Browsing by Author "Clement, Meredith E"

Now showing 1 - 6 of 6
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    Application of Whole-Genome Sequencing to an Unusual Outbreak of Invasive Group A Streptococcal Disease.
    (Open Forum Infect Dis, 2016-01) Galloway-Peña, Jessica; Clement, Meredith E; Sharma Kuinkel, Batu K; Ruffin, Felicia; Flores, Anthony R; Levinson, Howard; Shelburne, Samuel A; Moore, Zack; Fowler, Vance G
    Whole-genome analysis was applied to investigate atypical point-source transmission of 2 invasive group A streptococcal (GAS) infections. Isolates were serotype M4, ST39, and genetically indistinguishable. Comparison with MGAS10750 revealed nonsynonymous polymorphisms in ropB and increased speB transcription. This study demonstrates the usefulness of whole-genome analyses for GAS outbreaks.
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    Health Care Utilization Behaviors Predict Disengagement From HIV Care: A Latent Class Analysis.
    (Open forum infectious diseases, 2018-05) Okeke, Nwora Lance; Clement, Meredith E; McKellar, Mehri S; Stout, Jason E

    Background

    The traditional definition of engagement in HIV care in terms of only clinic attendance and viral suppression provides a limited understanding of how persons living with HIV (PLWH) interact with the health care system.

    Methods

    We conducted a retrospective analysis of patients with ≥1 HIV clinic visits at the Duke Adult Infectious Diseases Clinic between 2008 and 2013. Health care utilization was characterized by 4 indicators: clinic attendance in each half of the year (yes/no), number of emergency department (ED) visits/year (0, 1, or 2+), inpatient admissions/year (0, 1, 2+), and viral suppression (never, intermittent, always). Health care engagement patterns were modeled using latent class/latent transition analysis.

    Results

    A total of 2288 patients (median age, 46.4 years; 59% black, 71% male) were included in the analysis. Three care engagement classes were derived from the latent class model: "adherent" "nonadherent," and "sick." Patients age ≤40 years were more likely to be in the nonadherent class (odds ratio, 2.64; 95% confidence interval, 1.38-5.04) than other cohort members. Whites and males were more likely to transition from nonadherent to adherent the following year. Nonadherent patients were significantly more likely to disengage from care the subsequent year than adherent patients (23.6 vs 0.2%, P < .001).

    Conclusions

    A broader definition of health care engagement revealed distinct and dynamic patterns among PLWH that would have been hidden had only previous HIV clinic attendance had been considered. These patterns may be useful for designing engagement-targeted interventions.
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    Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis.
    (PloS one, 2022-01) Di Stefano, Leon; Ogburn, Elizabeth L; Ram, Malathi; Scharfstein, Daniel O; Li, Tianjing; Khanal, Preeti; Baksh, Sheriza N; McBee, Nichol; Gruber, Joshua; Gildea, Marianne R; Clark, Megan R; Goldenberg, Neil A; Bennani, Yussef; Brown, Samuel M; Buckel, Whitney R; Clement, Meredith E; Mulligan, Mark J; O'Halloran, Jane A; Rauseo, Adriana M; Self, Wesley H; Semler, Matthew W; Seto, Todd; Stout, Jason E; Ulrich, Robert J; Victory, Jennifer; Bierer, Barbara E; Hanley, Daniel F; Freilich, Daniel; Pandemic Response COVID-19 Research Collaboration Platform for HCQ/CQ Pooled Analyses

    Background

    Results from observational studies and randomized clinical trials (RCTs) have led to the consensus that hydroxychloroquine (HCQ) and chloroquine (CQ) are not effective for COVID-19 prevention or treatment. Pooling individual participant data, including unanalyzed data from trials terminated early, enables more detailed investigation of the efficacy and safety of HCQ/CQ among subgroups of hospitalized patients.

    Methods

    We searched ClinicalTrials.gov in May and June 2020 for US-based RCTs evaluating HCQ/CQ in hospitalized COVID-19 patients in which the outcomes defined in this study were recorded or could be extrapolated. The primary outcome was a 7-point ordinal scale measured between day 28 and 35 post enrollment; comparisons used proportional odds ratios. Harmonized de-identified data were collected via a common template spreadsheet sent to each principal investigator. The data were analyzed by fitting a prespecified Bayesian ordinal regression model and standardizing the resulting predictions.

    Results

    Eight of 19 trials met eligibility criteria and agreed to participate. Patient-level data were available from 770 participants (412 HCQ/CQ vs 358 control). Baseline characteristics were similar between groups. We did not find evidence of a difference in COVID-19 ordinal scores between days 28 and 35 post-enrollment in the pooled patient population (odds ratio, 0.97; 95% credible interval, 0.76-1.24; higher favors HCQ/CQ), and found no convincing evidence of meaningful treatment effect heterogeneity among prespecified subgroups. Adverse event and serious adverse event rates were numerically higher with HCQ/CQ vs control (0.39 vs 0.29 and 0.13 vs 0.09 per patient, respectively).

    Conclusions

    The findings of this individual participant data meta-analysis reinforce those of individual RCTs that HCQ/CQ is not efficacious for treatment of COVID-19 in hospitalized patients.
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    Incidence, Long-Term Outcomes, and Healthcare Utilization of Patients With Human Immunodeficiency Virus/Acquired Immune Deficiency Syndrome and Disseminated Mycobacterium avium Complex From 1992-2015.
    (Open Forum Infect Dis, 2017) Collins, Lauren F; Clement, Meredith E; Stout, Jason E
    BACKGROUND: Despite the advent of combination antiretroviral therapy (cART), patients with human immunodeficiency virus (HIV) continue to develop late-stage complications including acquired immune deficiency syndrome (AIDS), disseminated Mycobacterium avium complex (DMAC), and death. METHODS: We performed an observational retrospective cohort study of HIV-infected adults who developed DMAC in the Duke University Health System from 1992 to 2015 to determine the incidence, long-term outcomes, and healthcare utilization of this population at high risk for poor outcomes. Findings were stratified by the "pre-cART" era (before January 1, 1996) and "post-cART" thereafter. RESULTS: We identified 330 adult HIV-infected patients newly diagnosed with DMAC, the majority (75.2%) of whom were male and non-Hispanic black (69.1%), with median age of 37 years. Incidence of DMAC declined significantly from 65.3/1000 in 1992 to 2.0/1000 in 2015, and the proportion of females and non-Hispanic blacks was significantly higher in the post-cART era. The standardized mortality ratios for DMAC patients who received cART were 69, 58, 27, 5.9, and 6.8 at years 1-5, respectively, after DMAC diagnosis. For patients diagnosed with DMAC in 2000 or later (n = 135), 20% were newly diagnosed with HIV in the 3 months preceding presentation with DMAC. Those with established HIV had a median time from HIV diagnosis to DMAC diagnosis of 7 years and were more likely to be black, rehospitalized in the 6 months after DMAC diagnosis, and die in the long term. CONCLUSIONS: Disseminated Mycobacterium avium complex continues to be a lethal diagnosis in the cART era, disproportionately afflicts minority populations, and reflects both delayed entry into care and failure to consistently engage care.
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    Lower likelihood of cardiac procedures after acute coronary syndrome in patients with human immunodeficiency virus/acquired immunodeficiency syndrome.
    (Medicine, 2018-02) Clement, Meredith E; Lin, Li; Navar, Ann Marie; Okeke, Nwora Lance; Naggie, Susanna; Douglas, Pamela S
    Cardiovascular disease (CVD) is an increasing cause of morbidity and mortality in human immunodeficiency virus (HIV)-infected adults; however, this population may be less likely to receive interventions during hospitalization for acute coronary syndrome (ACS). The degree to which this disparity can be attributed to poorly controlled HIV infection is unknown.In this large cohort study, we used the National Inpatient Sample (NIS) to compare rates of cardiac procedures among patients with asymptomatic HIV-infection, symptomatic acquired immunodeficiency syndrome (AIDS), and uninfected adults hospitalized with ACS from 2009 to 2012. Multivariable analysis was used to compare procedure rates by HIV status, with appropriate weighting to account for NIS sampling design including stratification and hospital clustering.The dataset included 1,091,759 ACS hospitalizations, 0.35% of which (n = 3783) were in HIV-infected patients. Patients with symptomatic AIDS, asymptomatic HIV, and uninfected patients differed by sex, race, and income status. Overall rates of cardiac catheterization and revascularization were 53.3% and 37.4%, respectively. In multivariable regression, we found that relative to uninfected patients, those with symptomatic AIDS were less likely to undergo catheterization (odds ratio [OR] 0.48, confidence interval [CI] 0.43-0.55), percutaneous coronary intervention (OR 0.69, CI 0.59-0.79), and coronary artery bypass grafting (0.75, CI 0.61-0.93). No difference was seen for those with asymptomatic HIV relative to uninfected patients (OR 0.93, CI 0.81-1.07; OR 1.06, CI 0.93-1.21; OR 0.88, CI 0.72-1.06, respectively).We found that lower rates of cardiovascular procedures in HIV-infected patients were primarily driven by less frequent procedures in those with AIDS.
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    Machine learning for early detection of sepsis: an internal and temporal validation study.
    (JAMIA open, 2020-07) Bedoya, Armando D; Futoma, Joseph; Clement, Meredith E; Corey, Kristin; Brajer, Nathan; Lin, Anthony; Simons, Morgan G; Gao, Michael; Nichols, Marshall; Balu, Suresh; Heller, Katherine; Sendak, Mark; O'Brien, Cara

    Objective

    Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice.

    Materials and methods

    We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP-RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed.

    Results

    The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP-RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP-RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP-RNN outperform all 7 clinical risk score and machine learning comparisons.

    Conclusions

    We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.