367. Procalcitonin as a Potential Biomarker in the Study of Human Babesiosis. (15th December 2022)

• Record Type: Journal Article
• Authors: Lum, Michael D; Gauvin, Caitlin; Pham, Sophia; Papamanoli, Aikaterini; Spitzer, Eric; Kalogeropoulos, Andreas P; Marcos, Luis A
• Is Part Of: Open forum infectious diseases. Volume 9:(2022)Supplement 2
• Publication Date: 2022-12-15
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofac492.445
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: Procalcitonin (Pct) has been gaining momentum as a potential biomarker during parasitic infections as studies have demonstrated that Pct levels are significantly elevated during certain forms of protozoal sepsis. Given this, the goal of this pilot study is to determine the relationship between Pct levels and parasitemia (Par) during infection with Babesia microti and to elucidate the potential for Pct to be used as a biomarker in the study of babesiosis. Methods: Babesia cases were collected from Stony Brook University Hospital (SBUH) and South Hampton Hospital (SHH) from 2012 to 2019. Median values of maximum Par and Pct throughout hospitalization were recorded and cases were referenced for admission to the intensive care unit (ICU). Correlation between maximum Par and Pct values were examined with the nonparametric Spearman rank correlation and fractional polynomials were used to assess the functional form of the correlation. Receiver-operator characteristic (ROC) curve analyses were used to identify cutoff points for Pct and maximum Par as markers for prediction of ICU admission. Results: A total of 33 patients met the inclusion criteria for acute babesiosis. In fractional polynomial analysis, log-transformed Pct levels had a linear correlation with log-transformed maximum Par, r=0.556 (P=0.001, Fig 1A). In ROC curve analysis, a cut off level of ≥ 1.2 ng/mL for Pct had optimal prediction characteristics for ICU admission (sensitivity 62.5%, specificity 88%; correct classification 82%) with a relevant AUC of 0.77 (95% CI: 0.58–0.89; P=0.005, Fig 1B). A cut off level of ≥ 3.6% for maximum Par had optimal prediction characteristics for ICU admission (sensitivity 75%, specificity 92%, correct classification 88%) with a relevant AUC of 0.75 (95% CI 0.58—0.89; P= 0.005, Fig 1B). Comparison of AUC for Pct and Par yielded no significant difference (P=0.8). Figure 1A. Linear correlation between log10-parasitemia and log10- procalcitonin levels. The linear correlation of the log-transformed variables was r=0.556 (P=0.001), similar to the nonparametric (Spearman) correlation coefficient (ρ=0.567). The red dots represent patients who were admitted to the intensive care unit. Figure 1B. Receiver-operator characteristic curve of parasitemia for prediction of intensive care unit admission (0.75; 95%CI: 0.58—0.89; P=0.005); optimal cut-off is 3.6%. Receiver-operator characteristic curve of procalcitonin for prediction of intensive care unit admission (C=0.77; 95%CI: 0.58–0.89; P=0.005); optimal cut-off is 1.2 ng/mL. Conclusion: Our analysis demonstrated a positive linear correlation between Pct and maximum Par, which is the current standard marker for severe disease. Also, our ROC analyses estimate that Pct values ≥ 1.2 ng/mL and Par ≥ 3.6% suggests severe disease and need for ICU admission (Fig 1A, 1B). Given this, our findings suggest that Pct may have potential for predicting severe disease during babesiosis. Disclosures: All Authors : No reported disclosures.