Improving Equity in Deep Learning Medical Applications with the Gerchberg-Saxton Algorithm.

Deep learning (DL) has gained prominence in healthcare for its ability to facilitate early diagnosis, treatment identification with associated prognosis, and varying patient outcome predictions. However, because of highly variable medical practices and unsystematic data collection approaches, DL can unfortunately exacerbate biases and distort estimates. For example, the presence of sampling bias poses a significant challenge to the efficacy and generalizability of any statistical model. Even with DL approaches, selection bias can lead to inconsistent, suboptimal, or inaccurate model results, especially for underrepresented populations. Therefore, without addressing bias, wider implementation of DL approaches can potentially cause unintended harm. In this paper, we studied a novel method for bias reduction that leverages the frequency domain transformation via the Gerchberg-Saxton and corresponding impact on the outcome from a racio-ethnic bias perspective.

Connecting Chronic Pain and Opioid Use Disorder Clinical Trials Through Data Harmonization: Wake Forest IMPOWR Dissemination, Education, and Coordination Center (IDEA-CC).

The National Institutes of Health (NIH) has developed the NIH HEAL Integrative Management of chronic Pain and OUD for Whole Recovery (IMPOWR) network to address the interconnected nature of chronic pain (CP) and opioid use disorder (OUD), which are influenced by mental health. The network aims to develop integrated treatment pathways across multiple sites in the United States. The IMPOWR Dissemination, Education, and Coordination Center (IDEA-CC) is proposed to support the NIH HEAL IMPOWR network by developing a CP- and OUD-focused infrastructure that includes measures of stigma, trauma, and quality of life. This includes deploying a data framework to link clinical sites, developing an educational infrastructure to address stigma and health disparities, and disseminating research findings. The IDEA-CC will standardize data collection processes, develop web-based data commons, and facilitate data sharing opportunities. The IDEA-CC will support the development and validation of composite CP and OUD measures and will develop educational materials to address stigma and health disparities. Overall, the IDEA-CC will create a research community and data commons that connect NIH HEAL IMPOWR centers to translate findings and develop a key CP-OUD research data, and education infrastructure.

Sample average treatment effect on the treated (SATT) analysis using counterfactual explanation identifies BMT and SARS-CoV-2 vaccination as protective risk factors associated with COVID-19 severity and survival in patients with multiple myeloma.

Patients with multiple myeloma (MM), an age-dependent neoplasm of antibody-producing plasma cells, have compromised immune systems and might be at increased risk for severe COVID-19 outcomes. This study characterizes risk factors associated with clinical indicators of COVID-19 severity and all-cause mortality in myeloma patients utilizing NCATS' National COVID Cohort Collaborative (N3C) database. The N3C consortium is a large, centralized data resource representing the largest multi-center cohort of COVID-19 cases and controls nationwide (>16 million total patients, and >6 million confirmed COVID-19+ cases to date). Our cohort included myeloma patients (both inpatients and outpatients) within the N3C consortium who have been diagnosed with COVID-19 based on positive PCR or antigen tests or ICD-10-CM diagnosis code. The outcomes of interest include all-cause mortality (including discharge to hospice) during the index encounter and clinical indicators of severity (i.e., hospitalization/emergency department/ED visit, use of mechanical ventilation, or extracorporeal membrane oxygenation (ECMO)). Finally, causal inference analysis was performed using the Coarsened Exact Matching (CEM) and Propensity Score Matching (PSM) methods. As of 05/16/2022, the N3C consortium included 1,061,748 cancer patients, out of which 26,064 were MM patients (8,588 were COVID-19 positive). The mean age at COVID-19 diagnosis was 65.89 years, 46.8% were females, and 20.2% were of black race. 4.47% of patients died within 30 days of COVID-19 hospitalization. Overall, the survival probability was 90.7% across the course of the study. Multivariate logistic regression analysis showed histories of pulmonary and renal disease, dexamethasone, proteasome inhibitor/PI, immunomodulatory/IMiD therapies, and severe Charlson Comorbidity Index/CCI were significantly associated with higher risks of severe COVID-19 outcomes. Protective associations were observed with blood-or-marrow transplant/BMT and COVID-19 vaccination. Further, multivariate Cox proportional hazard analysis showed that high and moderate CCI levels, International Staging System (ISS) moderate or severe stage, and PI therapy were associated with worse survival, while BMT and COVID-19 vaccination were associated with lower risk of death. Finally, matched sample average treatment effect on the treated (SATT) confirmed the causal effect of BMT and vaccination status as top protective factors associated with COVID-19 risk among US patients suffering from multiple myeloma. To the best of our knowledge, this is the largest nationwide study on myeloma patients with COVID-19.

Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology.

In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.

Use of comprehensive genomic profiling for biomarker discovery for the management of non-small cell lung cancer brain metastases.

Background: Clinical biomarkers for brain metastases remain elusive. Increased availability of genomic profiling has brought discovery of these biomarkers to the forefront of research interests. Method: In this single institution retrospective series, 130 patients presenting with brain metastasis secondary to Non-Small Cell Lung Cancer (NSCLC) underwent comprehensive genomic profiling conducted using next generation circulating tumor deoxyribonucleic acid (DNA) (Guardant Health, Redwood City, CA). A total of 77 genetic mutation identified and correlated with nine clinical outcomes using appropriate statistical tests (general linear models, Mantel-Haenzel Chi Square test, and Cox proportional hazard regression models). For each outcome, a genetic signature composite score was created by summing the total genes wherein genes predictive of a clinically unfavorable outcome assigned a positive score, and genes with favorable clinical outcome assigned negative score. Results: Seventy-two genes appeared in at least one gene signature including: 14 genes had only unfavorable associations, 36 genes had only favorable associations, and 22 genes had mixed effects. Statistically significant associated signatures were found for the clinical endpoints of brain metastasis velocity, time to distant brain failure, lowest radiosurgery dose, extent of extracranial metastatic disease, concurrent diagnosis of brain metastasis and NSCLC, number of brain metastases at diagnosis as well as distant brain failure. Some genes were solely associated with multiple favorable or unfavorable outcomes. Conclusion: Genetic signatures were derived that showed strong associations with different clinical outcomes in NSCLC brain metastases patients. While these data remain to be validated, they may have prognostic and/or therapeutic impact in the future. Statement of translation relevance: Using Liquid biopsy in NSCLC brain metastases patients, the genetic signatures identified in this series are associated with multiple clinical outcomes particularly these ones that lead to early or more numerous metastases. These findings can be reverse-translated in laboratory studies to determine if they are part of the genetic pathway leading to brain metastasis formation.

An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C).

Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.

Guiding principles for technical infrastructure to support computable biomedical knowledge.

Over the past 4 years, the authors have participated as members of the Mobilizing Computable Biomedical Knowledge Technical Infrastructure working group and focused on conceptualizing the infrastructure required to use computable biomedical knowledge. Here, we summarize our thoughts and lay the foundation for future work in the development of CBK infrastructure, including: explaining the difference between computable knowledge and data, and contextualizing the conversation with the Learning Health Systems and the FAIR principles. Specifically, we provide three guiding principles to advance the development of CBK infrastructure: (a) Promote interoperable systems for data and knowledge to be findable, accessible, interoperable, and reusable. (b) Enable stable, trustworthy knowledge representations that are human and machine readable. (c) Computable knowledge resources should, when possible, be open. Standards supporting computable knowledge infrastructures must be open.

Transfer Learning Approach to Vascular Permeability Changes in Brain Metastasis Post-Whole-Brain Radiotherapy.

The purpose of this study is to further validate the utility of our previously developed CNN in an alternative small animal model of BM through transfer learning. Unlike the glioma model, the BM mouse model develops multifocal intracranial metastases, including both contrast enhancing and non-enhancing lesions on DCE MRI, thus serving as an excellent brain tumor model to study tumor vascular permeability. Here, we conducted transfer learning by transferring the previously trained GBM CNN to DCE MRI datasets of BM mice. The CNN was re-trained to learn about the relationship between BM DCE images and target permeability maps extracted from the Extended Tofts Model (ETM). The transferred network was found to accurately predict BM permeability and presented with excellent spatial correlation with the target ETM PK maps. The CNN model was further tested in another cohort of BM mice treated with WBRT to assess vascular permeability changes induced via radiotherapy. The CNN detected significantly increased permeability parameter Ktrans in WBRT-treated tumors (p < 0.01), which was in good agreement with the target ETM PK maps. In conclusion, the proposed CNN can serve as an efficient and accurate tool for characterizing vascular permeability and treatment responses in small animal brain tumor models.

Prognostic Mutational Signatures of NSCLC Patients treated with chemotherapy, immunotherapy and chemoimmunotherapy.

Different types of therapy are currently being used to treat non-small cell lung cancer (NSCLC) depending on the stage of tumor and the presence of potentially druggable mutations. However, few biomarkers are available to guide clinicians in selecting the most effective therapy for all patients with various genetic backgrounds. To examine whether patients' mutation profiles are associated with the response to a specific treatment, we collected comprehensive clinical characteristics and sequencing data from 524 patients with stage III and IV NSCLC treated at Atrium Health Wake Forest Baptist. Overall survival based Cox-proportional hazard regression models were applied to identify mutations that were "beneficial" (HR < 1) or "detrimental" (HR > 1) for patients treated with chemotherapy (chemo), immune checkpoint inhibitor (ICI) and chemo+ICI combination therapy (Chemo+ICI) followed by the generation of mutation composite scores (MCS) for each treatment. We also found that MCS is highly treatment specific that MCS derived from one treatment group failed to predict the response in others. Receiver operating characteristics (ROC) analyses showed a superior predictive power of MCS compared to TMB and PD-L1 status for immune therapy-treated patients. Mutation interaction analysis also identified novel co-occurring and mutually exclusive mutations in each treatment group. Our work highlights how patients' sequencing data facilitates the clinical selection of optimized treatment strategies.

Modulation of oxidative phosphorylation and mitochondrial biogenesis by cigarette smoke influence the response to immune therapy in NSCLC patients.

The treatment regimen of non-small cell lung cancer (NSCLC) has drastically changed owing to the superior anti-cancer effects generated by the immune-checkpoint blockade (ICB). However, only a subset of patients experience benefit after receiving ICBs. Therefore, it is of paramount importance to increase the response rate by elucidating the underlying molecular mechanisms and identifying novel therapeutic targets to enhance the efficacy of IBCs in non-responders. We analyzed the progression-free survival (PFS) and overall survival (OS) of 295 NSCLC patients who received anti-PD-1 therapy by segregating them with multiple clinical factors including sex, age, race, smoking history, BMI, tumor grade and subtype. We also identified key signaling pathways and mutations that are enriched in patients with distinct responses to ICB by gene set enrichment analysis (GSEA) and mutational analyses. We found that former and current smokers have a higher response rate to anti-PD-1 treatment than non-smokers. GSEA results revealed that oxidative phosphorylation (OXPHOS) and mitochondrial related pathways are significantly enriched in both responders and smokers, suggesting a potential role of cellular metabolism in regulating immune response to ICB. We also demonstrated that all-trans retinoic acid (ATRA) which enhances mitochondrial function significantly enhanced the efficacy of anti-PD-1 treatment in vivo. Our clinical and bioinformatics based analyses revealed a connection between smoking induced metabolic switch and the response to immunotherapy, which can be the basis for developing novel combination therapies that are beneficial to never smoked NSCLC patients.

NIH HEAL Clinical Data Elements (CDE) implementation: NIH HEAL Initiative IMPOWR network IDEA-CC.

OBJECTIVE: The National Institutes of Health (NIH) HEAL Initiative is making data findable, accessible, interoperable, and reusable (FAIR) to maximize the value of the unprecedented federal investment in pain and opioid-use disorder research. This involves standardizing the use of common data elements (CDE) for clinical research. METHODS: This work describes the process of the selection, processing, harmonization, and design constraints of CDE across a pain and opioid use disorder clinical trials network (NIH HEAL IMPOWR). RESULTS: The network alignment allowed for incorporation of newer data standards across the clinical trials. Specific advances included geographic coding (RUCA), deidentified patient identifiers (GUID), shareable clinical survey libraries (REDCap), and concept mapping to standardized concepts (UMLS). CONCLUSIONS: While complex, harmonization across a network of chronic pain and opioid use disorder clinical trials with separate interventions can be optimized through use of CDEs and data standardization processes. This standardization process will support the robust secondary data analyses. Scaling this process could standardize CDE results across interventions or disease state which could help inform insurance companies or government organizations about coverage determinations. The development of the HEAL CDE program supports connecting isolated studies and solutions to each other, but the practical aspects may be challenging for some studies to implement. Leveraging tools and technology to simplify process and create ready to use resources may support wider adoption of consistent data standards.

Adaptation of a Personalized Electronic Care Planning Tool for Cancer Follow-up Care: Formative Study.

BACKGROUND: Most patients diagnosed with colorectal cancer will survive for at least 5 years; thus, engaging patients to optimize their health will likely improve outcomes. Clinical guidelines recommend patients receive a comprehensive care plan (CP) when transitioning from active treatment to survivorship, which includes support for ongoing symptoms and recommended healthy behaviors. Yet, cancer care providers find this guideline difficult to implement. Future directions for survivorship care planning include enhancing information technology support for developing personalized CPs, using CPs to facilitate self-management, and assessing CPs in clinical settings. OBJECTIVE: We aimed to develop an electronic tool for colorectal cancer follow-up care (CFC) planning. METHODS: Incorporating inputs from health care professionals and patient stakeholders is fundamental to the successful integration of any tool into the clinical workflow. Thus, we followed the Integrate, Design, Assess, and Share (IDEAS) framework to adapt an existing application for stroke care planning (COMPASS-CP) to meet the needs of colorectal cancer survivors (COMPASS-CP CFC). Constructs from the Consolidated Framework for Implementation Research (CFIR) guided our approach. We completed this work in 3 phases: (1) gathering qualitative feedback from stakeholders about the follow-up CP generation design and workflow; (2) adapting algorithms and resource data sources needed to generate a follow-up CP; and (3) optimizing the usability of the adapted prototype of COMPASS-CP CFC. We also quantitatively measured usability (target average score ≥70; range 0-100), acceptability, appropriateness, and feasibility. RESULTS: In the first phase, health care professionals (n=7), and patients and caregivers (n=7) provided qualitative feedback on COMPASS-CP CFC that informed design elements such as selection, interpretation, and clinical usefulness of patient-reported measures. In phase 2, we built a minimal viable product of COMPASS-CP CFC. This tool generated CPs based on the needs identified by patient-completed measures (including validated patient-reported outcomes) and electronic health record data, which were then matched with resources by zip code and preference to support patients' self-management. Elements of the CFIR assessed revealed that most health care professionals believed the tool would serve patients' needs and had advantages. In phase 3, the average System Usability Scale score was above our target score for health care professionals (n=5; mean 71.0, SD 15.2) and patients (n=5; mean 95.5, SD 2.1). Participants also reported high levels of acceptability, appropriateness, and feasibility. Additional CFIR-informed feedback, such as desired format for training, will inform future studies. CONCLUSIONS: The data collected in this study support the initial usability of COMPASS-CP CFC and will inform the next steps for implementation in clinical care. COMPASS-CP CFC has the potential to streamline the implementation of personalized CFC planning to enable systematic access to resources that will support self-management. Future research is needed to test the impact of COMPASS-CP CFC on patient health outcomes.

A transformer-based deep-learning approach for classifying brain metastases into primary organ sites using clinical whole-brain MRI images.

Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site and are currently made with biopsy and histology. Here, we develop a deep-learning approach for accurate non-invasive digital histology with whole-brain magnetic resonance imaging (MRI) data. Contrast-enhanced T1-weighted and fast spoiled gradient echo brain MRI exams (n = 1,582) were preprocessed and input to the proposed deep-learning workflow for tumor segmentation, modality transfer, and primary site classification into one of five classes. Tenfold cross-validation generated an overall area under the receiver operating characteristic curve (AUC) of 0.878 (95% confidence interval [CI]: 0.873,0.883). These data establish that whole-brain imaging features are discriminative enough to allow accurate diagnosis of the primary organ site of malignancy. Our end-to-end deep radiomic approach has great potential for classifying metastatic tumor types from whole-brain MRI images. Further refinement may offer an invaluable clinical tool to expedite primary cancer site identification for precision treatment and improved outcomes.

Patient-reported outcomes via electronic health record portal versus telephone: a pragmatic randomized pilot trial of anxiety or depression symptoms in epilepsy.

Objective: To close gaps between research and clinical practice, tools are needed for efficient pragmatic trial recruitment and patient-reported outcome collection. The objective was to assess feasibility and process measures for patient-reported outcome collection in a randomized trial comparing electronic health record (EHR) patient portal questionnaires to telephone interview among adults with epilepsy and anxiety or depression symptoms. Materials and Methods: Recruitment for the randomized trial began at an epilepsy clinic visit, with EHR-embedded validated anxiety and depression instruments, followed by automated EHR-based research screening consent and eligibility assessment. Fully eligible individuals later completed telephone consent, enrollment, and randomization. Participants were randomized 1:1 to EHR portal versus telephone outcome assessment, and patient-reported and process outcomes were collected at 3 and 6 months, with primary outcome 6-month retention in EHR arm (feasibility target: ≥11 participants retained). Results: Participants (N = 30) were 60% women, 77% White/non-Hispanic, with mean age 42.5 years. Among 15 individuals randomized to EHR portal, 10 (67%, CI 41.7%-84.8%) met the 6-month retention endpoint, versus 100% (CI 79.6%-100%) in the telephone group (P = 0.04). EHR outcome collection at 6 months required 11.8 min less research staff time per participant than telephone (5.9, CI 3.3-7.7 vs 17.7, CI 14.1-20.2). Subsequent telephone contact after unsuccessful EHR attempts enabled near complete data collection and still saved staff time. Discussion: In this randomized study, EHR portal outcome assessment did not meet the retention feasibility target, but EHR method saved research staff time compared to telephone. Conclusion: While EHR portal outcome assessment was not feasible, hybrid EHR/telephone method was feasible and saved staff time.

Applying FHIR Genomics for Research - From Sequencing to Database.

The availability of next-generation sequencing (NGS) technologies and their continually declining costs have resulted in the accumulation of large genomic data sets. NGS results have traditionally been delivered in PDF format, and in some cases, structured data, e.g., XML or JSON formats, are also made available, but there is a lack of uniformity around the profiling of external vendor testing platforms. Atrium Health Wake Forest Baptist and TriNetX have harmonized and mapped genomic data to FHIR Genomic standards and imported it into the TriNetX database through a data pipeline. This process is translatable to other sequencing platforms and to other institutions. The addition of genotypic data to the TriNetX database to the reservoir of phenotypic data will promote enhanced industry trial recruitment, (ii) comprehensive intra-institutional genomic benchmarking/quality improvement, and eventually (iii) sweeping inter-institutional genomic research and treatment paradigm shifts.

VOC-alarm: mutation-based prediction of SARS-CoV-2 variants of concern.

SUMMARY: Mutation is the key for a variant of concern (VOC) to overcome selective pressures, but this process is still unclear. Understanding the association of the mutational process with VOCs is an unmet need. Motivation: Here, we developed VOC-alarm, a method to predict VOCs and their caused COVID surges, using mutations of about 5.7 million SARS-CoV-2 complete sequences. We found that VOCs rely on lineage-level entropy value of mutation numbers to compete with other variants, suggestive of the importance of population-level mutations in the virus evolution. Thus, we hypothesized that VOCs are a result of a mutational process across the globe. Results: Analyzing the mutations from January 2020 to December 2021, we simulated the mutational process by estimating the pace of evolution, and thus divided the time period, January 2020-March 2022, into eight stages. We predicted Alpha, Delta, Delta Plus (AY.4.2) and Omicron (B.1.1.529) by their mutational entropy values in the Stages I, III, V and VII with accelerated paces, respectively. In late November 2021, VOC-alarm alerted that Omicron strongly competed with Delta and Delta plus to become a highly transmissible variant. Using simulated data, VOC-alarm also predicted that Omicron could lead to another COVID surge from January 2022 to March 2022. AVAILABILITY AND IMPLEMENTATION: Our software implementation is available at https://github.com/guangxujin/VOC-alarm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exosomal miR-4466 from nicotine-activated neutrophils promotes tumor cell stemness and metabolism in lung cancer metastasis.

Smoking is associated with lung cancer and has a profound impact on tumor immunity. Nicotine, the addictive and non-carcinogenic smoke component, influences various brain cells and the immune system. However, how long-term use of nicotine affects brain metastases is poorly understood. We, therefore, examined the mechanism by which nicotine promotes lung cancer brain metastasis. In this study, we conducted a retrospective analysis of 810 lung cancer patients with smoking history and assessed brain metastasis. We found that current smoker's lung cancer patients have significantly higher brain metastatic incidence compared to the never smokers. We also found that chronic nicotine exposure recruited STAT3-activated N2-neutrophils within the brain pre-metastatic niche and secreted exosomal miR-4466 which promoted stemness and metabolic switching via SKI/SOX2/CPT1A axis in the tumor cells in the brain thereby enabling metastasis. Importantly, exosomal miR-4466 levels were found to be elevated in serum/urine of cancer-free subjects with a smoking history and promote tumor growth in vivo, suggesting that exosomal miR-4466 may serve as a promising prognostic biomarker for predicting increased risk of metastatic disease among smoker(s). Our findings suggest a novel pro-metastatic role of nicotine-induced N2-neutrophils in the progression of brain metastasis. We also demonstrated that inhibiting nicotine-induced STAT3-mediated neutrophil polarization effectively abrogated brain metastasis in vivo. Our results revealed a novel mechanistic insight on how chronic nicotine exposure contributes to worse clinical outcome of metastatic lung cancer and implicated the risk of using nicotine gateway for smoking cessation in cancer patients.

Risk and Outcome of Breakthrough COVID-19 Infections in Vaccinated Patients With Cancer: Real-World Evidence From the National COVID Cohort Collaborative.

PURPOSE: To provide real-world evidence on risks and outcomes of breakthrough COVID-19 infections in vaccinated patients with cancer using the largest national cohort of COVID-19 cases and controls. METHODS: We used the National COVID Cohort Collaborative (N3C) to identify breakthrough infections between December 1, 2020, and May 31, 2021. We included patients partially or fully vaccinated with mRNA COVID-19 vaccines with no prior SARS-CoV-2 infection record. Risks for breakthrough infection and severe outcomes were analyzed using logistic regression. RESULTS: A total of 6,860 breakthrough cases were identified within the N3C-vaccinated population, among whom 1,460 (21.3%) were patients with cancer. Solid tumors and hematologic malignancies had significantly higher risks for breakthrough infection (odds ratios [ORs] = 1.12, 95% CI, 1.01 to 1.23 and 4.64, 95% CI, 3.98 to 5.38) and severe outcomes (ORs = 1.33, 95% CI, 1.09 to 1.62 and 1.45, 95% CI, 1.08 to 1.95) compared with noncancer patients, adjusting for age, sex, race/ethnicity, smoking status, vaccine type, and vaccination date. Compared with solid tumors, hematologic malignancies were at increased risk for breakthrough infections (adjusted OR ranged from 2.07 for lymphoma to 7.25 for lymphoid leukemia). Breakthrough risk was reduced after the second vaccine dose for all cancers (OR = 0.04; 95% CI, 0.04 to 0.05), and for Moderna's mRNA-1273 compared with Pfizer's BNT162b2 vaccine (OR = 0.66; 95% CI, 0.62 to 0.70), particularly in patients with multiple myeloma (OR = 0.35; 95% CI, 0.15 to 0.72). Medications with major immunosuppressive effects and bone marrow transplantation were strongly associated with breakthrough risk among the vaccinated population. CONCLUSION: Real-world evidence shows that patients with cancer, especially hematologic malignancies, are at higher risk for developing breakthrough infections and severe outcomes. Patients with vaccination were at markedly decreased risk for breakthrough infections. Further work is needed to assess boosters and new SARS-CoV-2 variants.

Creating learning health systems and the emerging role of biomedical informatics.

INTRODUCTION: The nature of information used in medicine has changed. In the past, we were limited to routine clinical data and published clinical trials. Today, we deal with massive, multiple data streams and easy access to new tests, ideas, and capabilities to process them. Whereas in the past getting information for decision-making was a challenge, now, it is how to analyze, evaluate and prioritize all that is readily available through the multitude of data-collecting devices. Clinicians must become adept with the tools needed to deal with the era of big data, requiring a major change in how we learn to make decisions. Major change is often met with resistance and questions about value. A Learning Health System is an enabler to encourage the development of such tools and demonstrate value in improved decision-making. METHODS: We describe how we are developing a Biomedical Informatics program to help our medical institution's evolution as an academic Learning Health System, including strategy, training for house staff and examples of the role of informatics from operations to research. RESULTS: We described an array of learning health system implementations and educational programs to improve healthcare and prepare a cadre of physicians with basic information technology skills. The programs have been well accepted with, for example, increasing interest and enrollment in the educational programs. CONCLUSIONS: We are now in an era when large volumes of a wide variety of data are readily available. The challenge is not so much in the acquisition of data, but in assessing the quality, relevance and value of the data. The data we can get may not be the data we need. In the past, sources of data were limited, and trial results published in journals were the major source of evidence for decision making. The advent of powerful analytics systems has changed the concept of evidence. Clinicians will have to develop the skills necessary to work in the era of big data. It is not reasonable to expect that all clinicians will also be data scientists. However, understanding the role of AI and predictive analytics, and how to apply them, will become progressively more important. Programs such as the one being implemented at Wake Forest fill that need.