OBJECTIVES: The association between tobacco smoke exposure and critical illness is not well studied, largely because obtaining an accurate smoking history from critically ill patients is difficult. Biomarkers can provide quantitative data on active and secondhand cigarette smoke exposure. We sought to compare cigarette smoke exposure as measured by biomarkers to exposure by self-report in a cohort of critically ill patients and to determine how well biomarkers of cigarette smoke exposure correlate with each other in this population. DESIGN, SETTING, AND PATIENTS: Serum and urine cotinine and trans-3'-hydroxycotinine, urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol, and hair and nail nicotine levels were measured in 60 subjects enrolled in an observational cohort of critically ill subjects at a tertiary academic medical center in Tennessee. Smoking history was obtained from patients, their surrogates, or the medical chart. Cigarette smoke exposure as measured by biomarkers was compared to exposure by history. MEASUREMENTS AND MAIN RESULTS: By smoking history, 29 subjects were identified as smokers, 28 were identified as nonsmokers, and 3 were identified as unknown. The combination of serum cotinine and urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol identified 27 of the 28 nonsmokers by history either as active smokers (n = 6, 21%) or as exposed to secondhand smoke (n = 21, 75%). All biomarker levels were strongly correlated with each other (r = .69-.95, p < .0001). CONCLUSIONS: The combination of serum cotinine and urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol identified considerably more active smokers than did smoking history and detected a high prevalence of secondhand smoke exposure in a critically ill population. These markers will be important for future studies investigating the relationship between active smoking and secondhand smoke exposure and critical illness.
OBJECTIVES: The association between tobacco smoke exposure and critical illness is not well studied, largely because obtaining an accurate smoking history from critically ill patients is difficult. Biomarkers can provide quantitative data on active and secondhand cigarette smoke exposure. We sought to compare cigarette smoke exposure as measured by biomarkers to exposure by self-report in a cohort of critically ill patients and to determine how well biomarkers of cigarette smoke exposure correlate with each other in this population. DESIGN, SETTING, AND PATIENTS: Serum and urine cotinine and trans-3'-hydroxycotinine, urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol, and hair and nail nicotine levels were measured in 60 subjects enrolled in an observational cohort of critically ill subjects at a tertiary academic medical center in Tennessee. Smoking history was obtained from patients, their surrogates, or the medical chart. Cigarette smoke exposure as measured by biomarkers was compared to exposure by history. MEASUREMENTS AND MAIN RESULTS: By smoking history, 29 subjects were identified as smokers, 28 were identified as nonsmokers, and 3 were identified as unknown. The combination of serum cotinine and urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol identified 27 of the 28 nonsmokers by history either as active smokers (n = 6, 21%) or as exposed to secondhand smoke (n = 21, 75%). All biomarker levels were strongly correlated with each other (r = .69-.95, p < .0001). CONCLUSIONS: The combination of serum cotinine and urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol identified considerably more active smokers than did smoking history and detected a high prevalence of secondhand smoke exposure in a critically ill population. These markers will be important for future studies investigating the relationship between active smoking and secondhand smoke exposure and critical illness.
Authors: Andrew F Shorr; Stephanie L Scoville; Steven B Cersovsky; G Dennis Shanks; Christian F Ockenhouse; Bonnie L Smoak; Warner W Carr; Bruno P Petruccelli Journal: JAMA Date: 2004-12-22 Impact factor: 56.272
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Authors: Suzaynn F Schick; Benjamin C Blount; Peyton Jacob; Najat A Saliba; John T Bernert; Ahmad El Hellani; Peter Jatlow; R Steven Pappas; Lanqing Wang; Jonathan Foulds; Arunava Ghosh; Stephen S Hecht; John C Gomez; Jessica R Martin; Clementina Mesaros; Sanjay Srivastava; Gideon St Helen; Robert Tarran; Pawel K Lorkiewicz; Ian A Blair; Heather L Kimmel; Claire M Doerschuk; Neal L Benowitz; Aruni Bhatnagar Journal: Am J Physiol Lung Cell Mol Physiol Date: 2017-05-18 Impact factor: 5.464
Authors: Brendan J Clark; André Williams; Laura M Cecere Feemster; Katharine A Bradley; Madison Macht; Marc Moss; Ellen L Burnham Journal: Crit Care Med Date: 2013-06 Impact factor: 7.598