Immune profiling of patients with extranodal natural killer/T cell lymphoma treated with daratumumab.

Natural killer/T cell lymphoma (NKTCL) is a highly aggressive, heterogeneous non-Hodgkin lymphoma resulting from malignant proliferation of cytotoxic natural killer (NK) or T cells. Previous studies demonstrated variable expression of CD38 on NKTCL tumors. Daratumumab, a human IgGκ monoclonal antibody targeting CD38 with a direct on-tumor and immunomodulatory mechanism of action, was hypothesized to be a novel therapeutic option for patients with relapsed or refractory (R/R) NKTCL. In the phase 2 NKT2001 study (ClinicalTrials.gov Identifier: NCT02927925) assessing the safety and efficacy of daratumumab, a suboptimal overall response rate was seen in R/R NKTCL patients. One patient, whose tumors did not express CD38, responded to treatment, suggesting that the immunomodulatory activities of daratumumab may be sufficient to confer clinical benefit. To understand the suboptimal response rate and short duration of response, we investigated the immune profile of NKTCL patients from NKT2001 in the context of daratumumab anti-tumor activity. Tumor tissue and whole blood were, respectively, analyzed for CD38 expression and patient immune landscapes, which were assessed via cytometry by time-of-flight (CyTOF), multiparameter flow cytometry (MPFC), clonal sequencing, and plasma Epstein-Barr virus (EBV)-DNA level measurements. Changes observed in the immune profiles of NKTCL patients from NKT2001, including differences in B and T cell populations between responders and nonresponders, suggest that modulation of the immune environment is crucial for daratumumab anti-tumor activities in NKTCL. In conclusion, these findings highlight that the clinical benefit of daratumumab in NKTCL may be enriched by B/T cell-related biomarkers.

The efficacy of mobile health interventions used to manage acute or chronic pain: A systematic review.

Chronic pain is a significant health issue that affects approximately 50 million adults in the United States. Traditional interventions are not always an effective treatment strategy for pain control. However, the wide adoption of smartphones and the rapid growth of health information technologies over the past decade have created opportunities to use mobile health (mHealth) applications (apps) for pain tracking and self-management. In this PRISMA-compliant systematic review, we assessed the current U.S.-based research on pain-related mHealth apps to describe the app components and determine the efficacy of these interventions for persons with acute or chronic pain. We conducted a comprehensive search of five databases based on methodological guidelines from the Joanna Briggs Institute. We included articles reporting original data on mHealth interventions with pain intensity as a primary or secondary outcome and excluded articles that utilized multimodal interventions. Of the original 4959 articles, only five studies met the eligibility criteria. Most of the interventions included feasibility or pilot studies, and all studies were published between 2015 and 2018. Two of the five studies used visual analog scales. Only two of the studies reported statistically significant pain intensity outcomes, and considerable heterogeneity between the studies limited our ability to generalize findings or conduct a meta-analysis. Research investigating the components and efficacy of pain-related mHealth apps as interventions is an emerging field. To better understand the potential clinical benefits of mHealth apps designed to manage pain, further research is needed.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review.

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Modeling Flowsheet Data to Support Secondary Use.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thromboembolism, genitourinary system including catheter-associated urinary tract infection, and pain management) and five high-volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for falls to 78% for the respiratory system. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.

Big data science: A literature review of nursing research exemplars.

BACKGROUND: Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge. PURPOSE: The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals. METHODS: A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice. DISCUSSION: Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods. CONCLUSION: There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science.

Performance of a multiplexed dual analyte immunoassay for the early detection of non-small cell lung cancer.

OBJECTIVES: "PAULA's" test (Protein Assays Utilizing Lung cancer Analytes) is a novel multiplex immunoassay blood test that incorporates both tumor antigens and autoantibodies to determine the risk that lung cancer (LC) is present in individuals from a high-risk population. The test's performance characteristics were evaluated in a study using 380 retrospective clinical serum samples. METHODS: PAULA's test is performed on the Luminex xMAP technology platform, and detects a panel of 3 tumor antigens (CEA, CA-125, and CYFRA 21-1) and 1 autoantibody marker (NY-ESO-1). A training set (n = 230) consisting of 115 confirmed diagnoses of non-small cell lung carcinoma (NSCLC) cases and 115 age- and smoking history-matched controls was used to develop the LC predictive model. Data from an independent matched validation set (n = 150) was then used to evaluate the model developed, and determine the ability of the test to distinguish NSCLC cases from controls. RESULTS: The 4-biomarker panel was able to discriminate NSCLC cases from controls with 74% sensitivity, 80% specificity, and 0.81 AUC in the training set and with 77% sensitivity, 80% specificity, and 0.85 AUC in the independent validation set. The use of NY-ESO-1 autoantibodies substantially increased the overall sensitivity of NSCLC detection as compared to the 3 tumor markers alone. Overall, the multiplexed 4-biomarker panel assay demonstrated comparable performance to a previously employed 8-biomarker non-multiplexed assay. CONCLUSIONS: These studies confirm the value of using a mixed panel of tumor antigens and autoantibodies in the early detection of NSCLC in high-risk individuals. The results demonstrate that the performance of PAULA's test makes it suitable for use as an aid to determine which high-risk patients need to be directed to appropriate noninvasive diagnostic follow-up testing, especially low-dose CT (LDCT).

End Users' Perceived Engagement with Clinical Dashboards: A Rapid Review.

Clinical dashboards are an emerging and fast-evolving technology used to support frontline clinicians' practice. Understanding end users' perceived engagement with clinical dashboards is essential to co-design, implementation, and adoption. There is a lack of literature exploring the integration of dashboards into clinical workflow. This rapid review explores clinical end users' perceived engagement with dashboards that support workflow. We conducted a literature search in PubMed and CINAHL. Four articles met our eligibility criteria. Findings reveal variations in taxonomy and measures used to evaluate clinicians' perceived engagement. There are also a variety of reported barriers and facilitators to adoption. Standardized frameworks and vocabulary are needed to facilitate a common understanding of clinical end users' perceived engagement with dashboards.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

BACKGROUND: Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap. OBJECTIVES: This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process. METHODS: We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories. RESULTS: The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales. CONCLUSION: This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

BACKGROUND: Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap. OBJECTIVES: This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process. METHODS: We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories. RESULTS: The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales. CONCLUSION: This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.

Sulfur incorporation generally improves Ricin inhibition in pterin-appended glycine-phenylalanine dipeptide mimics.

Several 7-aminoamido-pterins were synthesized to evaluate the electronic and biochemical subtleties observed in the 'linker space' when N-{N-(pterin-7-yl)carbonylglycyl}-l-phenylalanine 1 was bound to the active site of RTA. The gylcine-phenylalanine dipeptide analogs included both amides and thioamides. Decarboxy gly-phe analog 2 showed a 6.4-fold decrease in potency (IC50 = 128 µM), yet the analogous thioamide 7 recovered the lost activity and performed similarly to the parent inhibitor (IC50 = 29 µM). Thiourea 12 exhibited an IC50 nearly six times lower than the oxo analog 13. All inhibitors showed the pterin head-group firmly bound in their X-ray structures yet the pendants were not fully resolved suggesting that all pendants are not firmly bound in the RTA linker space. Calculated log P values do not correlate to the increase in bioactivity suggesting other factors dominate.

Leveraging A Clinical Dashboard and Process Mappings to Improve Treatment Access and Outcomes for Women Veterans with Urinary Incontinence.

In support of the Improving Primary Care Understanding of Resources and Screening for Urinary Incontinence to Enhance Treatment initiative with the Veterans Health Administration, we developed a clinical dashboard to support primary care providers in identifying underdiagnosed, undertreated women Veterans with urinary incontinence. This paper describes our dashboard development and evaluation. We employed a user-centered design in determining dashboard requirements, interface design, and functionality. We invited early users at three pilot sites to formal usability reviews. We quantified the dashboard usability using the System Usability Scale and administered surveys and interviews for insights on performance. We employed process maps to uncover processes of end-users' dashboard engagements within local environments. User evaluations demonstrated the dashboard as a helpful instrument in identifying women Veterans with good to excellent usability performance. User feedback offers a user-driven pathway to develop our dashboard that supports clinicians to better care for women Veterans with urinary incontinence.

Trans-segmental imaging in the spinal cord of behaving mice.

Spinal cord circuits play crucial roles in transmitting pain, but the underlying activity patterns within and across spinal segments in behaving mice have remained elusive. We developed a wearable widefield macroscope with a 7.9-mm2 field of view, ~3- to 4-µm lateral resolution, 2.7-mm working distance and <10-g overall weight and show that highly localized painful mechanical stimuli evoke widespread, coordinated astrocyte excitation across multiple spinal segments.

Nurses' Roles in mHealth App Development: Scoping Review.

BACKGROUND: Although mobile health (mHealth) apps for both health consumers and health care providers are increasingly common, their implementation is frequently unsuccessful when there is a misalignment between the needs of the user and the app's functionality. Nurses are well positioned to help address this challenge. However, nurses' engagement in mHealth app development remains unclear. OBJECTIVE: This scoping review aims to determine the extent of the evidence of the role of nurses in app development, delineate developmental phases in which nurses are involved, and to characterize the type of mHealth apps nurses are involved in developing. METHODS: We conducted a scoping review following the 6-stage methodology. We searched 14 databases to identify publications on the role of nurses in mHealth app development and hand searched the reference lists of relevant publications. Two independent researchers performed all screening and data extraction, and a third reviewer resolved any discrepancies. Data were synthesized and grouped by the Software Development Life Cycle phase, and the app functionality was described using the IMS Institute for Healthcare Informatics functionality scoring system. RESULTS: The screening process resulted in 157 publications being included in our analysis. Nurses were involved in mHealth app development across all stages of the Software Development Life Cycle but most frequently participated in design and prototyping, requirements gathering, and testing. Nurses most often played the role of evaluators, followed by subject matter experts. Nurses infrequently participated in software development or planning, and participation as patient advocates, research experts, or nurse informaticists was rare. CONCLUSIONS: Although nurses were represented throughout the preimplementation development process, nurses' involvement was concentrated in specific phases and roles.

Multiplex translaminar imaging in the spinal cord of behaving mice.

While the spinal cord is known to play critical roles in sensorimotor processing, including pain-related signaling, corresponding activity patterns in genetically defined cell types across spinal laminae have remained challenging to investigate. Calcium imaging has enabled cellular activity measurements in behaving rodents but is currently limited to superficial regions. Here, using chronically implanted microprisms, we imaged sensory and motor-evoked activity in regions and at speeds inaccessible by other high-resolution imaging techniques. To enable translaminar imaging in freely behaving animals through implanted microprisms, we additionally developed wearable microscopes with custom-compound microlenses. This system addresses multiple challenges of previous wearable microscopes, including their limited working distance, resolution, contrast, and achromatic range. Using this system, we show that dorsal horn astrocytes in behaving mice show sensorimotor program-dependent and lamina-specific calcium excitation. Additionally, we show that tachykinin precursor 1 (Tac1)-expressing neurons exhibit translaminar activity to acute mechanical pain but not locomotion.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.

OBJECTIVES: The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools. METHODS: We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework. RESULTS: Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care. CONCLUSION: In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.

Amivantamab plus lazertinib in osimertinib-relapsed EGFR-mutant advanced non-small cell lung cancer: a phase 1 trial.

Patients with epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) often develop resistance to current standard third-generation EGFR tyrosine kinase inhibitors (TKIs); no targeted treatments are approved in the osimertinib-relapsed setting. In this open-label, dose-escalation and dose-expansion phase 1 trial, the potential for improved anti-tumor activity by combining amivantamab, an EGFR-MET bispecific antibody, with lazertinib, a third-generation EGFR TKI, was evaluated in patients with EGFR-mutant NSCLC whose disease progressed on third-generation TKI monotherapy but were chemotherapy naive (CHRYSALIS cohort E). In the dose-escalation phase, the recommended phase 2 combination dose was established; in the dose-expansion phase, the primary endpoints were safety and overall response rate, and key secondary endpoints included progression-free survival and overall survival. The safety profile of amivantamab and lazertinib was generally consistent with previous experience of each agent alone, with 4% experiencing grade ≥3 events; no new safety signals were identified. In an exploratory cohort of 45 patients who were enrolled without biomarker selection, the primary endpoint of investigator-assessed overall response rate was 36% (95% confidence interval, 22-51). The median duration of response was 9.6 months, and the median progression-free survival was 4.9 months. Next-generation sequencing and immunohistochemistry analyses identified high EGFR and/or MET expression as potential predictive biomarkers of response, which will need to be validated with prospective assessment. ClinicalTrials.gov identifier: NCT02609776 .

Post-translational N-glycosylation of type I transmembrane KCNE1 peptides: implications for membrane protein biogenesis and disease.

N-Glycosylation of membrane proteins is critical for their proper folding, co-assembly and subsequent matriculation through the secretory pathway. Here, we examine the kinetics of N-glycan addition to type I transmembrane KCNE1 K(+) channel ß-subunits, where point mutations that prevent N-glycosylation at one consensus site give rise to disorders of the cardiac rhythm and congenital deafness. We show that KCNE1 has two distinct N-glycosylation sites: a typical co-translational site and a consensus site ∼20 residues away that unexpectedly acquires N-glycans after protein synthesis (post-translational). Mutations that ablate the co-translational site concomitantly reduce glycosylation at the post-translational site, resulting in unglycosylated KCNE1 subunits that cannot reach the cell surface with their cognate K(+) channel. This long range inhibition is highly specific for post-translational N-glycosylation because mutagenic conversion of the KCNE1 post-translational site into a co-translational site restored both monoglycosylation and anterograde trafficking. These results directly explain how a single point mutation can prevent N-glycan attachment at multiple sites, providing a new biogenic mechanism for human disease.

Developing and Evaluating a Diversity, Equity, and Inclusion Storytelling Model in Data Courses in an Academic Electronic Health Record.

Meaningful use of data generated from electronic health records (EHRs) exerts influential impacts on every aspect of healthcare to facilitate clinically intelligent decision-making and improve healthcare outcomes. As nurses are called to chart a path of equity in healthcare, there presents a growing need of incorporating diversity, equity, inclusion (DEI) perspectives into data courses generated from academic EHRs in academic informatics education. This paper describes the development of a DEI data standard model and the evaluation of data courses residing within an academic EHR platform using a cognitive walkthrough method. Data points selected for learning activities in the evaluated data courses appeared to be predominantly clinically driven and lack DEI-informed data features. To facilitate DEI-informed graduate health informatics education and the seamless transfer of health professional students to workforces, data courses built within academic EHRs should integrate DEI-informed data measures and thinking in course curriculum design and development.

Human iPSC-derived astrocytes generated from donors with globoid cell leukodystrophy display phenotypes associated with disease.

Globoid cell leukodystrophy (Krabbe disease) is a fatal neurodegenerative, demyelinating disease caused by dysfunctional activity of galactosylceramidase (GALC), leading to the accumulation of glycosphingolipids including psychosine. While oligodendrocytes have been extensively studied due to their high levels of GALC, the contribution of astrocytes to disease pathogenesis remains to be fully elucidated. In the current study, we generated induced pluripotent stem cells (iPSCs) from two donors with infantile onset Krabbe disease and differentiated them into cultures of astrocytes. Krabbe astrocytes recapitulated many key findings observed in humans and rodent models of the disease, including the accumulation of psychosine and elevated expression of the pro-inflammatory cytokine IL-6. Unexpectedly, Krabbe astrocytes had higher levels of glucosylceramide and ceramide, and displayed compensatory changes in genes encoding glycosphingolipid biosynthetic enzymes, suggesting a shunting away from the galactosylceramide and psychosine pathway. In co-culture, Krabbe astrocytes negatively impacted the survival of iPSC-derived human neurons while enhancing survival of iPSC-derived human microglia. Substrate reduction approaches targeting either glucosylceramide synthase or serine palmitoyltransferase to reduce the sphingolipids elevated in Krabbe astrocytes failed to rescue their detrimental impact on neuron survival. Our results suggest that astrocytes may contribute to the progression of Krabbe disease and warrant further exploration into their role as therapeutic targets.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.

BACKGROUND: The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. OBJECTIVES: This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. METHODS: We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. RESULTS: Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. CONCLUSION: This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.