ArticleBMC infectious diseases2026
Clinical presentation and laboratory findings: key indicators for early diagnosis and severity prediction of leptospirosis in Sri Lanka.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe diagnosis and treatment of leptospirosis in high-burden, low-resource settings pose a challenge due to the limited availability of confirmatory diagnostics. Therefore, the initiation of treatment relies primarily on the clinical assessment of patients suspected of having leptospirosis in resource-poor settings. This study aimed to both early diagnostic indicators and predictors of disease severity.
methodsA total of 170 patients suspected of having leptospirosis admitted to selected hospitals in Western Province, Sri Lanka were enrolled. MAT, qPCR, and culture were used for confirmatory diagnosis. Patients with confirmed leptospirosis were grouped as severe and non severe based on presenting with at least one organ failure and/or dysfunction.
resultsOut of 170 leptospirosis suspected patients, 79 (46.47%) were confirmed by PCR (n = 49), MAT (n = 44) and/or culture (n = 2). Myalgia, oliguria, elevated neutrophils, serum creatinine, serum urea, SGOT, SGPT and CRP, decreased lymphocytes, haemoglobin and platelets had a significant association with confirmed leptospirosis. Of patients with confirmed leptospirosis, 51 were categorized as non severe and 28 were categorized as severe. Leptospirosis severity was correlated with decreased haemoglobin, lymphocyte and platelet counts, and elevated WBC, neutrophil, K+, SGOT, SGPT, serum urea, serum creatinine, total and direct bilirubin and CRP. A bacterial load between 1.21 × 102 to 1.26 × 106 (median 3,326) Leptospira/ml was reported with no significant association between severity of the disease and leptospiraemia.
conclusionClinical and basic laboratory findings play a crucial role in supporting the diagnosis of leptospirosis and predicting disease severity, particularly in tropical, resource-poor settings.
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