Browsing by Author "Crowley, Anna Lisa"

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    Critical Review of Current Approaches for Echocardiographic Reproducibility and Reliability Assessment in Clinical Research.
    (Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography, 2016-12) Crowley, Anna Lisa; Yow, Eric; Barnhart, Huiman X; Daubert, Melissa A; Bigelow, Robert; Sullivan, Daniel C; Pencina, Michael; Douglas, Pamela S

    Background

    There is no broadly accepted standard method for assessing the quality of echocardiographic measurements in clinical research reports, despite the recognized importance of this information in assessing the quality of study results.

    Methods

    Twenty unique clinical studies were identified reporting echocardiographic data quality for determinations of left ventricular (LV) volumes (n = 13), ejection fraction (n = 12), mass (n = 9), outflow tract diameter (n = 3), and mitral Doppler peak early velocity (n = 4). To better understand the range of possible estimates of data quality and to compare their utility, reported reproducibility measures were tabulated, and de novo estimates were then calculated for missing measures, including intraclass correlation coefficient (ICC), 95% limits of agreement, coefficient of variation (CV), coverage probability, and total deviation index, for each variable for each study.

    Results

    The studies varied in approaches to reproducibility testing, sample size, and metrics assessed and values reported. Reported metrics included mean difference and its SD (n = 7 studies), ICC (n = 5), CV (n = 4), and Bland-Altman limits of agreement (n = 4). Once de novo estimates of all missing indices were determined, reasonable reproducibility targets for each were identified as those achieved by the majority of studies. These included, for LV end-diastolic volume, ICC > 0.95, CV < 7%, and coverage probability > 0.93 within 30 mL; for LV ejection fraction, ICC > 0.85, CV < 8%, and coverage probability > 0.85 within 10%; and for LV mass, ICC > 0.85, CV < 10%, and coverage probability > 0.60 within 20 g.

    Conclusions

    Assessment of data quality in echocardiographic clinical research is infrequent, and methods vary substantially. A first step to standardizing echocardiographic quality reporting is to standardize assessments and reporting metrics. Potential benefits include clearer communication of data quality and the identification of achievable targets to benchmark quality improvement initiatives.
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    Effect of Algorithm-Based Therapy vs Usual Care on Clinical Success and Serious Adverse Events in Patients with Staphylococcal Bacteremia: A Randomized Clinical Trial.
    (JAMA, 2018-09) Holland, Thomas L; Raad, Issam; Boucher, Helen W; Anderson, Deverick J; Cosgrove, Sara E; Aycock, P Suzanne; Baddley, John W; Chaftari, Anne-Marie; Chow, Shein-Chung; Chu, Vivian H; Carugati, Manuela; Cook, Paul; Corey, G Ralph; Crowley, Anna Lisa; Daly, Jennifer; Gu, Jiezhun; Hachem, Ray; Horton, James; Jenkins, Timothy C; Levine, Donald; Miro, Jose M; Pericas, Juan M; Riska, Paul; Rubin, Zachary; Rupp, Mark E; Schrank, John; Sims, Matthew; Wray, Dannah; Zervos, Marcus; Fowler, Vance G; Staphylococcal Bacteremia Investigators

    Importance

    The appropriate duration of antibiotics for staphylococcal bacteremia is unknown.

    Objective

    To test whether an algorithm that defines treatment duration for staphylococcal bacteremia vs standard of care provides noninferior efficacy without increasing severe adverse events.

    Design, setting, and participants

    A randomized trial involving adults with staphylococcal bacteremia was conducted at 16 academic medical centers in the United States (n = 15) and Spain (n = 1) from April 2011 to March 2017. Patients were followed up for 42 days beyond end of therapy for those with Staphylococcus aureus and 28 days for those with coagulase-negative staphylococcal bacteremia. Eligible patients were 18 years or older and had 1 or more blood cultures positive for S aureus or coagulase-negative staphylococci. Patients were excluded if they had known or suspected complicated infection at the time of randomization.

    Interventions

    Patients were randomized to algorithm-based therapy (n = 255) or usual practice (n = 254). Diagnostic evaluation, antibiotic selection, and duration of therapy were predefined for the algorithm group, whereas clinicians caring for patients in the usual practice group had unrestricted choice of antibiotics, duration, and other aspects of clinical care.

    Main outcomes and measures

    Coprimary outcomes were (1) clinical success, as determined by a blinded adjudication committee and tested for noninferiority within a 15% margin; and (2) serious adverse event rates in the intention-to-treat population, tested for superiority. The prespecified secondary outcome measure, tested for superiority, was antibiotic days among per-protocol patients with simple or uncomplicated bacteremia.

    Results

    Among the 509 patients randomized (mean age, 56.6 [SD, 16.8] years; 226 [44.4%] women), 480 (94.3%) completed the trial. Clinical success was documented in 209 of 255 patients assigned to algorithm-based therapy and 207 of 254 randomized to usual practice (82.0% vs 81.5%; difference, 0.5% [1-sided 97.5% CI, -6.2% to ∞]). Serious adverse events were reported in 32.5% of algorithm-based therapy patients and 28.3% of usual practice patients (difference, 4.2% [95% CI, -3.8% to 12.2%]). Among per-protocol patients with simple or uncomplicated bacteremia, mean duration of therapy was 4.4 days for algorithm-based therapy vs 6.2 days for usual practice (difference, -1.8 days [95% CI, -3.1 to -0.6]).

    Conclusions and relevance

    Among patients with staphylococcal bacteremia, the use of an algorithm to guide testing and treatment compared with usual care resulted in a noninferior rate of clinical success. Rates of serious adverse events were not significantly different, but interpretation is limited by wide confidence intervals. Further research is needed to assess the utility of the algorithm.

    Trial registration

    ClinicalTrials.gov Identifier: NCT01191840.