Predicting Neutropenic Sepsis in Patients with Hematologic Malignancy: A Retrospective Case–Control Study

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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Publication Details

Journal
Clinical Nursing Research
Published
2024-09-08
DOI
https://doi.org/10.1177/10547738241273862
Primary Topic
Neutropenia and Cancer Infections
Type
article
Field-Weighted Citation Impact
0.00

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article

Predicting Neutropenic Sepsis in Patients with Hematologic Malignancy: A Retrospective Case–Control Study

Ji‐Won Lee, Hee‐Ju Kim
Clinical Nursing Research
Neutropenia and Cancer Infections
article

Predicting Neutropenic Sepsis in Patients with Hematologic Malignancy: A Retrospective Case–Control Study

Ji‐Won Lee, Hee‐Ju Kim
article en

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.

Clinical Nursing ResearchVol. 33(8)
The Catholic University of Korea Seoul St. Mary's Hospital (KR), Catholic University of Korea (KR)
National Research Foundation of Korea
Good health and well-being
Openalex Percentile: Top 74%
Neutropenia and Cancer Infections
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