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National Trends in Hospitalization for Fever and Neutropenia in Children with Cancer, 2007-2014
ARTICLE IN PRESS THE JOURNAL OF PEDIATRICS • www.jpeds.com
ORIGINAL ARTICLES
National Trends in Hospitalization for Fever and Neutropenia in Children with Cancer, 2007-2014 Anusha Lekshminarayanan, MD1,*, Parth Bhatt, MD, MPH2,*, Vijay Gandhi Linga, MD2, Riddhi Chaudhari, MBBS3, Brian Zhu, BA2, Mihir Dave, MD4, Keyur Donda, MBBS5, Sejal Savani, MD6, Samir V. Patel, MD, MPH7, Zeenia C. Billimoria, MD8, Smita Bhaskaran, MD2, Samer Zaid-Kaylani, MD2, Fredrick Dapaah-Siakwan, MD, FAAP5, and Neel S. Bhatt, MBBS, MPH9 Objective To assess the trends of inpatient resource use and mortality in pediatric hospitalizations for fever with neutropenia in the US from 2007 to 2014.
Study design Using National (Nationwide) Inpatient Sample (NIS) and International Classification of Diseases, Ninth Revision, Clinical Modification codes, we studied pediatric cancer hospitalizations with fever with neutropenia between 2007 and 2014. Using appropriate weights for each NIS discharge, we created national estimates of median cost, length of stay, and in-hospital mortality rates. Results Between 2007 and 2014, there were 104 315 hospitalizations for pediatric fever with neutropenia. The number of weighted fever with neutropenia hospitalizations increased from 12.9 (2007) to 18.1 (2014) per 100 000 US population. A significant increase in fever with neutropenia hospitalizations trend was seen in the 5- to 14-year age group, male sex, all races, and in Midwest and Western US hospital regions. Overall mortality rate remained low at 0.75%, and the 15- to 19-year age group was at significantly greater risk of mortality (OR 2.23, 95% CI 1.363.68, P = .002). Sepsis, pneumonia, meningitis, and mycosis were the comorbidities with greater risk of mortality during fever with neutropenia hospitalizations. Median length of stay (2007: 4 days, 2014: 5 days, P < .001) and cost of hospitalization (2007: $8771, 2014: $11 202, P < .001) also significantly increased during the study period. Conclusions Our study provides information regarding inpatient use associated with fever with neutropenia in pediatric hospitalizations. Continued research is needed to develop standardized risk stratification and costeffective treatment strategies for fever with neutropenia hospitalizations considering increasing costs reported in our study. Future studies also are needed to address the greater observed mortality in adolescents with cancer. (J Pediatr 2018;■■:■■-■■).
F
ever with neutropenia is a common oncologic emergency seen in pediatric patients with cancer. Prompt administration of broad-spectrum antibiotics and inpatient hospitalization are the standards of care.1 Hospitalizations due to fever with neutropenia have increased steadily by nearly 50% over the recent decades.2-4 There has been significant interest in improving risk stratification to identify patients at high risk for mortality and potentially treating patients who are at low risk as outpatients.5,6 The current literature provides limited information regarding the overall healthcare burden of fever with neutropenia–related hospitalizations in the pediatric population. Previous epidemiologic studies have provided details of inpatient use for fever with neutropenia in children for the years 2009 and 20127,8; however, these studies did not provide trends and outcomes of fever with neutropenia–related pediatric hospitalizations in the US. The objectives of this study were to describe temporal trends of hospitalizations for fever with neutropenia in children between 2007 and 2014; to characterize the trends in children according to encounterlevel and hospital-level factors; and to study factors associated with mortality, length of stay (LOS), and cost of fever with neutropenia–associated hospitalization. 1
Methods We derived our study cohort from the National (Nationwide) Inpatient Sample (NIS) database from 2007 to 2014. The NIS database is part of the Healthcare Cost
CCS HCUP ICD-9-CM LOS NIS URI
Clinical Classification Software Healthcare Cost and Utilization Project International Classification of Diseases, Ninth Revision, Clinical Modification Length of stay National (Nationwide) Inpatient Sample Upper respiratory infection
From the Department of Internal Medicine, Functional Cholesterol, Diabetes, and Endocrinology Center, Springdale, OH; 2Department of Pediatrics, Texas Tech University Health Sciences Center, Amarillo, TX; 3 Department of Pediatrics, University of Connecticut, Hartford, CT; 4Department of Public Health, Icahn School of Medicine at Mount Sinai, New York, NY; 5Department of Pediatrics, University of Miami, Coral Gables, FL; 6Department of Public Health, New York University, New York, NY; 7Department of Internal Medicine, Sparks Health Systems, Fort Smith, AR; 8Department of Pediatrics, University of Washington, Seattle, WA; and 9 Department of Pediatrics, Division of Hematology/ Oncology/BMT, Medical College of Wisconsin, Milwaukee, WI *Contributed equally. The authors declare no conflicts of interest. 0022-3476/$ - see front matter. © 2018 Elsevier Inc. All rights reserved. https://doi.org10.1016/j.jpeds.2018.06.056
1 FLA 5.5.0 DTD ■ YMPD10092_proof ■ July 17, 2018
THE JOURNAL OF PEDIATRICS • www.jpeds.com and Utilization Project (HCUP), sponsored by the Agency of Healthcare Research and Quality. NIS is the largest publicly available all-payer inpatient healthcare database in the US, yielding national estimates of hospital inpatient stays.9 The 2014 NIS sampling frame comprises 44 states and 4411 hospitals estimating 35 million hospitalizations, covering more than 96% of the US population.10 Data for each hospitalization consist of 1 primary discharge diagnosis, <30 secondary diagnoses, and 15 procedural codes recorded during the encounter. The NIS database has been used previously to study inpatient use trends.11-13 Study Population and Design We queried the NIS database from 2007 to 2014 to derive pediatric cancer–related hospitalizations using HCUP Clinical Classification Software (CCS). The CCS for the International Classification of Diseases, Ninth Revision, Clinical Modification Codes (ICD-9-CM) is a diagnosis and procedure categorization scheme that can be used in many types of projects analyzing data on diagnoses and procedures.14 CCS codes from “11” to “45” were used to include all cancer-related hospitalizations. Furthermore, to avoid duplication, we excluded those admissions that were transferred out, using the “DISPUNIFORM” variable. We also excluded elective hospitalizations. Admissions of patients with fever with neutropenia were derived using the ICD-9-CM code “780.6x” AND (“288.5x” OR “288.0x” OR “284.1x”). Similar methodology has been used previously.7,8 Details of the population derivation are shown in Figure 1. Definition of Variables Encounter- and hospital-level characteristics were studied for fever with neutropenia–related hospitalizations. Encounterlevel characteristics such as age, sex, race (white, black, Hispanic, and other), median household income based on zip codes (0-25th percentile, 26th-50th percentile, 51st-75th percentile,
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and 76th-100th percentile), expected primary payer (Medicaid/ Medicare, private, and other), type of cancer, comorbidities such as viral upper respiratory infections (URIs), meningitis, gastroenteritis, mycoses/fungal infections, otitis media, and skin and subcutaneous infections based on the ICD-9-CM and CCS codes were included (Table I; available at www.jpeds.com). Hospital-level characteristics such as hospital location and teaching status (rural, urban nonteaching, and urban teaching), size by number of hospital beds (small, medium, and large), and hospital region (Northeast, Midwest, South, and West) were studied. Hospital size was defined using region of the US, urbanrural designation of the hospital, in addition to the teaching status.15 Cost-to-charge ratio provided by HCUP was used to calculate the actual costs. Cost-to-charge ratio allows conversion of charge data to cost estimates. Inflation-adjusted cost was calculated based on 2014 for all years according to the Consumer Price index Data released by the US government.16 NIS provides weights, which were used to generate national estimates. Statistical Analyses For continuous variables, medians and IQRs were reported. For categorical variables, percentages were reported. The c2 test, t test, or Wilcoxon rank-sum test were used to compare baseline characteristics depending on the distribution of the variables. For trend analysis, c2 test of trend for proportion was generated via the “trend” command. To generate national estimates for trend analysis, “trendwt” provided by the NIS was used.17 Because of the complex survey design of NIS, the “survey” command was used to perform multivariable analysis. Predictors of mortality were analyzed by using survey logistic regression. Hierarchal regression was used to analyze predictors of LOS and cost. Two-level hierarchical models with encounter-level factors nested within hospitals were created using unique hospital identification number provided by NIS.18 SAS 9.4 (SAS Institute, Cary, North Carolina) was used for all
Figure 1. Derivation of the study population. 2
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analyses. Two-sided P value <.05 was considered as significant for all analyses.
Results Baseline Clinical Characteristics of Hospitalizations for Fever with Neutropenia During the study period 2007-2014, we found 104 315 pediatric inpatient encounters of fever with neutropenia. Fever with neutropenia hospitalizations increased from 12.9 per 100 000 US population (10 636 weighted encounters) in 2007 to 18.1 per 100 000 US population (14 865 weighted encounters) in 2014 using the US population as the denominator. Table II (available at www.jpeds.com) demonstrates the baseline characteristics of the study population. Of the study population, 54.6% were male, 52.4% were of white race, 51.6% had the primary diagnosis of leukemia, 53% had a private-payer insurance, 90.5% of hospitalizations occurred at urban teaching hospitals, and 62.9% of patients were treated at large hospitals. Trends of Hospitalizations for Fever with Neutropenia Table III demonstrates the fever with neutropenia hospitalization trends during the study period using baseline encounterlevel and hospital-level characteristics. A statistically significant increasing trend in the number of fever with neutropenia hospitalizations was observed among the following groups from 2007 to 2014: age group 5-9 years (27.8%-28.6%, P < .001), 10-14 years (16.2%-19.2%, P < .001), male patients (54.1%54.2%, P = .003), all races (white: 47.2%-53%, P < .001; black: 6.8%-7.2%, P < .001; Hispanic: 15.4%-21.8%, P = .003; and other: 6.1%-9.8%, P < .001), and in the Midwest (16.4%18.5%, P < .001) and Western regions (13.2%-21.4%, P < .001). Significantly declining fever with neutropenia hospitalizations trend was noted for the 0- to 4-year age group and in Northeast and Southern hospital region. Predictors of Mortality during Fever with Neutropenia Inpatient Hospitalizations The average mortality rate was 0.75% in the hospitalizations for fever with neutropenia; however, a minor but statistically significant increasing trend in mortality was noted (0.66% in 2007 to 0.74% in 2014, P = .001). Results of multivariable analysis assessing predictors of mortality during fever with neutropenia hospitalizations are shown in Table IV. Significantly greater risk of mortality was noted in the 15- to 19-year age group (OR 2.23, 95% CI 1.36-3.68, P = .002) compared with the 0- to 4-year age group. None of the other age groups, sex, insurance status, hospital location, bed size, or region were significantly associated with mortality. Greater odds for mortality also were seen with comorbidity infections such as mycosis (OR 3.02, 95% CI 1.92-4.76, P < .001), meningitis (OR 4.83, 95% CI 1.45-16.16, P = .01), pneumonia (OR 4.87, 95% CI 3.157.51, P < .001), and sepsis (OR 5.82, 95% CI 3.96-8.56, P < .001). None of the disease diagnoses such as leukemia, lymphoma, bone and soft tissue, central nervous system tumors, kidney,
genital tumors, or neuroblastoma was associated with mortality. Similarly, the presence of other comorbidities such as viral URI, gastroenteritis, otitis media, or skin and subcutaneous infections was not associated with mortality. LOS and Cost of Hospitalization Model A significant increase in LOS and cost was observed during the study period. Median LOS significantly increased from 4 days (2007) to 5 days (2014), and median cost increased from $8771 (2007) to $11 202 (2014) (Figure 2). Results of multivariable analysis showing predictors of LOS and cost of hospitalization during fever with neutropenia hospitalizations are shown in Table V (available at www.jpeds.com). Significantly longer LOS was noted in the age group 15-19 years (0.7 days, 95% CI 0.1-0.7, P = .02), with primary diagnosis of leukemia (2.8 days, 95% CI 2.1-3.4, P < .001), with Medicare/Medicaid insurance (0.7 days, 95% CI 0.3-1.1, P < .001), other insurance (1.2 days, 95% CI 0.4-2, P = .003), and with following comorbidities: sepsis (6.8 days, 95% CI 6.3-7.4, P < .001), pneumonia (6.5 days, 95% CI 5.7-7.4, P < .001), URI (2.1 days, 95% CI 1.4-2.8, P < .001), meningitis (25.2 days, 95% CI 20.929.5, P < .001), gastroenteritis (5.5 days, 95% CI 4.6-6.4, P < .001), mycosis (5.7 days, 95% CI 5.0-6.5, P < .001), and skin and subcutaneous infections (2.6 days, 95% CI 1.7-3.6, P < .001). Significantly lower LOS was noted in the 5- to 9-year age group (−0.9 days, 95% CI −1.4 to −0.5, P < .001), with diagnosis of bone tumors (−0.9 days, 95% CI −1.7 to −0.2, P = .02), and otitis media (−1.4 days, 95% CI −2.5 to −0.4, P = .0081). Significantly greater costs were incurred in hospitalizations among the age groups 10-14 years ($5510, 95% CI $35217499, P < .001), 15-19 years ($7507, 95% CI $5459-9555, P < .001), those with Medicaid/Medicare insurance ($2455, 95% CI $1003-3907, P = .001), other insurance ($5410, 95% CI $2501-8319, P < .001), and in the Western US ($4641, 95% CI $590-8691, P = .03) compared with the referent groups as shown in Table V. Statistically significant increase in cost also was seen with primary diagnosis of leukemia ($5881, 95% CI $36068155, P < .001), comorbidities of sepsis ($21 457, 95% CI $19 314-23 600, P < .001), pneumonia ($28 654, 95% CI $25 61531 693, P < .001), URI ($8743, 95% CI $6309-11 178, P < .001), meningitis ($125 373, 95% CI $107 719-143 027, P < .001), gastroenteritis ($18 593, 95% CI $15 358-21 828, P < .001), mycosis ($16 750, 95% CI $14 054-19 446, P < .001), and skin and subcutaneous infections ($8440, 95% CI 5012-11 868, P < .001). Significantly lower cost of hospitalization was seen in large hospitals (−$6394, 95% CI −$10 683 to −$2106, P = .0038), bone tumors (−$5844, 95% CI −$8603 to −3084, P < .001), and otitis media (−$4828, 95% CI −$8687 to −969, P = .02).
Discussion This study assessed the temporal trends of inpatient encounters for children with fever with neutropenia and cancer and factors affecting LOS, cost of hospitalizations, and mortality. The number of fever with neutropenia hospitalizations increased from 2007 to 2014. Overall mortality rate remained
National Trends in Hospitalization for Fever and Neutropenia in Children with Cancer, 2007-2014 FLA 5.5.0 DTD ■ YMPD10092_proof ■ July 17, 2018
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2007
2008
2009
2010
2011
2012
2013
2014
Total
Febrile neutropenia hospitalizations, unweighted, n Febrile neutropenia hospitalizations, weighted,* n Fever with neutropenia hospitalization per 100 000 US population, n Age groups, % 0-4 y 5-9 y 10-14 y 15-19 y Sex, % Male Female Race, % White Black Hispanic Other Hospital region, % Northeast Midwest South West Mortality, % Median cost of hospitalization, US $, (IQR) Median LOS, d (IQR)
2389
2223
2385
3737
2393
2817
2759
2973
21 676
P value
10 636
10 575
11 728
18 167
10 463
14 085
13 795
14 865
104 315
12.9
12.7
14.1
21.8
12.6
17.1
16.8
18.1
38.2 27.8 16.2 17.9
36.9 23.8 19.9 19.4
31.4 25.7 18.7 24.3
34.4 29.3 17.7 18.6
36.2 27.6 17.9 18.4
34.3 27.8 18.0 19.9
31.9 29.5 19.6 19.1
32.7 28.6 19.2 19.5
34.3 27.7 18.4 19.6
<.001 <.001 <.001 .2
54.1 45.9
52.3 47.7
54.6 45.4
54.5 45.6
55.6 44.4
55.7 44.2
55.2 44.8
54.2 45.9
54.6 45.4
.003 .003
47.2 6.8 15.4 6.1
47.6 5.5 23.6 11.7
49.6 5.7 25.3 8.8
57.7 6.8 21.9 8.1
53.0 11.1 18.1 7.8
54.6 7.6 20.2 9.2
52.4 7.1 21.8 10.0
53.0 7.2 21.8 9.8
52.4 7.2 21.1 9.0
<.001 <.001 .003 <.001
19.1 16.4 46.9 13.2 0.66 8771 (4756-17 675)
25.7 13.9 21.6 34.7 0.51 8828 (4759-18 197)
12.9 15.2 17.2 50.7 0.9 9905 (5327-23 880)
21.5 12.1 40.3 23.3 0.64 10 200 (5558-21 565)
8.2 15.5 61.8 10.6 0.88 10 852 (5990-21 921)
15.8 20.7 33.4 20.5 0.92 10 539 (5653-24 059)
13.8 16.8 36.9 22.5 0.76 10 396 (5482-23 411)
14.0 18.5 36.4 21.4 0.74 11 202 (5717-27 333)
16.5 16.1 36.7 24.5 0.75 10 102 (5428-22 158)
<.001 <.001 <.001 <.001 .001 <.001
4 (3-8)
4 (3-8)
5 (3-9)
5 (3-9)
4 (3-8)
5 (3-9)
4 (3-8)
5 (3-9)
5 (3-8)
<.001
*Weights provided by the NIS were used to generate national weighted estimates.
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Table III. Trends of pediatric hospitalizations for fever with neutropenia by age, sex, race, hospital region, and outcomes (mortality, LOS, and cost of hospitalization) between 2007 and 2014
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Table IV. Multivariate predictors of mortality for pediatric fever with neutropenia hospitalizations between 2007 and 2014 95% CI Variables Age, y 0-4 5-9 10-14 15-19 Sex Male Female Insurance status Private Medicaid Others Hospital location and teaching status Rural Urban nonteaching Urban teaching Size by number of hospital beds Small Medium Large Hospital region NE MW S W Primary diagnosis Leukemia Lymphoma Bone and soft-tissue tumors CNS tumors Neuroblastoma Kidney tumors Genital tumors Comorbidities Sepsis Pneumonia Viral URI Meningitis Gastroenteritis Mycosis Otitis media Skin and subcutaneous infections
OR
Lower limit Upper limit P value
1.33 1.26 2.23
0.80 0.76 1.36
Reference 2.23 2.07 3.68
.3 .4 .002
0.79
0.56
Reference 1.13
.2
1.05 0.73
0.75 0.33
Reference 1.48 1.60
.8 .4
1.61 1.09
0.18 0.14
Reference 14.18 8.40
.7 .9
1.06 0.92
0.51 0.48
Reference 2.18 1.76
.9 .8
1.55 1.46 1.31
0.90 0.89 0.77
Reference 2.67 2.41 2.24
.1 .1 .3
1.53 0.65 0.64 0.30 0.49 1.16 1.17
0.75 0.24 0.24 0.06 0.10 0.30 0.13
3.12 1.75 1.67 1.46 2.39 4.44 10.45
.2 .4 .4 .1 .4 .8 .9
5.82 4.87 1.03 4.83 1.15 3.02 0.48 1.45
3.96 3.15 0.64 1.45 0.63 1.92 0.12 0.74
8.56 7.51 1.66 16.16 2.10 4.76 1.95 2.82
<.001 <.001 .9 .01 .7 <.001 .3 .3
CNS, central nervous system; MW, Midwest; NE, Northeast; S, South; W, West.
at 0.75%, and mortality was significantly greater in the older age group (15-19 years) and with sepsis, pneumonia, meningitis, and mycosis. In addition, increasing trends in cost of hospitalization and LOS also were noted through the study period. The number of fever with neutropenia hospitalizations reported in our study is similar to those reported by Mueller et al using another administrative claims database.7,8 These studies reported fever with neutropenia hospitalizations in children in 2009 and 2012, respectively, and overall an increasing trend was noted. The rate of comorbidities such as sepsis and viral URI were similar to those reported in our study. Basu et al also used ICD-9 codes to identify fever with neutropenia discharge diagnoses between 1995 and 2002, although temporal
trends were not reported.19 In that study, the number of fever with neutropenia discharge diagnoses were lower than in our study (1224 to 1853 per year), which could be due to the difference in study design, because they used information from the US academic institutions only. In comparison, NIS is a large nationally representative dataset and includes community hospital discharges. However, it is important to note that readmissions during the specified time period cannot be excluded using HCUP datasets, which could account for a greater number of hospitalizations for fever with neutropenia in our study and other studies using similar datasets. The mortality rate reported in our study (0.8%) is similar to previous studies by Mueller et al, who reported 0.4%-0.5%.7,8 Predictors of mortality also were assessed in our study. Age was a significant predictor of mortality, with the oldest age group in our study being at greater risk of death during fever with neutropenia encounters compared with the youngest, the 0to 4-year age group. Basu et al studied the predictors of mortality in pediatric fever with neutropenia hospitalizations and also found that adolescents were at significantly greater risk of mortality compared with younger children.19 A previous analysis from population-based cancer registries from 27 European countries also documented lower 5-year overall survival in adolescents and young adults with cancer compared with younger patients.20 Similarly, the statistics of American Cancer Society showed greater mortality rates in 15- to 19year age group compared with the 0- to 14-year age group between 2011 and 2015.21 It is possible that the disease biology is different in this age group compared with younger cohort, contributing to unfavorable outcomes. Other factors, which were not assessed in our study, could have contributed to greater mortality, such as variation in care (pediatric vs adult healthcare setting), social support, delay in seeking care during these episodes, or socioeconomic status.20,22 Several comorbidities also predicted greater risk of death in fever with neutropenia hospitalizations. Sepsis conferred a 5-fold greater risk of death, both pneumonia and meningitis a 4-fold greater, and mycosis a 3-fold higher risk of death. These findings show that despite advancements and widespread availability of antimicrobial therapy for prophylaxis and treatment and supportive care, infections remain a significant cause of death and lead to increasing healthcare burden. Current management practices for fever with neutropenia in children are not standardized and remain specific to institutions,23,24 despite growing interest in developing and validating risk-stratification models6,25-27 and the existence of clinical practice guidelines.5 Although inpatient hospitalization remains the standard of care for the management of fever with neutropenia, improvement in cost-effectiveness and quality of life have been reported with the outpatient management of low-risk patients compared with hospitalization with mortality rates remaining unchanged.28-31 Although we are unable to create a risk-stratification model in this study, because of a lack of pivotal information such as treatment details, treatment intensity, vital signs, clinical status at admission, and laboratory test results, previous studies using administrative claims databases have used longer
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Figure 2. LOS and cost of hospitalization trend in pediatric hospitalizations for fever with neutropenia. The X-axis represents the year of hospitalization, the primary Y-axis shows the median cost (US $) of pediatric fever with neutropenia hospitalizations, and the secondary Y-axis shows the median LOS (days) of pediatric hospitalizations for fever with neutropenia.
LOS as a surrogate for illness severity.7,8,19 A median LOS of 5 days and a median cost of hospitalization of $10 102 were reported in our study, which were lower than other studies focusing on pediatric and adult hospitalizations for fever with neutropenia.7,8,22,32 These studies only used single-year design to assess fever with neutropenia-related hospitalizations, which could explain the differences. Previous studies have not reported trends in LOS or cost of hospitalization. We reported a significant increase in LOS, which could in turn explain the significant increase in cost of hospitalization during our study period. In addition, the presence of comorbidities such as sepsis, pneumonia, viral URI, meningitis, gastroenteritis, mycosis, and skin and subcutaneous infections predicted longer LOS, which could be considered indicators of severe illness compared with hospitalizations without these comorbidities. Primary diagnosis of bone tumor and otitis media as a comorbidity were associated with shorter LOS, which could imply their low-risk nature. However, caution should be exercised in interpreting these results due to the lack of granular data. Continued research in this field is needed to develop a universal, validated risk-stratification model to judiciously use healthcare resources and to improve treatment delivery options considering cost-effectiveness and quality of life in this population. There are several limitations to our study. The NIS data are collected through discharge diagnoses. Although elective admissions were excluded, we cannot exclude other possible nonelective admission diagnoses in our study or development of
neutropenia during hospitalization. Because the NIS dataset is available through ICD-9-CM codes, its accuracy depends heavily on appropriate coding practices, and undercoding or overcoding could not be ruled out. It is also possible that our study underestimates the true incidence of fever with neutropenia during this time period through our ICD-9-CM search strategy, as encounters with presence of comorbidities (eg, pneumonia, meningitis, sepsis) at the time of admission may only be coded for the comorbidity, not as fever with neutropenia. Although NIS provides national estimates and yearly trends of inpatient hospitalizations, NIS is an administrative claims database; therefore, we were unable to study specific details regarding cancer staging, cancer-directed treatment, treatment intensity, time to antibiotic administration, clinical status, or laboratory test results at admission or discharge. In addition, we were unable to rule out readmissions, and the actual incidence of fever with neutropenia hospitalization could be different during the specified time period. Although each hospital may have institution-specific guidelines for the documentation and the management of fever with neutropenia, which could introduce selection bias in the cohort, no significant impact was noted in our study, as we used hierarchical regression to take hospital-level variations into account. ■ We acknowledge HCUP and its partner organizations providing data to the HCUP. A list of all HCUP data partners is available at https://www.hcup-us.ahrq.gov/db/hcupdatapartners.jsp. Submitted for publication May 5, 2018; last revision received Jun 5, 2018; accepted Jun 18, 2018
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Reprint requests: Neel S. Bhatt, MBBS, MPH, Fellow–Pediatric Hematology/ Oncology/BMT, Medical College of Wisconsin, Milwaukee, WI 53226. E-mail: [email protected]
References 1. Fletcher M, Hodgkiss H, Zhang S, Browning R, Hadden C, Hoffman T, et al. Prompt administration of antibiotics is associated with improved outcomes in febrile neutropenia in children with cancer. Pediatr Blood Cancer 2013;60:1299-306. 2. Alvarez E, Chamberlain LJ, Aftandilian C, Saynina O, Wise P. Pediatric oncology discharges with febrile neutropenia: variation in location of care. J Pediatr Hematol Oncol 2017;39:e1-7. 3. Alkan A, Bir K, Utkan G. Can obesity be an advantage for febrile neutropenia? Ann Oncol 2015;26:439-40. 4. Ku BC, Bailey C, Balamuth F. Neutropenia in the febrile child. Pediatr Emerg Care 2016;32:329-34. 5. Lehrnbecher T, Robinson P, Fisher B, Alexander S, Ammann RA, Beauchemin M, et al. Guideline for the management of fever and neutropenia in children with cancer and hematopoietic stem-cell transplantation recipients: 2017 update. J Clin Oncol 2017;35:208294. 6. Paganini HR, Aguirre C, Puppa G, Garbini C, Javier RG, Ensinck G, et al. A prospective, multicentric scoring system to predict mortality in febrile neutropenic children with cancer. Cancer 2007;109:2572-9. 7. Mueller EL, Walkovich KJ, Mody R, Gebremariam A, Davis MM. Hospital discharges for fever and neutropenia in pediatric cancer patients: United States, 2009. BMC Cancer 2015;15:388. 8. Mueller EL, Croop J, Carroll AE. Fever and neutropenia hospital discharges in children with cancer: a 2012 update. Pediatr Hematol Oncol 2016;33:39-48. 9. HCUP Databases. Healthcare Cost and Utilization Project (HCUP). February 2018. Agency for Healthcare Research and Quality, Rockville, MD [Internet]. www.hcup-us.ahrq.gov/nisoverview.jsp. Accessed November 19, 2017. 10. 2014 Introduction to the NIS. Healthcare Cost and Utilization Project (HCUP). December 2016. Agency for Healthcare Research and Quality, Rockville, MD [Internet]. www.hcup-us.ahrq.gov/db/nation/nis/ NIS_Introduction_2014.jsp. Accessed May 2, 2018. 11. Patel A, Singh D, Bhatt P, Thakkar B, Akingbola OA, Srivastav SK. Incidence, trends, and outcomes of cerebral edema among children with diabetic ketoacidosis in the United States. Clin Pediatr (Phila) 2016;55:94351. 12. Patel NJ, Pau D, Nalluri N, Bhatt P, Thakkar B, Kanotra R, et al. Temporal trends, predictors, and outcomes of in-hospital gastrointestinal bleeding associated with percutaneous coronary intervention. Am J Cardiol 2016;118:1150-7. 13. Bhatt NS, Bhatt P, Donda K, Dapaah-Siakwan F, Chaudhari R, Linga VG, et al. Temporal trends of splenectomy in pediatric hospitalizations with immune thrombocytopenia. Pediatr Blood Cancer 2018;65:e27072. 14. HCUP CCS. Healthcare Cost and Utilization Project (HCUP). March 2017. Agency for Healthcare Research and Quality, Rockville, MD [Internet]. www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed May 2, 2018. 15. HCUP NIS Description of Data Elements. Healthcare Cost and Utilization Project (HCUP). September 2008. Agency for Healthcare Research and Quality, Rockville, MD [Internet]. www.hcup-us.ahrq.gov/db/vars/ hosp_bedsize/nisnote.jsp. Accessed May 2, 2018. 16. United States Department of Labor, Bureau of Labor Statistics, Division of Consumer Prices and Price Indexes, Washington, DC. Consumer Price Index [Internet]. https://www.bls.gov/cpi/. Accessed May 2, 2018.
17. HCUP NIS Trend Weights. Healthcare Cost and Utilization Project (HCUP). May 2015. Agency for Healthcare Research and Quality, Rockville, MD [Internet]. www.hcup-us.ahrq.gov/db/nation/nis/trendwghts.jsp. Accessed November 19, 2017. 18. Houchens R, Chu B, Steiner C. Hierarchical Modeling using HCUP Data HCUP Methods Series Report # 2007-01 Online. January 10, 2007. U.S. Agency for Healthcare Research and Quality [Internet]. http://www.hcup -us.ahrq.gov/reports/methods.jsp. Accessed November 19, 2017. 19. Basu SK, Fernandez ID, Fisher SG, Asselin BL, Lyman GH. Length of stay and mortality associated with febrile neutropenia among children with cancer. J Clin Oncol 2005;23:7958-66. 20. Trama A, Botta L, Foschi R, Ferrari A, Stiller C, Desandes E, et al. Survival of European adolescents and young adults diagnosed with cancer in 200-07: population-based data from EUROCARE-5. Lancet Oncol 2016;17:896-906. 21. American Cancer Society. Cancer Statistics Center [Internet]. http://cancerstatisticscenter.cancer.org. Accessed April 28, 2018.. 22. Tai E, Hallisey E, Peipins LA, Flanagan B, Lunsford NB, Wilt G, et al. Geographic access to cancer care and mortality among adolescents. J Adolesc Young Adult Oncol 2018;7:22-9. 23. Boragina M, Patel H, Reiter S, Dougherty G. Management of febrile neutropenia in pediatric oncology patients: a Canadian survey. Pediatr Blood Cancer 2007;48:521-6. 24. Phillips B, Selwood K, Lane SM, Skinner R, Gibson F, Chisholm JC, et al. Variation in policies for the management of febrile neutropenia in United Kingdom Children’s Cancer Study Group centres. Arch Dis Child 2007;92:495-8. 25. Alexander SW, Wade KC, Hibberd PL, Parsons SK. Evaluation of risk prediction criteria for episodes of febrile neutropenia in children with cancer. J Pediatr Hematol Oncol 2002;24:38-42. 26. Santolaya ME, Alvarez AM, Becker A, Cofré J, Enríquez N, O’Ryan M, et al. Prospective, multicenter evaluation of risk factors associated with invasive bacterial infection in children with cancer, neutropenia, and fever. J Clin Oncol 2001;19:3415-21. 27. Rondinelli PIP, Ribeiro Kde CB, de Camargo B. A proposed score for predicting severe infection complications in children with chemotherapyinduced febrile neutropenia. J Pediatr Hematol Oncol 2006;28:66570. 28. Orme LM, Babl FE, Barnes C, Barnett P, Donath S, Ashley DM. Outpatient versus inpatient IV antibiotic management for pediatric oncology patients with low risk febrile neutropenia: a randomised trial. Pediatr Blood Cancer 2014;61:1427-33. 29. Brack E, Bodmer N, Simon A, Leibundgut K, Kühne T, Niggli FK, et al. First-day step-down to oral outpatient treatment versus continued standard treatment in children with cancer and low-risk fever in neutropenia. A randomized controlled trial within the multicenter SPOG 2003 FN study. Pediatr Blood Cancer 2012;59:423-30. 30. Ahmed N, El-Mahallawy HA, Ahmed IA, Nassif S, El-Beshlawy A, ElHaddad A. Early hospital discharge versus continued hospitalization in febrile pediatric cancer patients with prolonged neutropenia: a randomized, prospective study. Pediatr Blood Cancer 2007;49:78692. 31. Santolaya ME, Alvarez AM, Avilés CL, Becker A, Cofré J, Cumsille MA, et al. Early hospital discharge followed by outpatient management versus continued hospitalization of children with cancer, fever, and neutropenia at low risk for invasive bacterial infection. J Clin Oncol 2004;22:37849. 32. Kuderer NM, Dale DC, Crawford J, Cosler LE, Lyman GH. Mortality, morbidity, and cost associated with febrile neutropenia in adult cancer patients. Cancer 2006;106:2258-66.
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Table I. Description of ICD-9-CM codes and HCUP CCS used in the study ICD-9-CM codes/HCUP CCS ICD-9-CM “780.6x”
ICD-9-CM “288.5x” ICD-9-CM “288.0x” ICD-9-CM “284.1x” HCUP CCS “39” HCUP CCS “38” and “37” “170.x” HCUP CCS “35” ICD-9-CM “194.x” HCUP CCS “35” HCUP CCS “27,” “28,” “30,” or “31” HCUP CCS “2” HCUP CCS “122” HCUP CCS “7” HCUP CCS “76” HCUP CCS “135” HCUP CCS “4” HCUP CCS “92” HCUP CCS “197”
Description Fever and other physiologic disturbances of temperature regulation Decreased white blood cell count Neutropenia Pancytopenia Leukemias Lymphomas Bone tumors Brain and nervous system tumors Neuroblastoma Kidney and renal pelvis tumors Genital tumors Sepsis Pneumonia URI (viral) Meningitis Gastroenteritis Mycosis Otitis media Skin and subcutaneous infections
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Table II. Baseline characteristics of pediatric hospitalizations for fever with neutropenia between 2007 and 2014 Characteristics Age, % 0-4 y 5-9 y 10-14 y 15-19 y Sex, % Male Female Race, % White Black Hispanic Other Missing Primary payer, % Private Medicaid Other Median household income category for patient's zip code, % 0-25th percentile 26-50th percentile 51-75th percentile 76-100th percentile Primary diagnosis Leukemia, % Lymphoma, % Bone and connective tissue, % Bone tumors, % Soft-tissue sarcoma, % Cancer of brain and nervous system, % Neuroblastoma, % Kidney tumors, % Wilms tumors, % Cancer ovary/testis, % Comorbidities Sepsis, % Pneumonia, % Viral URIs, % Meningitis, % Gastroenteritis, % Mycoses/fungal infections, % Otitis media, % Skin and subcutaneous infections, % Hospital characteristics Size by number of hospital beds, % Small Medium Large Hospital region, % Northeast Midwest South West Hospital location and teaching status, % Rural Urban—nonteaching Urban—teaching
2007
2008
2009
2010
2011
2012
2013
2014
Total
38.2 27.8 16.2 17.9
36.9 23.8 19.9 19.4
31.4 25.7 18.7 24.3
34.4 29.3 17.7 18.6
36.2 27.6 17.9 18.4
34.3 27.8 18.0 19.9
31.9 29.5 19.6 19.1
32.7 28.6 19.2 19.5
34.3 27.7 18.4 19.6
54.1 45.9
52.3 47.7
54.6 45.4
54.5 45.6
55.6 44.4
55.7 44.2
55.2 44.8
54.2 45.9
54.6 45.4
47.2 6.8 15.4 6.1 24.4
47.6 5.5 23.6 11.7 11.6
49.6 5.7 25.3 8.8 10.6
57.7 6.8 21.9 8.1 5.5
53.0 11.1 18.1 7.8 10.0
54.6 7.6 20.2 9.2 8.5
52.4 7.1 21.8 10.0 8.8
53.0 7.2 21.8 9.8 8.2
52.4 7.2 21.1 9.0 10.3
55.9 36.5 7.1
53.3 41.1 5.5
55.4 39.8 4.8
55.1 38.4 6.0
49.9 38.6 10.2
51.0 40.7 8.1
52.2 41.4 6.3
51.4 40.2 7.9
53.0 39.6 6.9
26.4 21.7 20.7 27.6
21.5 22.5 25.6 28.0
18.9 23.6 27.6 28.8
23.8 22.7 25.3 24.7
28.7 22.4 25.8 20.6
25.4 21.3 25.6 25.3
23.3 23.6 25.2 25.3
23.4 24.3 24.3 25.2
23.9 22.8 25.1 25.6
53.3 7.8 12.1 7.8 4.3 7.9 5.6 3.0 2.5 1.2
49.1 9.2 13.2 9.2 4.0 8.9 3.7 4.5 4.1 1.3
51.1 9.8 15.6 11.2 4.4 6.3 4.3 3.4 2.5 0.9
52.1 8.8 14.7 10.0 4.7 7.1 4.8 2.6 2.5 1.0
52.1 8.4 13.8 9.3 4.5 8.5 4.0 2.6 2.3 0.5
50.2 10.3 13.8 9.9 4.0 7.4 4.2 3.6 3.3 0.8
51.7 9.6 15.7 10.4 5.3 6.3 4.2 3.5 3.0 0.8
53.0 10.3 15.9 10.8 5.2 5.5 3.6 3.0 2.7 0.7
51.6 9.3 14.5 9.9 4.6 7.1 4.3 3.2 2.8 0.9
10.7 4.4 5.6 0.2 3.8 7.4 3.0 3.5
10.3 5.3 10.3 0.0 4.1 7.5 3.3 4.2
12.7 5.3 8.0 0.2 4.9 6.6 3.3 3.5
11.0 5.3 8.1 0.4 4.4 7.4 2.8 3.9
10.2 5.5 11.0 0.1 5.8 7.4 3.0 4.5
11.5 5.3 11.0 0.1 6.4 8.2 2.8 4.1
10.7 6.0 14.0 0.0 5.8 7.4 2.7 5.3
12.6 4.5 17.2 0.0 6.5 7.8 2.5 3.7
11.3 5.2 10.8 0.2 5.3 7.5 2.9 4.1
25.6 6.7 67.6
4.6 25.2 70.2
8.2 30.0 61.4
2.1 19.7 64.8
6.2 14.0 59.1
14.5 24.0 61.5
13.9 25.4 60.6
16.5 24.1 59.4
11.1 21.5 62.9
20.1 17.4 48.3 14.2
26.2 14.7 23.1 36.0
13.2 16.3 18.5 51.9
21.9 12.7 41.3 24.1
8.5 16.6 63.4 11.6
17.4 23.3 36.6 22.8
15.4 19.4 40.3 24.9
15.5 20.8 39.6 24.1
17.5 17.6 38.8 26.1
1.4 8.3 90.4
1.7 6.0 92.3
1.5 4.5 93.5
1.3 3.6 81.8
0.4 2.7 76.1
1.0 3.1 96.0
0.7 3.8 95.5
0.3 2.1 97.6
1.0 4.1 90.5
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Table V. Predictors of LOS and cost of pediatric hospitalizations for fever with neutropenia between 2007 and 2014 Predictors for LOS Variables Age, y 0-4 5-9 10-14 15-19 Female sex (vs male) Insurance type Private Medicaid/Medicare Others Hospital location and teaching status Rural Urban nonteaching Urban teaching Size by number of hospital beds Small Medium Large Hospital region NE MW S W Primary diagnosis Leukemia Lymphoma Bone tumors CNS Neuroblastoma Kidney tumors Genital tumors Comorbidities Sepsis Pneumonia Viral URI Meningitis Gastroenteritis Mycosis Otitis media Skin and subcutaneous infections
Coefficient (days)
95% CI
P value
Coefficient (US $)
95% CI
P value
−0.5 0.9 1.2 0.7
<.001 .2 .02 .08
−964 5510 7507 783
Reference −2672 3521 5459 −563
745 7499 9555 2130
.3 <.001 <.001 .3
0.7 1.2
Reference 0.3 1.1 0.4 2.0
<.001 .003
2455 5410
Reference 1003 2501
3907 8319
.001 <.001
0.5 1.6
Reference −2.1 3.0 −0.7 3.9
.7 .2
1327 6592
0.2 0.5
Reference −1.2 1.6 −0.7 1.6
.8 .4
−643 −6394
Reference −5701 −10 683
4415 −2106
.8 .004
0.0 0.3 1.1
Reference −1.1 1.0 −0.7 1.3 0.0 2.1
.9 .6 .05
1015 −244 4641
Reference −3047 −3934 590
5076 3445 8691
.6 .9 .03
5881 −2152 −5844 871 3551 −2549 −7723
3606 −5256 −8603 −2366 −165 −6730 −15 406
8155 953 −3084 4108 7267 1633 −40
<.001 .2 <.001 .6 .06 .2 .06
21 457 28 654 8743 125 373 18 593 16 750 −4828 8440
19 314 25 615 6309 107 719 15 358 14 054 −8687 5012
23 600 31 693 11 178 143 027 21 828 19 446 −969 11 868
<.001 <.001 <.001 <.001 <.001 <.001 .02 <.001
−0.9 0.4 0.7 0.3
Reference −1.4 −0.2 0.1 0.0
Predictors for cost of hospitalization
2.8 0.3 −0.9 0.6 1.0 −0.3 −1.5
2.1 −0.5 −1.7 −0.3 −0.1 −1.5 −3.6
3.4 1.1 −0.2 1.5 2.0 0.8 0.5
<.001 .5 .02 .2 .07 .6 .1
6.8 6.5 2.1 25.2 5.5 5.7 −1.4 2.6
6.3 5.7 1.4 20.9 4.6 5.0 −2.5 1.7
7.4 7.4 2.8 29.5 6.4 6.5 −0.4 3.6
<.001 <.001 <.001 <.001 <.001 <.001 .0081 <.001
Reference −8635 11 289 −2379 15 563
.8 .2
CNS, central nervous system; MW, Midwest; NE, Northeast; S, South; W, West.
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National Trends in Hospitalization for Fever and Neutropenia in Children with Cancer, 2007-2014 Anusha Lekshminarayanan, MD1,*, Parth Bhatt, MD, MPH2,*, Vijay Gandhi Linga, MD2, Riddhi Chaudhari, MBBS3, Brian Zhu, BA2, Mihir Dave, MD4, Keyur Donda, MBBS5, Sejal Savani, MD6, Samir V. Patel, MD, MPH7, Zeenia C. Billimoria, MD8, Smita Bhaskaran, MD2, Samer Zaid-Kaylani, MD2, Fredrick Dapaah-Siakwan, MD, FAAP5, and Neel S. Bhatt, MBBS, MPH9 Objective To assess the trends of inpatient resource use and mortality in pediatric hospitalizations for fever with neutropenia in the US from 2007 to 2014.
Study design Using National (Nationwide) Inpatient Sample (NIS) and International Classification of Diseases, Ninth Revision, Clinical Modification codes, we studied pediatric cancer hospitalizations with fever with neutropenia between 2007 and 2014. Using appropriate weights for each NIS discharge, we created national estimates of median cost, length of stay, and in-hospital mortality rates. Results Between 2007 and 2014, there were 104 315 hospitalizations for pediatric fever with neutropenia. The number of weighted fever with neutropenia hospitalizations increased from 12.9 (2007) to 18.1 (2014) per 100 000 US population. A significant increase in fever with neutropenia hospitalizations trend was seen in the 5- to 14-year age group, male sex, all races, and in Midwest and Western US hospital regions. Overall mortality rate remained low at 0.75%, and the 15- to 19-year age group was at significantly greater risk of mortality (OR 2.23, 95% CI 1.363.68, P = .002). Sepsis, pneumonia, meningitis, and mycosis were the comorbidities with greater risk of mortality during fever with neutropenia hospitalizations. Median length of stay (2007: 4 days, 2014: 5 days, P < .001) and cost of hospitalization (2007: $8771, 2014: $11 202, P < .001) also significantly increased during the study period. Conclusions Our study provides information regarding inpatient use associated with fever with neutropenia in pediatric hospitalizations. Continued research is needed to develop standardized risk stratification and costeffective treatment strategies for fever with neutropenia hospitalizations considering increasing costs reported in our study. Future studies also are needed to address the greater observed mortality in adolescents with cancer. (J Pediatr 2018;■■:■■-■■).
F
ever with neutropenia is a common oncologic emergency seen in pediatric patients with cancer. Prompt administration of broad-spectrum antibiotics and inpatient hospitalization are the standards of care.1 Hospitalizations due to fever with neutropenia have increased steadily by nearly 50% over the recent decades.2-4 There has been significant interest in improving risk stratification to identify patients at high risk for mortality and potentially treating patients who are at low risk as outpatients.5,6 The current literature provides limited information regarding the overall healthcare burden of fever with neutropenia–related hospitalizations in the pediatric population. Previous epidemiologic studies have provided details of inpatient use for fever with neutropenia in children for the years 2009 and 20127,8; however, these studies did not provide trends and outcomes of fever with neutropenia–related pediatric hospitalizations in the US. The objectives of this study were to describe temporal trends of hospitalizations for fever with neutropenia in children between 2007 and 2014; to characterize the trends in children according to encounterlevel and hospital-level factors; and to study factors associated with mortality, length of stay (LOS), and cost of fever with neutropenia–associated hospitalization. 1
Methods We derived our study cohort from the National (Nationwide) Inpatient Sample (NIS) database from 2007 to 2014. The NIS database is part of the Healthcare Cost
CCS HCUP ICD-9-CM LOS NIS URI
Clinical Classification Software Healthcare Cost and Utilization Project International Classification of Diseases, Ninth Revision, Clinical Modification Length of stay National (Nationwide) Inpatient Sample Upper respiratory infection
From the Department of Internal Medicine, Functional Cholesterol, Diabetes, and Endocrinology Center, Springdale, OH; 2Department of Pediatrics, Texas Tech University Health Sciences Center, Amarillo, TX; 3 Department of Pediatrics, University of Connecticut, Hartford, CT; 4Department of Public Health, Icahn School of Medicine at Mount Sinai, New York, NY; 5Department of Pediatrics, University of Miami, Coral Gables, FL; 6Department of Public Health, New York University, New York, NY; 7Department of Internal Medicine, Sparks Health Systems, Fort Smith, AR; 8Department of Pediatrics, University of Washington, Seattle, WA; and 9 Department of Pediatrics, Division of Hematology/ Oncology/BMT, Medical College of Wisconsin, Milwaukee, WI *Contributed equally. The authors declare no conflicts of interest. 0022-3476/$ - see front matter. © 2018 Elsevier Inc. All rights reserved. https://doi.org10.1016/j.jpeds.2018.06.056
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THE JOURNAL OF PEDIATRICS • www.jpeds.com and Utilization Project (HCUP), sponsored by the Agency of Healthcare Research and Quality. NIS is the largest publicly available all-payer inpatient healthcare database in the US, yielding national estimates of hospital inpatient stays.9 The 2014 NIS sampling frame comprises 44 states and 4411 hospitals estimating 35 million hospitalizations, covering more than 96% of the US population.10 Data for each hospitalization consist of 1 primary discharge diagnosis, <30 secondary diagnoses, and 15 procedural codes recorded during the encounter. The NIS database has been used previously to study inpatient use trends.11-13 Study Population and Design We queried the NIS database from 2007 to 2014 to derive pediatric cancer–related hospitalizations using HCUP Clinical Classification Software (CCS). The CCS for the International Classification of Diseases, Ninth Revision, Clinical Modification Codes (ICD-9-CM) is a diagnosis and procedure categorization scheme that can be used in many types of projects analyzing data on diagnoses and procedures.14 CCS codes from “11” to “45” were used to include all cancer-related hospitalizations. Furthermore, to avoid duplication, we excluded those admissions that were transferred out, using the “DISPUNIFORM” variable. We also excluded elective hospitalizations. Admissions of patients with fever with neutropenia were derived using the ICD-9-CM code “780.6x” AND (“288.5x” OR “288.0x” OR “284.1x”). Similar methodology has been used previously.7,8 Details of the population derivation are shown in Figure 1. Definition of Variables Encounter- and hospital-level characteristics were studied for fever with neutropenia–related hospitalizations. Encounterlevel characteristics such as age, sex, race (white, black, Hispanic, and other), median household income based on zip codes (0-25th percentile, 26th-50th percentile, 51st-75th percentile,
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and 76th-100th percentile), expected primary payer (Medicaid/ Medicare, private, and other), type of cancer, comorbidities such as viral upper respiratory infections (URIs), meningitis, gastroenteritis, mycoses/fungal infections, otitis media, and skin and subcutaneous infections based on the ICD-9-CM and CCS codes were included (Table I; available at www.jpeds.com). Hospital-level characteristics such as hospital location and teaching status (rural, urban nonteaching, and urban teaching), size by number of hospital beds (small, medium, and large), and hospital region (Northeast, Midwest, South, and West) were studied. Hospital size was defined using region of the US, urbanrural designation of the hospital, in addition to the teaching status.15 Cost-to-charge ratio provided by HCUP was used to calculate the actual costs. Cost-to-charge ratio allows conversion of charge data to cost estimates. Inflation-adjusted cost was calculated based on 2014 for all years according to the Consumer Price index Data released by the US government.16 NIS provides weights, which were used to generate national estimates. Statistical Analyses For continuous variables, medians and IQRs were reported. For categorical variables, percentages were reported. The c2 test, t test, or Wilcoxon rank-sum test were used to compare baseline characteristics depending on the distribution of the variables. For trend analysis, c2 test of trend for proportion was generated via the “trend” command. To generate national estimates for trend analysis, “trendwt” provided by the NIS was used.17 Because of the complex survey design of NIS, the “survey” command was used to perform multivariable analysis. Predictors of mortality were analyzed by using survey logistic regression. Hierarchal regression was used to analyze predictors of LOS and cost. Two-level hierarchical models with encounter-level factors nested within hospitals were created using unique hospital identification number provided by NIS.18 SAS 9.4 (SAS Institute, Cary, North Carolina) was used for all
Figure 1. Derivation of the study population. 2
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analyses. Two-sided P value <.05 was considered as significant for all analyses.
Results Baseline Clinical Characteristics of Hospitalizations for Fever with Neutropenia During the study period 2007-2014, we found 104 315 pediatric inpatient encounters of fever with neutropenia. Fever with neutropenia hospitalizations increased from 12.9 per 100 000 US population (10 636 weighted encounters) in 2007 to 18.1 per 100 000 US population (14 865 weighted encounters) in 2014 using the US population as the denominator. Table II (available at www.jpeds.com) demonstrates the baseline characteristics of the study population. Of the study population, 54.6% were male, 52.4% were of white race, 51.6% had the primary diagnosis of leukemia, 53% had a private-payer insurance, 90.5% of hospitalizations occurred at urban teaching hospitals, and 62.9% of patients were treated at large hospitals. Trends of Hospitalizations for Fever with Neutropenia Table III demonstrates the fever with neutropenia hospitalization trends during the study period using baseline encounterlevel and hospital-level characteristics. A statistically significant increasing trend in the number of fever with neutropenia hospitalizations was observed among the following groups from 2007 to 2014: age group 5-9 years (27.8%-28.6%, P < .001), 10-14 years (16.2%-19.2%, P < .001), male patients (54.1%54.2%, P = .003), all races (white: 47.2%-53%, P < .001; black: 6.8%-7.2%, P < .001; Hispanic: 15.4%-21.8%, P = .003; and other: 6.1%-9.8%, P < .001), and in the Midwest (16.4%18.5%, P < .001) and Western regions (13.2%-21.4%, P < .001). Significantly declining fever with neutropenia hospitalizations trend was noted for the 0- to 4-year age group and in Northeast and Southern hospital region. Predictors of Mortality during Fever with Neutropenia Inpatient Hospitalizations The average mortality rate was 0.75% in the hospitalizations for fever with neutropenia; however, a minor but statistically significant increasing trend in mortality was noted (0.66% in 2007 to 0.74% in 2014, P = .001). Results of multivariable analysis assessing predictors of mortality during fever with neutropenia hospitalizations are shown in Table IV. Significantly greater risk of mortality was noted in the 15- to 19-year age group (OR 2.23, 95% CI 1.36-3.68, P = .002) compared with the 0- to 4-year age group. None of the other age groups, sex, insurance status, hospital location, bed size, or region were significantly associated with mortality. Greater odds for mortality also were seen with comorbidity infections such as mycosis (OR 3.02, 95% CI 1.92-4.76, P < .001), meningitis (OR 4.83, 95% CI 1.45-16.16, P = .01), pneumonia (OR 4.87, 95% CI 3.157.51, P < .001), and sepsis (OR 5.82, 95% CI 3.96-8.56, P < .001). None of the disease diagnoses such as leukemia, lymphoma, bone and soft tissue, central nervous system tumors, kidney,
genital tumors, or neuroblastoma was associated with mortality. Similarly, the presence of other comorbidities such as viral URI, gastroenteritis, otitis media, or skin and subcutaneous infections was not associated with mortality. LOS and Cost of Hospitalization Model A significant increase in LOS and cost was observed during the study period. Median LOS significantly increased from 4 days (2007) to 5 days (2014), and median cost increased from $8771 (2007) to $11 202 (2014) (Figure 2). Results of multivariable analysis showing predictors of LOS and cost of hospitalization during fever with neutropenia hospitalizations are shown in Table V (available at www.jpeds.com). Significantly longer LOS was noted in the age group 15-19 years (0.7 days, 95% CI 0.1-0.7, P = .02), with primary diagnosis of leukemia (2.8 days, 95% CI 2.1-3.4, P < .001), with Medicare/Medicaid insurance (0.7 days, 95% CI 0.3-1.1, P < .001), other insurance (1.2 days, 95% CI 0.4-2, P = .003), and with following comorbidities: sepsis (6.8 days, 95% CI 6.3-7.4, P < .001), pneumonia (6.5 days, 95% CI 5.7-7.4, P < .001), URI (2.1 days, 95% CI 1.4-2.8, P < .001), meningitis (25.2 days, 95% CI 20.929.5, P < .001), gastroenteritis (5.5 days, 95% CI 4.6-6.4, P < .001), mycosis (5.7 days, 95% CI 5.0-6.5, P < .001), and skin and subcutaneous infections (2.6 days, 95% CI 1.7-3.6, P < .001). Significantly lower LOS was noted in the 5- to 9-year age group (−0.9 days, 95% CI −1.4 to −0.5, P < .001), with diagnosis of bone tumors (−0.9 days, 95% CI −1.7 to −0.2, P = .02), and otitis media (−1.4 days, 95% CI −2.5 to −0.4, P = .0081). Significantly greater costs were incurred in hospitalizations among the age groups 10-14 years ($5510, 95% CI $35217499, P < .001), 15-19 years ($7507, 95% CI $5459-9555, P < .001), those with Medicaid/Medicare insurance ($2455, 95% CI $1003-3907, P = .001), other insurance ($5410, 95% CI $2501-8319, P < .001), and in the Western US ($4641, 95% CI $590-8691, P = .03) compared with the referent groups as shown in Table V. Statistically significant increase in cost also was seen with primary diagnosis of leukemia ($5881, 95% CI $36068155, P < .001), comorbidities of sepsis ($21 457, 95% CI $19 314-23 600, P < .001), pneumonia ($28 654, 95% CI $25 61531 693, P < .001), URI ($8743, 95% CI $6309-11 178, P < .001), meningitis ($125 373, 95% CI $107 719-143 027, P < .001), gastroenteritis ($18 593, 95% CI $15 358-21 828, P < .001), mycosis ($16 750, 95% CI $14 054-19 446, P < .001), and skin and subcutaneous infections ($8440, 95% CI 5012-11 868, P < .001). Significantly lower cost of hospitalization was seen in large hospitals (−$6394, 95% CI −$10 683 to −$2106, P = .0038), bone tumors (−$5844, 95% CI −$8603 to −3084, P < .001), and otitis media (−$4828, 95% CI −$8687 to −969, P = .02).
Discussion This study assessed the temporal trends of inpatient encounters for children with fever with neutropenia and cancer and factors affecting LOS, cost of hospitalizations, and mortality. The number of fever with neutropenia hospitalizations increased from 2007 to 2014. Overall mortality rate remained
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2007
2008
2009
2010
2011
2012
2013
2014
Total
Febrile neutropenia hospitalizations, unweighted, n Febrile neutropenia hospitalizations, weighted,* n Fever with neutropenia hospitalization per 100 000 US population, n Age groups, % 0-4 y 5-9 y 10-14 y 15-19 y Sex, % Male Female Race, % White Black Hispanic Other Hospital region, % Northeast Midwest South West Mortality, % Median cost of hospitalization, US $, (IQR) Median LOS, d (IQR)
2389
2223
2385
3737
2393
2817
2759
2973
21 676
P value
10 636
10 575
11 728
18 167
10 463
14 085
13 795
14 865
104 315
12.9
12.7
14.1
21.8
12.6
17.1
16.8
18.1
38.2 27.8 16.2 17.9
36.9 23.8 19.9 19.4
31.4 25.7 18.7 24.3
34.4 29.3 17.7 18.6
36.2 27.6 17.9 18.4
34.3 27.8 18.0 19.9
31.9 29.5 19.6 19.1
32.7 28.6 19.2 19.5
34.3 27.7 18.4 19.6
<.001 <.001 <.001 .2
54.1 45.9
52.3 47.7
54.6 45.4
54.5 45.6
55.6 44.4
55.7 44.2
55.2 44.8
54.2 45.9
54.6 45.4
.003 .003
47.2 6.8 15.4 6.1
47.6 5.5 23.6 11.7
49.6 5.7 25.3 8.8
57.7 6.8 21.9 8.1
53.0 11.1 18.1 7.8
54.6 7.6 20.2 9.2
52.4 7.1 21.8 10.0
53.0 7.2 21.8 9.8
52.4 7.2 21.1 9.0
<.001 <.001 .003 <.001
19.1 16.4 46.9 13.2 0.66 8771 (4756-17 675)
25.7 13.9 21.6 34.7 0.51 8828 (4759-18 197)
12.9 15.2 17.2 50.7 0.9 9905 (5327-23 880)
21.5 12.1 40.3 23.3 0.64 10 200 (5558-21 565)
8.2 15.5 61.8 10.6 0.88 10 852 (5990-21 921)
15.8 20.7 33.4 20.5 0.92 10 539 (5653-24 059)
13.8 16.8 36.9 22.5 0.76 10 396 (5482-23 411)
14.0 18.5 36.4 21.4 0.74 11 202 (5717-27 333)
16.5 16.1 36.7 24.5 0.75 10 102 (5428-22 158)
<.001 <.001 <.001 <.001 .001 <.001
4 (3-8)
4 (3-8)
5 (3-9)
5 (3-9)
4 (3-8)
5 (3-9)
4 (3-8)
5 (3-9)
5 (3-8)
<.001
*Weights provided by the NIS were used to generate national weighted estimates.
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Table III. Trends of pediatric hospitalizations for fever with neutropenia by age, sex, race, hospital region, and outcomes (mortality, LOS, and cost of hospitalization) between 2007 and 2014
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Table IV. Multivariate predictors of mortality for pediatric fever with neutropenia hospitalizations between 2007 and 2014 95% CI Variables Age, y 0-4 5-9 10-14 15-19 Sex Male Female Insurance status Private Medicaid Others Hospital location and teaching status Rural Urban nonteaching Urban teaching Size by number of hospital beds Small Medium Large Hospital region NE MW S W Primary diagnosis Leukemia Lymphoma Bone and soft-tissue tumors CNS tumors Neuroblastoma Kidney tumors Genital tumors Comorbidities Sepsis Pneumonia Viral URI Meningitis Gastroenteritis Mycosis Otitis media Skin and subcutaneous infections
OR
Lower limit Upper limit P value
1.33 1.26 2.23
0.80 0.76 1.36
Reference 2.23 2.07 3.68
.3 .4 .002
0.79
0.56
Reference 1.13
.2
1.05 0.73
0.75 0.33
Reference 1.48 1.60
.8 .4
1.61 1.09
0.18 0.14
Reference 14.18 8.40
.7 .9
1.06 0.92
0.51 0.48
Reference 2.18 1.76
.9 .8
1.55 1.46 1.31
0.90 0.89 0.77
Reference 2.67 2.41 2.24
.1 .1 .3
1.53 0.65 0.64 0.30 0.49 1.16 1.17
0.75 0.24 0.24 0.06 0.10 0.30 0.13
3.12 1.75 1.67 1.46 2.39 4.44 10.45
.2 .4 .4 .1 .4 .8 .9
5.82 4.87 1.03 4.83 1.15 3.02 0.48 1.45
3.96 3.15 0.64 1.45 0.63 1.92 0.12 0.74
8.56 7.51 1.66 16.16 2.10 4.76 1.95 2.82
<.001 <.001 .9 .01 .7 <.001 .3 .3
CNS, central nervous system; MW, Midwest; NE, Northeast; S, South; W, West.
at 0.75%, and mortality was significantly greater in the older age group (15-19 years) and with sepsis, pneumonia, meningitis, and mycosis. In addition, increasing trends in cost of hospitalization and LOS also were noted through the study period. The number of fever with neutropenia hospitalizations reported in our study is similar to those reported by Mueller et al using another administrative claims database.7,8 These studies reported fever with neutropenia hospitalizations in children in 2009 and 2012, respectively, and overall an increasing trend was noted. The rate of comorbidities such as sepsis and viral URI were similar to those reported in our study. Basu et al also used ICD-9 codes to identify fever with neutropenia discharge diagnoses between 1995 and 2002, although temporal
trends were not reported.19 In that study, the number of fever with neutropenia discharge diagnoses were lower than in our study (1224 to 1853 per year), which could be due to the difference in study design, because they used information from the US academic institutions only. In comparison, NIS is a large nationally representative dataset and includes community hospital discharges. However, it is important to note that readmissions during the specified time period cannot be excluded using HCUP datasets, which could account for a greater number of hospitalizations for fever with neutropenia in our study and other studies using similar datasets. The mortality rate reported in our study (0.8%) is similar to previous studies by Mueller et al, who reported 0.4%-0.5%.7,8 Predictors of mortality also were assessed in our study. Age was a significant predictor of mortality, with the oldest age group in our study being at greater risk of death during fever with neutropenia encounters compared with the youngest, the 0to 4-year age group. Basu et al studied the predictors of mortality in pediatric fever with neutropenia hospitalizations and also found that adolescents were at significantly greater risk of mortality compared with younger children.19 A previous analysis from population-based cancer registries from 27 European countries also documented lower 5-year overall survival in adolescents and young adults with cancer compared with younger patients.20 Similarly, the statistics of American Cancer Society showed greater mortality rates in 15- to 19year age group compared with the 0- to 14-year age group between 2011 and 2015.21 It is possible that the disease biology is different in this age group compared with younger cohort, contributing to unfavorable outcomes. Other factors, which were not assessed in our study, could have contributed to greater mortality, such as variation in care (pediatric vs adult healthcare setting), social support, delay in seeking care during these episodes, or socioeconomic status.20,22 Several comorbidities also predicted greater risk of death in fever with neutropenia hospitalizations. Sepsis conferred a 5-fold greater risk of death, both pneumonia and meningitis a 4-fold greater, and mycosis a 3-fold higher risk of death. These findings show that despite advancements and widespread availability of antimicrobial therapy for prophylaxis and treatment and supportive care, infections remain a significant cause of death and lead to increasing healthcare burden. Current management practices for fever with neutropenia in children are not standardized and remain specific to institutions,23,24 despite growing interest in developing and validating risk-stratification models6,25-27 and the existence of clinical practice guidelines.5 Although inpatient hospitalization remains the standard of care for the management of fever with neutropenia, improvement in cost-effectiveness and quality of life have been reported with the outpatient management of low-risk patients compared with hospitalization with mortality rates remaining unchanged.28-31 Although we are unable to create a risk-stratification model in this study, because of a lack of pivotal information such as treatment details, treatment intensity, vital signs, clinical status at admission, and laboratory test results, previous studies using administrative claims databases have used longer
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Figure 2. LOS and cost of hospitalization trend in pediatric hospitalizations for fever with neutropenia. The X-axis represents the year of hospitalization, the primary Y-axis shows the median cost (US $) of pediatric fever with neutropenia hospitalizations, and the secondary Y-axis shows the median LOS (days) of pediatric hospitalizations for fever with neutropenia.
LOS as a surrogate for illness severity.7,8,19 A median LOS of 5 days and a median cost of hospitalization of $10 102 were reported in our study, which were lower than other studies focusing on pediatric and adult hospitalizations for fever with neutropenia.7,8,22,32 These studies only used single-year design to assess fever with neutropenia-related hospitalizations, which could explain the differences. Previous studies have not reported trends in LOS or cost of hospitalization. We reported a significant increase in LOS, which could in turn explain the significant increase in cost of hospitalization during our study period. In addition, the presence of comorbidities such as sepsis, pneumonia, viral URI, meningitis, gastroenteritis, mycosis, and skin and subcutaneous infections predicted longer LOS, which could be considered indicators of severe illness compared with hospitalizations without these comorbidities. Primary diagnosis of bone tumor and otitis media as a comorbidity were associated with shorter LOS, which could imply their low-risk nature. However, caution should be exercised in interpreting these results due to the lack of granular data. Continued research in this field is needed to develop a universal, validated risk-stratification model to judiciously use healthcare resources and to improve treatment delivery options considering cost-effectiveness and quality of life in this population. There are several limitations to our study. The NIS data are collected through discharge diagnoses. Although elective admissions were excluded, we cannot exclude other possible nonelective admission diagnoses in our study or development of
neutropenia during hospitalization. Because the NIS dataset is available through ICD-9-CM codes, its accuracy depends heavily on appropriate coding practices, and undercoding or overcoding could not be ruled out. It is also possible that our study underestimates the true incidence of fever with neutropenia during this time period through our ICD-9-CM search strategy, as encounters with presence of comorbidities (eg, pneumonia, meningitis, sepsis) at the time of admission may only be coded for the comorbidity, not as fever with neutropenia. Although NIS provides national estimates and yearly trends of inpatient hospitalizations, NIS is an administrative claims database; therefore, we were unable to study specific details regarding cancer staging, cancer-directed treatment, treatment intensity, time to antibiotic administration, clinical status, or laboratory test results at admission or discharge. In addition, we were unable to rule out readmissions, and the actual incidence of fever with neutropenia hospitalization could be different during the specified time period. Although each hospital may have institution-specific guidelines for the documentation and the management of fever with neutropenia, which could introduce selection bias in the cohort, no significant impact was noted in our study, as we used hierarchical regression to take hospital-level variations into account. ■ We acknowledge HCUP and its partner organizations providing data to the HCUP. A list of all HCUP data partners is available at https://www.hcup-us.ahrq.gov/db/hcupdatapartners.jsp. Submitted for publication May 5, 2018; last revision received Jun 5, 2018; accepted Jun 18, 2018
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Reprint requests: Neel S. Bhatt, MBBS, MPH, Fellow–Pediatric Hematology/ Oncology/BMT, Medical College of Wisconsin, Milwaukee, WI 53226. E-mail: [email protected]
References 1. Fletcher M, Hodgkiss H, Zhang S, Browning R, Hadden C, Hoffman T, et al. Prompt administration of antibiotics is associated with improved outcomes in febrile neutropenia in children with cancer. Pediatr Blood Cancer 2013;60:1299-306. 2. Alvarez E, Chamberlain LJ, Aftandilian C, Saynina O, Wise P. Pediatric oncology discharges with febrile neutropenia: variation in location of care. J Pediatr Hematol Oncol 2017;39:e1-7. 3. Alkan A, Bir K, Utkan G. Can obesity be an advantage for febrile neutropenia? Ann Oncol 2015;26:439-40. 4. Ku BC, Bailey C, Balamuth F. Neutropenia in the febrile child. Pediatr Emerg Care 2016;32:329-34. 5. Lehrnbecher T, Robinson P, Fisher B, Alexander S, Ammann RA, Beauchemin M, et al. Guideline for the management of fever and neutropenia in children with cancer and hematopoietic stem-cell transplantation recipients: 2017 update. J Clin Oncol 2017;35:208294. 6. Paganini HR, Aguirre C, Puppa G, Garbini C, Javier RG, Ensinck G, et al. A prospective, multicentric scoring system to predict mortality in febrile neutropenic children with cancer. Cancer 2007;109:2572-9. 7. Mueller EL, Walkovich KJ, Mody R, Gebremariam A, Davis MM. Hospital discharges for fever and neutropenia in pediatric cancer patients: United States, 2009. BMC Cancer 2015;15:388. 8. Mueller EL, Croop J, Carroll AE. Fever and neutropenia hospital discharges in children with cancer: a 2012 update. Pediatr Hematol Oncol 2016;33:39-48. 9. HCUP Databases. Healthcare Cost and Utilization Project (HCUP). February 2018. Agency for Healthcare Research and Quality, Rockville, MD [Internet]. www.hcup-us.ahrq.gov/nisoverview.jsp. Accessed November 19, 2017. 10. 2014 Introduction to the NIS. Healthcare Cost and Utilization Project (HCUP). December 2016. Agency for Healthcare Research and Quality, Rockville, MD [Internet]. www.hcup-us.ahrq.gov/db/nation/nis/ NIS_Introduction_2014.jsp. Accessed May 2, 2018. 11. Patel A, Singh D, Bhatt P, Thakkar B, Akingbola OA, Srivastav SK. Incidence, trends, and outcomes of cerebral edema among children with diabetic ketoacidosis in the United States. Clin Pediatr (Phila) 2016;55:94351. 12. Patel NJ, Pau D, Nalluri N, Bhatt P, Thakkar B, Kanotra R, et al. Temporal trends, predictors, and outcomes of in-hospital gastrointestinal bleeding associated with percutaneous coronary intervention. Am J Cardiol 2016;118:1150-7. 13. Bhatt NS, Bhatt P, Donda K, Dapaah-Siakwan F, Chaudhari R, Linga VG, et al. Temporal trends of splenectomy in pediatric hospitalizations with immune thrombocytopenia. Pediatr Blood Cancer 2018;65:e27072. 14. HCUP CCS. Healthcare Cost and Utilization Project (HCUP). March 2017. Agency for Healthcare Research and Quality, Rockville, MD [Internet]. www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed May 2, 2018. 15. HCUP NIS Description of Data Elements. Healthcare Cost and Utilization Project (HCUP). September 2008. Agency for Healthcare Research and Quality, Rockville, MD [Internet]. www.hcup-us.ahrq.gov/db/vars/ hosp_bedsize/nisnote.jsp. Accessed May 2, 2018. 16. United States Department of Labor, Bureau of Labor Statistics, Division of Consumer Prices and Price Indexes, Washington, DC. Consumer Price Index [Internet]. https://www.bls.gov/cpi/. Accessed May 2, 2018.
17. HCUP NIS Trend Weights. Healthcare Cost and Utilization Project (HCUP). May 2015. Agency for Healthcare Research and Quality, Rockville, MD [Internet]. www.hcup-us.ahrq.gov/db/nation/nis/trendwghts.jsp. Accessed November 19, 2017. 18. Houchens R, Chu B, Steiner C. Hierarchical Modeling using HCUP Data HCUP Methods Series Report # 2007-01 Online. January 10, 2007. U.S. Agency for Healthcare Research and Quality [Internet]. http://www.hcup -us.ahrq.gov/reports/methods.jsp. Accessed November 19, 2017. 19. Basu SK, Fernandez ID, Fisher SG, Asselin BL, Lyman GH. Length of stay and mortality associated with febrile neutropenia among children with cancer. J Clin Oncol 2005;23:7958-66. 20. Trama A, Botta L, Foschi R, Ferrari A, Stiller C, Desandes E, et al. Survival of European adolescents and young adults diagnosed with cancer in 200-07: population-based data from EUROCARE-5. Lancet Oncol 2016;17:896-906. 21. American Cancer Society. Cancer Statistics Center [Internet]. http://cancerstatisticscenter.cancer.org. Accessed April 28, 2018.. 22. Tai E, Hallisey E, Peipins LA, Flanagan B, Lunsford NB, Wilt G, et al. Geographic access to cancer care and mortality among adolescents. J Adolesc Young Adult Oncol 2018;7:22-9. 23. Boragina M, Patel H, Reiter S, Dougherty G. Management of febrile neutropenia in pediatric oncology patients: a Canadian survey. Pediatr Blood Cancer 2007;48:521-6. 24. Phillips B, Selwood K, Lane SM, Skinner R, Gibson F, Chisholm JC, et al. Variation in policies for the management of febrile neutropenia in United Kingdom Children’s Cancer Study Group centres. Arch Dis Child 2007;92:495-8. 25. Alexander SW, Wade KC, Hibberd PL, Parsons SK. Evaluation of risk prediction criteria for episodes of febrile neutropenia in children with cancer. J Pediatr Hematol Oncol 2002;24:38-42. 26. Santolaya ME, Alvarez AM, Becker A, Cofré J, Enríquez N, O’Ryan M, et al. Prospective, multicenter evaluation of risk factors associated with invasive bacterial infection in children with cancer, neutropenia, and fever. J Clin Oncol 2001;19:3415-21. 27. Rondinelli PIP, Ribeiro Kde CB, de Camargo B. A proposed score for predicting severe infection complications in children with chemotherapyinduced febrile neutropenia. J Pediatr Hematol Oncol 2006;28:66570. 28. Orme LM, Babl FE, Barnes C, Barnett P, Donath S, Ashley DM. Outpatient versus inpatient IV antibiotic management for pediatric oncology patients with low risk febrile neutropenia: a randomised trial. Pediatr Blood Cancer 2014;61:1427-33. 29. Brack E, Bodmer N, Simon A, Leibundgut K, Kühne T, Niggli FK, et al. First-day step-down to oral outpatient treatment versus continued standard treatment in children with cancer and low-risk fever in neutropenia. A randomized controlled trial within the multicenter SPOG 2003 FN study. Pediatr Blood Cancer 2012;59:423-30. 30. Ahmed N, El-Mahallawy HA, Ahmed IA, Nassif S, El-Beshlawy A, ElHaddad A. Early hospital discharge versus continued hospitalization in febrile pediatric cancer patients with prolonged neutropenia: a randomized, prospective study. Pediatr Blood Cancer 2007;49:78692. 31. Santolaya ME, Alvarez AM, Avilés CL, Becker A, Cofré J, Cumsille MA, et al. Early hospital discharge followed by outpatient management versus continued hospitalization of children with cancer, fever, and neutropenia at low risk for invasive bacterial infection. J Clin Oncol 2004;22:37849. 32. Kuderer NM, Dale DC, Crawford J, Cosler LE, Lyman GH. Mortality, morbidity, and cost associated with febrile neutropenia in adult cancer patients. Cancer 2006;106:2258-66.
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Table I. Description of ICD-9-CM codes and HCUP CCS used in the study ICD-9-CM codes/HCUP CCS ICD-9-CM “780.6x”
ICD-9-CM “288.5x” ICD-9-CM “288.0x” ICD-9-CM “284.1x” HCUP CCS “39” HCUP CCS “38” and “37” “170.x” HCUP CCS “35” ICD-9-CM “194.x” HCUP CCS “35” HCUP CCS “27,” “28,” “30,” or “31” HCUP CCS “2” HCUP CCS “122” HCUP CCS “7” HCUP CCS “76” HCUP CCS “135” HCUP CCS “4” HCUP CCS “92” HCUP CCS “197”
Description Fever and other physiologic disturbances of temperature regulation Decreased white blood cell count Neutropenia Pancytopenia Leukemias Lymphomas Bone tumors Brain and nervous system tumors Neuroblastoma Kidney and renal pelvis tumors Genital tumors Sepsis Pneumonia URI (viral) Meningitis Gastroenteritis Mycosis Otitis media Skin and subcutaneous infections
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Table II. Baseline characteristics of pediatric hospitalizations for fever with neutropenia between 2007 and 2014 Characteristics Age, % 0-4 y 5-9 y 10-14 y 15-19 y Sex, % Male Female Race, % White Black Hispanic Other Missing Primary payer, % Private Medicaid Other Median household income category for patient's zip code, % 0-25th percentile 26-50th percentile 51-75th percentile 76-100th percentile Primary diagnosis Leukemia, % Lymphoma, % Bone and connective tissue, % Bone tumors, % Soft-tissue sarcoma, % Cancer of brain and nervous system, % Neuroblastoma, % Kidney tumors, % Wilms tumors, % Cancer ovary/testis, % Comorbidities Sepsis, % Pneumonia, % Viral URIs, % Meningitis, % Gastroenteritis, % Mycoses/fungal infections, % Otitis media, % Skin and subcutaneous infections, % Hospital characteristics Size by number of hospital beds, % Small Medium Large Hospital region, % Northeast Midwest South West Hospital location and teaching status, % Rural Urban—nonteaching Urban—teaching
2007
2008
2009
2010
2011
2012
2013
2014
Total
38.2 27.8 16.2 17.9
36.9 23.8 19.9 19.4
31.4 25.7 18.7 24.3
34.4 29.3 17.7 18.6
36.2 27.6 17.9 18.4
34.3 27.8 18.0 19.9
31.9 29.5 19.6 19.1
32.7 28.6 19.2 19.5
34.3 27.7 18.4 19.6
54.1 45.9
52.3 47.7
54.6 45.4
54.5 45.6
55.6 44.4
55.7 44.2
55.2 44.8
54.2 45.9
54.6 45.4
47.2 6.8 15.4 6.1 24.4
47.6 5.5 23.6 11.7 11.6
49.6 5.7 25.3 8.8 10.6
57.7 6.8 21.9 8.1 5.5
53.0 11.1 18.1 7.8 10.0
54.6 7.6 20.2 9.2 8.5
52.4 7.1 21.8 10.0 8.8
53.0 7.2 21.8 9.8 8.2
52.4 7.2 21.1 9.0 10.3
55.9 36.5 7.1
53.3 41.1 5.5
55.4 39.8 4.8
55.1 38.4 6.0
49.9 38.6 10.2
51.0 40.7 8.1
52.2 41.4 6.3
51.4 40.2 7.9
53.0 39.6 6.9
26.4 21.7 20.7 27.6
21.5 22.5 25.6 28.0
18.9 23.6 27.6 28.8
23.8 22.7 25.3 24.7
28.7 22.4 25.8 20.6
25.4 21.3 25.6 25.3
23.3 23.6 25.2 25.3
23.4 24.3 24.3 25.2
23.9 22.8 25.1 25.6
53.3 7.8 12.1 7.8 4.3 7.9 5.6 3.0 2.5 1.2
49.1 9.2 13.2 9.2 4.0 8.9 3.7 4.5 4.1 1.3
51.1 9.8 15.6 11.2 4.4 6.3 4.3 3.4 2.5 0.9
52.1 8.8 14.7 10.0 4.7 7.1 4.8 2.6 2.5 1.0
52.1 8.4 13.8 9.3 4.5 8.5 4.0 2.6 2.3 0.5
50.2 10.3 13.8 9.9 4.0 7.4 4.2 3.6 3.3 0.8
51.7 9.6 15.7 10.4 5.3 6.3 4.2 3.5 3.0 0.8
53.0 10.3 15.9 10.8 5.2 5.5 3.6 3.0 2.7 0.7
51.6 9.3 14.5 9.9 4.6 7.1 4.3 3.2 2.8 0.9
10.7 4.4 5.6 0.2 3.8 7.4 3.0 3.5
10.3 5.3 10.3 0.0 4.1 7.5 3.3 4.2
12.7 5.3 8.0 0.2 4.9 6.6 3.3 3.5
11.0 5.3 8.1 0.4 4.4 7.4 2.8 3.9
10.2 5.5 11.0 0.1 5.8 7.4 3.0 4.5
11.5 5.3 11.0 0.1 6.4 8.2 2.8 4.1
10.7 6.0 14.0 0.0 5.8 7.4 2.7 5.3
12.6 4.5 17.2 0.0 6.5 7.8 2.5 3.7
11.3 5.2 10.8 0.2 5.3 7.5 2.9 4.1
25.6 6.7 67.6
4.6 25.2 70.2
8.2 30.0 61.4
2.1 19.7 64.8
6.2 14.0 59.1
14.5 24.0 61.5
13.9 25.4 60.6
16.5 24.1 59.4
11.1 21.5 62.9
20.1 17.4 48.3 14.2
26.2 14.7 23.1 36.0
13.2 16.3 18.5 51.9
21.9 12.7 41.3 24.1
8.5 16.6 63.4 11.6
17.4 23.3 36.6 22.8
15.4 19.4 40.3 24.9
15.5 20.8 39.6 24.1
17.5 17.6 38.8 26.1
1.4 8.3 90.4
1.7 6.0 92.3
1.5 4.5 93.5
1.3 3.6 81.8
0.4 2.7 76.1
1.0 3.1 96.0
0.7 3.8 95.5
0.3 2.1 97.6
1.0 4.1 90.5
National Trends in Hospitalization for Fever and Neutropenia in Children with Cancer, 2007-2014 FLA 5.5.0 DTD ■ YMPD10092_proof ■ July 17, 2018
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THE JOURNAL OF PEDIATRICS • www.jpeds.com
Volume ■■
Table V. Predictors of LOS and cost of pediatric hospitalizations for fever with neutropenia between 2007 and 2014 Predictors for LOS Variables Age, y 0-4 5-9 10-14 15-19 Female sex (vs male) Insurance type Private Medicaid/Medicare Others Hospital location and teaching status Rural Urban nonteaching Urban teaching Size by number of hospital beds Small Medium Large Hospital region NE MW S W Primary diagnosis Leukemia Lymphoma Bone tumors CNS Neuroblastoma Kidney tumors Genital tumors Comorbidities Sepsis Pneumonia Viral URI Meningitis Gastroenteritis Mycosis Otitis media Skin and subcutaneous infections
Coefficient (days)
95% CI
P value
Coefficient (US $)
95% CI
P value
−0.5 0.9 1.2 0.7
<.001 .2 .02 .08
−964 5510 7507 783
Reference −2672 3521 5459 −563
745 7499 9555 2130
.3 <.001 <.001 .3
0.7 1.2
Reference 0.3 1.1 0.4 2.0
<.001 .003
2455 5410
Reference 1003 2501
3907 8319
.001 <.001
0.5 1.6
Reference −2.1 3.0 −0.7 3.9
.7 .2
1327 6592
0.2 0.5
Reference −1.2 1.6 −0.7 1.6
.8 .4
−643 −6394
Reference −5701 −10 683
4415 −2106
.8 .004
0.0 0.3 1.1
Reference −1.1 1.0 −0.7 1.3 0.0 2.1
.9 .6 .05
1015 −244 4641
Reference −3047 −3934 590
5076 3445 8691
.6 .9 .03
5881 −2152 −5844 871 3551 −2549 −7723
3606 −5256 −8603 −2366 −165 −6730 −15 406
8155 953 −3084 4108 7267 1633 −40
<.001 .2 <.001 .6 .06 .2 .06
21 457 28 654 8743 125 373 18 593 16 750 −4828 8440
19 314 25 615 6309 107 719 15 358 14 054 −8687 5012
23 600 31 693 11 178 143 027 21 828 19 446 −969 11 868
<.001 <.001 <.001 <.001 <.001 <.001 .02 <.001
−0.9 0.4 0.7 0.3
Reference −1.4 −0.2 0.1 0.0
Predictors for cost of hospitalization
2.8 0.3 −0.9 0.6 1.0 −0.3 −1.5
2.1 −0.5 −1.7 −0.3 −0.1 −1.5 −3.6
3.4 1.1 −0.2 1.5 2.0 0.8 0.5
<.001 .5 .02 .2 .07 .6 .1
6.8 6.5 2.1 25.2 5.5 5.7 −1.4 2.6
6.3 5.7 1.4 20.9 4.6 5.0 −2.5 1.7
7.4 7.4 2.8 29.5 6.4 6.5 −0.4 3.6
<.001 <.001 <.001 <.001 <.001 <.001 .0081 <.001
Reference −8635 11 289 −2379 15 563
.8 .2
CNS, central nervous system; MW, Midwest; NE, Northeast; S, South; W, West.
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Lekshminarayanan et al FLA 5.5.0 DTD ■ YMPD10092_proof ■ July 17, 2018