Abstract

Neutropenic sepsis (NS) is one of the leading causes of death among patients with hematologic malignancies. Identifying its predictive factors is fundamental for early detection. Few studies have evaluated the predictive factors in relation to microbial infection confirmation, which is clinically important for initiating sepsis treatment. This study aimed to determine whether selected biomarkers (i.e., body temperature, C-reactive protein, albumin, procalcitonin), treatment-related characteristics (i.e., diagnosis, duration of neutropenia, treatment modality), and infection-related characteristics (i.e., infection source, causative organisms) can predict NS in patients with hematologic malignancies. We also aimed to identify the optimal predictive cutoff points for these parameters. This retrospective case-control study used the data from a total of 163 patients (58 in the sepsis group and 105 in the non-sepsis group). We collected data with reference to the day of specimen collection, with which microbial infection was confirmed. Multiple logistic regression was used to determine predictive risk factors and the area under the curve (AUC) of the receiver operating characteristic for the optimal predictive cutoff points. The independent predictors of NS were average body temperature during a fever episode and procalcitonin level. The odds for NS rose by 9.97 times with every 1°C rise in average body temperature (95% confidence interval, CI [1.33, 75.05]) and by 2.09 times with every 1 ng/mL rise in the procalcitonin level (95% CI [1.08, 4.04]). Average body temperature (AUC = 0.77, 95% CI [0.68, 0.87]) and procalcitonin levels (AUC = 0.71, 95% CI [0.59, 0.84]) have fair accuracy for predicting NS, with the optimal cutoff points of 37.9°C and 0.55 ng/mL, respectively. This study found that average body temperature during a fever episode and procalcitonin are useful in predicting NS. Thus, nurses should carefully monitor body temperature and procalcitonin levels in patients with hematologic malignancies to detect the onset of NS.

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