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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

; 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 (r0.48), but institu-tional average patient severity was negatively correlated with average institutional costs (r ⴝ ⴚ0.26). Maximal severity score correlated well with costs (r0.66) and length of stay (LOS; r0.64) at the patient level but poorly at the institutional level (r0.07 costs;r0.40 LOS). The institutional intensity of therapy was nega-tively correlated with admission severity (rⴝ ⴚ0.03) but strongly correlated with costs (r0.84) and LOS (r0.83).

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.

1,2

Variation in the treatment of these children

has been well described and reflects a lack of

con-sensus regarding optimal therapy.

3–5

Ascertainment

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 – 8

we 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

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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 using␹2and 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

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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

3

noted 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

3

had 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%

Continuous␤agonists 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%

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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.

16,17

Similarly, despite a demonstrated lack of

efficacy of corticosteroids in VLRI,

18 –21

1 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.

22–34

There

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.

4

Patients 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

Continuous␤agonists 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

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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

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DOI: 10.1542/peds.108.4.851

2001;108;851

Pediatrics

Gassaway

Douglas F. Willson, Susan D. Horn, J. Owen Hendley, Randall Smout and Julie

Viral Lower Respiratory Illness

Effect of Practice Variation on Resource Utilization in Infants Hospitalized for

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DOI: 10.1542/peds.108.4.851

2001;108;851

Pediatrics

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Douglas F. Willson, Susan D. Horn, J. Owen Hendley, Randall Smout and Julie

Viral Lower Respiratory Illness

Effect of Practice Variation on Resource Utilization in Infants Hospitalized for

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Figure

TABLE 2.Correlation Coefficients of PCSI Scores and Re-source Utilization at Patient Level Versus Institutional Level
TABLE 4.Institutional Comparative Ranking of Interventions (Rank Sums)*

References

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