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Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 remains a global threat with few proven efficacious treatments. Transfusion of convalescent plasma collected from donors who have recovered from COVID-19 disease has emerged as a promising therapy and has been granted emergency use authorization by the U.S. Food and Drug Administration (FDA). We recently reported results from interim analysis of a propensity-score matched study suggesting that early treatment of COVID-19 patients with convalescent plasma containing high titer anti-spike protein receptor binding domain (RBD) IgG significantly decreases mortality. We here present results from 60-day follow up of our cohort of 351 transfused hospitalized patients. Prospective determination of ELISA anti-RBD IgG titer facilitated selection and transfusion of the highest titer units available. Retrospective analysis by the Ortho VITROS IgG assay revealed a median signal/cutoff (S/C) ratio of 24.0 for transfused units, a value far exceeding the recently FDA-required cutoff of 12.0 for designation of high titer convalescent plasma. With respect to altering mortality, our analysis identified an optimal window of 44 hours post-hospitalization for transfusing COVID-19 patients with high titer convalescent plasma. In the aggregate, the analysis confirms and extends our previous preliminary finding that transfusion of COVID-19 patients soon after hospitalization with high titer anti-spike protein RBD IgG present in convalescent plasma significantly reduces mortality.
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BackgroundCOVID-19 disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally, and no proven treatments are available. Convalescent plasma therapy has been used with varying degrees of success to treat severe microbial infections for more than 100 years. MethodsPatients (n=25) with severe and/or life-threatening COVID-19 disease were enrolled at the Houston Methodist hospitals from March 28 - April 14, 2020. Patients were transfused with convalescent plasma obtained from donors with confirmed SARS-CoV-2 infection and had been symptom free for 14 days. The primary study outcome was safety, and the secondary outcome was clinical status at day 14 post-transfusion. Clinical improvement was assessed based on a modified World Health Organization 6-point ordinal scale and laboratory parameters. Viral genome sequencing was performed on donor and recipient strains. ResultsAt baseline, all patients were receiving supportive care, including anti-inflammatory and anti-viral treatments, and all patients were on oxygen support. At day 7 post-transfusion with convalescent plasma, nine patients had at least a 1-point improvement in clinical scale, and seven of those were discharged. By day 14 post-transfusion, 19 (76%) patients had at least a 1-point improvement in clinical status and 11 were discharged. No adverse events as a result of plasma transfusion were observed. The whole genome sequencing data did not identify a strain genotype-disease severity correlation. ConclusionsThe data indicate that administration of convalescent plasma is a safe treatment option for those with severe COVID-19 disease. Randomized, controlled trials are needed to determine its efficacy.
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Computational tools in the form of software packages are burgeoning in the field of medical imaging and biomedical research. These tools enable biomedical researchers to analyze a variety of data using modern machine learning and statistical analysis techniques. While these publicly available software packages are a great step towards a multiplicative increase in the biomedical research productivity, there are still many open issues related to validation and reproducibility of the results. A key gap is that while scientists can validate domain insights that are implicit in the analysis, the analysis itself is coded in a programming language and that domain scientist may not be a programmer. Thus, there is no/limited direct validation of the program that carries out the desired analysis. We propose a novel solution, building upon recent successes in natural language understanding, to address this problem. Our platform allows researchers to perform, share, reproduce and interpret the analysis pipelines and results via natural language. While this approach still requires users to have a conceptual understanding of the techniques, it removes the burden of programming syntax and thus lowers the barriers to advanced and reproducible neuroimaging and biomedical research.
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BACKGROUND: Computer self-administration may help busy pediatricians' offices increase adolescent substance use screening rates efficiently and effectively, if proven to yield valid responses. The CRAFFT screening protocol for adolescents has demonstrated validity as an interview, but a computer self-entry approach needs validity testing. The aim of this study was to evaluate the criterion validity and time efficiency of a computerized adolescent substance use screening protocol implemented by self-administration or clinician-administration. METHODS: Twelve- to 17-year-old patients coming for routine care at 3 primary care clinics completed the computerized screen by both self-administration and clinician-administration during their visit. To account for order effects, we randomly assigned participants to self-administer the screen either before or after seeing their clinician. Both were conducted using a tablet computer and included identical items (any past-12-month use of tobacco, alcohol, drugs; past-3-month frequency of each; and 6 CRAFFT items). The criterion measure for substance use was the Timeline Follow-Back, and for alcohol/drug use disorder, the Adolescent Diagnostic Interview, both conducted by confidential research assistant interview after the visit. Tobacco dependence risk was assessed with the self-administered Hooked on Nicotine Checklist (HONC). Analyses accounted for the multisite cluster sampling design. RESULTS: Among 136 participants, mean age was 15.0 ± 1.5 years, 54% were girls, 53% were black or Hispanic, and 67% had ≥3 prior visits with their clinician. Twenty-seven percent reported any substance use (including tobacco) in the past 12 months, 7% met criteria for an alcohol or cannabis use disorder, and 4% were HONC positive. Sensitivity/specificity of the screener were high for detecting past-12-month use or disorder and did not differ between computer and clinician. Mean completion time was 49 seconds (95% confidence interval [CI]: 44-54) for computer and 74 seconds (95% CI: 68-87) for clinician (paired comparison, P < .001). CONCLUSIONS: Substance use screening by computer self-entry is a valid and time-efficient alternative to clinician-administered screening.