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.

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.

SMART COVID Navigator, a Clinical Decision Support Tool for COVID-19 Treatment: Design and Development Study.

BACKGROUND: COVID-19 caused by SARS-CoV-2 has infected 219 million individuals at the time of writing of this paper. A large volume of research findings from observational studies about disease interactions with COVID-19 is being produced almost daily, making it difficult for physicians to keep track of the latest information on COVID-19's effect on patients with certain pre-existing conditions. OBJECTIVE: In this paper, we describe the creation of a clinical decision support tool, the SMART COVID Navigator, a web application to assist clinicians in treating patients with COVID-19. Our application allows clinicians to access a patient's electronic health records and identify disease interactions from a large set of observational research studies that affect the severity and fatality due to COVID-19. METHODS: The SMART COVID Navigator takes a 2-pronged approach to clinical decision support. The first part is a connection to electronic health record servers, allowing the application to access a patient's medical conditions. The second is accessing data sets with information from various observational studies to determine the latest research findings about COVID-19 outcomes for patients with certain medical conditions. By connecting these 2 data sources, users can see how a patient's medical history will affect their COVID-19 outcomes. RESULTS: The SMART COVID Navigator aggregates patient health information from multiple Fast Healthcare Interoperability Resources-enabled electronic health record systems. This allows physicians to see a comprehensive view of patient health records. The application accesses 2 data sets of over 1100 research studies to provide information on the fatality and severity of COVID-19 for several pre-existing conditions. We also analyzed the results of the collected studies to determine which medical conditions result in an increased chance of severity and fatality of COVID-19 progression. We found that certain conditions result in a higher likelihood of severity and fatality probabilities. We also analyze various cancer tissues and find that the probabilities for fatality vary greatly depending on the tissue being examined. CONCLUSIONS: The SMART COVID Navigator allows physicians to predict the fatality and severity of COVID-19 progression given a particular patient's medical conditions. This can allow physicians to determine how aggressively to treat patients infected with COVID-19 and to prioritize different patients for treatment considering their prior medical conditions.

Bipolar disorder: Trimodal age-at-onset distribution.

OBJECTIVE: Bipolar disorder (BD) is a chronic mental health disorder with significant morbidity and mortality. Age at onset (AAO) may be a key variable in delineating more homogeneous subgroups of BD patients. However, no known research has systematically assessed how BD age-at-onset subgroups should be defined. METHODS: We systematically searched the following databases: Cochrane Central Register of Controlled Trials, PsycINFO, MEDLINE, Embase, CINAHL, Scopus, Proquest Dissertations and Theses, Google Scholar and BIOSIS Previews. Original quantitative English language studies investigating AAO in BD were sought. RESULTS: A total of 9454 unique publications were identified. Twenty-one of these were included in data analysis (n = 22981 BD participants). Fourteen of these studies (67%, n = 13626 participants) found a trimodal AAO distribution: early-onset (µ = 17.3, σ = 1.19, 45% of sample), mid-onset (µ = 26.0, σ = 1.72, 35%), and late-onset (µ = 41.9, σ = 6.16, 20%). Five studies (24%, n = 1422 participants) described a bimodal AAO distribution: early-onset (µ = 24.3, σ = 6.57, 66% of sample) and late-onset (µ = 46.3, σ = 14.15, 34%). Two studies investigated cohort effects on BD AAO and found that when the sample was not split by cohort, a trimodal AAO was the winning model, but when separated by cohort a bimodal distribution fit the data better. CONCLUSIONS: We propose that the field conceptualises bipolar disorder age-at-onset subgroups as referring broadly to life stages. Demarcating BD AAO groups can inform treatment and provide a framework for future research to continue to investigate potential mechanisms of disease onset.

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.

Office-Based Forehead Flaps: A Safe and Reliable Reconstructive Option.

BACKGROUND: Forehead flaps are one of the workhorse flaps for nasal reconstruction, especially for large defects involving the nasal tip, ala, or multiple nasal subunits. Forehead flaps are often performed on older patients who have accompanying comorbidities and who may be at higher risk for anesthetic complications. The aim of this retrospective study was to compare the safety and success of forehead flap nasal reconstruction in 2 different clinical settings: those performed under local anesthesia in an office-based procedure room, compared with those performed in the operating room under either general anesthesia or intravenous sedation. METHODS: A retrospective chart review was performed on all patients who underwent forehead flap reconstruction between the years of 2011 and 2018 by the senior author. Patient demographics, operative details, and postoperative complications were recorded and analyzed. Patients were followed for 1 year postoperatively or until the end of the study period. Patients were excluded if they had an accompanying unrelated cosmetic procedures performed during first-stage forehead flap reconstruction. RESULTS: A total of 96 forehead flaps were performed, 35 of which (36.5%) were done in an office-based procedure room using local anesthesia only. Patient ages ranged from 45 to 92 years, with an average age of 71.9 years. The majority of flaps (n = 81, 85.3%) were divided at the second-stage procedure. There was no statistically significant difference in time elapsed between first- and second-stage procedures between groups (procedure room: 22.6 days; operating room: 23.8 days). There were 13 total postoperative complications (13.5%), but there was no statistically significant difference in complication rate between groups (office-based: 3 complications, 8.6%; operating room: 10 complications, 16.4%). CONCLUSIONS: Our data suggest that forehead flap reconstruction can be done safely with acceptable postoperative results when performed under local anesthesia in an office-based outpatient setting.

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.

Patient satisfaction with nipple-sparing mastectomy: A prospective study of patient reported outcomes using the BREAST-Q.

BACKGROUND AND OBJECTIVES: The authors sought to study patient-reported outcomes following nipple-sparing mastectomy (NSM). METHODS: From 2008 to 2011, the BREAST-Q was administered to women undergoing NSM surgery for cancer treatment or risk-reduction prior to surgery and at 2 years after completion of reconstruction. The change in score over time and the impact of surgical indication, complication occurrence, and laterality on scores were analyzed. RESULTS: The BREAST-Q was prospectively administered to 39 women undergoing NSM for cancer treatment (n = 17) or risk-reduction (RR) (n = 22). At 2 years after operation, median overall satisfaction with breasts was 75 (IQR = 67,100). There were significant postoperative increases in scores for overall satisfaction with breasts (+8, P = 0.021) and psychosocial well-being (+14, P = 0.003). Postoperatively, RR patients had significantly higher scores for psychosocial wellness, physical impact (chest), and overall satisfaction with outcome compared to cancer treatment patients (P < 0.05). Also, increase from preoperative to postoperative psychosocial wellness was higher in the RR compared to cancer treatment patients (+17 vs. +1, P = 0.043). Complication occurrence did not significantly impact postoperative scores. CONCLUSIONS: Following NSM for cancer treatment or RR, patients demonstrated high levels of satisfaction and quality of life as measured by BREAST-Q. Satisfaction level increased 2 years following operation. J. Surg. Oncol. 2016;114:416-422. © 2016 Wiley Periodicals, Inc.

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.

Graphical Presentations in Systemic Anticancer Treatment Network Meta-Analyses: A Systematic Review.

Network meta-analysis (NMA) draws conclusions about indirect comparisons of randomized clinical trials and is considered high-level evidence. Most NMA publications make use of network plots to portray results. Network plots are complex graphics that can have many visual attributes to portray useful information, such as node size, color, and graph layout. We analyzed the network plots from 162 NMAs of systemic anticancer therapy efficacy using a set of 16 attributes. Our review showed that the current state of network plot data visualizations within the NMA space lacks diversity and does not make use of many of the visual attributes available to convey information. More thoughtful design choices should be placed behind these important visualizations, which can carry clinical significance and help derive treatment plans for patients.

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.

The evolving posology and administration of immune checkpoint inhibitors: subcutaneous formulations.

Immune checkpoint inhibitors (ICIs) have transformed cancer care. Recently, atezolizumab gained its first global approval in a subcutaneous (SC) formulation by the UK medicines regulator, being notable as the first time an FDA- and/or European Medicines Agency (EMA)-approved ICI has been licensed via this administration route. Here, we discuss this approval, other SC ICIs in development, and the benefits and challenges of this administration route, including potential implications for patient care.

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.

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.