Shelton W Wright1, Taniya Kaewarpai2, Lara Lovelace-Macon3, Deirdre Ducken3, Viriya Hantrakun4, Kristina E Rudd5, Prapit Teparrukkul6, Rungnapa Phunpang4, Peeraya Ekchariyawat7, Adul Dulsuk4, Boonhthanom Moonmueangsan8, Chumpol Morakot8, Ekkachai Thiansukhon9, Direk Limmathurotsakul4,10, Narisara Chantratita2,4, T Eoin West3. 1. Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle, Washington, USA. 2. Department of Microbiology and Immunology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand. 3. Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington, USA. 4. Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand. 5. Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. 6. Department of Internal Medicine, Sunpasitthiprasong Hospital, Ubon Ratchathani, Thailand. 7. Department of Microbiology, Faculty of Public Health, Mahidol University, Bangkok, Thailand. 8. Department of Medicine, Mukdahan Hospital, Mukdahan, Thailand. 9. Department of Medicine, Udon Thani Hospital, Udon Thani, Thailand. 10. Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Abstract
BACKGROUND: Melioidosis, infection caused by Burkholderia pseudomallei, is a common cause of sepsis with high associated mortality in Southeast Asia. Identification of patients at high likelihood of clinical deterioration is important for guiding decisions about resource allocation and management. We sought to develop a biomarker-based model for 28-day mortality prediction in melioidosis. METHODS: In a derivation set (N = 113) of prospectively enrolled, hospitalized Thai patients with melioidosis, we measured concentrations of interferon-γ, interleukin-1β, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-ɑ, granulocyte-colony stimulating factor, and interleukin-17A. We used least absolute shrinkage and selection operator (LASSO) regression to identify a subset of predictive biomarkers and performed logistic regression and receiver operating characteristic curve analysis to evaluate biomarker-based prediction of 28-day mortality compared with clinical variables. We repeated select analyses in an internal validation set (N = 78) and in a prospectively enrolled external validation set (N = 161) of hospitalized adults with melioidosis. RESULTS: All 8 cytokines were positively associated with 28-day mortality. Of these, interleukin-6 and interleukin-8 were selected by LASSO regression. A model consisting of interleukin-6, interleukin-8, and clinical variables significantly improved 28-day mortality prediction over a model of only clinical variables [AUC (95% confidence interval [CI]): 0.86 (.79-.92) vs 0.78 (.69-.87); P = .01]. In both the internal validation set (0.91 [0.84-0.97]) and the external validation set (0.81 [0.74-0.88]), the combined model including biomarkers significantly improved 28-day mortality prediction over a model limited to clinical variables. CONCLUSIONS: A 2-biomarker model augments clinical prediction of 28-day mortality in melioidosis.
BACKGROUND: Melioidosis, infection caused by Burkholderia pseudomallei, is a common cause of sepsis with high associated mortality in Southeast Asia. Identification of patients at high likelihood of clinical deterioration is important for guiding decisions about resource allocation and management. We sought to develop a biomarker-based model for 28-day mortality prediction in melioidosis. METHODS: In a derivation set (N = 113) of prospectively enrolled, hospitalized Thai patients with melioidosis, we measured concentrations of interferon-γ, interleukin-1β, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-ɑ, granulocyte-colony stimulating factor, and interleukin-17A. We used least absolute shrinkage and selection operator (LASSO) regression to identify a subset of predictive biomarkers and performed logistic regression and receiver operating characteristic curve analysis to evaluate biomarker-based prediction of 28-day mortality compared with clinical variables. We repeated select analyses in an internal validation set (N = 78) and in a prospectively enrolled external validation set (N = 161) of hospitalized adults with melioidosis. RESULTS: All 8 cytokines were positively associated with 28-day mortality. Of these, interleukin-6 and interleukin-8 were selected by LASSO regression. A model consisting of interleukin-6, interleukin-8, and clinical variables significantly improved 28-day mortality prediction over a model of only clinical variables [AUC (95% confidence interval [CI]): 0.86 (.79-.92) vs 0.78 (.69-.87); P = .01]. In both the internal validation set (0.91 [0.84-0.97]) and the external validation set (0.81 [0.74-0.88]), the combined model including biomarkers significantly improved 28-day mortality prediction over a model limited to clinical variables. CONCLUSIONS: A 2-biomarker model augments clinical prediction of 28-day mortality in melioidosis.
Authors: Kristina E Rudd; Christopher W Seymour; Adam R Aluisio; Marc E Augustin; Danstan S Bagenda; Abi Beane; Jean Claude Byiringiro; Chung-Chou H Chang; L Nathalie Colas; Nicholas P J Day; A Pubudu De Silva; Arjen M Dondorp; Martin W Dünser; M Abul Faiz; Donald S Grant; Rashan Haniffa; Nguyen Van Hao; Jason N Kennedy; Adam C Levine; Direk Limmathurotsakul; Sanjib Mohanty; François Nosten; Alfred Papali; Andrew J Patterson; John S Schieffelin; Jeffrey G Shaffer; Duong Bich Thuy; C Louise Thwaites; Olivier Urayeneza; Nicholas J White; T Eoin West; Derek C Angus Journal: JAMA Date: 2018-06-05 Impact factor: 56.272
Authors: F N Lauw; A J Simpson; J M Prins; M D Smith; M Kurimoto; S J van Deventer; P Speelman; W Chaowagul; N J White; T van der Poll Journal: J Infect Dis Date: 1999-12 Impact factor: 5.226
Authors: Hector R Wong; Natalie Cvijanovich; Derek S Wheeler; Michael T Bigham; Marie Monaco; Kelli Odoms; William L Macias; Mark D Williams Journal: Am J Respir Crit Care Med Date: 2008-05-29 Impact factor: 21.405
Authors: Carmen Mikacenic; William O Hahn; Brenda L Price; Susanna Harju-Baker; Ronit Katz; Kevin C Kain; Jonathan Himmelfarb; W Conrad Liles; Mark M Wurfel Journal: PLoS One Date: 2015-10-22 Impact factor: 3.240
Authors: Sergei S Biryukov; Christopher K Cote; Christopher P Klimko; Jennifer L Dankmeyer; Nathaniel O Rill; Jennifer L Shoe; Melissa Hunter; Zain Shamsuddin; Ivan Velez; Zander M Hedrick; Raysa Rosario-Acevedo; Yuli Talyansky; Lindsey K Schmidt; Caitlyn E Orne; David P Fetterer; Mary N Burtnick; Paul J Brett; Susan L Welkos; David DeShazer Journal: Front Microbiol Date: 2022-08-17 Impact factor: 6.064