Norrie, Felicity C

ABSTRACT Background Babesia microti , a parasite transmitted by the Ixodes scapularis tick, can cause infections ranging in presentation from asymptomatic to fulminant, life-threatening disease. Ocrelizumab, an anti-CD20 B-lymphocyte monoclonal antibody used to treat multiple sclerosis, increases the risk of severe or relapsing infections with tick-borne pathogens. In rare cases, babesiosis may trigger hemophagocytic lymphohistiocytosis (HLH). Case Summary We report a case of babesiosis manifesting in a subacute fashion with secondary HLH in a 51-year-old female on B cell-depleting therapy. She presented in December with generalized weakness and fever and was found to have pancytopenia, transaminitis, and hyperinflammatory markers consistent with HLH. A microbial cell-free DNA next-generation sequencing test detected Babesia microti , with subsequent confirmation by blood PCR and smear demonstrating <0.01% parasitemia. The patient improved following treatment with atovaquone and azithromycin, along with corticosteroids for HLH. Conclusion This case highlights the heterogeneity of babesiosis and its potential to present with minimal parasitemia, yet severe systemic inflammation in immunocompromised hosts. Additionally, this case underscores the importance of maintaining a broad differential in patients on B cell-depleting agents who present with new pancytopenia and systemic inflammatory symptoms. Early utilization of microbial cell-free DNA next-generation sequencing tests may aid in timely diagnosis and treatment.

Abstract Objectives Recent work has demonstrated that automated fluorescence flow cytometry (FLC) is a potential alternative for the detection and quantification of Plasmodium parasites. The objective of this study was to apply this novel FLC method to detect and quantify Babesia parasites in venous blood and compare results to light microscopy and polymerase chain reaction methods. Methods An automated hematology/malaria analyzer (XN-31; Sysmex) was used to detect and quantify B microti–infected red blood cells from residual venous blood samples (n = 250: Babesia positive, n = 170; Babesia negative, n = 80). As no instrument software currently exists for Babesia, qualitative and quantitative machine learning (ML) algorithms were developed to facilitate analysis. Results Performance of the ML models was verified against the XN-31 software using P falciparum–infected samples. When applied to Babesia-infected samples, the qualitative ML model demonstrated an area under the curve (AUC) of 0.956 (sensitivity, 95.9%; specificity, 83.3%) relative to polymerase chain reaction. For valid scattergrams, the qualitive model achieved an AUC of 1.0 (sensitivity and specificity, 100%), while the quantitative model demonstrated an AUC of 0.986 (sensitivity, 94.4%; specificity, 100%). Conclusions This investigation demonstrates that Babesia parasites can be detected and quantified directly from venous blood using FLC. Although promising, opportunities remain to improve the general applicability of the method.