Objective:To develop and evaluate an automated algorithm to identify sepsis onset, referred to as time-zero (t0), from the electronic medical records (EMR). Materials and Methods:We developed an algorithm to construct a comprehensive timeline of systemic inflammatory response syndrome (SIRS) criteria and organ dysfunction (OD) using structured data, and documentation of infection (DOI) using both structured data and unstructured clinical notes. Algorithm performance was assessed using 2030 manually abstracted adult sepsis cases from a multicenter health system in southeast Michigan. Results:On average, the algorithm DOI time was significantly earlier than abstractors (mean: -0.33 hour, 95% Cl, -0.55 to -0.11), resulting in a significantly earlier t0 (mean: -0.60 hour, 95% CI, -0.74 to -0.45). Discussion:Automated approaches to analyzing EMR data offer a scalable framework for SEP-1 monitoring, research, and quality improvement. Conclusion:Incorporating unstructured clinical notes improves DOI detection.

Objective We aimed to use machine learning (ML) models to investigate the impact of clinical, social and behavioural factors on 1-year progression from pre-diabetes to type 2 diabetes mellitus (DM). Design A retrospective cohort study. Setting A large health system including eight sites in southeast Michigan. Participants Adults with haemoglobin A1c (HbA1c) between 5.7% and 6.4% for two consecutive years between 1 January 2008 and 31 December 2023, and no prior history of type 2 DM or metformin use. Primary outcome measure New-onset type 2 DM (HbA1c ≥6.5%) in 1 year. Results Among 11 809 individuals, 815 (6.9%) progressed to type 2 DM within 1 year. CatBoost demonstrated the best performance (average area under the curve 0.78). Prior-year HbA1c was the most influential covariate (SHapley Additive exPlanations 1.02). Traditional metabolic factors (high-density lipoprotein, body mass index (BMI), white blood cell, age, gender, triglycerides) also contributed. Lastly, while inclusion of social and behavioural determinants of health (SBDH) did not significantly improve the overall model performance, depression emerged as the prominent SBDH covariate. Depression was a stronger predictor of diabetes progression in individuals with higher baseline BMI and lower baseline HbA1c. Conclusions Inclusion of social and behavioural covariates provided no incremental value for prediction of diabetes progression from pre-diabetes. However, machine learning revealed that depression may play a role in progression to type 2 DM.

BackgroundDuring the COVID-19 pandemic healthcare workers reported using a variety of positive or negative health behaviors as a strategy to cope with the increased stress at work and home. This study examined the extent of healthcare workers' engagement in various health behaviors and the association of these behaviors with changes in self-reported physical and psychological well-being during the pandemic.MethodsA survey was developed and administered to healthcare workers in a large healthcare system in Southeast Michigan between November 2, 2020, and January 21, 2021, during Michigan's second COVID-19 surge.ResultsDuring the study period 368 healthcare workers completed the survey. The majority of participants were female (83.2%) and aged between 25-34 years (29.3%). Most participants reported a drop in their psychological well-being and physical health during the pandemic. Overall, 14.8% to 47.7% of participants rated their engagement in a positive health behavior as "often" or "very often". Engagement in most positive health behaviors was associated with higher self-rating of physical health and psychological well-being. The estimated decrease in psychological well-being scores for participants who often/very often engaged in pleasurable activities was 0.91 points less (p = 0.003, 95% CI [-1.50, -0.32]) than for those who rarely/never engaged in such activity. Exercise, healthy eating, getting enough sleep, engaging in pleasurable activities, focusing on gratitude and positive things, and getting social support appeared to positively influence the degree to which participant's physical health declined. After controlling for age, gender and other potential confounders, each additional positive health behavior was associated with a 0.22-point decrease in the rate of decline in overall physical health (p = 0.000, 95% CI -0.35, -0.10).DiscussionHealthcare workers who regularly engaged in positive health behaviors during the pandemic reported better psychological and physically well-being than those who did not before and during COVID-19. Engaging in pleasurable activities was the only health behavior in our study that demonstrated a potential protective effect against the decline in psychological well-being during the pandemic. Several other positive health behaviors were associated with a lower rate of decline of physical health during the pandemic. Health systems should consider implementing strategies to increase opportunities for pleasurable activities for healthcare workers, both in the workplace and at home, especially during times of increased stress.

Dually-eligible beneficiaries (or duals) are enrolled in both Medicare and Medicaid. Duals have high and complex healthcare needs, and face substantial barriers to quality healthcare access. Thus, it is important to understand how dual-eligibility status is associated with patterns of healthcare services use and the factors that drive these patterns. We used data from the Medical Expenditure Panel Survey (2008-2022) to evaluate the association of dual-eligibility status and health care use patterns for older Medicare beneficiaries (65+-years). We used Latent Class Analysis to sequentially fit and assess multiple class solutions based on 10 healthcare services (e.g. hospital use, dental visits, home healthcare, medications) and generated healthcare use patterns profiles. Subsequently, we fit survey-weighted unadjusted and adjusted multinomial logistic regression models to link dual-eligibility status to the best fitting use patterns profiles. We found that the three class solution (typical [67.8%], high [17.5%], and low [14.7%] users) provided the best statistical and substantive fit to the data. Unadjusted models showed that duals are more likely to be both high (RRR = 2.07, p < 0.001) and low users (RRR=1.45, p < 0.001) as compared to non-duals. After adjusting for sociodemographics, comorbidities, and access to usual source of care, duals remained more likely to be high users (RRR=1.7, p < 0.001), but no longer low users as compared to non-duals. These results highlight the need for more nuanced approaches, by stakeholders, to policies and interventions that streamline the use of health services in a way that aligns with efficient, quality and cost-effective healthcare for this complex need population.

Abstract Individuals enrolled in both Medicaid and Medicare, also known as duals, have complex and high healthcare needs and disproportionately account for higher state and federal spending. In recent years, most of these beneficiaries have been enrolled in some form of managed care plans. In this study, using the 2020-2021 Medical Expenditure Panel Survey, we evaluate the association of dual-eligibility status and different forms of healthcare use and expenditures: inpatient, emergency room and outpatient. We also explore if enrollment in a managed care affects this relationship. We estimate a series of Poisson regressions for use variables and generalized linear models for expenditure variables that account for the interaction between dual-eligibility status and enrollment in managed care. Postestimation, we calculate marginal effects and use analysis of variance contrast tests to estimate the differences in the predicted use events and average expenditure based on managed care and dual-eligibility status. We find that, on average, duals eligibles have a higher use of inpatient and emergency room than non-duals but similar outpatient visits. We also find that non-duals enrolled in managed care have lower outpatient and emergency room visits than their non-managed care counterparts. No significant differences are observed for dual eligibles in terms of their managed care status for both use and expenditures. While more research and years of data are needed to establish whether managed care improves utilization and lowers spending for dual eligibles, we find that managed care affects duals and non-duals differently and stakeholders should consider this to provide equitable health.

Social and behavioral determinants of health (SBDH) are associated with a variety of health and utilization outcomes, yet these factors are not routinely documented in the structured fields of electronic health records (EHR). The objective of this study was to evaluate different machine learning approaches for detection of SBDH from the unstructured clinical notes in the EHR. Latent Semantic Indexing (LSI) was applied to 2,083,180 clinical notes corresponding to 46,146 patients in the MIMIC-III dataset. Using LSI, patients were ranked based on conceptual relevance to a set of keywords (lexicons) pertaining to 15 different SBDH categories. For Generative Pretrained Transformer (GPT) models, API requests were made with a Python script to connect to the OpenAI services in Azure, using gpt-3.5-turbo-1106 and gpt-4-1106-preview models. Prediction of SBDH categories were performed using a logistic regression model that included age, gender, race and SBDH ICD-9 codes. LSI retrieved patients according to 15 SBDH domains, with an overall average PPV ≥ 83

Abstract Disclosure: E.M. Niedzialkowska: None. F. El Sayed: None. M. Townsend: None. U. Bhatia: None. S. Roy: None. R. Homayouni: None. A. Halalau: None. Background: Pre-diabetes (PD), characterized by hyperglycemia influenced by lifestyle factors, genetics, age, and ethnicity, is considered a risk factor for development of diabetes. However, only a small proportion of PD individuals progress to type-2 diabetes (T2D) each year. More refined diabetes risk prediction tools are needed to direct proactive health management strategies for at-risk populations. Question and Significance: Our study explored the associations between age, race, gender and BMI in PD individuals who either progressed to T2D or reverted to normoglycemia (NG) over a one-year follow-up period. Methods and Design: This is a retrospective observational study of 38,228 patients with PD, based on having two consecutive years of hemoglobin A1c (HbA1c) between 5.7% and 6.5%. We compared the demographic characteristics of individuals who progressed to T2D or regressed to NG within one year using bivariate (Kruskal-Wallis or Chi-squared) or multivariate logistic regression analysis. Results: The study cohort included 57.36% females, 66.13% Whites, 19.41% Blacks and 4.36% Asians with a median age of 69. Bivariate analysis revealed significant age differences between PD patients who reverted to NG with median age of 67 in those who reverted vs 70 in those who did not revert (p< 0.0001). In addition, the proportion of males who progressed to T2D was significantly higher and the proportion of Blacks who reverted to NG was significantly lower.Using multivariate regression (adjusting for age, gender, race and BMI), we found that each year in age significantly lowered the odds of reversion from PD to NG (OR 0.98, p<0.0001) but was not associated with progression to T2D. PD males had significantly higher odds of progression to T2D (OR 1.28, p<0.0001). Both Asians (OR 0.62, p<0.0007) and Blacks (OR 0.68, p<0.0004) had significantly lower odds of reversion to NG from PD compared to Whites. Surprisingly, BMI was not associated with neither reversion to NG nor progression to T2D, after adjusting for age, gender, and race. Discussion: Our findings underscore significant racial disparities and gender differences in regression to normoglycemia and progression to diabetes. This emphasizes the need for personalized interventions and targeted strategies for prediabetic populations to mitigate progression to T2D and to achieve normoglycemia. Presentation: 6/1/2024

Aims. The purpose of this article is to share the qualitative results of digital stories from 21 healthcare workers during the second wave of the pandemic and compare them with stories analyzed during the first wave. Background. Everyone has personal COVID-19 experiences and memories. Yet, the literature is lacking in how the stories from the healthcare team during the pandemic affected the individual healthcare worker at that time. Methods. A descriptive qualitative study analyzing digital stories was conducted during the second wave of the pandemic of a large Midwest healthcare system. Results. Twenty-one audio stories were analyzed. Four themes revealed were negative emotional response/impact, feelings/statements of optimism and hope, imposed or changing role expectations, and leadership/administration concerns. These themes aligned with the first wave analysis. Conclusions. Healthcare workers during COVID-19 experienced profound upheaval in their usual daily rhythm. This study revealed that there was a paradox of experiences consistently shared through their own stories including perceptions healthcare workers had of their leadership and the perceived system failure. Implications for Nursing Management. The pandemic resulted in healthcare teams expressing anxiety in caring for COVID patients, yet trying to remain hopeful. They also expressed awareness of how their own roles changed and the leadership failures during this time. Strong leadership is required to create a path forward for healthcare workers.

COPYRIGHT © 2023 Homayouni, Manda, Tan and Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: AI and data science in drug development and public health: Highlights from the MCBIOS 2022 conference

Background: Serologic analysis is an important tool towards assessing the humoral response to COVID-19 infection and vaccination. Numerous serologic tests and platforms are currently available to support this line of testing. Two broad antibody testing categories are point-of-care lateral flow immunoassays and semi -quantitative immunoassays performed in clinical laboratories, which typically require blood collected from a finger-stick and a standard venipuncture blood draw, respectively. This study evaluated the use of dried blood spot (DBS) collections as a sample source for COVID-19 antibody testing using an automated clinical laboratory test system.Methods: Two hundred and ninety-four participants in the BLAST COVID-19 seroprevalence study (NCT04349202) were recruited at the time of a scheduled blood draw to have an additional sample taken via finger stick as a DBS collection. Using the EUROIMMUN assay to assess SARS-CoV-2 anti-spike IgG status, DBS specimens were tested on 7, 14, 21, and 28 days post-collection and compared to the reference serum sample obtained from a blood draw for the BLAST COVID-19 study.Results: SARS-CoV-2 anti-spike IgG status from DBS collections demonstrated high concordance with serum across all time points (7-28 days). However, the semi-quantitative value from DBS collections was lower on average than that from serum, resulting in increased uncertainty around the equivocal-to-positive analytical decision point.Conclusions: DBS collections can be substituted for venipuncture when assaying for COVID-19 IgG antibody, with samples being stable for at least 28 days at room temperature. Finger-stick sampling can therefore be advan-tageous for testing large populations for SARS-CoV-2 antibodies without the need for phlebotomists or immediate processing of samples. We have high confidence in serostaus determination from DBS collections, although the reduced semi-quantitative value may cause some low-level positives to fall into the equivocal or even negative range.

Background:Some studies conducted before the Delta and Omicron variant-dominant periods have indicated that influenza vaccination provided protection against COVID-19 infection or hospitalization, but these results were limited by small study cohorts and a lack of comprehensive data on patient characteristics. No studies have examined this question during the Delta and Omicron periods (08/01/2021 to 2/22/2022).Methods:We conducted a retrospective cohort study of influenza-vaccinated and unvaccinated patients in the Corewell Health East(CHE, formerly known as Beaumont Health), Corewell Health West(CHW, formerly known as Spectrum Health) and Michigan Medicine (MM) healthcare system during the Delta-dominant and Omicron-dominant periods. We used a test-negative, case-control analysis to assess the effectiveness of the influenza vaccine against hospitalized SARS-CoV-2 outcome in adults, while controlling for individual characteristics as well as pandameic severity and waning immunity of COVID-19 vaccine.Results:The influenza vaccination has shown to provided some protection against SARS-CoV-2 hospitalized outcome across three main healthcare systems. CHE site (odds ratio [OR]=0.73, vaccine effectiveness [VE]=27%, 95% confidence interval [CI]: [18-35], p<0.001), CHW site (OR=0.85, VE=15%, 95% CI: [6-24], p<0.001), MM (OR=0.50, VE=50%, 95% CI: [40-58], p <0.001) and overall (OR=0.75, VE=25%, 95% CI: [20-30], p <0.001).Conclusion:The influenza vaccine provides a small degree of protection against SARS-CoV-2 infection across our study sites.

PURPOSE:To gain a deeper understanding of healthcare workers experiences during COVID-19 using an anonymous, web-based, audio narrative platform.METHODS:Data were collected from healthcare workers in the midwestern United States using a web-enabled audio diary approach. Participant recordings were analysed using a narrative coding and conceptualization process derived from grounded theory coding techniques.RESULTS:Fifteen healthcare workers, in direct patient care or non-patient care roles, submitted 18 audio narratives. Two paradoxical themes emerged: 1) A paradox of distress and meaningfulness, where a harsh work environment resulted in psychological distress while simultaneously resulting in new rewarding experiences, sense of purpose and positive outlooks. 2) A paradox of social isolation and connection, where despite extreme isolation, healthcare workers formed intense and meaningful interpersonal connections with patients and colleagues in new ways.CONCLUSIONS:A web-enabled audio diary approach provided an opportunity for healthcare workers to reflect deeper on their experiences without investigator influence, which led to some unique findings. Paradoxically, amid social isolation and extreme distress, a sense of value, meaning and rewarding human connections emerged. These findings suggest that interventions addressing healthcare worker burnout and distress might be enhanced by leveraging naturally occurring positive experiences as much as mitigating negative ones.

Abstract Background and objective The notion of prediabetes, defined by the ADA as glycated hemoglobin A1c (HbA1c) of 5.7–6.4%, implies increased vascular inflammatory and immunologic processes and higher risk for developing diabetes mellitus and major cardiovascular events. We aimed to determine the risk factors associated with rapid progression of normal and prediabetes patients to type 2 diabetes mellitus (T2DM). Methods Retrospective cohort study in a single 8-hospital health system in southeast Michigan, between 2006 and 2020. All patients with HbA1c <6.5% at baseline and at least 2 other HbA1c measurements were clustered in five trajectories encompassing more than 95% of the study population. Multivariate linear regression analysis was performed to examine the association of demographic and comorbidities with HbA1c trajectories progressing to diabetes. Results A total of 5,347 prediabetic patients were clustered based on their HbA1c progression (C1: 4,853, C2: 253, C66: 102, C12: 85, C68: 54). The largest cluster (C1) had a baseline median HbA1c value of 6.0% and exhibited stable HbA1c levels in prediabetic range across all subsequent years. The smallest cluster (C68) had the lowest median baseline HbA1c value and also remained stable across subsequent years. The proportion of normal HbA1c in each of the pre-diabetic trajectories ranged from 0 to 12.7%, whereas 81.5% of the reference cluster (C68) were normal HbA1c at baseline. The C2 (steady rising) trajectory was significantly associated with BMI (adj OR 1.10, 95%CI 1.03–1.17), and family history of DM (adj OR 2.75, 95%CI 1.32–5.74). With respect to the late rising trajectories, baseline BMI was significantly associated with both C66 and C12 trajectory (adj OR 1.10, 95%CI 1.03–1.18) and (adj OR 1.13, 95%CI 1.05–1.23) respectively, whereas, the C12 trajectory was also significantly associated with age (adj OR 1.62, 95%CI 1.04–2.53) and history of MACE (adj OR 3.20, 95%CI 1.14–8.93). Conclusions We suggest that perhaps a more aggressive preventative approach should be considered in patients with a family history of T2DM who have high BMI and year-to-year increase in HbA1c, whether they have normal hemoglobin A1c or they have prediabetes. KEY MESSAGES Progression to diabetes from normal or prediabetic hemoglobin A1c within four years is associated with baseline BMI. A steady rise in HbA1c during a four-year period is associated with age and family history of T2DM, whereas age and personal history of MACE are associated with a rapid rise in HbA1c. A more aggressive preventative approach should be considered in patients with a family history of T2DM who have high BMI and year-to-year increase in HbA1c.

EDITORIAL article Front. Artif. Intell., 25 February 2022 | https://doi.org/10.3389/frai.2022.859700

In general, we agree with the comments and suggestions raised by Escandon and colleagues [1], and we are grateful to them for identifying the error discussed below. The limitations pointed out in their letter were raised in the Discussion section of our original article. The suggestions about sensitivity analysis, more comprehensive case definitions, and precise mask type during the relevant exposure are valid but unavoidable given the timing of the study relative to the start of the pandemic. None of the issues raised affect our conclusions regarding associations between seropositivity or asymptomatic rate and job grouping or mask use. Escandon et al correctly point out that the 30-day window for exhibiting symptoms prior to blood collection may have resulted in an overestimation of the asymptomatic rate in this study. Further analysis of the data revealed that 76 seropositive individuals indicated that they were previously diagnosed with coronavirus disease...

Since their discovery over two decades ago, the molecular and cellular functions of the NIPSNAP family of proteins (NIPSNAPs) have remained elusive until recently. NIPSNAPs interact with a variety of mitochondrial and cytoplasmic proteins. They have been implicated in multiple cellular processes and associated with different physiologic and pathologic conditions, including pain transmission, Parkinson's disease, and cancer. Recent evidence demonstrated a direct role for NIPSNAP1 and NIPSNAP2 proteins in regulation of mitophagy, a process that is critical for cellular health and maintenance. Importantly, NIPSNAPs contain a 110 amino acid domain that is evolutionary conserved from mammals to bacteria. However, the molecular function of the conserved NIPSNAP domain and its potential role in mitophagy have not been explored. It stands to reason that the highly conserved NIPSNAP domain interacts with a substrate that is ubiquitously present across all species and can perhaps act as a sensor for mitochondrial health.