Browsing by Author "Lindsell, Christopher J."

Now showing 1 - 6 of 6
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    Best Practices for Biostatistical Consultation and Collaboration in Academic Health Centers
    (Informa UK Limited, 2016) Perkins, Susan M.; Bacchetti, Peter; Davey, Cynthia S.; Lindsell, Christopher J.; Mazumdar, Madhu; Oster, Robert A.; Rocke, David M.; Rudser, Kyle D.; Kim, Mimi; Biostatistics, School of Public Health
    Given the increasing level and scope of biostatistics expertise needed at academic health centers today, we developed best practices guidelines for biostatistics units to be more effective in providing biostatistical support to their institutions, and in fostering an environment in which unit members can thrive professionally. Our recommendations focus on the key areas of: 1) funding sources and mechanisms; 2) providing and prioritizing access to biostatistical resources; and 3) interacting with investigators. We recommend that the leadership of biostatistics units negotiate for sufficient long-term infrastructure support to ensure stability and continuity of funding for personnel, align project budgets closely with actual level of biostatistical effort, devise and consistently apply strategies for prioritizing and tracking effort on studies, and clearly stipulate with investigators prior to project initiation policies regarding funding, lead time, and authorship.
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    Guidance for biostatisticians on their essential contributions to clinical and translational research protocol review
    (Cambridge University Press, 2021-07-12) Ciolino, Jody D.; Spino, Cathie; Ambrosius, Walter T.; Khalatbari, Shokoufeh; Messinger Cayetano, Shari; Lapidus, Jodi A.; Nietert, Paul J.; Oster, Robert A.; Perkins, Susan M.; Pollock, Brad H.; Pomann, Gina-Maria; Price, Lori Lyn; Rice, Todd W.; Tosteson, Tor D.; Lindsell, Christopher J.; Spratt, Heidi; Biostatistics and Health Data Science, School of Medicine
    Rigorous scientific review of research protocols is critical to making funding decisions, and to the protection of both human and non-human research participants. Given the increasing complexity of research designs and data analysis methods, quantitative experts, such as biostatisticians, play an essential role in evaluating the rigor and reproducibility of proposed methods. However, there is a common misconception that a statistician’s input is relevant only to sample size/power and statistical analysis sections of a protocol. The comprehensive nature of a biostatistical review coupled with limited guidance on key components of protocol review motived this work. Members of the Biostatistics, Epidemiology, and Research Design Special Interest Group of the Association for Clinical and Translational Science used a consensus approach to identify the elements of research protocols that a biostatistician should consider in a review, and provide specific guidance on how each element should be reviewed. We present the resulting review framework as an educational tool and guideline for biostatisticians navigating review boards and panels. We briefly describe the approach to developing the framework, and we provide a comprehensive checklist and guidance on review of each protocol element. We posit that the biostatistical reviewer, through their breadth of engagement across multiple disciplines and experience with a range of research designs, can and should contribute significantly beyond review of the statistical analysis plan and sample size justification. Through careful scientific review, we hope to prevent excess resource expenditure and risk to humans and animals on poorly planned studies.
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    Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases
    (Frontiers Media, 2021-07-28) Challa, Anup P.; Zaleski, Nicole M.; Jerome, Rebecca N.; Lavieri, Robert R.; Shirey-Rice, Jana K.; Barnado, April; Lindsell, Christopher J.; Aronoff, David M.; Crofford, Leslie J.; Harris, Raymond C.; Ikizler, T. Alp; Mayer, Ingrid A.; Holroyd, Kenneth J.; Pulley, Jill M.; Medicine, School of Medicine
    Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study-a powerful regression method for generating hypotheses about new indications for an approved drug-with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, "go-no go" decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.
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    Integrated PERSEVERE and endothelial biomarker risk model predicts death and persistent MODS in pediatric septic shock: a secondary analysis of a prospective observational study
    (BMC, 2022-07-11) Atreya, Mihir R.; Cvijanovich, Natalie Z.; Fitzgerald, Julie C.; Weiss, Scott L.; Bigham, Michael T.; Jain, Parag N.; Schwarz, Adam J.; Lutfi, Riad; Nowak, Jeffrey; Allen, Geoffrey L.; Thomas, Neal J.; Grunwell, Jocelyn R.; Baines, Torrey; Quasney, Michael; Haileselassie, Bereketeab; Lindsell, Christopher J.; Alder, Matthew N.; Wong, Hector R.; Pediatrics, School of Medicine
    Background: Multiple organ dysfunction syndrome (MODS) is a critical driver of sepsis morbidity and mortality in children. Early identification of those at risk of death and persistent organ dysfunctions is necessary to enrich patients for future trials of sepsis therapeutics. Here, we sought to integrate endothelial and PERSEVERE biomarkers to estimate the composite risk of death or organ dysfunctions on day 7 of septic shock. Methods: We measured endothelial dysfunction markers from day 1 serum among those with existing PERSEVERE data. TreeNet® classification model was derived incorporating 22 clinical and biological variables to estimate risk. Based on relative variable importance, a simplified 6-biomarker model was developed thereafter. Results: Among 502 patients, 49 patients died before day 7 and 124 patients had persistence of MODS on day 7 of septic shock. Area under the receiver operator characteristic curve (AUROC) for the newly derived PERSEVEREnce model to predict death or day 7 MODS was 0.93 (0.91-0.95) with a summary AUROC of 0.80 (0.76-0.84) upon tenfold cross-validation. The simplified model, based on IL-8, HSP70, ICAM-1, Angpt2/Tie2, Angpt2/Angpt1, and Thrombomodulin, performed similarly. Interaction between variables-ICAM-1 with IL-8 and Thrombomodulin with Angpt2/Angpt1-contributed to the models' predictive capabilities. Model performance varied when estimating risk of individual organ dysfunctions with AUROCS ranging from 0.91 to 0.97 and 0.68 to 0.89 in training and test sets, respectively. Conclusions: The newly derived PERSEVEREnce biomarker model reliably estimates risk of death or persistent organ dysfunctions on day 7 of septic shock. If validated, this tool can be used for prognostic enrichment in future pediatric trials of sepsis therapeutics.
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    Prospective clinical testing and experimental validation of the Pediatric Sepsis Biomarker Risk Model
    (American Association for the Advancement of Science, 2019-11-13) Wong, Hector R.; Caldwell, J. Timothy; Cvijanovich, Natalie Z.; Weiss, Scott L.; Fitzgerald, Julie C.; Bigham, Michael T.; Jain, Parag N.; Schwarz, Adam; Lutfi, Riad; Nowak, Jeffrey; Allen, Geoffrey L.; Thomas, Neal J.; Grunwell, Jocelyn R.; Baines, Torrey; Quasney, Michael; Haileselassie, Bereketeab; Lindsell, Christopher J.; Pediatrics, School of Medicine
    Sepsis remains a major public health problem with no major therapeutic advances over the last several decades. The clinical and biological heterogeneity of sepsis have limited success of potential new therapies. Accordingly, there is considerable interest in developing a precision medicine approach to inform more rational development, testing, and targeting of new therapies. We previously developed the Pediatric Sepsis Biomarker Risk Model (PERSEVERE) to estimate mortality risk and proposed its use as a prognostic enrichment tool in sepsis clinical trials; prognostic enrichment selects patients based on mortality risk independent of treatment. Here, we show that PERSEVERE has excellent performance in a diverse cohort of children with septic shock with potential for use as a predictive enrichment strategy; predictive enrichment selects patients based on likely response to treatment. We demonstrate that the PERSEVERE biomarkers are reliably associated with mortality in mice challenged with experimental sepsis, thus providing an opportunity to test precision medicine strategies in the preclinical setting. Using this model, we tested two clinically feasible therapeutic strategies, guided by the PERSEVERE-based enrichment, and found that mice identified as high risk for mortality had a greater bacterial burden and could be rescued by higher doses of antibiotics. The association between higher pathogen burden and higher mortality risk was corroborated among critically ill children with septic shock. This bedside to bench to bedside approach provides proof of principle for PERSEVERE-guided application of precision medicine in sepsis.
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    Walk before you run: feasibility challenges and lessons learned from the PROCLAIM Study, a multicenter randomized controlled trial of misoprostol for prevention of recurrent C. difficile during COVID-19
    (Elsevier, 2023) Lavieri, Robert R.; Dubberke, Erik R.; McGill, Sarah K.; Bartelt, Luther; Smith, Stephanie A.; Pandur, Balint K.; Phillips, Sharon E.; Vermillion, Krista; Shirey-Rice, Jana; Pulley, Jill; Xu, Yaomin; Lindsell, Christopher J.; Zaleski, Nicole; Jerome, Rebecca; Doster, Ryan S.; Aronoff, David M.; Medicine, School of Medicine
    We analyzed our challenging experience with a randomized controlled trial of misoprostol for prevention of recurrent C. difficile. Despite careful prescreening and thoughtful protocol modifications to facilitate enrollment, we closed the study early after enrolling just 7 participants over 3 years. We share lessons learned, noting the importance of feasibility studies, inclusion of biomarker outcomes, and dissemination of such findings to inform future research design and implementation successes.