METHODOLOGY AND PRINCIPLE FINDINGS: A literature search was performed in Scopus, PubMed, MEDLINE and non-indexed citations (via Ovid) by using suitable keyword combinations. Studies evaluating the performance of nucleic acid assays targeting leptospire genes in human or animal clinical samples against a reference test were included. Of the 1645 articles identified, 42 eligible studies involving 7414 samples were included in the analysis. The diagnostic performance of nucleic acid assays targeting the rrs, lipL32, secY and flaB genes was pooled and analyzed. Among the genetic markers analyzed, the secY gene showed the highest diagnostic accuracy measures, with a pooled sensitivity of 0.56 (95% CI: 0.50-0.63), a specificity of 0.98 (95% CI: 0.97-0.98), a diagnostic odds ratio of 46.16 (95% CI: 6.20-343.49), and an area under the curve of summary receiver operating characteristics curves of 0.94. Nevertheless, a high degree of heterogeneity was observed in this meta-analysis. Therefore, the present findings here should be interpreted with caution.
CONCLUSION: The diagnostic accuracies of the studies examined for each genetic marker showed a significant heterogeneity. The secY gene exhibited higher diagnostic accuracy measures compared with other genetic markers, such as lipL32, flaB, and rrs, but the difference was not significant. Thus, these genetic markers had no significant difference in diagnostic accuracy for leptospirosis. Further research into these genetic markers is warranted.
METHODS: In this single-centre retrospective study, comparative analysis on clinical presentations and laboratory findings was performed between confirmed leptospirosis versus non-leptospirosis cases.
RESULTS: In multivariate logistic regression evidenced by a Hosmer-Lemeshow significance value of 0.979 and Nagelkerke R square of 0.426, the predictors of a leptospirosis case are hypocalcemia (calcium <2.10mmol/L), hypochloremia (chloride <98mmol/L), and eosinopenia (absolute eosinophil count <0.040×109/L). The proposed diagnostic scoring model has a discriminatory power with area under the curve (AUC) 0.761 (p<0.001). A score value of 6 reflected a sensitivity of 0.762, specificity of 0.655, a positive predictive value of 0.38, negative predictive value of 0.91, a positive likelihood ratios of 2.21, and a negative likelihood ratios of 0.36.
CONCLUSION: With further validation in clinical settings, implementation of this diagnostic scoring model is helpful to manage presumed leptospirosis especially in the absence of leptospirosis confirmatory tests.
METHOD: Two hundred sixty eight serum specimens collected from patients that were diagnosed for dengue fever were confirmed for dengue virus serotyping by real-time polymerase chain reaction. Clinical, laboratory and demographic data were extracted from the hospital database to identify patients with confirmed leptospirosis infection among the dengue patients. Thus, frequency of co-infection was calculated and association of the dataset with dengue-leptospirosis co-infection was statistically determined.
RESULTS: The frequency of dengue co-infection with leptospirosis was 4.1%. Male has higher preponderance of developing the co-infection and end result of shock as clinical symptom is more likely present among co-infected cases. It is also noteworthy that, DENV 1 is the common dengue serotype among all cases identified as dengue-leptospirosis co-infection in this study.
CONCLUSION: The increasing incidence of leptospirosis among dengue infected patients has posed the need to precisely identify the presence of co-infection for the betterment of treatment without mistakenly ruling out either one of them. Thus, anticipating the possible clinical symptoms and laboratory results of dengue-leptospirosis co-infection is essential.