QJM Advance Access originally published online on February 19, 2009
QJM 2009 102(4):251-259; doi:10.1093/qjmed/hcp006
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Prediction of outcome after paraquat poisoning by measurement of the plasma paraquat concentration
From the 1South Asian Clinical Toxicology Research Collaboration, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka, 2Scottish Poisons Information Bureau, Royal Infirmary of Edinburgh, Edinburgh, UK, 3Clinical Pharmacology Unit, Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK, 4Syngenta Crop Protection AG, Basel, Switzerland, 5Syngenta CTL, Macclesfield, UK, 6Causation Ltd, Macclesfield, Cheshire, UK, 7Australian National University Medical School, Canberra ACT, Australia and 8POW Hospital Clinical School, University of NSW, Sydney, Australia
Address correspondence to Nick Buckley, Prince of Wales Hospital Clinical School, University of NSW, Level 1, South Wing Edmund Blackett Building, Randwick 2031, Australia. email: n.buckley{at}unsw.edu.au
Received 10 September 2008 and in revised form 11 January 2009
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Abstract |
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Background: Paraquat is a herbicide with a good occupational safety record, but a high mortality after intentional ingestion that has proved refractory to treatment. For nearly three decades paraquat concentration–time data have been used to predict the outcome following ingestion. However, none of the published methods has been independently or prospectively validated. We aimed to use prospectively collected data to test the published predictive methods and to determine if any is superior.
Methods: Plasma paraquat concentrations were measured on admission for 451 patients in 10 hospitals in Sri Lanka as part of large prospective cohort study. All deaths in hospital were recorded; patients surviving to hospital discharge were followed up after 3 months to detect delayed deaths. Five prediction methods that are based on paraquat concentration–time data were then evaluated in all eligible patients.
Results: All methods showed comparable performance within their range of application. For example, between 4- and 24-h prediction of prognosis was most variable between Sawada and Proudfoot methods but these differences were relatively small [specificity 0.96 (95% CI: 0.90–0.99) vs. 0.89 (0.82–0.95); sensitivity 0.57 vs. 0.79, positive and negative likelihood ratios 14.8 vs. 7.40 and 0.44 vs. 0.23 and positive predictive values 0.96 vs. 0.92, respectively].
Conclusions: All five published methods were better at predicting death than survival. These predictions may also serve as tools to identify patients who need treatment and for some assessment to be made of new treatments that are trialled without a control group.