Inconsistency analysis between metagenomic next-generation sequencing results of cerebrospinal fluid and clinical diagnosis with suspected central nervous system infection.

backgroundRecently, with the rapid progress of metagenomic next-generation sequencing (mNGS), inconsistency between mNGS results and clinical diagnoses has become more common. There is currently no reasonable explanation for this, and the interpretation of mNGS reports still needs to be standardised.

methodsA retrospective analysis was conducted on 47 inpatients with suspected central nervous system (CNS) infections, and clinical data were recorded. The final diagnosis was determined by an expert group based on the patient's clinical manifestation, laboratory examination, and response to treatment. mNGS results were compared with the final diagnosis, and any inconsistencies that occurred were investigated. Finally, the credibility of mNGS results was evaluated using the integral approach, which consists of three parts: typical clinical features, positive results with the traditional method, and cerebrospinal fluid cells ≥ 100 (× 10

resultsForty-one patients with suspected CNS infection were assigned to infected (ID, 31/41, 75.61%) and non-infected groups (NID, 10/41, 24.39%) after assessment by a panel of experts according to the composite diagnostic criteria. For mNGS-positive results, 20 of the 24 pathogens were regarded as contaminants when the final score was ≤ 1. The remaining 11 pathogens detected by mNGS were all true positives, which was consistent with the clinical diagnosis when the score was ≥ 2. For mNGS negative results, when the score was ≥ 2, the likelihood of infection may be greater than when the score is ≤ 1.

conclusionThe integral method is effective for evaluating mNGS results. Regardless of whether the mNGS result was positive or negative, the possibility of infection was greater when the score was ≥ 2. A negative mNGS result does not necessarily indicate that the patient was not clinically infected, and, therefore, clinical features are more important.