Download Risk Prediction in Pediatric Cancer Patients With Fever and

ORIGINAL STUDIES
Risk Prediction in Pediatric Cancer Patients With Fever
and Neutropenia
Hana Hakim, MD,* Patricia M. Flynn, MD,*†‡ Deo Kumar Srivastava, PhD,§ Katherine M. Knapp, MD,*†
Chenghong Li, PhD,§ James Okuma, MS,§ and Aditya H. Gaur, MD*
Background: To identify predictors for 2 risk measures—“proven invasive bacterial infection or culture-negative sepsis (IBD)” and “clinical
complications (CC)”—in pediatric cancer patients with fever and neutropenia (FN).
Methods: Records of 390 patients with FN hospitalized over 2 years were
reviewed. For the 332 who met inclusion criteria, one FN episode was
randomly selected. Independent predictors at presentation were analyzed
using multiple regression models. Optimal cut-off risk prediction scores
were determined. These models were validated by bootstrap analysis.
Results: Patients’ median age was 6.0 years; 66% had an underlying
diagnosis of leukemia. Independent predictors of IBD (n ⫽ 56) were
absolute neutrophil count ⬍100, temperature at presentation ⱖ39.0°C,
“sick” clinical appearance, and underlying diagnosis of acute myeloid
leukemia. A total weighted score ⬍24 reliably identified patients at low
risk for IBD. Independent predictors of CC (n ⫽ 47) were relapse of
malignancy, non-white race, “sick” clinical appearance, and underlying
diagnosis of acute myeloid leukemia. A total weighted score ⬍19 predicted
patients at low risk for CC. Of those misclassified as low risk, 11 of 12 with
IBD and 3 of 9 with CC had the outcome within 24 hours of presentation.
Of the remaining patients classified as low-risk for IBD and CC, 99.5% and
97.1%, respectively, remained outcome-free after 24 hours of observation.
Conclusions: This study identifies predictors of infection/complications in
pediatric patients with FN, establishes clinical cut-off scores and highlights
the importance of the initial clinical impression and 24 hours of observation. These prediction models warrant prospective validation.
Key Words: fever, neutropenia, children, cancer, risk, prediction
(Pediatr Infect Dis J 2010;29: 53–59)
F
tient setting is the mainstay of therapy for FN in pediatric
patients.1–3 However, this patient population is heterogeneous, and
not all pediatric patients with FN are at the same risk for complications and bacterial infections. Models of risk prediction for
FN-related morbidity and mortality and risk-based stratification
of care including outpatient and oral antibiotics have been
suggested4 –21 and are being increasingly used for adults. Risk
prediction can result in tremendous cost saving, better resource
utilization, and improved quality of care.22
The key factor in risk prediction is to consistently and
reliably identify patients “at risk.” Of the several risk prediction
models proposed,23–35 few apply to pediatric patients. While many
previous studies focused on derivation datasets using retrospective
study designs, the 2 conducted prospectively,26 –28 found different
predictor variables, did not establish weighted risk prediction
scores and, unlike the approach now commonly used in adult
patients,23,24 had limited focus on clinical complications (CC) as
an outcome measure. Clinical complications evaluated in previous
models did not include adverse events occurring without associated hemodynamic instability or sepsis syndrome, such as respiratory failure and altered mental status. With the above summarized limitations and needs, as the first step of a planned 3-part
evaluation, we designed a retrospective study to examine candidate
predictor variables for 2 measures of risk for FN: proven invasive
bacterial infection or culture-negative sepsis (IBD) and clinical
complications (CC). The next steps of our planned study will be to
use this information to build prediction models that could be
further optimized and validated prospectively, and as a final step,
to evaluate risk-stratified FN management in a randomized clinical
trial.
ever and neutropenia (FN) is a common complication in patients undergoing chemotherapy to treat cancer. Empiric treatment with intravenous (IV) broad-spectrum antibiotics in an inpa-
Study Population and Design
Accepted for publication October 1, 2009.
From the *Department of Infectious Diseases, St. Jude Children’s Research
Hospital, Memphis, TN; Departments of †Pediatrics and ‡Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN; and
§Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN.
Supported by a National Institutes of Health grant (CA21765) and the American
Lebanese Syrian Associated Charities (ALSAC).
Presented as a poster at the 45th Annual Meeting of Infectious Diseases Society
of America (IDSA), San Diego, CA, October 2007. Another part was presented
as a poster at the 48th Annual Interscience Conference on Antimicrobial Agents
and Chemotherapy (ICAAC)/45th Annual Infectious Diseases Society of
America (IDSA) Meeting, Washington, DC, October 2008.
The authors report no conflicts of interest.
Address for correspondence: Hana Hakim, MD, Department of Infectious Diseases,
Mail Stop 600, St. Jude Children’s Research Hospital, 262 Danny Thomas
Place, Memphis, TN 38105-3678. E-mail: [email protected].
Supplemental digital content is available for this article. Direct URL citations
appear in the printed text and are provided in the HTML and PDF versions
of this article on the journal’s Web site (www.pidj.com).
Copyright © 2009 by Lippincott Williams & Wilkins
ISSN: 0891-3668/10/2901-0053
DOI: 10.1097/INF.0b013e3181c3f6f0
We retrospectively reviewed the records of 390 pediatric
(⬍22 years) patients with cancer who developed FN in an outpatient setting and were hospitalized for further management at St.
Jude Children’s Research Hospital (St. Jude), Memphis, TN from
January 1, 2004 to December 31, 2005. Data for patients who had
already received a stem cell transplant or developed FN during
hospitalization for other reasons were excluded from analysis. We
developed a list of candidate variables (Table, Supplemental Digital Content 1, http://links.lww.com/INF/A314) on the basis of
variables identified by previous risk-prediction studies23,24,26 –29
and by this retrospective medical record review. These included:
demographics; underlying cancer; relapse; granulocyte-colony
stimulating factor (G-CSF) use; steroid use within the past 14
days; duration of neutropenia; presence of indwelling central
venous catheter (CVC); time since last chemotherapy; antifungal
therapy for probable or proven fungal infection within the past 6
months; colonization by methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), or
Pseudomonas aeruginosa within the past 4 weeks; prophylactic or
treatment antibiotics; vital signs; and laboratory tests. Blood cul-
METHODS
The Pediatric Infectious Disease Journal • Volume 29, Number 1, January 2010
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Hakim et al
The Pediatric Infectious Disease Journal • Volume 29, Number 1, January 2010
tures were collected at admission in continuously-read BACTEC
bottles (BD Diagnostics, Sparks, MD), and routinely included both
AEROBIC/F bottles to detect aerobic organisms and MYCO/F
Lytic bottles to detect fungi and mycobacteria. Data on comorbidities at the time of presentation were also analyzed. These
included abdominal pain, altered mental status, blood loss requiring transfusion, clinical appearance, signs of viral infection, dehydration requiring IV fluids, mucositis, rectal pain, respiratory
distress, vomiting, and suspected focus of infection. Definitions for
the 2 study outcomes, (1) proven invasive bacterial infection or
culture-negative sepsis and (2) clinical complications occurring
after presentation until fever resolution for 5 consecutive days,
were established a priori. This study was approved by the St. Jude
Institutional Review Board.
Children with FN were managed as inpatients as per institutional guidelines, which are consistent with the recommendations of the Infectious Diseases Society of America.3 Cefepime
1500 mg/m2/dose IV every 8 hours was empirically started at
presentation. Vancomycin 400 mg/m2/dose IV every 8 hours was
added for any of the following: obvious CVC or soft tissue
infection, history of recent cytarabine treatment, quinolone prophylaxis or therapy at the time of fever, known colonization with
MRSA, evidence of sepsis, suspected central nervous system shunt
infection, and known or suspected infection with Bacillus cereus.
Empiric antifungal therapy was added if the patient remained
febrile on day 5 of antibiotics and if neutropenia was expected to
last longer than 5 to 7 days. After fever resolution for ⬎24 hours,
patients were discharged on oral cefpodoxime or IV cefepime if
blood cultures remained sterile and there was no evidence of
pneumonia, suspected bacterial infection, or sepsis during the
episode, and no vomiting, stomatitis, or diarrhea at discharge.
Antibiotics were continued until fever resolved for at least 24
hours and there was recovery from neutropenia; if neutropenia
persisted, antibiotics were continued for at least 5 afebrile days and
there was no evidence of documented infection or signs and
symptoms of sepsis. The chemotherapeutic protocol for children
with acute myeloid leukemia (AML) required administration of
voriconazole for antifungal prophylaxis.
Definitions
Fever was defined as an oral temperature of ⱖ38.3°C or of
ⱖ38.0°C persisting for ⱖ1 hour. Neutropenia was defined as an
absolute neutrophil count of ⱕ500 cells/mm3. Hypotension was
defined as a systolic blood pressure less than fifth percentile for
age and sex, or need for vasopressor support. Respiratory failure
was defined as an arterial oxygen pressure of ⬍60 mm Hg on room
air or the need for mechanical ventilation or oxygen in a patient
with no known supplemental oxygen requirement at baseline.
Clinical appearance was classified as either well, sick, or toxic.28
A patient was classified as “well” if the admitting physician made
no specific comment about the patient’s general appearance at the
time of admission, or documented that the patient looked well, in
no distress, or playful. A patient was classified as sick if he/she
was noted to be irritable or looking ill, and classified as toxic if not
breathing or noted to appear toxic, lethargic, or obtunded. Bacteremia was defined as a blood culture growing an organism determined not to be a contaminant. An organism was considered a
contaminant if it was isolated from only one culture receptacle and
was a member of the skin flora (diphtheroids except Corynebacterium jeikeium, Bacillus spp. except B. cereus, Propionibacterium
spp., coagulase-negative staphylococcus or Micrococcus spp.).
Proven invasive bacterial infection was defined as isolation of a
pathogen from a sterile body site or as proven by histology.
Culture-negative sepsis was defined, in the absence of a positive
culture, as a systemic response to a possible infection because of
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hemodynamic instability; focal or multiple organ involvement; or
altered mental status or lethargy. CC from presentation until fever
resolution for 5 consecutive days included altered mental status,
arrhythmia or new electrocardiographic changes, bleeding requiring transfusion, congestive heart failure, disseminated intravascular coagulation (DIC), hypotension, admission to the intensive care
unit (ICU), respiratory failure, renal failure, and other complications that the investigator judged as clinically significant.
Statistical Analysis
Derivation
Only one episode per patient was randomly selected using a
computer algorithm to avoid bias, given the possibility that multiple episodes in the same patient may not necessarily be independent. Frequency tables for categorical variables and descriptive
parameters (median and range) for continuous variables were
calculated for the predictor variables assessed at the time of
presentation. For each outcome variable, univariate and multiple
logistic regressions were used to determine the association between the variables and outcome. The multiple logistic regression
model was fit to the data by using a stepwise selection method.
Episodes that had the outcomes present at the time of presentation
were excluded from the model building. A P value of less than 0.1
was used as the criterion for entry of the covariate into the model
and a P value of less than 0.05 as that for staying in the model. The
Hosmer-Lemeshow test was used to evaluate the model’s goodness-of-fit to the data. A P value of less than 0.05 was considered
significant. All P values were 2-sided. The final multiple regression model was used to develop a scoring prediction rule by
summation of weights assigned to each variable by multiplying the
␤-regression coefficients by 10 and rounding to the nearest integer.
Receiver operating characteristic (ROC) curves were used to
determine the optimal cut-off score. Optimal cut-off points were
determined by 3 methods36: (1) minimizing the absolute difference
between sensitivity and specificity; (2) minimizing the distance to
the point (0, 1), and; (3) maximizing the Youden index, J, the
vertical distance to the chance line. Values equal to or greater than
the cut-off point identified high-risk patients. Misclassification rate
associated with the original predictive model was calculated by
applying the ROC cut-off point to the original data that were used
to develop the model. Predicted low-risk patients were re-evaluated within 24 hours of presentation for occurrence of the outcome.
Validation
The predictive model was validated using the bootstrap approach described by Efron and Chong.37 Within each bootstrap
replication, a bootstrap sample was generated by drawing randomly
with replacement from the original data and fitting a predictive
logistic model as well as obtaining the ROC cut-off point using the
same steps described above. The model was validated by applying the
cut-off point to the episodes that were not selected in the bootstrap
sample and calculating the misclassification rate. A total of 500
bootstrap replications were performed; the average misclassification
rate and frequency distribution of each candidate variable selected in
500 predictive models were obtained. SAS version 9.1.3 (SAS institute, Cary, NC) was used.
RESULTS
Overall Description of the FN Episodes
A list of 885 FN episodes was obtained from the Medical
Records Department. One episode was randomly selected per patient.
Of the 390 medical records reviewed, 58 were excluded because of
absence of: fever (23), neutropenia (23), both (9), or FN development
© 2009 Lippincott Williams & Wilkins
The Pediatric Infectious Disease Journal • Volume 29, Number 1, January 2010
while inpatient (3). A total of 332 FN episodes were analyzed. The
median number of FN episodes per patient is 2 (range: 1–12). Table,
Supplemental Digital Content 1, http://links.lww.com/INF/A314,
shows patients’ baseline characteristics at the time of presentation.
Fifty-six patients had IBD: bacteremia (41), urinary tract infection (2),
and culture-negative sepsis (13). Forty-seven patients developed CC:
hypotension (21), respiratory failure (14), altered mental status or
seizure (6), congestive heart failure (1), DIC (1), arrhythmia (1),
paraplegia (1), deep venous thrombosis (1), and persistent hypoglycemia (1). Twenty-five patients developed both IBD and CC.
Prediction Rule for the Outcome “Proven Invasive
Bacterial Infection or Culture-Negative Sepsis (IBD)”
Table 1 shows the predictor variables at the time patients
presented with FN that were analyzed. Using multiple regression
analysis, independent predictors in the final model for IBD included an underlying diagnosis of AML (OR: 7.46; 95% CI:
2.76 –20.14), “sick” clinical appearance (OR: 4.23; 95% CI: 2.10 –
8.53), temperature of ⱖ39.0°C at presentation (OR: 2.91; 95% CI:
1.30 – 6.49), and an absolute neutrophil count ⬍100 (OR: 2.68;
95% CI: 1.25–5.76). Weighted scores were assigned (Table, Supplemental Digital Content 2, http://links.lww.com/INF/A315) and the
ROC curve was plotted (Fig. 1A). A total weighted score of ⬍24
points identified patients at low risk for IBD, with a high negative
predictive value (NPV) but a low positive predictive value (PPV)
(75% sensitivity, 77% specificity; 95% NPV, 36% PPV). The 3
statistical methods agreed on the optimal cut-off score of 24 points.
Prediction Rule for the Outcome “Clinical
Complications (CC)”
Table 1 summarizes the results for the outcome CC. Independent predictors of CC included “sick” clinical appearance (OR:
7.41; 95% CI: 3.50 –15.72), relapse of malignancy (OR: 3.14; 95%
CI: 1.36 –7.25), nonwhite race (OR: 2.21; 95% CI: 1.00 – 4.90),
and an underlying diagnosis of AML (OR: 3.15; 95% CI: 1.10 –
9.04). Weighted scores were assigned (Table, Supplemental Digital Content 2, http://links.lww.com/INF/A315) and the ROC
curve was plotted (Fig. 1B). A total weighted score of ⬍19 points
predicted patients at low risk for CC (78% sensitivity, 73%
specificity; 29% PPV, 96% NPV).
Bootstrap Validation for the Outcome “Proven
Invasive Bacterial Infection or Culture-Negative
Sepsis (IBD)”
The 4 IBD predictors in the proposed predictive model were
most frequently identified in 500 bootstrap replications, suggesting
the proposed model was appropriate. The misclassification rate of
the original model was 0.23, which is smaller than the average
misclassification rate from the bootstrap replications (0.31). This
agrees with Efron and Chong37 who showed that error rate obtained from the original model-building sample is an underestimate of the true error rate.
Bootstrap Validation for the Outcome “Clinical
Complications (CC)”
The 4 predictors in the proposed predicting model for CC
were most frequently selected in 500 bootstrap replications. The
average misclassification rate from the bootstrap replications
(0.28) was similar to that of the original predicting model (0.26).
Inpatient Observation of the Low-Risk Group for
24 Hours
The “serious error” or false-negative misclassification rate
in the predicted low-risk group for IBD was 3.7%. Within the first
24 hours of presentation (Fig., Supplemental Digital Content 3,
© 2009 Lippincott Williams & Wilkins
Infection and Cancer
http://links.lww.com/INF/A316), 91% of the IBD and 56% of the
CC occurred. Re-evaluating low-risk patients 24 hours after presentation for IBD occurrence decreased the serious misclassification error to 0.3% (Fig. 2A). The one patient who developed IBD
and was misclassified by the suggested prediction scheme did
clinically well. This patient was a 7-year-old with T-cell lymphoma, febrile for ⬍1 day, and hospitalized for 2 days, whose
admission blood culture yielded Capnocytophaga sputigena after
143.7 hours of incubation.
Similarly, the “serious error” or false-negative misclassification rate in the predicted low-risk group for CC was 2.7%. By
re-evaluating low-risk patients 24 hours after presentation for CC
occurrence, the serious misclassification error decreased to 1.8%
(Fig. 2B). The 6 patients who were misclassified by the sequential
prediction rule at presentation followed by a 24-hour observation
had an average age of 9.6 years (range: 0.8 –18.0 years). Their
complications were respiratory distress (4), seizure (1), and altered
mental status (1). The median time to complications was 86.5
hours after presentation (range: 68.5–130.7 hours). The diagnoses
of their FN episodes were Clostridium difficile colitis (1), pneumonia (2), rotavirus gastroenteritis (1), Streptococcus mitis bacteremia detected within 11 hours of presentation (1) and no identified focus (1). No deaths occurred.
DISCUSSION
This study identifies predictors of 2 measures of “risk” in
pediatric and adolescent cancer patients with FN–invasive bacterial
disease and clinical complications. The 2 outcome measures have
some overlap but are complementary and not necessarily redundant
when making treatment decisions for patients with FN. The risk of
clinical complications influences the decision of whether the patient
should receive care inpatient or outpatient, whereas the risk of IBD
determines the route of antibiotic: intravenous or oral.
Our study highlights the importance of having an initial
period of inpatient observation, which minimizes the chance of a
high-risk FN patient being inadvertently categorized as low-risk at
admission and subsequently managed less aggressively using an
outpatient approach. Risk assessment at admission combined with
24-hour observation shows a high negative predictive value.
Despite the considerable progress made over the last decade
in developing prediction rules for infections or clinical complications in patients with FN, there remain many limitations to the
universal use of these rules, including lack of direct applicability of
adult models to children, lack of multinational prospective validation of pediatric models, lack of pediatric prediction models with
weighted scores for ease of use in clinical practice, and fundamental differences in definitions of “low-risk” and prediction end
points such as bacterial infections, clinical complications or lack of
response to therapy.22 To date, our model is the only one that
evaluates clinical complications not occurring in association with
sepsis syndrome, such as altered mental status or respiratory
failure caused by a viral etiology. The presence of hemodynamic
instability or sepsis syndrome is an integral part of the model
developed by Santolaya et al.26
Table,
Supplemental
Digital
Content
4,
http://links.lww.com/INF/A317, compares the performance of
various prediction models with that of our model. Our study
addresses some of the above-mentioned limitations. Ours is the
first study to examine the ability to predict the risk of invasive
bacterial disease and clinical complications in pediatric and adolescent patients with FN, and provides an easily usable scoring tool
for clinicians. Ours is also the only model that takes into account
the value of including clinical appearance in risk prediction. The
importance of “clinical impression,” which all clinicians consider in
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The Pediatric Infectious Disease Journal • Volume 29, Number 1, January 2010
Hakim et al
TABLE 1. Risk Predictors for “Proven Invasive Bacterial Infection or Culture-Negative Sepsis” and “Clinical
Complications”
Univariate Analysis
Variables (n)
Outcome
Proven invasive bacterial infection or
culture-negative sepsis† (n ⫽
320)
Cancer diagnosis (332)
AML (16%)
ALL/lymphoma (50%)
Solid tumor (34%)
Receiving G-CSF (332)
Yes (29%)
No (71%)
Tmax at presentation (332)
ⱖ39.0°C (18%)
⬍39.0°C (82%)
ANC (330)
⬍100 (56%)
ⱖ100 (44%)
Plts (330)
⬍50,000 (44%)
ⱖ50,000 (56%)
Clinical appearance (331)
Sick/toxic (25%)
Well (75%)
Prior relapse (332)
Yes (18%)
No (82%)
Anticipated neutropenia
⬍7 d? (319)
Yes (51%)
No (49%)
Outcome
Clinical complications‡ (n ⫽ 324)
Antifungal therapy within the
past 6 mo? (313)
Yes (8%)
No (92%)
Race (332)
Nonwhite (25%)
White (75%)
Duration from neutropenia to
fever onset (304)
⬎7 d (23%)
ⱕ7 d (77%)
Clinical appearance (331)
Sick/toxic (25%)
Well (75%)
Prior relapse (332)
Yes (18%)
No (82%)
At least one comorbidity at
presentation (332)
Yes (50%)
No (50%)
Cancer diagnosis (332)
AML (16%)
ALL/lymphoma (50%)
Solid tumor (34%)
OR
95% CI
6.91
1.93
1.00
2.77–17.24
0.83– 4.51
0.39
1.00
0.17– 0.90
2.05
1.00
Multiple Logistic Regression*
␤ Regression
Coefficient
OR
95% CI
P
2.0095
0.7218
7.46
2.06
1.00
2.76 –20.14
0.84 –5.02
⬍0.0001
0.1100
1.00 – 4.20
1.0675
2.91
1.00
1.30 – 6.49
0.009
2.82
1.00
1.41–5.65
0.9864
2.68
1.00
1.25–5.76
0.012
2.33
1.00
1.25– 4.34
3.84
1.00
2.02–7.28
1.4423
4.23
1.00
2.10 – 8.53
⬍0.0001
1.84
1.00
0.90 –3.74
0.43
1.00
0.23– 0.83
3.59
1.00
1.37–9.39
1.99
1.00
0.99 – 4.00
0.7950
2.21
1.00
1.00 – 4.90
0.0495
2.35
1.00
1.11– 4.99
6.39
1.00
3.18 –12.85
2.0032
7.41
1.00
3.50 –15.72
⬍0.0001
2.87
1.00
1.39 –5.92
1.1457
3.14
1.00
1.36 –7.25
0.007
3.58
1.00
1.69 –7.59
2.14
0.96
1.00
0.87–5.23
0.44 –2.09
1.1484
⫺0.0333
3.15
0.97
1.00
1.10 –9.04
0.40 –2.33
0.033
0.94
*The multiple logistic regression model was built by using stepwise selection method and included only the observations having complete data.
The Hosmer-Lemeshow goodness-of-fit test for “proven invasive bacterial infection or culture-negative sepsis” did not indicate lack of fit (P ⫽ 0.14).
‡
The Hosmer-Lemeshow goodness-of-fit test for “clinical complications” model did not indicate lack of fit (P ⫽ 0.47).
AML indicates acute myelogenous leukemia; ALL, acute lymphoblastic leukemia; G-CSF, granulocyte colony-stimulating factor; ANC, absolute neutrophil count; AMC,
absolute monocyte count; Plts, platelets.
†
medical decision-making but find difficult to define, is a key finding
of this study and perhaps underemphasized in previous studies in the
FN literature.18,34
Consistent with 2 previous reports, nonwhite race (primarily
African-American) was an independent predictor of CC in our
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study.38,39 A longitudinal epidemiologic study that evaluated sepsis-related disparities in risk and outcome in a nationally representative sample of hospitalizations in 8.9 million patients with
cancer in the United States has shown that white cancer patients
had lower risk for sepsis than nonwhites.38
© 2009 Lippincott Williams & Wilkins
The Pediatric Infectious Disease Journal • Volume 29, Number 1, January 2010
Infection and Cancer
FIGURE 1. Receiver operating characteristic (ROC) curve for the outcome (A) “proven invasive bacterial infection or culture-negative sepsis,” using the scoring method, AUC (95% CI) ⫽ 0.776 (0.700 – 0.852) and (B) for the outcome “clinical
complications,” using the scoring method, AUC (95% CI) ⫽ 0.803 (0.730 – 0.876).
While our study has limitations that we recognized a priori
and made attempts to minimize, the consistency of study results
with 500 random bootstrap sample runs suggests robust findings.
The retrospective study design introduces limitations because of
the inability to capture clinical information in a standardized,
consistent fashion. This limitation is especially pertinent to an
important variable, “clinical appearance,” which came out as a
strong predictor in both risk prediction models. Although we used
a priori definitions while abstracting information on this variable,
it will be imperative to examine it in a prospective study wherein
the definition is available as a guide to the examining clinician.
Overall, to minimize the limitations related to the retrospective
nature of this study, we used a priori definitions, a standardized
data abstraction form, and only 2 chart abstractors. Second, the
exclusion of inflammatory markers in the model because they were
© 2009 Lippincott Williams & Wilkins
not routinely assessed in patients with FN has both advantages and
disadvantages. While CRP is inexpensive and accessible, other
promising inflammatory markers such as procalcitonin and IL6
remain expensive and not routinely used in patient care.40 – 44 Our
current risk prediction model remains relevant to clinical settings
where access to inflammatory markers is not easy and routine. That
said, the consideration of inflammatory markers in further optimization of risk prediction is clear and is one of the objectives of our
ongoing prospective validation study.
In summary, the field of risk-stratified management of
patients with FN has advanced and transitioned from research to
practice much more for adult than for pediatric and adolescent
patients. What is lacking is the systematic development of an
easy-to-use risk prediction tool. Our study contributes to the
existing data by providing a simple scoring tool for use by
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The Pediatric Infectious Disease Journal • Volume 29, Number 1, January 2010
Hakim et al
Anil Thridandapani, BS, Department of Infectious Diseases, St.
Jude Children’s Research Hospital for constructing the study’s
database and data quality assurance; Georgeta Vaidean, MD,
PhD, Department of Preventive Medicine, University of Tennessee
Health Science Center, Memphis, TN for her helpful comments
about the manuscript; and Vani J. Shanker, PhD, ELS, Department of Scientific Editing, St. Jude Children’s Research Hospital
for scientific editing of the manuscript.
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FIGURE 2. Effect of the scoring prediction model application at presentation followed by a 24-hour period of inpatient observation on the ability to detect patients with outcome events (A) “proven invasive bacterial infection or
culture-negative sepsis” and (B) “clinical complications.”
clinicians that predicts pediatric and adolescent patients with FN at
risk for invasive bacterial disease and clinical complications.
ACKNOWLEDGMENTS
The authors thank Jawwad Yusuf, MBBS, and Jenine Worley for chart review and data abstraction; Wally Bitar, MS, and
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