Discussion
This study identified that temperature 1-hr after initial acetaminophen
dosing was significantly higher in individuals with culture confirmed
bacteremia. We demonstrated that temperature 1-hr after initial
acetaminophen had significant value in predicting bacteremia using both
a binary logistic regression and a CART machine learning model, with
AUC-ROC comparable to other prospectively validated
CDRs.16,19-22 Similar to prior reports, we observed
that presenting temperature ≥ 39.0°C, evidence of focal infection and
hypotension were also all predictive of bacteremia5,8,9,15. However, when examining the relative
importance of input variables in our CART model, temperature 1-hr after
acetaminophen was far and away the most important predictor of
bacteremia when compared to pre-acetaminophen temperature ≥ 39.0°C,
evidence of focal infection, and hypotension. Generation of an optimal
decision tree using CART analysis also demonstrated that 85.7% of
patients with temperature ≤ 37.3°C (99.2°F) 1-hr after acetaminophen had
negative blood cultures. Conversely, 80% of patients with temperature
> 39.2°C (102.5°F) 1-hr after acetaminophen were culture
positive. These results suggest there may be some utility in using
post-acetaminophen temperatures at both extremes of the spectrum to aid
current CDR’s in identifying patients at low- and high-risk of
bacteremia. This may be especially useful in assessing patients with a
mix of low- and high-risk features (ex: high degree of neutropenia with
low-risk category of malignancy), in which prompt and accurate
determination of risk can be difficult.
Temperature has primarily been used as a binary variable (ex.
Temperature ≥ or < 39.0°C) in CDRs for FN risk stratification,
with few if any studies investigating the relationship between fever
response to antipyretics and bacteremia. Alali et al. described the
creation of a random forest model for predicting BSI and need for
transfer to an intensive care unit (TIC), in which the authors detail
that treating temperature as a continuous variable and including peak
temperature as an input variable increased model
performance.16 The authors also identified max
temperature to be the most important predictor of bacteremia and the
second most important predictor of TIC (behind only
hypotension).16 While our study did not demonstrate a
significant difference in Tmax between patients with bacteremia and
culture negative individuals, the concept of using temperature to
predict bacteremia as well as the relative importance of temperature as
a predictor of bacteremia are congruent with the results of this study.
Another study by Haeusler et al. utilized prospective data from the
Australian-PICNICC study to investigate the association between 10
commonly utilized CDR variables and the presence of bacterial
infection.22 Using both univariate and multivariate
analysis of 858 episodes of FN, the authors demonstrated that increasing
temperature was significantly associated with bacterial infection and
was one of only 3 variables that maintained significant predictability
for bacteremia after multivariate analysis (the other two being
decreasing platelet count and appearing clinically
unwell).22 These results again highlight the important
relationship between temperature and bacteremia in FN and lend credence
to the idea that fever response 1-hr after acetaminophen is capable of
lending valuable insight into which patients are likely to have
bacteremia and should not be categorized as low risk.
Many contemporary CDRs utilize a host of patient or disease related
factors to help risk stratify patients presenting with FN (specific type
of leukemia/lymphoma, presence of relapse with marrow involvement,
chemotherapy within 7 days of FN presentation,
etc.).6-8,10 To control for these factors, we split
patients into groups based on their category of malignancy
(leukemia/lymphoma, solid tumor or HSCT), and matched them based not
only on this category but also on the degree of neutropenia, using this
as a proxy for relative immunosuppression. Another commonly utilized
patient factor for risk stratification is presence of a central venous
catheter (CVC). CVCs are present almost ubiquitously across this
institution for patients undergoing active treatment and thus was deemed
largely unhelpful and non-discriminatory for the purposes of this
investigation. Rondinelli et al. also cited age as a patient factor that
could increase the risk of serious infectious complications, assigning
increased risk to patients ≤ 5 years old. In our study, by matching
cohorts-controls based on age and ensuring all cohort patients ≤ 5 years
old were also matched with controls ≤ 5 years old, we made the response
to acetaminophen variable independent of age.
Several laboratory results and biomarkers are also commonly utilized in
FN CDRs. Inflammatory markers such as C-reactive protein (CRP),
procalcitonin (PCT), interleukin-6 (IL-6) or interleukin-8 (IL-8) have
all been touted as helpful means of FN risk
stratification.6,10,23-25 The use of CRP, PCT, IL-6
and IL-8 as adjunct to fever response to acetaminophen were investigated
for this study; however, at our institution like many others, it is not
routine practice to obtain these inflammatory markers on presentation
and thus the retrospective data available for our patient population was
too sparse to provide any meaningful insights. AMC is another laboratory
variable seen in several CDRs. AMC cutoffs for determination of
high-risk patients range between < 100 cells/µL to <
155 cells/µL depending on the CDR.5,11,26 In this
study we did not see a significant relationship between AMC and
bacteremia at a cutoff of < 100 cells/µL. One prospective
validation study of several CDRs found that by altering the cutoff
parameters and recalibrating several of these CDRs they were able to
raise the overall AUC-ROC and low-risk yield for the risk stratification
models.22 These results were obtained in part by
lowering the AMC cutoff from < 100 or < 155 cells/µL
to < 15 cells/µL, suggesting that the lack of significance
between AMC and bacteremia seen in our study may be due in part to
questions regarding the optimal AMC threshold.
Our study has several limitations. As an analysis of a single tertiary
academic medical center, our results may not necessarily be
generalizable to other institutions. Our study is also limited by its
retrospective nature which hindered the accurate collection and
incorporation of clinical and laboratory variables (ex. GI symptoms,
chills/rigors, mucositis, evidence of focal infection, CRP, PCT, IL-6,
IL-8) into our models; however, all single-center retrospective analyses
suffer similar drawbacks, making the need for prospective analysis and
multicenter validation critical.