 |
|
Use of Simple Heuristics to Target Macrolide Prescription in Children With Community-Acquired Pneumonia
Joachim E. Fischer, MD, MSc;
Felicitas Steiner, MD;
Franziska Zucol, MD;
Christoph Berger, MD;
Laura Martignon, PhD;
Walter Bossart, PhD;
Martin Altwegg, PhD;
David Nadal, MD
Arch Pediatr Adolesc Med. 2002;156:1005-1008.
ABSTRACT
| |
Background Macrolides are the first-line antibiotic treatment of community-acquired
pneumonia (CAP). Owing to alarming resistance rates among invasive Streptococcus pneumoniae isolates, particularly in young children,
macrolide use should be restricted to patients infected with susceptible pathogens,
eg, Mycoplasma pneumoniae.
Objective To develop a simple clinical prediction rule for identifying M pneumoniae as the cause of CAP in children.
Design and Setting Prospective cohort study in 253 children with radiologically confirmed
CAP in a walk-in clinic of a tertiary care hospital.
Main Outcome Measures Mycoplasma infection, proven by results of antibody testing of paired
serum samples (gold standard). We compared the area under the receiver operating
characteristic curve (c statistic) of the following 2 prediction models: a
scoring system derived from logistic regression analysis and a fast-and-frugal
decision tree.
Results Mycoplasma pneumoniae infection was confirmed
in 32 (13%) of 253 children. A scoring system based on duration of fever and
patient age yielded a c statistic of 0.84 (95% confidence interval [CI], 0.77-0.91),
compared with that of the decision tree (c = 0.76 [95% CI, 0.70-0.83]). The
scoring system identified 75% of all cases as being at high or very high risk
for M pneumoniae infection; the decision tree, 72%
at high risk. The scoring system would curtail macrolide prescriptions by
75%; the decision tree, by 68%.
Conclusions In children with CAP, simple clinical decision rules identify patients
at risk for M pneumoniae infection. At present US
macrolide resistance rates among invasive S pneumoniae
isolates, both rules increase the chance of prescribing effective first-line
antibiotics compared with general macrolide administration.
INTRODUCTION
THE MOST COMMON bacterial cause of community-acquired pneumonia (CAP)
is Streptococcus pneumoniae.1-2
Infections with Mycoplasma pneumoniae are rarer.3-5 In Canada and the United
States, macrolides are recommended as first-line antibiotics for adults with
CAP.6 From 1993 to 1999, macrolide prescription
rates increased 3-fold, particularly in children.7
Unfortunately, macrolides no longer cover all bacterial causes of CAP. Resistance
rates of the most common pathogen isolated from patients with CAP, S pneumoniae, are dramatically rising.7
A recent study in Pittsburgh, Pa, schoolchildren demonstrated macrolide resistance
in 48% of all group A streptococci isolates from throat cultures.8 In consideration of these alarming resistance patterns,
the Active Bacterial Core Surveillance/Emergence Infections Program Network
has urged physicians to reduce inappropriate prescription practices, particularly
in young children.7
After confirming the diagnosis of CAP in a child, the physician must
decide on antibiotic prescription and further diagnostic efforts. Although
macrolides remain the antibiotic of choice in patients with M pneumoniae,9 ß-lactams offer
an alternative for other frequent bacterial infections. Rapid detection of M pneumoniae, which is now possible by means of polymerase
chain reaction analysis,10 precludes inadequate
prescriptions. However, application of this test to all children is costly.
Moreover, while awaiting the polymerase chain reaction results, most physicians
prescribe a first-line antibiotic. Risk stratification by simple clinical
decision rules, which assist targeted use of costly tests and increase the
chance of prescribing the most appropriate antibiotics in the first place,
is highly desirable.
Time is crucial in the domain of clinical decision rules.11
In general, most clinical decision rules do not provide perfect information
but assist in gauging the likelihood of a disease11
or in the triage of patients.11-12
The rules incorporate key variables from patient histories, results of the
physical examination, and initial laboratory evaluation.11
Potentially useful rules achieve risk gradients of greater than 10 between
the groups at lowest and highest risk or an area under the receiver operating
characteristic curve (c statistic) of greater than 0.75.11-12
Based on logistic regression analysis, decision rules13
providing algorithms for the computation of scores and disease risk12 or, alternatively, fast-and-frugal heuristic decision
trees may have derived. The latter are easier to memorize than many rules.
These trees model everyday human decision making and are based on elementary,
sequential inference rules.14-15
Their underlying principle is elimination of alternative diagnoses in a few
simple and successive steps. Trees start with the most diagnostic criterion
at the top.16 Well-performing trees should
be as effective clinically as full logistic regression models. Theoretically,
the inherent advantage of fast-and-frugal trees is a robustness in settings
other than the derivation data set.16 They
also eliminate the need for computations or consultation of scoring tables.17
Decision rules for predicting the underlying etiology in pediatric CAP
are lacking. Therefore, we developed 2 prediction rules from readily available
clinical criteria for rapid risk stratification of M pneumoniae as a cause in children with CAP. The first rule was a score derived
from logistic regression analysis; the second, a fast-and-frugal decision
tree.
METHODS
We conducted a 24-month cohort study at the emergency department and
walk-in clinic of a tertiary care university hospital. Children and adolescents
(aged 1 month to 16 years) with CAP confirmed (by 2 reviewers) by findings
on chest x-rays were eligible. The initial laboratory workup included a differential
white blood cell count and measurement of C-reactive protein level. To establish
the cause of CAP, blood cultures and nasopharyngeal secretions for detection
of respiratory viruses, Chlamydia pneumoniae, and M pneumoniae were obtained. Antigens from influenza A and
B viruses; parainfluenza viruses 1, 2, and 3; respiratory syncytial virus;
and adenoviruses were identified by means of an enzyme-linked immunosorbent
assay. Antibody testing in paired serum samples (at baseline and 4-week follow-up)
served as the diagnostic gold standard for acute M pneumoniae infection. Patients without follow-up samples were excluded. Further
details have been reported elsewhere.10 The
study was approved by the institutional ethics committee.
The starting point for the model development was a data set containing
all variables from history, clinical examination, and initial laboratory and
radiological workups that were available when the diagnosis of CAP was established.
For development of the logistic regression model, we subjected all variables
to a univariate screen for an association with the outcome (confirmed infection
with M pneumoniae). We tested 2-variable combinations
to rule out negative confounding. Using a stepwise inclusion procedure, we
identified a simple model with only few variables. We transformed the regression
coefficients to derive a scoring system by means of a 2-step procedure. First,
we multiplied and rounded the regression coefficients to achieve full numbers.
Next, we tested further simplifications to arrive at a scoring system with
a sum of 10 points. Of the models that did not significantly differ according
to the Akaike information criterion, we selected the model with the best fit
(checked by means of the Hosmer-Lemeshow test13)
and a simple scoring table.
For development of the fast-and-frugal decision tree, first, we identified
the variable with the highest sensitivity for M pneumoniae. In this case, a negative result assists in ruling out M pneumoniae infection. In fast-and-frugal trees, the most discriminative
variable is placed at the top of the tree.15
The simplicity of the trees arises from the specific choice of variables and
cutoffs; these allow physicians to rule out (or to rule in) a particular disease
at each decision knot for a large proportion of patients by deliberately accepting
a small false-negative (or false-positive) rate.
We used 2 criteria to compare the performance of the decision rules.
First, we compared the c statistics (mathematically equivalent to the area
under the receiver operating characteristic curve) of the logistic regression
model, the score, and the fast-and-frugal tree. We used the following 2 methods
to compare the performance of the models: (1) the Akaike information criterion,13 and (2) the algorithm suggested by Hanley and McNeil.18 Second, we determined the rate at which each rule
would allow reduction of macrolide prescriptions compared with a strategy
of giving macrolides to all patients with CAP. This comparison was performed
for the same false-negative rate in both models. In the absence of a validation
data set, we derived confidence interval (CI) estimates by the observed distribution
of c statistics from 1000 bootstrap cycles. We used SAS software (Version
8.1; SAS Institute Inc, Cary, NC) for the analyses.
RESULTS
Informed consent was obtained for 323 of 472 children with confirmed
CAP. Paired serum samples were available in 253 patients. Seventy children
failed to attend the follow-up visit (data not shown). However, their baseline
characteristics did not differ from those of the patients included. Acute
infection with M pneumoniae was serologically proved
in 32 children (13%). The remaining 221 cases were classified as due to C pneumoniae (n = 1), positive bacterial blood cultures
(n = 13), positive respiratory viral antigen test (n = 40), and mixed or unknown
causes (n = 167).
Compared with other children with CAP, patients with M pneumoniae were older, had a longer duration of fever, lower leukocyte
levels, and lower absolute neutrophil counts. No other variable retained a
significant univariate association (Table
1).
|
|
|
|
Table 1. Univariate Associations Between Clinical Variables and Serologically
Proven Mycoplasma pneumoniae Infection in Children
With CAP*
|
|
|
Multivariable logistic regression analysis identified a model consisting
of age and the logarithm of duration of fever (c = 0.84; Hosmer-Lemeshow test, P = .29). Table 2
and Table 3 present the scoring
system derived from the regression coefficients. The score yielded c statistics
of 0.84 (observed 95% CI, 0.77-0.91). The system stratified children into
groups at low (absolute risk [AR], 2%), moderate (AR, 7%), high (AR, 28%),
and very high (AR, 65%) risk for M pneumoniae. Using
the strata instead of the full score resulted in a c statistic of 0.84 (95%
CI, 0.77-0.90). Based on the Akaike information criterion, the full model
performed significantly better than the strata model ( 2 test, P<.05).
|
|
|
|
Table 2. Prediction Rule for Risk for Mycoplasma
pneumoniae Infection in Children With CAP*
|
|
|
|
|
|
|
Table 3. Interpretation of Risk for Mycoplasma pneumoniae Infection in Confirmed CAP*
|
|
|
Figure 1 shows the fast-and-frugal
decision tree. The first question on the history of fever eliminated 95 (38%)
of 253 patients, at the cost of missing 3 patients with M pneumoniae infection (sensitivity, 90%; 95% CI, 75%-98%). The second
question (age  3 years) eliminated an additional 85 patients (34%) at the
cost of missing 6 patients with M pneumoniae infection
(sensitivity, 79%; 95% CI, 60%-92%). In the remaining 73 patients (29%), the
AR for M pneumoniae infection was 32%. The c statistic
of the tree was 0.76 (95% CI, 0.70-0.83). According to the Akaike information
criterion, the score model discriminated significantly better than the fast-and-frugal
tree (P = .008). However, using the more conservative
algorithm suggested by Hanley and McNeil18
and considering the correlation between the 2 systems (r = 0.63), the difference of the areas under the curve was not significant
(2-sided P>.20).
|
|
|
|
A fast-and-frugal decision tree for ruling out Mycoplasma pneumoniae infection in children with community-acquired
pneumonia. Clinical features are considered sequentially, with a possible
stop decision after each question. The numbers indicate the patients ruled
out or remaining in the decision tree. AR indicates absolute risk; CI, confidence
interval.
|
|
|
To assess the agreement between the scoring system and the fast-and-frugal
tree, we collapsed the high- and very-high-risk categories from the scoring
system to a single category. The weighted  , which indicates the agreement
beyond chance of the 2 classification systems, was 0.68. Most of the disagreement
between the systems occurred at the discrimination between low and moderate
risk (Table 4).
|
|
|
|
Table 4. Agreement Between Risk Strata Obtained From 2 Classification
Systems*
|
|
|
COMMENT
Our objective was to compare a clinical decision rule derived from logistic
regression analysis and a fast-and-frugal decision tree for risk stratification
of M pneumoniae infection in children with CAP. Simple
data gathered while establishing the diagnosis of CAP (duration of fever and
patient age) identified patients at low, medium, high, and very high risk.
The summary score yielded a gradient of greater than 30 between the low- and
the very-high-risk groups. Compared with the score, which requires looking
up data in a table, the fast-and-frugal decision tree can easily be memorized.
Asking the simple question regarding duration of fever and the age of the
child allows identification of the following group at high risk (32%) for
CAP due to M pneumoniae: children with CAP who have
had fever for more than 2 days and who are older than 3 years. The score model
places 75% of all patients with M pneumoniae infection
into the high- or very-high-risk group. The fast-and-frugal tree achieves
a marginally lower correct classification rate (sensitivity, 72%; Table 2 and Table 3 and Figure 1).
The agreement between both systems was moderate ( = 0.68), with most
of the disagreement occurring at the distinction between low and moderate
risk. This indicates that the 2 systems represent 2 distinct approaches that
best agree at identifying patients at high risk for M pneumoniae infection.
When the external validity of the rules is established in an independent
patient population, would either of these rules have any clinical usefulness?
They do not provide perfect information, which is counterbalanced by the benign
natural course of CAP due to M pneumoniae. In otherwise
healthy patients, M pneumoniae infection rarely proceeds
to a life-threatening disease. More often, the disease resolves spontaneously.
The probability of a dramatic progression of the disease during the next 48
hours in patients infected with M pneumoniae should
be considered when applying the rule, because up to 1 in 4 patients will be
missed if initial prescriptions are based on the score alone. This false-negative
rate would be unacceptable for patients at high risk for adverse outcomes.
Physicians in the United States who prescribe macrolides to all children with
CAP who are younger than 5 years face a resistance or decreased susceptibility
rate of 30%7 or, according to a recent survey,
even 48%.8 Applying the decision rules would
mean that 25% of all patients with CAP due to M pneumoniae will not receive macrolides. Because CAP due to S pneumoniae is more common than CAP due to M pneumoniae,3 physicians applying either of the
decision rules have a better chance of prescribing effective antibiotics in
the first place. Basing decisions about first-line antibiotic treatment on
the rules would reduce macrolide prescription rates by 75% when using the
scoring system and by 68% when using the fast-and-frugal tree rule. These
reductions may be rewarded by declining resistance rates of S pneumoniae.19 An independent issue
is the appropriateness of treating infections due to M pneumoniae with macrolides. Future studies will have to demonstrate that prescribing
macrolides to children with CAP due to M pneumoniae
is associated with improved outcomes.
An alternative strategy for curtailing inappropriate macrolide prescriptions
is to establish the underlying cause, in particular M pneumoniae, in every case. At present, applying a rapid molecular diagnostic
test for M pneumoniae in all children with CAP would
incur diagnostic costs (in US dollars) of $70 per patient undergoing testing.
An analysis of the costs per additionally identified case of M pneumoniae (marginal cost comparison) shows that the decision rules
may also aid cost-conscious decision making about further tests. Application
of the scoring system results in estimated costs per identified case in the
very-high-risk group of $108, whereas extension of testing to the high-risk
group results in marginal costs of $248 per detected case. Addition of the
medium-risk group incurs marginal costs of $1061, and inclusion of the low-risk
group escalates the costs to $3465 per additionally identified case. The corresponding
figures using the fast-and-frugal tree are $190 for each case detected when
children older than 3 years who have had fever for more than 2 days undergo
testing. The marginal costs rise to $883 if only duration of fever is used,
and to $2216 if testing is extended to all. Considering the clinical consequences
of missing M pneumoniae infection, molecular testing
in the low-risk groups is probably not cost-effective.
We acknowledge the possibility of recall bias regarding the duration
of fever. However, it is unlikely that recall bias would have differed across
the causes. Because of the small number of true-positive cases, we were unable
to divide the data into a derivation and a validation data set, and we were
forced to test the robustness of the models by bootstrap simulation. However,
the CIs remain large in the very-high-risk group of the scoring system. Therefore,
the presented scoring system and the decision tree should be validated in
an independent patient sample. They may not be applicable during an epidemic
of M pneumoniae.
CONCLUSIONS
We demonstrated that a scoring system and a fast-and-frugal decision
tree provide a rapid probability estimate of the cause of childhood CAP. These
simple rules may aid physicians in cost-conscious and efficient ordering of
costly diagnostic tests and increase the chance of prescribing appropriate
first-line antibiotics. The fast-and-frugal decision tree suggests that first-line
macrolide therapy may be restricted to children with CAP who have had fever
for more than 2 days and who are older than 3 years.
| What This Study Adds
Liberal prescription of macrolides in children with CAP is associated
with increasing prevalence of resistant pathogens. Reserving macrolide prescription
to patients infected with Mycoplasma pneumoniae is
an option to curb excessive use. Simple decision rules are desirable to assist
identifying patients at high risk for M pneumoniae
infection.
The study presents 2 simple prediction rules that allow stratification
of children with CAP into groups at low, medium, and high risk for M pneumoniae as a causative agent.
|
|
AUTHOR INFORMATION
Accepted for publication May 20, 2002.
This study was supported in part by Pfizer AG, Zurich, Switzerland.
Corresponding author and reprints: David Nadal, MD, Division of Infectious
Diseases, University Children's Hospital of Zurich, Steinwiesstrasse 75, CH-8032
Zurich, Switzerland (e-mail: david.nadal{at}kispi.unizh.ch).
From the Horten-Zentrum (Dr Fischer), the Institute of Medical Virology
(Dr Bossart), and the Department of Medical Microbiology (Dr Altwegg), University
of Zurich, and the Division of Infectious Diseases, University Children's
Hospital of Zurich (Drs Steiner, Zucol, Berger, and Nadal), Zurich, Switzerland;
and the Max Planck Institute for Human Development, Berlin, Germany (Dr Martignon).
REFERENCES
| |
1. Marston BJ, Plouffe JF, File TM Jr, et al for the Community-Based Pneumonia Incidence Study Group. Incidence of community-acquired pneumonia requiring hospitalization. Arch Intern Med. 1997;157:1709-1718.
ABSTRACT
2. Korppi M, Heiskanen-Kosma T, Jalonen E, et al. Aetiology of community-acquired pneumonia in children treated in hospital. Eur J Pediatr. 1993;152:24-30.
FULL TEXT
|
ISI
| PUBMED
3. Wubbel L, Muniz L, Ahmed A, et al. Etiology and treatment of community-acquired pneumonia in ambulatory
children. Pediatr Infect Dis J. 1999;18:98-104.
FULL TEXT
|
ISI
| PUBMED
4. Hammerschlag MR. Atypical pneumonias in children. Adv Pediatr Infect Dis. 1995;10:1-39.
PUBMED
5. Heiskanen-Kosma T, Korppi M, Jokinen C, et al. Etiology of childhood pneumonia. Pediatr Infect Dis J. 1998;17:986-991.
FULL TEXT
|
ISI
| PUBMED
6. Bartlett JG, Dowell SSF, Mandell LA, File TM Jr, Musher DM, Fine MJ. Practice guidelines for the management of community-acquired pneumonia
in adults. Clin Infect Dis. 2000;31:347-382
FULL TEXT
| PUBMED
7. Hyde TB, Gay K, Stephens DS, et al. Macrolide resistance among invasive Streptococcus
pneumoniae isolates. JAMA. 2001;286:1857-1862.
FREE FULL TEXT
8. Martin JM, Green M, Barbadora KA, Wald ER. Erythromycin-resistant group A streptococci in schoolchildren in Pittsburgh. N Engl J Med. 2002;346:1200-1206.
FREE FULL TEXT
9. Jadavji T, Law B, Lebel MH, Kennedy WA, Gold R, Wang EE. A practical guide for the diagnosis and treatment of pediatric pneumonia. CMAJ. 1997;156(suppl):S703-S711.
10. Nadal D, Bossart W, Zucol F, et al. Community-acquired pneumonia in children due to Mycoplasma pneumoniae. Diagn Microbiol Infect Dis. 2001;39:15-19.
FULL TEXT
|
ISI
| PUBMED
11. Reynolds T. Disease prediction models aim to guide medical decision making. Ann Intern Med. 2001;135:637-640.
12. Morrow DA, Antman EM, Giugliano RP, et al. A simple risk index for rapid initial triage of patients with ST-elevation
myocardial infarction: an InTIME II substudy. Lancet. 2001;358:1571-1575.
FULL TEXT
|
ISI
| PUBMED
13. Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons Inc; 1989.
14. Todd PM, Gigerenzer G. Simple heuristics that make us smart. Behav Brain Sci. 2000;23:727-741.
FULL TEXT
|
ISI
| PUBMED
15. Gigerenzer G. The psychology of good judgment: frequency formats and simple algorithms. Med Decis Making. 1996;16:273-280.
FREE FULL TEXT
16. Gigerenzer G, Goldstein DG. Reasoning the fast and frugal way: models of bounded rationality. Psychol Rev. 1996;103:650-669.
FULL TEXT
|
ISI
| PUBMED
17. Gigerenzer G, Martignon L, Czerlinski J. How good are fast and frugal heuristics. In: Shanteau J, Mellers B, Schum D, eds. Decision
Science and Technology: Reflections on the Contributions of Ward Edwards. Norwell, Mass: Kluwer Academic Publishers; 1999:81-103.
18. Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic
curves derived from the same cases. Radiology. 1983;148:839-843.
FREE FULL TEXT
19. Seppala H, Klaukka T, Vuopio-Varkila J, et al and the Finnish Study Group for Antimicrobial Resistance. The effect of changes in the consumption of macrolide antibiotics on
erythromycin resistance in group A streptococci in Finland. N Engl J Med. 1997;337:441-446.
FREE FULL TEXT
|
