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Deepankar Datta

Centre for Inflammation Research

ORCID: 0000-0001-9971-9434

Publishes on Sepsis Diagnosis and Treatment, Hemodynamic Monitoring and Therapy, Immune Response and Inflammation. 18 papers and 512 citations.

18Publications
512Total Citations

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Top publicationsby citations

Cell-surface signatures of immune dysfunction risk-stratify critically ill patients: INFECT study
Andrew Conway Morris, Deepankar Datta, Manu Shankar‐Hari et al.|Intensive Care Medicine|2018
Cited by 131Open Access

Cellular immune dysfunctions, which are common in intensive care patients, predict a number of significant complications. In order to effectively target treatments, clinically applicable measures need to be developed to detect dysfunction. The objective was to confirm the ability of cellular markers associated with immune dysfunction to stratify risk of secondary infection in critically ill patients. Multi-centre, prospective observational cohort study of critically ill patients in four UK intensive care units. Serial blood samples were taken, and three cell surface markers associated with immune cell dysfunction [neutrophil CD88, monocyte human leucocyte antigen-DR (HLA-DR) and percentage of regulatory T cells (Tregs)] were assayed on-site using standardized flow cytometric measures. Patients were followed up for the development of secondary infections. A total of 148 patients were recruited, with data available from 138. Reduced neutrophil CD88, reduced monocyte HLA-DR and elevated proportions of Tregs were all associated with subsequent development of infection with odds ratios (95% CI) of 2.18 (1.00–4.74), 3.44 (1.58–7.47) and 2.41 (1.14–5.11), respectively. Burden of immune dysfunction predicted a progressive increase in risk of infection, from 14% for patients with no dysfunction to 59% for patients with dysfunction of all three markers. The tests failed to risk stratify patients shortly after ICU admission but were effective between days 3 and 9. This study confirms our previous findings that three cell surface markers can predict risk of subsequent secondary infection, demonstrates the feasibility of standardized multisite flow cytometry and presents a tool which can be used to target future immunomodulatory therapies. The study was registered with clinicaltrials.gov (NCT02186522).

blandr: Bland-Altman Method Comparison
Deepankar Datta|Unknown|2017
Cited by 66Open Access

Carries out Bland Altman analyses (also known as a Tukey mean-difference plot) as described by JM Bland and DG Altman in 1986 &lt;<a href="https://doi.org/10.1016%2FS0140-6736%2886%2990837-8" target="_top">doi:10.1016/S0140-6736(86)90837-8</a>&gt;. This package was created in 2015 as existing Bland-Altman analysis functions did not calculate confidence intervals. This package was created to rectify this, and create reproducible plots. This package is also available as a module for the 'jamovi' statistical spreadsheet (see &lt;<a href="https://www.jamovi.org" target="_top">https://www.jamovi.org</a>&gt; for more information).