Citations 2
Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy
Morris, Alan H. , Horvat, Christopher M. , Grainger, David W. , Orme, James F., Clemmer, Terry P., Weaver, Lindell K., Thomas, Frank O, Grissom, Colin K. , Hirshberg, Ellie , East, Thomas D. , Wallace, C. Jane, Young, Michael , Sittig, Dean F. , Pearl, James E., Pesenti, Antinio, Bombino, Michela, Beck, Eduardo, Sward, Katherine , Phansalkar, Shobha, Bernard, Gordon R. , Brower, Roy G., Truwit, Jonathon D. , Steingrub, Jay S. , Hiten, R Duncan, Willson, Douglas F., Randolph, Adrienne G. , Curley, Martha A. Q. , Newth, Christopher J. L. , Lacroix, Jacques, Agus, Michael S. D. , Lee, Kang Hoe, deBoisblanc, Bennett P., Moore, Frederick A. , Evans, R. Scott, Sorenson, Dean, Wong, Anthony, Boland, Michael V. , Dere, W. , Crandall, Alan S. , Huff, Stanley M. , Haug, Peter J. , Pielmeier, Ulrike , Rees, Stephen Edward , Karbing, Dan Stieper , Berger, Kenneth I. , Oppenheimer, Beno W. , Pickering, Brian W. , Schoenfeld, David , Tocino, Irena , Savitz, Lucy A., Dreyfuss, Didier, Slutsky, Arthur S. , Crapo, James D. , Pinsky, Michael R. , James, Brent C., Berwick, Donald M. , Sittig, Dean F , Sward, Katherine A , Weir, Charlene , Huff, Stanley M , Gonnering, Russell S , Randolph, Adrienne G , Stagg, Brian C. , Lanspa, Michael J. , Suchyta, Mary R. , Thompson, Bruce , Zimmerman, Jerry J. , Nadkarni, Vinay , Facelli, Julio Cesar , Andreassen, Steen , Fan, Eddy , Goldring, Roberta M. , Ely, Eugene Wesley , Pronovost, Peter J.
Journal of the American Medical Informatics Association | September 20, 2022 | Volume: 30 | Issue: 1 | Pages: 178-194
AbstractHow to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.
Exploring the Predictive Ability of Dysfunctional High-Density Lipoprotein for Adverse Outcomes in Emergency Department Patients with Sepsis: A Preliminary Investigation
Guirgis, Faheem W. , Dodani, Sunita , Moldawer, Lyle L. , Leeuwenburgh, Christiaan , Bowman, Jennifer, Kalynych, Colleen , Jones, Alan E. , Reddy, Srinivasa T. , Moore, Frederick A.
Shock | April 28, 2017 | Volume: 48 | Issue: 5 | Pages: 539-544
ABSTRACT Background: High density lipoprotein (HDL) can be readily oxidized in inflammatory conditions and exhibit pro-inflammatory and dysfunctional (Dys-HDL) characteristics. We hypothesize that Dys-HDL may predict adverse outcomes and correlate with inflammatory cytokines in sepsis. Methods: Emergency department (ED) patients with sepsis were enrolled. Blood was drawn at enrollment and after 48 h. Dys-HDL, expressed as HDL inflammatory index (HII), and cytokines were measured. Multivariable logistic regression was used to determine the predictive ability of Dys-HDL for adverse outcomes (death, discharge to hospice, or nursing home). Results: Thirty-five patients were included in the study. HII was not significantly different at baseline or 48 h between patients with adverse outcomes versus those without. However, there was a significant difference in change in HII over the first 48 h between those with adverse outcomes (+0.21, 95% CI −0.13 to 0.31) versus those without (−0.11, 95% CI −1 to 0.11) (P = 0.025). Logistic regression revealed increasing HII to be an independent predictor of adverse outcomes (OR 5.2, 95% CI 1.1–25.1 P = 0.040). Of the 24 patents with cytokine measurements at both time points, significant inverse correlations between change in HII and change in GRO (rs = −0.52, P = 0.0088) and monocyte chemotactic protein-1 (rs = −0.61, P = 0.0014) concentrations over 48 h were observed. Conclusion: Increasing Dys-HDL concentrations in the first 48 h of sepsis are associated with an ongoing inflammatory response and adverse clinical outcomes. Early changes in HII may be a potential biomarker in ED patients admitted with sepsis.