Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics.

MOTIVATION: Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. RESULTS: Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. AVAILABILITY AND IMPLEMENTATION: phecodeX is available at https://github.com/PheWAS/phecodeX.

Oral antivirals for COVID-19 among patients with cancer.

PURPOSE: Immunocompromised individuals, such as those diagnosed with cancer, are at a significantly higher risk for severe illness and mortality when infected with SARS-CoV-2 (COVID-19) than the general population. Two oral antiviral treatments are approved for COVID-19: Paxlovid® (nirmatrelvir/ritonavir) and Lagevrio® (molnupiravir). There is a paucity of data regarding the benefit from these antivirals among immunocompromised patients with cancer, and recent studies have questioned their efficacy among vaccinated patients, even those with risk factors for severe COVID-19. METHODS: We evaluated the efficacy and safety of nirmatrelvir/ritonavir and molnupiravir in preventing severe illness and death using our database of 457 patients with cancer and COVID-19 from Brown University-affiliated hospitals. RESULTS: Sixty-seven patients received nirmatrelvir/ritonavir or molnupiravir and were compared to 45 concurrent controls who received no antiviral treatment despite being eligible to receive it. Administration of nirmatrelvir/ritonavir or molnupiravir was associated with improved survival and lower 90-day all-cause and COVID-19-attributed mortality (p < 0.05) and with lower peak O2 requirements (ordinal odds ratio [OR] 1.52, 95% confidence interval [CI] 0.92-2.56). CONCLUSION: Acknowledging the small size of our sample as a limitation, we concluded that early antiviral treatment might be beneficial to immunocompromised individuals, particularly those with cancer, when infected with SARS-CoV-2. Larger-scale, well-stratified studies are needed in this patient population.

Impact of COVID-19 in patients on active melanoma therapy and with history of melanoma.

INTRODUCTION: COVID-19 particularly impacted patients with co-morbid conditions, including cancer. Patients with melanoma have not been specifically studied in large numbers. Here, we sought to identify factors that associated with COVID-19 severity among patients with melanoma, particularly assessing outcomes of patients on active targeted or immune therapy. METHODS: Using the COVID-19 and Cancer Consortium (CCC19) registry, we identified 307 patients with melanoma diagnosed with COVID-19. We used multivariable models to assess demographic, cancer-related, and treatment-related factors associated with COVID-19 severity on a 6-level ordinal severity scale. We assessed whether treatment was associated with increased cardiac or pulmonary dysfunction among hospitalized patients and assessed mortality among patients with a history of melanoma compared with other cancer survivors. RESULTS: Of 307 patients, 52 received immunotherapy (17%), and 32 targeted therapy (10%) in the previous 3 months. Using multivariable analyses, these treatments were not associated with COVID-19 severity (immunotherapy OR 0.51, 95% CI 0.19 - 1.39; targeted therapy OR 1.89, 95% CI 0.64 - 5.55). Among hospitalized patients, no signals of increased cardiac or pulmonary organ dysfunction, as measured by troponin, brain natriuretic peptide, and oxygenation were noted. Patients with a history of melanoma had similar 90-day mortality compared with other cancer survivors (OR 1.21, 95% CI 0.62 - 2.35). CONCLUSIONS: Melanoma therapies did not appear to be associated with increased severity of COVID-19 or worsening organ dysfunction. Patients with history of melanoma had similar 90-day survival following COVID-19 compared with other cancer survivors.

Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC).

PURPOSE: Several professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. Although these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (eg, population frequency, functional, and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation, and, importantly, affects patient care. Therefore, it is essential to address this unmet need. METHODS: Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium, and the Variant Interpretation for Cancer Consortium used a consensus approach to develop a standard operating procedure (SOP) for the classification of oncogenicity of somatic variants. RESULTS: This comprehensive SOP has been developed to improve consistency in somatic variant classification and has been validated on 94 somatic variants in 10 common cancer-related genes. CONCLUSION: The comprehensive SOP is now available for classification of oncogenicity of somatic variants.

A retrospective approach to evaluating potential adverse outcomes associated with delay of procedures for cardiovascular and cancer-related diagnoses in the context of COVID-19.

OBJECTIVE: During the COVID-19 pandemic, health systems postponed non-essential medical procedures to accommodate surge of critically-ill patients. The long-term consequences of delaying procedures in response to COVID-19 remains unknown. We developed a high-throughput approach to understand the impact of delaying procedures on patient health outcomes using electronic health record (EHR) data. MATERIALS AND METHODS: We used EHR data from Vanderbilt University Medical Center's (VUMC) Research and Synthetic Derivatives. Elective procedures and non-urgent visits were suspended at VUMC between March 18, 2020 and April 24, 2020. Surgical procedure data from this period were compared to a similar timeframe in 2019. Potential adverse impact of delay in cardiovascular and cancer-related procedures was evaluated using EHR data collected from January 1, 1993 to March 17, 2020. For surgical procedure delay, outcomes included length of hospitalization (days), mortality during hospitalization, and readmission within six months. For screening procedure delay, outcomes included 5-year survival and cancer stage at diagnosis. RESULTS: We identified 416 surgical procedures that were negatively impacted during the COVID-19 pandemic compared to the same timeframe in 2019. Using retrospective data, we found 27 significant associations between procedure delay and adverse patient outcomes. Clinician review indicated that 88.9% of the significant associations were plausible and potentially clinically significant. Analytic pipelines for this study are available online. CONCLUSION: Our approach enables health systems to identify medical procedures affected by the COVID-19 pandemic and evaluate the effect of delay, enabling them to communicate effectively with patients and prioritize rescheduling to minimize adverse patient outcomes.

HemOnc: A new standard vocabulary for chemotherapy regimen representation in the OMOP common data model.

Systematic application of observational data to the understanding of impacts of cancer treatments requires detailed information models allowing meaningful comparisons between treatment regimens. Unfortunately, details of systemic therapies are scarce in registries and data warehouses, primarily due to the complex nature of the protocols and a lack of standardization. Since 2011, we have been creating a curated and semi-structured website of chemotherapy regimens, HemOnc.org. In coordination with the Observational Health Data Sciences and Informatics (OHDSI) Oncology Subgroup, we have transformed a substantial subset of this content into the OMOP common data model, with bindings to multiple external vocabularies, e.g., RxNorm and the National Cancer Institute Thesaurus. Currently, there are >73,000 concepts and >177,000 relationships in the full vocabulary. Content related to the definition and composition of chemotherapy regimens has been released within the ATHENA tool (athena.ohdsi.org) for widespread utilization by the OHDSI membership. Here, we describe the rationale, data model, and initial contents of the HemOnc vocabulary along with several use cases for which it may be valuable.

Systematic review of infectious events with the Bruton tyrosine kinase inhibitor ibrutinib in the treatment of hematologic malignancies.

OBJECTIVE: Ibrutinib is an irreversible inhibitor of Bruton tyrosine kinase (BTK) in B lymphocytes as well as other kinases including interleukin-2-inducible T-cell kinase (ITK) in CD4+ Th2 regulatory T cells. Increased infections have been observed in patients taking ibrutinib. The overall incidence has not been systematically evaluated. METHODS: The published literature and conference abstracts of prospective clinical trials using ibrutinib in hematologic malignancies were identified and reviewed using PubMed, Google Scholar, and HemOnc.org per PRISMA guidelines. Infectious events with a focus on pneumonia were collated per the Common Terminology Criteria for Adverse Events Version 4.03 grading. RESULTS: Infectious complications are common, occurring in 56% of patients taking single-agent ibrutinib and 52% of those on combination therapy. Approximately one in 5 patients developed pneumonia, which was the major contributor to a 2% rate of death from infections. Many of the cases of pneumonia were due to opportunistic pathogens. CONCLUSIONS: Ibrutinib use requires prudent consideration of the impacts on host immunity. We identified a high rate of serious adverse infectious events within prospective clinical trials. Data suggest a role of both BTK and ITK inhibition for the increased events. There was considerable variability in the reporting of adverse events between trials, journals, and conference reports.

Classification of hospital acquired complications using temporal clinical information from a large electronic health record.

Hospital acquired complications (HACs) are serious problems affecting modern day healthcare institutions. It is estimated that HACs result in an approximately 10% increase in total inpatient hospital costs across US hospitals. With US hospital spending totaling nearly $900 billion per annum, the damages caused by HACs are no small matter. Early detection and prevention of HACs could greatly reduce strains on the US healthcare system and improve patient morbidity & mortality rates. Here, we describe a machine-learning model for predicting the occurrence of HACs within five distinct categories using temporal clinical data. Using our approach, we find that at least $10 billion of excessive hospital costs could be saved in the US alone, with the institution of effective preventive measures. In addition, we also identify several keystone features that demonstrate high predictive power for HACs over different time periods following patient admission. The classifiers and features analyzed in this study show high promise of being able to be used for accurate prediction of HACs in clinical settings, and furthermore provide novel insights into the contribution of various clinical factors to the risk of developing HACs as a function of healthcare system exposure.

Introducing a Comprehensive Score of Systemic Anticancer Treatment Relevance.

Treatment patterns in systemic anticancer therapy (SACT) are extremely varied and complex. While professional society guidelines exist that suggest recommended treatment strategies, these guidelines are produced through an extremely laborious and sometimes opaque manual process, making it impossible for such guidelines to cover all relevant treatment scenarios. To complement these manually curated guidelines, we leveraged a database of 5818 clinical trials and 7012 supporting references from 1943-present to calculate a quantifiable "relevance score". In a pilot evaluation, this score was strongly associated with professional society guideline recommendations, while also providing relevance information on thousands of additional therapies. We show that this score also accurately illustrates trends in SACT adoption over time. We foresee that this score, which comprehensively evaluates the relevance of SACT overall and by cancer subtype, will have utility for clinical practitioners as well as researchers in real-world data.

Oral antivirals for COVID-19 among patients with cancer.

Purpose: Immunocompromised individuals, such as those diagnosed with cancer, are at a significantly higher risk for severe illness and mortality when infected with SARS-CoV-2 (COVID-19) than the general population. Two oral antiviral treatments are approved for COVID-19: Paxlovid® (nirmatrelvir/ritonavir) and Lagevrio® (molnupiravir). There is a paucity of data regarding the benefit from these antivirals among immunocompromised patients with cancer, and recent studies have questioned their efficacy among vaccinated patients, even those with risk factors for severe COVID-19. Methods: We evaluated the efficacy and safety of nirmatrelvir/ritonavir and molnupiravir in preventing severe illness and death using our database of 457 patients with cancer and COVID-19 from Brown University-affiliated hospitals. 67 patients received nirmatrelvir/ritonavir or molnupiravir and were compared to 56 concurrent controls who received no antiviral treatment despite being eligible to receive it. Results: Administration of nirmatrelvir/ritonavir or molnupiravir was associated with improved survival and lower 90-day all-cause and COVID-19-attributed mortality (p<0.05) and with lower peak O2 requirements (ordinal odds ratio [OR] 1.52, 95% confidence interval [CI] 0.92-2.56). Conclusion: Acknowledging the small size of our sample as a limitation, we concluded that early antiviral treatment might be beneficial to immunocompromised individuals, particularly those with cancer, when infected with SARS-CoV-2. Larger-scale, well-stratified studies are needed in this patient population.

Telehealth and Technology: New Directions in Cancer Care.

ABSTRACT: Telehealth is a broad concept that refers to any delivery of health care in real time using technologies to connect people or information that are not in the same physical location. Until fairly recently, telehealth was more aspiration than reality. This situation changed radically due in part to the COVID-19 pandemic, which led to a near-overnight inability for patients to be seen for routine management of chronic health conditions, including those with cancer. The purpose of this brief narrative review is to outline some areas where emerging and future technology may allow for innovations with specific implications for people with a current or past diagnosis of cancer, including underserved and/or historically excluded populations. Specific topics of telehealth are broadly covered in other areas of the special issue.

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition.

PURPOSE: The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results. METHODS: We proposed a complex, hybrid NLP system that automates the process of extracting, linking, and summarizing anticancer therapy and associated RECIST-like responses from narrative clinical text. The system consists of multiple machine learning-/deep learning-based and rule-based modules for diverse NLP tasks such as named entity recognition, assertion classification, relation extraction, and text normalization, to address different challenges associated with anticancer therapy and response information extraction. We then evaluated the system performances on two independent test sets from different institutions to demonstrate its effectiveness and generalizability. RESULTS: The system used domain-specific language models, BioBERT and BioClinicalBERT, for high-performance therapy mentions identification and RECIST responses extraction and categorization. The best-performing model achieved a 0.66 score in linking therapy and RECIST response mentions, with end-to-end performance peaking at 0.74 after relation normalization, indicating substantial efficacy with room for improvement. CONCLUSION: We developed, implemented, and tested an information extraction system from clinical notes for cancer treatment and efficacy assessment information. We expect this system will support future cancer research, particularly oncologic studies that focus on efficiently assessing the effectiveness and reliability of cancer therapeutics.

Optimizing word embeddings for small dataset: a case study on patient portal messages from breast cancer patients.

Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec (where PK stands for prior knowledge), for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec in a case study of patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7981 non-medical and 1116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p = 0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers ( p = 0.774 under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of Samples.

Background: Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. Objective: We introduce a novel adaptation of the word2vec model, PK-word2vec, for small-scale messages. Methods: PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec on patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. Results: The dataset was composed of 1,389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7,981 non-medical and 1,116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p=0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers (p = 0.774 under a paired t-test). Conclusions: PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Travel-Time Disparities in Access to Proton Beam Therapy for Cancer Treatment.

Importance: Proton beam therapy is an emerging radiotherapy treatment for patients with cancer that may produce similar outcomes as traditional photon-based therapy for many cancers while delivering lower amounts of toxic radiation to surrounding tissue. Geographic proximity to a proton facility is a critical component of ensuring equitable access both for indicated diagnoses and ongoing clinical trials. Objective: To characterize the distribution of proton facilities in the US, quantify drive-time access for the population, and investigate the likelihood of long commutes for certain population subgroups. Design, Setting, and Participants: This population-based cross-sectional study analyzed travel times to proton facilities in the US. Census tract variables in the contiguous US were measured between January 1, 2017, and December 31, 2021. Statistical analysis was performed from September to November 2023. Exposures: Drive time in minutes to nearest proton facility. Population totals and prevalence of specific factors measured from the American Community Survey: age; race and ethnicity; insurance, disability, and income status; vehicle availability; broadband access; and urbanicity. Main Outcomes and Measures: Poor access to proton facilities was defined as having a drive-time commute of at least 4 hours to the nearest location. Median drive time and percentage of population with poor access were calculated for the entire population and by population subgroups. Univariable and multivariable odds of poor access were also calculated for certain population subgroups. Results: Geographic access was considered for 327 536 032 residents of the contiguous US (60 594 624 [18.5%] Hispanic, 17 974 186 [5.5%] non-Hispanic Asian, 40 146 994 [12.3%] non-Hispanic Black, and 195 265 639 [59.6%] non-Hispanic White; 282 031 819 [86.1%] resided in urban counties). The median (IQR) drive time to the nearest proton facility was 96.1 (39.6-195.3) minutes; 119.8 million US residents (36.6%) lived within a 1-hour drive of the nearest proton facility, and 53.6 million (16.4%) required a commute of at least 4 hours. Persons identifying as non-Hispanic White had the longest median (IQR) commute time at 109.8 (48.0-197.6) minutes. Multivariable analysis identified rurality (odds ratio [OR], 2.45 [95% CI, 2.27-2.64]), age 65 years or older (OR, 1.09 [95% CI, 1.06-1.11]), and living below the federal poverty line (OR, 1.22 [1.20-1.25]) as factors associated with commute times of at least 4 hours. Conclusions and Relevance: This cross-sectional study of drive-time access to proton beam therapy found that disparities in access existed among certain populations in the US. These results suggest that such disparities present a barrier to an emerging technology in cancer treatment and inhibit equitable access to ongoing clinical trials.

SEETrials: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trials.

Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making. Methods: We collected clinical trial abstracts from key conferences and PubMed (2012-2023). The SEETrials system was developed with four modules: preprocessing, prompt modeling, knowledge ingestion and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types- multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies. Results: SEETrials achieved high precision (0.958), recall (sensitivity) (0.944), and F1 score (0.951) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.966-0.986 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88%, 95% CI: 84-92%; CR: 95%, 95% CI: 53-66%), bispecific antibodies (ORR: 64%, 95% CI: 55-73%; CR: 27%, 95% CI: 16-37%), and ADC (ORR: 51%, 95% CI: 37-65%; CR: 26%, 95% CI: 1-51%). Notable study heterogeneity was identified (>75% I 2 heterogeneity index scores) across several outcome entities analyzed within therapy subgroups. Conclusion: SEETrials demonstrated highly accurate data extraction and versatility across different therapeutics and various cancer domains. Its automated processing of large datasets facilitates nuanced data comparisons, promoting the swift and effective dissemination of clinical insights.

Frontal and anterior temporal hypometabolism post chemoradiation in head and neck cancer: A real-world PET study.

BACKGROUND AND PURPOSE: Adverse neurological effects after cancer therapy are common, but biomarkers to diagnose, monitor, or risk stratify patients are still not validated or used clinically. An accessible imaging method, such as fluorodeoxyglucose positron emission tomography (FDG PET) of the brain, could meet this gap and serve as a biomarker for functional brain changes. We utilized FDG PET to evaluate which brain regions are most susceptible to altered glucose metabolism after chemoradiation in patients with head and neck cancer (HNCa). METHODS: Real-world FDG PET images were acquired as standard of care before and after chemoradiation for HNCa in 68 patients. Linear mixed-effects voxelwise models assessed changes after chemoradiation in cerebral glucose metabolism quantified with standardized uptake value ratio (SUVR), covarying for follow-up time and patient demographics. RESULTS: Voxelwise analysis revealed two large clusters of decreased glucose metabolism in the medial frontal and polar temporal cortices following chemoradiation, with decreases of approximately 5% SUVR after therapy. CONCLUSIONS: These findings provide evidence that standard chemoradiation for HNCa can lead to decreased neuronal glucose metabolism, contributing to literature emphasizing the vulnerability of the frontal and anterior temporal lobes, especially in HNCa, where these areas may be particularly vulnerable to indirect radiation-induced injury. FDG PET shows promise as a sensitive biomarker for assessing these changes.

Overcome the Limitation of Phenome-Wide Association Studies (PheWAS): Extension of PheWAS to Efficient and Robust Large-Scale ICD Codes Analysis.

The Phenome-wide association studies (PheWAS) have become widely used for efficient, high-throughput evaluation of relationship between a genetic factor and a large number of disease phenotypes, typically extracted from a DNA biobank linked with electronic medical records (EMR). Phecodes, billing code-derived disease case-control status, are usually used as outcome variables in PheWAS and logistic regression has been the standard choice of analysis method. Since the clinical diagnoses in EMR are often inaccurate with errors which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classify controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased odds ratios. However, the accuracy of the list cannot be guaranteed without extensive data curation process. The costly curation process limits the efficiency of large-scale analyses that take full advantage of all structured phenotypic information available in EMR. Here, we proposed to estimate relative risks (RR) instead. We first demonstrated the desired nature of RR that overcomes the inaccuracy in the controls via theoretical formula. With simulation and real data application, we further confirmed that RR is unbiased without compiling exclusion criteria lists. With RR as estimates, we are able to efficiently extend PheWAS to a larger-scale, phenome construction agnostic analysis of phenotypes, using ICD 9/10 codes, which preserve much more disease-related clinical information than Phecodes.