen to represent a range of injury locations and intensities. Specimens are subjected to genomic, transcriptomic, proteomic, and metabolomic assays to describe their genetic, metabolic, immunologic, and microbiome profiles, providing a multidimensional landscape of the human response to injury. RESULTS: The highly multiplexed data generated includes changes in over 28,000 mRNA transcripts, 100 plasma metabolites, 200 urine metabolites, and 400 proteins over the longitudinal course of surgery and recovery. In our initial pilot dataset, we demonstrate the feasibility of collecting high quality multi-omic data at pre- and postoperative time points and are already seeing evidence of physiologic perturbation between timepoints. CONCLUSIONS: This repository allows for longitudinal, state-of-the-art geno-mic, transcriptomic, proteomic, metabolomic, immunologic, and clinical data collection and provides a rich and stable infrastructure on which to fuel further biomedical discovery.