Effect of Practice Variation on Resource Utilization in Infants
Hospitalized for Viral Lower Respiratory Illness
Douglas F. Willson, MD*; Susan D. Horn, PhD‡; J. Owen Hendley, MD
储
; Randall Smout, MS§; and
Julie Gassaway, RN, MSN§
ABSTRACT. Objective. Hospital care for children with viral lower respiratory illness (VLRI) is highly vari-able, and its relationship to severity and impact on out-come is unclear. Using the Pediatric Comprehensive Se-verity Index, we analyzed the correlation of institutional practice variation with severity and resource utilization in 10 children’s medical centers.
Methods. Demographics, clinical information, labo-ratory results, interventions, and outcomes were ex-tracted from the charts of consecutive infants with VLRI from 10 children’s medical centers. Pediatric Component of the Comprehensive Severity Index scoring was per-formed at admission and at maximum during hospital-ization. The correlation of patient variables, interven-tions, and resource utilization at the patient level was compared with their correlation at the aggregate institu-tional level.
Results. Of 601 patients, 1 died, 6 were discharged to home health care, 4 were discharged to rehabilitative care, and 2 were discharged to chronic nursing care. In-dividual patient admission severity score correlated pos-itively with patient hospital costs (rⴝ0.48), but institu-tional average patient severity was negatively correlated with average institutional costs (r ⴝ ⴚ0.26). Maximal severity score correlated well with costs (r ⴝ 0.66) and length of stay (LOS; r ⴝ 0.64) at the patient level but poorly at the institutional level (rⴝ0.07 costs;rⴝ 0.40 LOS). The institutional intensity of therapy was nega-tively correlated with admission severity (rⴝ ⴚ0.03) but strongly correlated with costs (r ⴝ 0.84) and LOS (r ⴝ 0.83).
Conclusions. Institutional differences in care prac-tices for children with VLRI were not explained by dif-ferences in patient severity and did not affect the chil-dren’s recovery but correlated significantly with hospital costs and LOS. Pediatrics 2001;108:851– 855; respiratory syncytial virus, bronchiolitis, lower respiratory illness, practice variation, pediatric intensive care, intravascular catheters, Comprehensive Severity Index, severity scoring.
ABBREVIATIONS. VLRI, viral lower respiratory illness; PCSI, Pediatric Comprehensive Severity Index; PICU, pediatric inten-sive care unit; LOS, length of stay;ICD-9, International Classification
of Diseases—Ninth Revision; RSV, respiratory syncytial virus; ISIS, International Severity Information Systems; CSI, Comprehensive Severity Index; ACSIC, Admission Comprehensive Severity Index; MCSIC, Maximum Comprehensive Severity Index;ICD-9-CM, In-ternational Classification of Diseases—Ninth Revision—Clinical Modi-fication.
O
ne percent to 2% of all children in the United
States are hospitalized for viral lower
respi-ratory illness (VLRI) during their
child-hood.
1,2Variation in the treatment of these children
has been well described and reflects a lack of
con-sensus regarding optimal therapy.
3–5Ascertainment
of optimal care is difficult because our therapies are
supportive, not curative, and most children do well
irrespective of differences in therapy. Consequently,
there is a propensity to persist in care practices that
may offer little or marginal benefit. Determination of
best care practices requires consideration of the
im-pact of treatment not only on morbidity and
mortal-ity but also on resource utilization.
We evaluated the care of children who were
hos-pitalized with VLRI across 10 children’s medical
centers. Using the Pediatric Comprehensive Severity
Index (PCSI),
6 – 8we examined the relative
contribu-tions of severity versus practice variation on
differ-ences in pediatric intensive care unit (PICU)
admis-sion rates, intubation, hospital costs, and hospital
length of stay (LOS) at these institutions.
METHODS
The Human Investigation Committees of each of the 10 partic-ipating centers approved this study, which was funded by a grant from the Agency for Health Care Policy and Research and directed by International Severity Information Systems, Inc. (ISIS; Salt Lake City, UT).
As part of a larger study of 16 506 pediatric hospital admis-sions, the hospital records of all children who were younger than 12 months and had the principal diagnoses of bronchiolitis ( Inter-national Classification of Disease—Ninth Revision[ICD-9] code 466.1) and respiratory syncytial virus (RSV) pneumonia (ICD-9 code 480.1) and were admitted to 10 children’s medical centers from April 1, 1995, to September 30, 1996, were reviewed. These 2ICD-9
codes were combined because we believed that these 2 diagnoses exist on a continuum, they constitute the bulk of pediatric VLRI, and the same severity matrix in the PCSI is used for both diag-noses. We chose children who were younger than 12 months because they are at the greatest risk of severe illness with VLRI. The 10 children’s medical centers all were tertiary care hospitals with accredited pediatric residency programs, full-time pediatric faculty, and PICUs staffed by full-time pediatric intensivists.
Comorbidities specifically examined included heart disease, a history of preterm birth, a history of hospitalization for wheezing in the previous 6 months, and a history of hospitalizations for bronchiolitis. Children with bronchopulmonary dysplasia (ICD-9
code 770.7) were excluded. From the *University of Virginia Children’s Medical Center, Charlottesville,
Virginia; ‡Department of Medical Informatics, Institute for Clinical Out-comes Research, §International Severity Information Systems, Inc, Salt Lake City, Utah; and储Division of Pediatric Infectious Disease, University of Virginia Children’s Medical Center, Charlottesville, Virginia.
Received for publication Oct 24, 2000; accepted Feb 5, 2001.
Reprint requests to (D.F.W.) Division of Pediatric Critical Care, University of Virginia Children’s Medical Center, Box 386, University of Virginia Health Sciences Center, Charlottesville, VA 22908. E-mail: dfw4m@unix. mail.virginia.edu
Severity scoring using the PCSI was done at admission (Ad-mission Comprehensive Severity Index [ACSIC]) and at maxi-mum (Maximaxi-mum Comprehensive Severity Index [MCSIC]) for the entire hospital course.
Data Extraction
All data collection was retrospective. Patient demographic data that included patient name, birth date, gender, admission and discharge dates, PICU admission and discharge dates, discharge disposition, Diagnostic Related Groups code, and International Classification of Diseases—Ninth Revision—Clinical Modification
(ICD-9-CM) diagnostic and procedure codes were submitted di-rectly on electronic media to ISIS, Inc, from hospital databases at each site. Fully allocated total costs for each patient, excluding physician’s fees, were used, as defined at each site. These were downloaded directly from patient files. Financial representatives from each institution met via telephone conference calls to stan-dardize the types of data sent. Cost outliers (⬍$4/d and
⬎$13 000/d) and transferred patients were excluded. One site did not record these data in a similar format, and their costs were excluded from the analysis. Information on clinical findings, lab-oratory results, interventions, and outcomes was collected by trained chart abstractors from the medical records. Every patient’s record was reviewed completely from admission to discharge.
All data were entered into a database using a software program developed for this purpose by ISIS, Inc, and subsequently trans-ferred directly into a SAS database (SAS, Inc, Cary, NC) for analysis.
To ensure comparability among chart abstractors, the data col-lector at each institution first underwent a 3-day training session with instructors from ISIS. For reliability, each chart abstractor sent photocopies of 4 charts along with corresponding clinical summary reports. Photocopied charts then were abstracted inde-pendently by a member of the training team responsible for reli-ability, and the results were compared. Nine of the 10 institutions achieved a 95% or better agreement rate after the first reliability test. After a review session with the data collector from the 10th institution, a second reliability test resulted in reliability scores at or above the desired 95% agreement rate. Reliability testing was repeated when data collection was at least 50% complete. Each data collector performed well, achieving at least a 95% agreement rate.
PCSI
The PCSI is a modification of the Comprehensive Severity Index (CSI) for adults. CSI was developed by a consensus panel of 150 specialist physicians at Johns Hopkins University Medical School.9 –15 In CSI, severity criteria for each diagnosis were
se-lected by the consensus panel on the basis of objective clinical findings, including elements of history, physical examination, and laboratory findings, and rated from 1 to 4 (1⫽normal or mild severity, 2⫽moderate, 3⫽severe, 4⫽life-threatening). These then were assembled into diagnosis-specific (ICD-9-CM) severity matrices. Final severity scoring considered all of the patient’s diagnoses to obtain an overall patient severity level and was scaled both categorically (from 1 to 4) and continuously (on a scale not subject to any preset maximum). Criteria that were common to more than 1 matrix were scored only once, using the matrix that gave the highest number of points. For both discrete and contin-uous measures, higher scores indicated higher severity.
CSI was designed to be diagnosis specific and defined severity a priori rather than on statistical regression models constructed to explain the variation in a defined outcome (eg, mortality). Because scoring is (relatively) independent of treatment, it does not require “recalibration” as treatments and/or outcomes change.
The PCSI was adapted from the adult CSI using similar meth-odology. Pediatric physician specialists from the 10 participating institutions modified the 838 severity matrices from the adult CSI to produce⬎1400 age- and diagnosis-specific matrices. Pediatric-specific algorithms for weighting principal and secondary diag-noses also were developed. Final computation of the overall PCSI severity score involves the following:
• Scoring of eachICD-9-CMdiagnosis encoded
• Weighting of principal and secondary diagnoses. Secondary diagnoses that are associated directly with either the principal or with other secondary diagnoses are not considered to avoid multiple coding of a single disease process.
• Points from severity criteria shared by more than 1 diagnosis are counted only once in computation of the continuous sever-ity score.
• Menu-driven microcomputer software that incorporated the matrix structure, algorithms, and logic of the PCSI and inte-grated this into the existing CSI software system was devel-oped. The specific matrix used for “lower respiratory illness” in this study included criteria such as blood gases, white blood cell counts, radiology findings, pulmonary findings on clinical ex-amination, temperature, and respiratory rate.
Statistical Analysis
Descriptive statistics (mean, standard deviation, percent) are used to describe patient characteristics. Patient, treatment, and outcome variables for children with and without various comor-bidities are compared using2and 2-samplettests. Correlation
analyses are used to associate patient variables and severity of illness with costs, LOS, PICU admission, and intubation. Comor-bidities, significant clinical and laboratory findings, and severity of illness are combined in regression analyses to determine the concurrent effects on measures of resource utilization (costs, LOS, PICU admission, and intubation).
Correlation and regression analyses were performed with 2 different units of analysis: 1) at the patient level with the patient as the unit of analysis and 2) at the institutional level. The aggregate institutional level analysis is subject to the problem of ecological inference, but we performed it to demonstrate in a succinct way the extensive variation in practice across these 10 sites.
RESULTS Demographics
A total of 853 consecutive infants with the
ICD-9
diagnostic code for bronchiolitis (466.1) or RSV
pneu-monia (480.1) as principal diagnosis were identified.
Excluded were 244 children who were older than 12
months, 4 with bronchopulmonary dysplasia and 4
with incomplete data. Thus, 601 (70%) children met
study criteria, 82 with the principal diagnosis of RSV
pneumonia (480.1) and 519 with bronchiolitis (466.1).
Demographic, comorbidity, and admission findings
for the 601 children are shown in Table 1.
Outcomes
There was 1 death. Six children (1%) were
dis-charged to short-term home health care, 4 (0.7%)
were discharged to rehabilitative care, and 2 (0.3%)
were discharged to chronic nursing care. The
aver-age hospital LOS was 4.5
⫾
4.2 days, and average
hospital costs were $6789
⫾
9131.
Correlation of Patient Variables and PCSI Scores With LOS and Costs at the Patient Level
shown), either PCSI score (ACSIC or MCSIC) can
serve as a reasonable proxy for the patient variables
that significantly correlated with LOS or costs and
thus are used in the subsequent analyses.
Correlation of Institutional Average PCSI Scores With Institutional Resource Utilization
In contrast to their performance at the patient
level, institutional average PCSI scores correlated
poorly with institutional average resource utilization
(Table 2). Institutional average ACSIC scores
corre-lated negatively with institutional frequency of PICU
admission, frequency of intubation, and average
costs. Institutional average MCSIC scores correlated
negatively with frequency of PICU admission and
had poor correlation with frequency of intubation
and average costs relative to their correlation at the
patient level.
Correlation of Institutional Care Practices With Severity and Resource Utilization
Because differences in average patient severity for
each institution did not account for institutional
dif-ferences in resource utilization, we examined the
correlation of institutional care practices with
sever-ity. The interventions that demonstrated significant
institutional variability are summarized in Table 3.
To quantify institutional differences in the intensity
of therapy, we ranked the institutions from 1 to 10
with respect to the frequency or duration of use of
each of these interventions (with 1 the lowest, 10 the
highest, and average ranks used for ties) and added
the ranks (Table 4). The resulting rank sums are
semiquantitative estimates of the relative intensity of
therapy at each institution. The institutional rank
sums of interventions were compared with the
insti-tutional average admission severity score,
institu-tional average costs, and instituinstitu-tional average LOS.
Institutional rank sums of interventions had a
nega-tive correlation with institutional average admission
severity score but, in contrast, correlated strongly
with institutional average costs and institutional
av-erage LOS (Table 5).
DISCUSSION
In a multi-institutional Canadian study, Wang et
al
3noted significant variation in the frequency of
interventions performed for children with VLRI.
Al-though they were unable to adjust for patient
vari-ables, they posited that such variation could not be
explained solely by differences in disease severity
and suggested that “practice preferences” among
hospitals must play a role. Our data delineate similar
practice variation in American hospitals. In addition,
our data demonstrate that intensity of therapy bore
little relationship to severity of illness but was a
primary determinant of resource utilization, as Wang
et al
3had suggested previously.
Such variation is not surprising. Nearly all of the
therapies used in VLRI are supportive with unclear
indications and indeterminate efficacy. Our study
suggests that many are used indiscriminately.
Anti-TABLE 1. Demographics, Comorbidity, and Admission Clin-ical Findings in 601 Patients With VLRI
Demographics
Age (mo) (mean⫾SD) 3.3⫾2.9
Gender (M/F) 58%/42%
Race
White 53%
Black 22%
Hispanic 21%
Other/unknown 4%
Comorbidity
Prematurity 19.6%
Cardiac disease 7.8%
History of wheezing* 9.3%
History of hospitalization† 11.3%
Clinical findings
Admission physical findings
Respiratory rate (mean⫾SD) 57⫾15
Fever (⬎38°C) 35%
Wheezing 65%
Rales 24.5%
Laboratory findings
WBC (mean⫾SD) 17.6⫾6
CXR findings (no CXR in 27.2%)
Atelectasis/infiltrate 41.5%
Hyperinflation 20.3%
Pleural effusion 1%
Normal 10%
RSV status (not tested in 25%)
RSV positive 62.2%
RSV negative 12.8%
SD indicates standard deviation; WBC, white blood cells; CXR, chest radiograph.
* History of wheezing episode in previous 6 months.
† History of hospitalization for bronchiolitis in previous 12 months.
TABLE 2. Correlation Coefficients of PCSI Scores and Re-source Utilization at Patient Level Versus Institutional Level
Admission CSI (ACSIC)
Maximum CSI (MCSIC)
n
Patient level
PICU admission 0.39 0.46 601
Intubation 0.49 0.60 601
Hospital LOS 0.49 0.64 598
Hospital costs 0.48 0.66 536
Institutional level
PICU admission ⫺0.29 ⫺0.10 10
Intubation ⫺0.10 0.14 10
Hospital LOS 0.24 0.40 10
Hospital costs ⫺0.26 0.07 9
TABLE 3. Institutional Variation in Interventions Intervention Population
Average
Institutional Range*
% PICU admission 28% 19%–56%
PICU LOS (d) 4.5 1.7–7.6
% Intubation 12% 0%–26%
Steroids 27% 8%–61%
agonists 92% 82%–100%
Continuousagonists 6% 0%–15%
Antibiotics 64% 39%–77%
⬎3 antibiotics 6% 0%–19%
Furosemide 9.5% 1%–28%
Ribavirin 2.5% 0%–15%
Chest Physiotherapy 28% 4%–71%
Arterial line 5.5% 0%–19%
Central line 3.5% 0%–16%
biotics were used in 64% of patients despite the
accepted viral causation of this disease and
convinc-ing data demonstratconvinc-ing that bacterial superinfection
is rare.
16,17Similarly, despite a demonstrated lack of
efficacy of corticosteroids in VLRI,
18 –211 institution
used them in 61% of patients. The use of inhaled

agonists in 92% of our patients is more difficult to
address because the literature is equivocal.
22–34There
are no data, however, on the benefit of continuous
nebulized bronchodilators, a therapy used in 6% of
these patients. Use of other therapies, including
riba-virin, chest physiotherapy, furosemide, and invasive
monitoring, was similarly idiosyncratic. Greater
in-stitutional use of each of the above interventions was
associated with higher costs and some with longer
LOS (data not shown).
The variable use of intensive care and intensive
care interventions merits particular comment, if only
because these are so costly. Although admission
se-verity score (ACSIC) correlated with PICU
admis-sion at the patient level (
r
⫽
0.39), it was actually
negatively correlated at the institutional level (
r
⫽
⫺
0.29). The rationale for PICU admission cannot be
discerned from our data, although the fact that most
PICU patients (82%) were not intubated suggests
that factors other than need for ventilatory support
were important. The institutional variation in the
frequency of intubation (0%–26%) and the
discor-dance between frequency of intubation and severity
across institutions suggest that the threshold for
in-tubation differed at different sites. Use of
intravas-cular catheters was similarly institution dependent.
The influence of physician practice preferences on
intensive care interventions in lower respiratory
ill-ness and the impact on costs and complications have
been reported previously.
4Patients with similar severity of illness received
very different care at different institutions. The
neg-ative correlation of institutional average admission
severity with the institutional rank sum of
interven-tions (
r
⫽ ⫺
0.03) suggests that disease severity on
admission was not a strong determinant of treatment
at the institutional level. The strong correlation of
rank sum of intervention with costs and LOS,
how-ever, is evidence that greater intensity of care had a
direct effect on resource utilization. There is no
evi-dence that greater intensity of care affected
morbid-ity or mortalmorbid-ity in view of the almost uniformly good
patient outcomes.
Our study demonstrates that institutional
“prac-tice preferences” are a major determinant of care for
children who are hospitalized with VLRI. These
practice preferences seem to have little impact on
recovery but have a significant impact on resource
utilization. This suggests the need for a more rational
and cost-effective approach to the care of these
pa-tients.
CONCLUSION
The treatment of children with VLRI differs widely
across children’s medical centers. Much of this
vari-ation seems to represent institutional or individual
physician practice preferences that bear little
rela-tionship to severity. Given the self-limited nature of
VLRI, as well as the costs and risks of interventions,
a more conservative approach to treatment should be
considered.
ACKNOWLEDGMENTS
This work was funded by a grant for the Agency for Health Care Policy and Research, Contract No. 290-95-0042.
Collaborating Members of the Rating of Illness Severity in Kids (RISK) Study Group: Douglas F. Willson, MD, University of Vir-ginia Children’s Medical Center, Charlottesville, VA; Robert Pet-tignano, MD, Egleston Children’s Hospital, Atlanta, GA; Adal-berto Torres, MD, Arkansas Children’s Hospital, Little Rock, AR; Ann Thompson, MD, Children’s Hospital of Pittsburgh, Pitts-burgh, PA; J. Michael Dean, MD, Primary Children’s Medical Center, Salt Lake City, UT; Britt Nelson, MD, Cook Children’s Medical Center, Fort Worth, TX; Stephen Johnson, MD, Kaiser Permanente Hospital, Los Angeles, CA; Gilbert Goldman, MD, Loyola University Medical Center, Chicago, IL; Robert Gomez, MD, Children’s Hospital of the King’s Daughters, Norfolk, VA;
TABLE 4. Institutional Comparative Ranking of Interventions (Rank Sums)*
Intervention Institution
A B C D E F G H I J
% PICU admission 9 10 2 4 8 3 6 1 5 7
PICU LOS 4 6 7 8 3 9 5 2 10 1
% Intubated 2 10 5 8 4 6 7 1 9 3
Steroids 2 7 3 5 10 1 8 9 4 6
Agonists 8 6 4 5 9 2 7 10 3 1
Continuousagonists 2.5 6 2.5 6 9 5 7 8 10 2.5
Antibiotics 1 8 4 6 3 7 5 10 9 2
ⱖ3 Antibiotics 1.5 10 7 3 4 6 5 8 9 1.5
Furosemide 8 5 7 6 2 3 9 1 10 4
Ribavirin 3.5 9 3.5 10 8 3.5 3.5 7 3.5 3.5
Chest physiotherapy 10 6 4 3 2 1 8 7 5 9
Arterial line 2 10 2 9 8 5 4 2 7 6
Central line 6 7 4 8 1.5 1.5 5 3 10 9
Rank sum 59.5 96.5 55 81 71.5 53 79.5 69 94.5 55.5
* Institutions are ranked from 1 to 10 (with 1 the lowest, 10 the highest, and average ranks used for ties) for the frequency or duration of interventions with significant institutional variation. The cumulative total is the rank sum of interventions for that institution.
TABLE 5. Correlation of Institutional Rank Sums With Aver-age Institutional Costs, LOS, and PCSI
Rank Sum
Average costs r⫽0.83
Average LOS r⫽0.84
and David Bergman, MD, Lucille S. Salter Children’s Hospital, Palo Alto, CA.
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BAD NEWS FROM HOLLAND
In adolescence, it is likely that as many as 40% of the survivors [or very preterm
birth:
⬍
32 weeks
⬍
1500 g] will not be able to become fully independent adults.
Walther FJ, den Ouden AL, Verloove-Vanhorick SP. Looking back in time: outcome of a national cohort of very preterm infants born in The Netherlands in 1983.Early Hum Dev.2000;59:175–191