Management of infants at risk for occult bacteremia: A decision analysis

ORIGINAL ARTICLES

M a n a g e m e n t of infants at risk for occult bacteremia: A decision analysis S t e p h e n M. Downs, MD, MS, Robert A. M c N u t t , MD, a n d Peter A. Margolis, MD From the Departments of Pediatrics and Internal Medicine, University of North Carolina at Chapel Hill

Because febrile infants with no obvious source of bacterial infection may have bacteremia, and because bacteremia is difficult to diagnose on clinical grounds, we used decision analysis to evaluate whether such infants should be treated with antibiotics, tested further, or sent home. Using a simple decision tree, we found that the decision to give empiric antibiotic treatment is the decision of choice. The difference in quality-adjusted life e x p e c t a n c y between the "best" and "worst" decisions was only 11 days. However, this difference translated to prevention of death or permanent disability in 60 cases per 100,000 febrile children. Further, empiric treatment remained the best m a n a g e m e n t alternative unless the probability of bacteremia was less than 1.4% (less than any published prevalence), or the efficacy of treatment was less than 21%. Our analysis demonstrated that a test with far greater sensitivity than leukocyte count or other tests currently in use is n e e d e d to justify testing rather than treating empirically. Further, an enormous patient population would be n e e d e d to find a difference of both clinical and statistical significance between treated and untreated patients in a controlled trial. In the absence of such trials, we recommend blood culture and empiric antibiotic treatment of all infants at rlsk for occult bacteremia. (J PEDIATR1991;118:11-20) Fever in young children is a common and vexing problem in pediatric practice. Most febrile infants have self-limited viral infections, but 3% to 12% of infants with no obvious source of infection have bacteremia. Although most patients with "occult" bacteremia improve spontaneously, some have complications such as meningitis, pneumonia, septic arthritis, osteomyelitis, and septic shock. Occult bacteremia may be difficult to identify on clinical grounds. Clinical examination1, 2 and laboratory tests 3 fail to identify 23% to 50% of cases. Furthermore, attempts to show that empiric antibiotic therapy prevents complications in patients with occult bacteremia have been inconclu-

sive. 4"6 Thus the practitioner, without knowing who has bacteremia or what value antibiotics offer, must decide whether to treat such patients empirically, to test them, or to send them home pending subsequent reevaluation. We used decision analysis, a quantitative method for comparing alternative approaches to patient management,

Supported in part by a grant from the Robert Wood Johnson Foundation, Princeton, N.J. The opinions, conclusions, and proposals expressed are those of the authors and do not necessarily represent the views of the Robert Wood Johnson Foundation. Submitted for publication June 1, 1990; accepted July 24, 1990. Reprint requests: Stephen M. Downs, MD, MS, Clinical Scholars Program, Universityof North Carolina, CB 7105, Chapel Hill, NC 27599-7105. 9/20/24088

to evaluate which alternative is best. Decision analysis can be useful not only because it offers a preferred alternative but because it identifies the most important variables for informed decision making. 7

See related articles, pp. 21 and 67.

QALE QOL ROC

Quality-adjusted life expectancy Quality of life Receiver-operator-characteristic [curve]

METHODS Clinical problem. A 2- to 24-month-old child has a rectal temperature _>39 o C and no identifiable source of bacterial

11

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Downs, McNutt, and Margolis

The Journal of l"eaiatrics January 1991

TreatmentDeathD Treat

+

9

NoBacteremiaD

Treatmen~

/ Bacteremia@ Me ~

I ,oool_ ]oo'tTreat

SendHome

9

1__<>

'acl,eioliiiii =i,i,i -

EEl

rv,ve+ ,S

PermanentNeurologiSequel c aeD

|

9 []

RecoverfromMeninclitisD Hospitalizfor e IVAntibioticsD

Fig.

t. Occult bacteremia decision model.Square nodes represent decisions;circular nodes represent chance occurences. Rectangular (terminal) nodes at end of tree represent outcomes of interest. All nonterminal nodes at left enter structurally identical subtrees, as depicted to right of bracket infection. Considering the risk of occult bacteremia, what is the best course of action? 1. Blood culture and empiric treatment with antibiotics may prevent subsequent complications in patients with bacteremia but would provide no benefit to those without bacteremia. It involves the pain and inconvenience of venipuncture and the administration of antibiotics, and there is a small risk of an adverse reaction to antibiotics. 2. A laboratory test may be used to identify patients with the highest risk of bacteremia. Patients with a positive test result would then receive antibiotics; those with a negative test result would not. This approach involves the pain of venipuncture, but reduces the number of patients without bacteremia who will be treated and increases the number with bacteremia who will be treated. 3. Watchful waiting may be the chosen approach. If no antibiotics are given, the patient would not suffer the discomfort and risks ofvenipuncture an d antibiotic therapy, but the patient with bacteremia may face an increased risk of complications that can lead to death or permanent disability. Assumptions. To frame this clinical problem for decision analysis, we made the following assumptions: l. The patient has no signs of meningitis. 2. Patients treated by any strategy will have blood specimens drawn for culture. 3. The result of the leukocyte count raises or lowers the probability of bacteremia, not the probability of subsequent complications if bacteremia is present. 4. Antibiotic therapy affects only the probability of re-

solving bacteremia, not the probability that complications may develop if bacteremia does not resolve. 8 5. Meningitis is the only complication of occult bacteremia that can lead to death or permanent sequelae. (Other complications, potentially preventable with antibiotics, will not occur in the model. This effect is greater in untreated than treated patients and therefore tends to make the model favor no treatment.) 6. All children in whom bacteremia does not resolve and in whom meningitis does not develop will remain febrile and will ultimately return for further evaluation and be hospitalized. Decision tree. The decision tree for this analysis is shown in Fig. 1. The branches represent the three management alternatives and subsequent chance events. Each chance event is assigned a probability. Each outcome is assigned a value (utility) that quantitates the decision maker's preference for the outcome. An expected utility is assigned to each nonterminal node by multiplying the value of each terminal node by the probability assigned to its branch and then summing over the branches. The decision with the highest expected utility is the decision of choice. 7 The first decision is among the three alternative approaches to management: (l) "treat" (blood culture and empiric antibiotic treatment), (2) "test" (leukocyte count, with blood culture and treatment of those whose leukocyte count is >~15,000/#1), or (3) "send home" (watchful waiting with follow-up in 24 hours). In the "treat" branch, the patient would be exposed to a minute risk of death related to treatment. After the "test" branch, the test result may be

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Management o f infants at risk f o r occult bacteremia

T a b l e I. Baseline probability and efficacy estimates in the model and range of reported values Variable

Baseline

Reported values"

Probability of death from

10-5

Not reported

0.65 0.75 0.05 0.70

0.30-0.79 0.55-0.77 0.03-0.12 0-0.90

0.80 0.20

0.16-1.00 0-0.40

0.10

0-0.18

0.10

0.05-0.38

treatment Sensitivity of test Specificity of test Probability of bacteremia Probability of spontaneous resolution Efficacy of treatment Probability of meningitis if persistent bacteremia Probability of death from meningitis Probability of complications of meningitis *See text.

positive (~15,000/#1) or negative (<15,000//A); if it is positive, the patient would be treated and face a small risk of treatment-related death. The patient with a negative test result and the patient sent home would be reevaluated at 24 hours. All these (nonterminal) branches enter a subtree of further events. The first chance node of the subtree depicts the probability that the patient has bacteremia, if the patient has bacteremia, it may or may not resolve. If the bacteremia does not resolve, meningitis may occur and the patient may die, permanent neurologic sequelae may develop, or the patient may recover. If meningitis does not occur, it is assumed that the patient will return for care because of the unresolved bacteremia and will be hospitalized. Probability. We used data from published studies and expert opinion to estimate the probabilities used in the model. These estimates are summarized in Table I and discussed briefly below. Death from antibiotic treatment has never been described in the setting of occult bacteremia, but on the basis of experience in other settings,9, 10 we chose a probability of 1/100,000. Lesser antibiotic reactions, such as rash and diarrhea, are short-term events and were represented as discounts to the utility model. Several investigators have reported the sensitivity and specificity of a leukocyte count >~15,000/#1 in identifying children with bacteremia or other serious bacterial infections.3,5,11-16 The reported sensitivities range from 30% to 79%, and the speeificities from 55% to 77%. We used values calculated from a study in which the leukocyte count and blood cultures were obtained for the entire sample of

13

T a b l e II. Adverse outcomes expected per 100,000 patients for each management strategy Outcome

Strategy

No. of patients treated

Hospitalizations

Death or disability

Treat Test Send home

100,000 27,000 0

300 720 1500

15 36 76

955 children at risk5; sensitivity was 65% and specificity was 75%. Many studies have reported the prevalence of bacteremia.5,11-13, 15,17-19 Although it varied with body temperature and age, the prevalence was consistently between 2.8% and 12%. For a 2- to 24-month-old patient with a temperature _>39~ C, we chose a baseline probability of bacteremia of 5%. Several studies have examined the rate at which occult bacteremia resolves spontaneously*; the extremes have been 0%4 and 90%. 21 The majority of studies reported spontaneous resolution of baeteremia at 68% to 71.4%. We therefore used 70% as a baseline. Several of the reports noted above also described the rate of resolution of occult bacteremia in children given oral doses of an antibiotic at the time that blood was drawn for culture, making it possible to estimate the efficacy of antibiotic therapy. Although in most reports the treatment was not allocated randomly, their figures indicate 69% to 100% efficacy.4-6, 11, 12:17The only two randomized studies found 16%5 and 100%4 efficacy, but these percentages were based on a very small number of outcomes. Using expert opinion, we chose a baseline of 80%. Lesser benefits of antibiotic therapy, such as earlier defervescence,5 were ignored, thus biasing the model against empiric treatment. Of the serious complications that occur in children with bacteremia, meningitis is the most common and best described. The probability of meningitis in patients in whom occult bacteremia does not resolve varies from 0%s, 21 to 40%.11, 20 We chose a baseline of 20% (i.e., 20% of the 30% whose bacteremia does not resolve, or 6% of all patients with occult baeteremia) based on a study in which all patients at risk had blood cultures and the patients resembled those in our model. 19 The reported mortality rate for meningitis of all bacterial causes ranges from 5.5% to 13%.8,22-24 Pretreatment with antibiotics does not appear to affect this outcome, s Our baseline of 10% was based on the largest studies available that considered all causes. 8, 22 The probability of permanent neurologic sequelae in patients with bacterial meningitis depends on the definition of sequelae. *References 4-6, 11, 12, 15, I7, 19-21.

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Fig. 2. Sensitivity analysis. Effect of varying probability (prevalence) of occult bacteremia on expected utility of each decision alternatives, in quality-adjusted life-years (QALY). Shaded region shows reported range of values for probability of bacteremia. Arrow marks baseline value used in model. Dotted lines mark (threshold) values at which decisionchanges.

We modeled only the most severe outcomes (e.g., IQ >_50, institutional care, quadriplegia) 25"28 to bias the model against empiric treatment. The probability of sequelae in these studies was between 5% and 38%. Our baseline of 10% was based on studies that included all bacterial causes.8, 23 Utility. Each outcome was assigned a value with the use of the quality-adjusted life expectancy utility model. This model best represents the outcomes of greatest impact: death and permanent disability. It consists of a baseline life expectancy that was adjusted for the quality of life. We used the declining exponential approximation of life expectancy,29 which estimates life expectancy by the-reciprocal of the age-, sex-, and race-adjusted mortality rate 0Zasr) plus any additional mortality rate associated with a particular state of health. In our model the additional deaths were due to severe neurologic damage (#cornplie).

The QOL decrement for permanent neurologic sequelae (which lasts a lifetime) was multiplied by the life expectancy. Undesirable events that reduce the QOL for only a brief period (i.e., hospitalization, antibiotic therapy, venipuncture) were subtracted from the life expectancy. Thus the QALE model has the following form: 1 QALE = QOL x (/Zasr ..[_#complic)

Ho - An - Ve

where Ho refers to hospitalization, An to antibiotic therapy, and Ve to venipuncture. Variables were included as appropriate for the outcome. We used a baseline age-, sex-, and race-adjusted mortality rate of 0.014/yr, 3~ corresponding to a life expectancy of approximately 70 years. The additional mortality rate associated with severe neurologic complications was 0.006/ yr. 31 The QOL adjustment for patients with severe neuro-

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Management o f injants at risk for occult bacteremia

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Fig. 3. Sensitivity analysis. Effect of varying efficacy of antibiotic therapy in clearing occult bacteremia on expeted utility of each decision alternative, in quality-adjusted life-years (QALY). Shaded region shows reported range of values for efficacy of antibiotic therapy. Arrow marks baseline value used in model.

logic damage was 80%. We subtracted 10 days of life for hospitalization and 1 day each for presumptive antibiotic therapy and venipuncture. Sensitivity analysis. We performed sensitivity analyses to see how changing each of our probability and utility estimates affected the decision. A one-way sensitivity anaP ysis varies the probability of an event over the entire range of estimates, from no probability to 100%. For each estimate the expected utility of each decision was recomputed. Thus the effect of changing probabilities on the decision of choice could easily be seen. We examined the effect of changing two variables simultaneously by using two-way sensitivity analysis. For every possible combination of values for the two variables, the best decision was calculated. A graph of the results defines regions in which each decision is best. To evaluate tests for occult bacteremia, we derived receiver-operator-characteristic curves 32 from published reports of test performance. The ROC curves compare the true positive rate (sensitivity) and false positive rate (1 - specificity) of a test for different definitions of test positivity; they are useful in choosing what test result should be considered the abnormal cutoff point. We superimposed

R O C curves on two-way sensitivity analysis plots to identify test results that would be of use clinically. RESULTS With the use of our baseline probabilities and utilities, the results were as follows: "Treat" = 69.99 Quality-adjusted life-years "Test" = 69.98 Quality-adjusted life-years "Send home" = 69.96 Quality-adjusted life-years Empiric antibiotic treatment of all febrile infants had the highest expected utility and was therefore the best management alternative. However, there was a difference of only 4 days of life between treating all patients and testing them, and only 11 days of life separated treating patients and sending them home, From the perspective of QALE, this decision was very close. We then examined these results from another perspective. We counted adverse events and compared the three strategies with regard to the number of adverse events prevented (Table II). Although the "treat" strategy involved treating nearly four times as many patients as the "test" strategy, it prevented 420 hospitalizations over the "test"

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The Journal of Pediatrics January. 199l

Fig. 4. Two-way sensitivity analysis on test sensitivity and specificitycompared with ROC curves for leukocyte count. Regions indicate test characteristics for which empiric treatment (clear area) or testing (shaded area) is best. ROC curves were derived from three major studies of occult bacteremia. Baselinevalues used in model are shown (X). (Data on ROC curves from McGowan JE Jr, Bratton L, Klein Jo, Finland M [N Engl J Med 1973;288:1309-12];Teele DW, Pelton SI, Grant MJA, et al [J PEDIATR1975;87:227-30];and McCarthy PL, Grundy GW, Spiesel SZ, Dolen TF Jr [Pediatrics 1976;57:861-9].)

strategy and 1200 over the "send home" strategy (per 100,000 cases). Further, empiric treatment prevented death or permanent disability in 20/100,000 cases over the "test" strategy and 60/100,000 cases over the "send home" strategy. In comparison, standard newborn screening prevents death or permanent disability in 0.3 to 5/100,000 cases) 3 From the perspective of numbers of adverse outcomes prevented, the decision to treat all patients with antibiotics was not so close. Sensitivity analysis. Because the literature contained a broad range of values for our probabilities, we performed sensitivity analysis. A one-way sensitivity analysis of the prevalence of occult bacteremia in febrile children (Fig. 2) showed that as the prevalence of bacteremia increased to 20% the expected utility for each strategy decreased. Points where the expected utilities of the strategies are equal are the threshold probabilities. The threshold probability between the "send home" and "test" strategies (the first crossing of lines) was 1%. If the prevalence was less than 1%,

the decision of choice was to send the patient home. For a prevalence between 1% and 1.4%, testing was best. However, when the prevalence of bacteremia was greater than 1.4%, the decision of choice became empiric treatment. Thus empiric treatment was best over the entire reported range, although the differences in expected utility among the strategies remained small. In the one-way sensitivity analysis of the efficacy of empiric antibiotic therapy (Fig. 3), as the efficacy increased from no efficacy to 100%, the expected utility of the "treat" and "test" strategies increased. The "treat" strategy increased faster than the "test" strategy because more patients with bacteremia would have received antibiotics. The expected utility of the "send home" strategy did not change because none of these patients would have received empiric antibiotic therapy. Between no efficacy and 17% efficacy, "send home" was the best strategy. Between 17% and 21% efficacy, "tesing" was best. When the efficacy of antibiotic therapy was greater than 21%, empiric antibiotic

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therapy was the management of choice. Again, the expected utility changed little regardless of efficacy. Even at 21% efficacy, empiric treatment prevented 182 hospitalizations and 10 cases of death or permanent disability over testing, and 382 hospitalizations and 20 cases of death or permanent disability over the "send home" strategy per 100,000 infants. Empiric antibiotic therapy would prevent more of these adverse outcomes than the other strategies regardless of efficacy; however, using the QALE model, which accounted for the tradeoff between risks and benefits, we found a 21% threshold efficacy below which empiric treatment is not justified. Table III shows the results of one-way sensitivity analyses of each of our probability and efficacy estimates. Only two variables had thresholds that fell within the reported range: the efficacy of antibiotic therapy and the probability of meningitis. At the threshold value for the probability of meningitis (6%), treatment still prevented death or permanent disability in 5/100,000 cases over testing and 17/ 100,000 cases over treating. However, below this threshold, the empiric use of antibiotics would not be justified by the smaller benefit. We also performed one-way sensitivity analyses of the mortality rates and utility variables. Only three thresholds were found (the age-, sex-, and race-adjusted mortality rate and the discounts for antibiotic treatment and venipuneture), and they were at clinically extreme values. For example, the decision to treat would change only if one were willing to lose 5 days of life to avoid a course of antibiotics or 9 days of life to avoid a venipuncture. The analysis is relatively insensitive to its most subjective variables, the utility variables. We performed two-way sensitivity analysis of the sensitivity and specificity of the leukocyte count. We found that the testing strategy was preferred only when the test characteristics fell into a narrow range at a high degree of sensitivity (Fig. 4). Thus, regardless of specificity, only an extremely sensitive test was preferred to the empiric treatment strategy. Superimposed on Fig. 4 are the ROC curves for the leukocyte count, derived from three major studies of occult bacteremia.ll, 15, 17 In the case of the leukocyte count, a cutoff point of 10,000/#1 gave higher sensitivity but lower specificity for occult bacteremia than a cutoff point of 15,000/#1. However, no point on any of these ROC curves fell into the shaded region in which testing would be the best strategy. Thus no cutoff point for the leukocyte count had sufficient sensitivity to make testing the preferred strategy. We also used sensitivity analysis to test our assumption that all patients with bacteremia who remained febrile at follow-up would be hospitalized. We changed our model by assuming that all children with non-culture-proved bacte-

Management o f infants at risk f o r occult bacteremia

17

T a b l e Ill. Sensitivity analysis: Probability and efficacy values for which each strategy is preferred Strategy Variable

Probability of death from treatment Sensitivity of test Specificity of test Probability of bacteremia Probability of spontaneous resolution Efficacy of treatment Probability of meningitis if persistent bacteremia Probability of death from meningitis Probability of complications of meningitis

Treat

Test

Send home

<0.00010.0001-0.0007

>0.0007

<0.91 0-1 >0.014

-->0.91 -0.014-0.010

<0.010

<0.92

0.92-0.94

>0.94

>0.21 >0.059

0.21-0.17 0.059-0.046

<0.17" <0.046*

0-1 0-1

--

*Thesevariableschangethe decisionat valuesthat fall withinthe reported range.

remia, who neither improved nor had meningitis, eventually became well without hospitalization. Despite this extreme bias against the "treat" strategy, the decision to treat empirically did not change. DISCUSSION The best approach to the management of the young child with a high fever and no obvious source of infection is hotly debated. This is not surprising, because fever in infants is common and there is uncertainty about the prevalence of occult bacteremia, the risk of complications of bacteremia, the ability of tests to identify children with bacteremia, and the efficacy of empiric antibiotic therapy. Decision analysis allowed us to examine all the assumptions, probabilities, and outcomes on which the decision may depend. The advantage of decision analysis is that these divergent issues may be combined to gain insight into which variables are most important for informed decision making. Our analysis suggested that the decision to treat all patients at risk for occult bacteremia with antibiotics is the best decision. In some respects, the decision seems a close one, and this may contribute to the debate over which strategy is best. Yet, on careful examination, the decision to treat empirically seems justified. The decision seems close because adverse outcomes are rare for all strategies. As a result, the difference in QALE among the alternatives appears small; yet this difference represents a significant clinical impact. The empiric treat-

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Downs, McNutt, and Margolis

ment strategy appeared to be 12 to 200 times more effective than, for example, standard newborn screening tests in preventing death or permanent neurologic disability. Unfortunately, adverse outcomes are so rare that an individual physician will not see enough cases of bacteremia to appreciate the differences among strategies. Consider, for example, a pediatrician who sees 30 patients a day, 250 clays a year. If 3% are children less than 2 years Of age with high fever, 34 and if the pediatrician uses the "send home" strategy, death or permanent disability would be seen in one of these patients every 6 years; the rest would do well. The same pediatrician using the "treat" strategy would see one of these dire outcomes once every 44 years. That adverse outcomes are so rare also makes meaningful clinical studies extremely difficult. Such studies depend on the number of important outcomes observed. Because severe cOmPlications occur in only one of several thousand febrile children, immense studies will be needed to achieve statistical significance. It follows that future studies of this problem should be designed carefully, and decision analysis can help guide the focus of such research. Our sensitivity analyses suggest which variables warrant further study. For example, the probability of bacteremia at which the decision changes is well below even the lowest reported prevalence. Therefore a more precise knowledge of the probability of bacteremia will not change the decision to treat, and further study of this variable may not be warranted. In contrast, the threshold value for the efficacy of empiric antibiotic therapy in clearing occult bacteremia (21%) did fall within the reported range. Thus an investigation of whether the "true" efficacy is above or below the threshold may determine which strategy is best. Moreover, the threshold values derived by decision analysis provide clinical criteria for choosing the sample size needed to study this variable. For example, a study demonstrating an efficacy at less than the 21% threshold would mean that the optimal decision would be to test, rather than to give empiric treatment. However, the demonstration of efficacy at such a low rate (at p _> 0.05 with 80% power) in a randomized trial would require at least 17,500 patients per group. The probability that meningitis will develop in a child with unresolved occult bacteremia may also be important because the threshold value (5.9%) falls within the reported range. However, to prove that the "true" probability is within a 95% confidence interval of 3% to 9%, one would need 3000 patients with bacteremia or 60,000 febrile infants. Trials of this magnitude are difficult to justify: Meanwhile, many clinicians object to obtaining blood cultures and treating all febrile infants because of the pain of venipuncture a short-term QOL decrement. A previous decision analysis35 concluded that no studies are indicated

The Journal of Pediatrics January 1991

for the child at risk for occult bacteremia (equivalent to our "send home" strategy), because the analysis discounted 2% of a lifetime for venipuncture. This huge discount is mathematically equivalent ot losing 1.4 years of life in our model. In reality, physicians willingly perform a heel-stick procedure on every newborn infant for screening tests that improve expected utility by roughly one fourth of one day. Thus, on the basis of our sensitivity analysis, venipuncture must be at least 36 times worse than the neonatal heel-stick procedure to outweight the benefit of blood culture and empiric treatment. Nonetheless, many pediatricians find it unappealing to treat all febrile infants without more diagnostic criteria. Unfortunately, no test now available appears to have sufficient sensitivity to rule out bacteremia and thereby change the decision from one of "treat" to "test." Other tests proposed to identify occult bacteremia (erythrocyte sedimentation rate and C-reactive protein concentration), like the leukocyte count, do not reach the sensitivity threshold found in our model. Even a serial combination of these tests has been shown to have insufficient sensitivity) However, McCarthy et al. 36 reported that any abnormal "response to social overture" had a 100% sensitivity for serious bacterial infections. If confirmed, this clinical test may be sufficient to rule out bacteremia, but this finding has not been repeated. Many pediatricians believe, however, that infants with bacteremia can be identified on clinical grounds. Unfortunately, the data on the success of clinicians in identifying bacteremia on clinical grounds find even experienced physicians lacking in appropriate sensitivity.2 Furthermore, the observation scales developed by McCarthy et al. 36 to identify serious illness in febrile children also were not sufficiently sensitive to cross our "test" threshold. Our results show that children at risk should be assumed to have bacteremia until there are sufficient data to prove that they do not. If a child appears well and has a normal leukocyte count, the risk of bacteremia may be low enough to justify not treating, but only if these two tests are independently predictive (i.e., leukocyte count and clinical appearance may correlate so closely that if one is known, the other adds little information). That issue has not yet been investigated. In the development of our simple tree, two alternatives were not modeled: (1) close daily observation, with treatment, of patients whose condition becomes worse and (2) blood culture without empiric treatment. To be sure, occult bacteremia that does not spontaneously resolve may become clinically obvious, or treatment may be initiated on the basis of culture results. However, the "observation" and "culture without treatment" strategies will fail because, as shown previously,2~complications of occult bacteremia develop during the delay before patients return or before cul-

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ture results are known. We also did not evaluate empiric treatment without blood culture as an option. This alternative may be appropriate, but it will require elaborate modeling beyond our simple decision tree. Trade-offs include the pain and cost of blood culture, early discontinuation of therapy for patients with negative cultures, inappropriate treatment of nonbacterial infections, and ineffective treatment of resistant organisms. When this work was presented to clinicians, 37 they challenged the assumption that all patients with bacteremia who remained febrile at follow-up would be hospitalized. It has also been argued 35 that obtaining blood specimens for culture leads to unnecessary hospitalization of children whose bacteremia would eventually resolve. However, sensitivity analysis reveals that the short-term cost of hospitalization is overshadowed by the permanent loss of quality life from death or severe neurologic disability. Our model is limited in that we did not consider the financial implications of the " t r e a t " strategy. It may be that empiric therapy, while avoiding the most adverse outcomes, is more costly than other strategies, but an appropriate cost per life-year saved has not been established. W e also did not model the possibility of encouraging the development of resistant strains of bacteria in the community. Despite these limitations, our decision analysis strongly supports empiric treatment of all children at risk for occult bacteremia. More important, decision analysis is a method that can be used to identify the most important factors in a complex decision. Thus our analysis suggests that the efficacy of antibiotic therapy and the probability of meningitis warrant further clinical studies, but shows that such studies will be extremely difficult. Finally, available laboratory tests and watchful waiting are not useful in ruling out occult bacteremia. We therefore recommend empiric antibiotic treatment of infants at risk for occult bacteremia.

REFERENCES

1. McCarthy PL, Jekel JF, Stashwick CA, Spiesel SZ, Dolan TF. History and observation variables in assessing febrile children. Pediatrics 1980;65:1090-5. 2. McCarthy PL, Jekel JF, Stashwick CA, et al. Further definition of history and observation variables in assessing febrile children. Pediatrics 1981;67:687-93. 3. McCarthy PL, Jekel JF, Dolan TF. Comparison of acutephase reactants in pediatric patients with fever. Pediatrics 1978;62:716-20. 4. Carroll WL, Farrell MK, Singer JI, Jackson MA, Lobel JS, Lewis ED. Treatment of occult bacteremia: a prospective clinical trial. Pediatrics 1983;72:608-12. 5. Jaffe DM, Tanz RR, Davis AT, Hernritig F, Fleisher G. Antibiotic administration to treat possible occult bacteremia in febrile children. 1987;317:1175-80. 6. Marshall R, Teele DW, Klein JO. Unsusptected bacteremia

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