57 results on '"Hoffman, Katherine L."'
Search Results
52. Association of urine mitochondrial DNA with clinical measures of COPD in the SPIROMICS cohort
- Author
-
Zhang, William Z., primary, Rice, Michelle C., additional, Hoffman, Katherine L., additional, Oromendia, Clara, additional, Barjaktarevic, Igor Z., additional, Wells, J. Michael, additional, Hastie, Annette T., additional, Labaki, Wassim W., additional, Cooper, Christopher B., additional, Comellas, Alejandro P., additional, Criner, Gerard J., additional, Krishnan, Jerry A., additional, Paine, Robert, additional, Hansel, Nadia N., additional, Bowler, Russell P., additional, Barr, R. Graham, additional, Peters, Stephen P., additional, Woodruff, Prescott G., additional, Curtis, Jeffrey L., additional, Han, Meilan K., additional, Ballman, Karla V., additional, Martinez, Fernando J., additional, Choi, Augustine M.K., additional, Nakahira, Kiichi, additional, Cloonan, Suzanne M., additional, and Choi, Mary E., additional
- Published
- 2020
- Full Text
- View/download PDF
53. Association of urine mitochondrial DNA with clinical measures of COPD in the SPIROMICS cohort.
- Author
-
Zhang WZ, Rice MC, Hoffman KL, Oromendia C, Barjaktarevic IZ, Wells JM, Hastie AT, Labaki WW, Cooper CB, Comellas AP, Criner GJ, Krishnan JA, Paine R 3rd, Hansel NN, Bowler RP, Barr RG, Peters SP, Woodruff PG, Curtis JL, Han MK, Ballman KV, Martinez FJ, Choi AM, Nakahira K, Cloonan SM, and Choi ME
- Subjects
- Humans, Male, Female, Middle Aged, Aged, Cohort Studies, Biomarkers urine, Spirometry, DNA, Mitochondrial genetics, DNA, Mitochondrial urine, Pulmonary Disease, Chronic Obstructive urine, Pulmonary Disease, Chronic Obstructive genetics, Pulmonary Disease, Chronic Obstructive diagnosis
- Published
- 2024
- Full Text
- View/download PDF
54. Learning Optimal Dynamic Treatment Regimes from Longitudinal Data.
- Author
-
Williams NT, Hoffman KL, Díaz I, and Rudolph KE
- Abstract
Studies often report estimates of the average treatment effect (ATE). While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect of treatment within any individual. A treatment strategy that uses an individual's information to tailor treatment to maximize benefit is known as an optimal dynamic treatment rule (ODTR). Treatment, however, is typically not limited to a single point in time; consequently, learning an optimal rule for a time-varying treatment may involve not just learning the extent to which the comparative treatments' benefits vary across the characteristics of individuals, but also learning the extent to which the comparative treatments' benefits vary as relevant circumstances evolve within an individual. The goal of this paper is to provide a tutorial for estimating ODTR from longitudinal observational and clinical trial data for applied researchers. We describe an approach that uses a doubly-robust unbiased transformation of the conditional average treatment effect. We then learn a time-varying ODTR for when to increase buprenorphine-naloxone (BUP-NX) dose to minimize return-to-regular-opioid-use among patients with opioid use disorder. Our analysis highlights the utility of ODTRs in the context of sequential decision making: the learned ODTR outperforms a clinically defined strategy., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
- Published
- 2024
- Full Text
- View/download PDF
55. Comparison of a Target Trial Emulation Framework to Cox Regression to Estimate the Effect of Corticosteroids on COVID-19 Mortality.
- Author
-
Hoffman KL, Schenck EJ, Satlin MJ, Whalen W, Pan D, Williams N, and Díaz I
- Abstract
Importance: Communication and adoption of modern study design and analytical techniques is of high importance for the improvement of clinical research from observational data., Objective: To compare (1) a modern method for causal inference including a target trial emulation framework and doubly robust estimation to (2) approaches common in the clinical literature such as Cox proportional hazards models. To do this, we estimate the effect of corticosteroids on mortality for moderate-to-severe coronavirus disease 2019 (COVID-19) patients. We use the World Health Organization's (WHO) meta-analysis of corticosteroid randomized controlled trials (RCTs) as a benchmark., Design: Retrospective cohort study using longitudinal electronic health record data for 28 days from time of hospitalization., Settings: Multi-center New York City hospital system., Participants: Adult patients hospitalized between March 1-May 15, 2020 with COVID-19 and not on corticosteroids for chronic use., Intervention: Corticosteroid exposure defined as >0.5mg/kg methylprednisolone equivalent in a 24-hour period. For target trial emulation, interventions are (1) corticosteroids for six days if and when patient meets criteria for severe hypoxia and (2) no corticosteroids. For approaches common in clinical literature, treatment definitions used for variables in Cox regression models vary by study design (no time frame, one-, and five-days from time of severe hypoxia)., Main Outcome: 28-day mortality from time of hospitalization., Results: 3,298 patients (median age 65 (IQR 53-77), 60% male). 423 receive corticosteroids at any point during hospitalization, 699 die within 28 days of hospitalization. Target trial emulation estimates corticosteroids to reduce 28-day mortality from 32.2% (95% CI 30.9-33.5) to 25.7% (24.5-26.9). This estimate is qualitatively identical to the WHO's RCT meta-analysis odds ratio of 0.66 (0.53-0.82)). Hazard ratios using methods comparable to current corticosteroid research range in size and direction from 0.50 (0.41-0.62) to 1.08 (0.80-1.47)., Conclusion and Relevance: Clinical research based on observational data can unveil true causal relationships; however, the correctness of these effect estimates requires designing the study and analyzing the data based on principles which are different from the current standard in clinical research.
- Published
- 2022
- Full Text
- View/download PDF
56. Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.
- Author
-
Batra R, Whalen W, Alvarez-Mulett S, Gómez-Escobar LG, Hoffman KL, Simmons W, Harrington J, Chetnik K, Buyukozkan M, Benedetti E, Choi ME, Suhre K, Schenck E, Choi AMK, Schmidt F, Cho SJ, and Krumsiek J
- Abstract
Background: Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions., Methods and Findings: In this study, we compared COVID-19 ARDS (n=43) and bacterial sepsis-induced (non-COVID-19) ARDS (n=24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within-ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation., Conclusion: We present a first comprehensive molecular characterization of differences between two ARDS etiologies - COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.
- Published
- 2022
- Full Text
- View/download PDF
57. Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.
- Author
-
Batra R, Uni R, Akchurin OM, Alvarez-Mulett S, Gómez-Escobar LG, Patino E, Hoffman KL, Simmons W, Chetnik K, Buyukozkan M, Benedetti E, Suhre K, Schenck E, Cho SJ, Choi AMK, Schmidt F, Choi ME, and Krumsiek J
- Abstract
Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. In this study, we performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). The comparison of these ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.
- Published
- 2022
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.