Reevaluation of an Acanthamoeba Molecular Diagnostic Algorithm following an Atypical Case of Amoebic Keratitis

在发生一例非典型阿米巴角膜炎病例后,对棘阿米巴分子诊断算法进行重新评估

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Abstract

Amoebic keratitis (AK) is a potentially blinding infection, the prompt diagnosis of which is essential for limiting ocular morbidity. We undertook a quality improvement initiative with respect to the molecular detection of acanthamoebae in our laboratory because of an unusual case of discordance. Nine ATCC strains of Acanthamoeba and 40 delinked, biobanked, surplus corneal scraping specimens were analyzed for the presence of acanthamoebae with four separate real-time PCR assays. The assay used by the Free-Living and Intestinal Amebas Laboratory of the CDC was considered the reference standard, and the performance characteristics of each individual assay and pairs of assays were calculated. Outcome measures were sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Of 49 included specimens, 14 (28.6%) were positive by the gold standard assay, and 35 (71.4%) were negative. The sensitivities of the individual assays ranged from 64.3% to 92.9%, compared to the gold standard, while the specificities ranged from 88.6% to 91.4%. The PPVs and NPVs ranged from 69.2% to 78.6% and from 86.1% to 96.9%, respectively. Combinations of assay pairs led to improved performance, with sensitivities ranging from 92.9% to 100% and specificities ranging from 97.1% to 100%. ATCC and clinical strains of Acanthamoeba that failed to be detected by certain individual assays included Acanthamoeba castellanii, Acanthamoeba culbertsoni, and Acanthamoeba lenticulata. For three clinical specimens, false negativity of the gold standard assay could not be excluded. Molecular diagnostic approaches, especially combinations of highly sensitive and specific assays, offer a reasonably performing, operator-independent, rapid strategy for the detection of acanthamoebae in clinical specimens and are likely to be more practical than either culture or direct microscopic detection.

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