Predictors of Follow-Up Appointment No-Shows Before and During COVID Among Adults with Type 2 Diabetes.

Background: The coronavirus disease 2019 (COVID-19) pandemic has rapidly transformed health care delivery into telehealth visits. Attending regular medical appointments are critical to prevent or delay diabetes-related complications. Although telehealth visits have addressed some barriers to in-person visits, appointment no-shows are still noted in the telehealth setting. It is not completely clear how the predictors of appointment no-shows differ between in-person and telehealth visits in diabetes care. Objective: This retrospective study examined if predictors of appointment no-shows differ (1) between pre-COVID (January 1, 2019-March 22, 2020) and COVID (March 23, 2020-December 31, 2020) periods and (2) by health care delivery modes (in-person or telehealth visits) during COVID among adults with type 2 diabetes mellitus (T2DM). Methods: We used electronic health records between January 1, 2019 and December 31, 2020 across four diabetes clinics in a tertiary academic hospital in Baltimore, Maryland. Appointments marked as completed or no-show by established adults with T2DM were included in the analyses. Results: Among 7,276 appointments made by 2,235 patients, overall appointment no-show was 14.99%. Being older and White were protective against appointment no-shows in both unadjusted and adjusted models during both time periods. The interaction terms of COVID periods (i.e., pre-COVID vs. COVID) were significant for when glycated hemoglobin drawn before this visit and for missing body mass index. Telehealth visits during COVID decreased more half of the odds of appointment no-shows. Conclusions: In the context of diabetes care, the implementation of telehealth reduced appointment no-shows. Future studies are needed to address social determinants of health, including access to internet access, to further reduce health disparities among adults with T2DM.

Patient Engagement in Diabetes Care: Missed Appointments Among Adults Living with Type 2 Diabetes

OBJECTIVES/GOALS: Missed appointments (MAs) negatively impact the health outcomes of adults living with type 2 diabetes mellitus (T2DM), causing disruptions in clinic operation and added financial cost to healthcare providers and systems. This study aimed to identify risk factors for MAs in both in-person and telehealth settings among adults living with T2DM. METHODS/STUDY POPULATION: Using a sequential multi-method design guided by the modified Quality-Caring Model, the quantitative phase of this study used electronic health records (EHR) data in Calendar Years 2019 and 2020 with 7,276 encounters made by 2,235 patients with T2DM from four diabetes clinics within a tertiary academic medical center in Baltimore, MD. Multivariable random effect logistic regression were used to examine the association between MAs and included predictors (i.e., patient characteristics [e.g., age, race, health status], health provider factors [e.g., types of provider], and health system factors [e.g., scheduling lag]). Based on the results of the quantitative phase, a purposive sample of 23 adults with T2DM and 10 providers were then interviewed individually via phone or zoom. RESULTS/ANTICIPATED RESULTS: The EHR data found that the following variables decreased the odds of MAs: having an activated patient portal account, patients with age over 46 or with white race. Telehealth was associated with 50% decreased odds of MAs during COVID (after 3/23/2020). On the other hand, longer scheduling lag increased the odds of MAs. Qualitative interviews revealed that MAs were often related to social needs, such as lack of/limited health-related transportation and its associated financial burden. Telehealth helped break these barriers for some adults with T2DM, but technical challenges in telehealth persisted for those with low digital health literacy and people who did not have a digital device and/or with unstable internet connection. Providers worried that these challenges might undermine the quality of diabetes care. DISCUSSION/SIGNIFICANCE: Disparities in MAs by age and race were noted, which might reflect the impact of unmeasured social needs in EHR. Perceived convenient telehealth may reduce MAs in T2DM care. However, the persistent technical challenges of telehealth should be addressed to optimize the quality of diabetes care and to promote care continuity for underserved populations.

Monocyte Distribution Width as a Diagnostic Marker for Infection: A Systematic Review and Meta-analysis.

BACKGROUND: Monocyte distribution width (MDW) is an emerging biomarker for infection. It is available easily and quickly as part of the CBC count, which is performed routinely on hospital admission. The increasing availability and promising results of MDW as a biomarker in sepsis has prompted an expansion of its use to other infectious diseases. RESEARCH QUESTION: What is the diagnostic performance of MDW across multiple infectious disease outcomes and care settings? STUDY DESIGN AND METHODS: A systematic review of the diagnostic performance of MDW across multiple infectious disease outcomes was conducted by searching PubMed, Embase, Scopus, and Web of Science through February 4, 2022. Meta-analysis was performed for outcomes with three or more reports identified (sepsis and COVID-19). Diagnostic performance measures were calculated for individual studies with pooled estimates created by linear mixed-effects models. RESULTS: We identified 29 studies meeting inclusion criteria. Most examined sepsis (19 studies) and COVID-19 (six studies). Pooled estimates of diagnostic performance for sepsis differed by reference standard (Second vs Third International Consensus Definitions for Sepsis and Septic Shock criteria) and tube anticoagulant used and ranged from an area under the receiver operating characteristic curve (AUC) of 0.74 to 0.94, with mean sensitivity of 0.69 to 0.79 and mean specificity of 0.57 to 0.86. For COVID-19, the pooled AUC of MDW was 0.76, mean sensitivity was 0.79, and mean specificity was 0.59. INTERPRETATION: MDW exhibited good diagnostic performance for sepsis and COVID-19. Diagnostic thresholds for sepsis should be chosen with consideration of reference standard and tube type used. TRIAL REGISTRY: Prospero; No.: CRD42020210074; URL: https://www.crd.york.ac.uk/prospero/.

Monocyte distribution width as a pragmatic screen for SARS-CoV-2 or influenza infection.

Monocyte distribution width (MDW) is a novel marker of monocyte activation, which is known to occur in the immune response to viral pathogens. Our objective was to determine the performance of MDW and other leukocyte parameters as screening tests for SARS-CoV-2 and influenza infection. This was a prospective cohort analysis of adult patients who underwent complete blood count (CBC) and SARS-CoV-2 or influenza testing in an Emergency Department (ED) between January 2020 and July 2021. The primary outcome was SARS-CoV-2 or influenza infection. Secondary outcomes were measures of severity of illness including inpatient hospitalization, critical care admission, hospital lengths of stay and mortality. Descriptive statistics and test performance measures were evaluated for monocyte percentage, MDW, white blood cell (WBC) count, and neutrophil to lymphocyte ratio (NLR). 3,425 ED patient visits were included. SARS-CoV-2 testing was performed during 1,922 visits with a positivity rate of 5.4%; influenza testing was performed during 2,090 with a positivity rate of 2.3%. MDW was elevated in patients with SARS-Cov-2 (median 23.0U; IQR 20.5-25.1) or influenza (median 24.1U; IQR 22.0-26.9) infection, as compared to those without (18.9U; IQR 17.4-20.7 and 19.1U; 17.4-21, respectively, P < 0.001). Monocyte percentage, WBC and NLR values were within normal range in patients testing positive for either virus. MDW identified SARS-CoV-2 and influenza positive patients with an area under the curve (AUC) of 0.83 (95% CI 0.79-0.86) and 0.83 (95% CI 0.77-0.88), respectively. At the accepted cut-off value of 20U for MDW, sensitivities were 83.7% (95% CI 76.5-90.8%) for SARS-CoV-2 and 89.6% (95% CI 80.9-98.2%) for influenza, compared to sensitivities below 45% for monocyte percentage, WBC and NLR. MDW negative predictive values were 98.6% (95% CI 98.0-99.3%) and 99.6% (95% CI 99.3-100.0%) respectively for SARS-CoV-2 and influenza. Monocyte Distribution Width (MDW), available as part of a routine complete blood count (CBC) with differential, may be a useful indicator of SARS-CoV-2 or influenza infection.

Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.

Demand has outstripped healthcare supply during the coronavirus disease 2019 (COVID-19) pandemic. Emergency departments (EDs) are tasked with distinguishing patients who require hospital resources from those who may be safely discharged to the community. The novelty and high variability of COVID-19 have made these determinations challenging. In this study, we developed, implemented and evaluated an electronic health record (EHR) embedded clinical decision support (CDS) system that leverages machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 h and inpatient care needs within 72 h into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. ML models were derived in a retrospective cohort of 21,452 ED patients who visited one of five ED study sites and were prospectively validated in 15,670 ED visits that occurred before (n = 4322) or after (n = 11,348) CDS implementation; model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. Incidence of critical care needs within 24 h and inpatient care needs within 72 h were 10.7% and 22.5%, respectively and were similar across study periods. ML model performance was excellent under all conditions, with AUC ranging from 0.85 to 0.91 for prediction of critical care needs and 0.80-0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after CDS implementation.

Monocyte anisocytosis increases during multisystem inflammatory syndrome in children with cardiovascular complications.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening complication that can develop weeks to months after an initial SARS-CoV-2 infection. A complex, time-consuming laboratory evaluation is currently required to distinguish MIS-C from other illnesses. New assays are urgently needed early in the evaluation process to expedite MIS-C workup and initiate treatment when appropriate. This study aimed to measure the performance of a monocyte anisocytosis index, obtained on routine complete blood count (CBC), to rapidly identify subjects with MIS-C at risk for cardiac complications. METHODS: We measured monocyte anisocytosis, quantified by monocyte distribution width (MDW), in blood samples collected from children who sought medical care in a single medical center from April 2020 to October 2020 (discovery cohort). After identifying an effective MDW threshold associated with MIS-C, we tested the utility of MDW as a tier 1 assay for MIS-C at multiple institutions from October 2020 to October 2021 (validation cohort). The main outcome was the early screening of MIS-C, with a focus on children with MIS-C who displayed cardiac complications. The screening accuracy of MDW was compared to tier 1 routine laboratory tests recommended for evaluating a child for MIS-C. RESULTS: We enrolled 765 children and collected 846 blood samples for analysis. In the discovery cohort, monocyte anisocytosis, quantified as an MDW threshold of 24.0, had 100% sensitivity (95% CI 78-100%) and 80% specificity (95% CI 69-88%) for identifying MIS-C. In the validation cohort, an initial MDW greater than 24.0 maintained a 100% sensitivity (95% CI 80-100%) and monocyte anisocytosis displayed a diagnostic accuracy greater that other clinically available hematologic parameters. Monocyte anisocytosis decreased with disease resolution to values equivalent to those of healthy controls. CONCLUSIONS: Monocyte anisocytosis detected by CBC early in the clinical workup improves the identification of children with MIS-C with cardiac complications, thereby creating opportunities for improving current practice guidelines.

Machine learning and artificial intelligence: applications in healthcare epidemiology.

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.

Responding to a Pandemic: COVID-19 Projects in the Malone Center.

As the scope and scale of the COVID-19 pandemic became clear in early March of 2020, the faculty of the Malone Center engaged in several projects aimed at addressing both immediate and long-term implications of COVID-19. In this article, we briefly outline the processes that we engaged in to identify areas of need, the projects that emerged, and the results of those projects. As we write, some of these projects have reached a natural termination point, whereas others continue. We identify some of the factors that led to projects that moved to implementation, as well as factors that led projects to fail to progress or to be abandoned.

Artificial Intelligence-Based Clinical Decision Support for COVID-19-Where Art Thou?

The COVID-19 crisis has brought about new clinical questions, new workflows, and accelerated distributed healthcare needs. Although artificial intelligence (AI)-based clinical decision support seemed to have matured, the application of AI-based tools for COVID-19 has been limited to date. In this perspective piece, the opportunities and requirements for AI-based clinical decision support systems are identified and challenges that impact "AI readiness" for rapidly emergent healthcare challenges are highlighted.