Medicine
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This repository contains the published and unpublished research of the Faculty of Medicine by the staff members of the faculty
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Item A Diagnostic scoring model for Leptospirosis in resource limited settings(Public Library of Science, 2016) Rajapakse, S.; Weeratunga, P.; Niloofa, R.; Fernando, N.; de Silva, N.L.; Rodrigo, C.; Maduranga, S.; Nandasiri, N.; Premawansa, S.; Karunanayake, L.; de Silva, H.J.; Handunnetti, S.Leptospirosis is a zoonotic infection with significant morbidity and mortality. The clinical presentation of leptospirosis is known to mimic the clinical profile of other prevalent tropical fevers. Laboratory confirmation of leptospirosis is based on the reference standard microscopic agglutination test (MAT), direct demonstration of the organism, and isolation by culture and DNA detection by polymerase chain reaction (PCR) amplification. However these methods of confirmation are not widely available in resource limited settings where the infection is prevalent, and reliance is placed on clinical features for provisional diagnosis. In this prospective study, we attempted to develop a model for diagnosis of leptospirosis, based on clinical features and standard laboratory test results. METHODS: The diagnostic score was developed based on data from a prospective multicentre study in two hospitals in the Western Province of Sri Lanka. All patients presenting to these hospitals with a suspected diagnosis of leptospirosis, based on the WHO surveillance criteria, were recruited. Confirmed disease was defined as positive genus specific MAT (Leptospira biflexa). A derivation cohort and a validation cohort were randomly selected from available data. Clinical and laboratory manifestations associated with confirmed leptospirosis in the derivation cohort were selected for construction of a multivariate regression model with correlation matrices, and adjusted odds ratios were extracted for significant variables. The odds ratios thus derived were subsequently utilized in the criteria model, and sensitivity and specificity examined with ROC curves. RESULTS: A total of 592 patients were included in the final analysis with 450 (180 confirmed leptospirosis) in the derivation cohort and 142 (52 confirmed leptospirosis) in the validation cohort. The variables in the final model were: history of exposure to a possible source of leptospirosis(adjusted OR = 2.827; 95% CI = 1.517-5.435; p = 0.001) serum creatinine > 150 micromol/l (adjusted OR = 2.735; 95% CI = 1.374-4.901; p = 0.001), neutrophil differential percentage > 80.0% of total white blood cell count (adjusted OR 2.163; 95% CI = 1.309-3.847; p = 0.032), serum bilirubin > 30 micromol/l (adjusted OR = 1.717; 95% CI 0.938-3.456; p = 0.049) and platelet count < 85,000/mm3 (adjusted OR = 2.350; 95% CI = 1.481-4.513; p = 0.006). Hosmer-Lemeshow test for goodness of fit was 0.931. The Nagelkerke R2 was 0.622. The area under the curve (AUC) was noted as 0.762. A score value of 14 reflected a sensitivity of 0.803, specificity of 0.602, a PPV of 0.54, NPV of 0.84, a positive LR of 2.01 and a negative LR of 0.32. CONCLUSIONS: The above diagnostic model for diagnosis of leptospirosis is suggested for use in clinical settings. It should be further validated in clinical practice.Item Identifying the biting species in snakebite by clinical features: an epidemiological tool for community surveys(Oxford University Press, 2006) Pathmeswaran, A.; Kasturiratne, A.; Fonseka, M.; Nandasena, S.; Lalloo, D.G.; de Silva, H.J.The outcome of snakebite is related to the biting species but it is often difficult to identify the biting snake, particularly in community settings. We have developed a clinical scoring system suitable for use in epidemiological surveys, with the main aim of identifying the presumed biting species in those with systemic envenoming who require treatment. The score took into account ten features relating to bites of the five medically important snakes in Sri Lanka, and an algorithm was developed applying different weightings for each feature for different species. A systematically developed artificial data set was used to fine tune the score and to develop criteria for definitive identification. The score was prospectively validated using 134 species-confirmed snakebites. It correctly differentiated the bites caused by the three snakes that commonly cause major clinical problems (Russell's viper (RV), kraits and cobra) from other snakes (hump-nosed viper (HNV) and saw-scaled viper (SSV)) with 80% sensitivity and 100% specificity. For individual species, sensitivity and specificity were, respectively: cobra 76%, 99%; kraits 85%, 99%; and RV 70%, 99%. As anticipated, the score was insensitive in the identification of bites due to HNV and SSV