Download Identifying Pulmonary Tuberculosis in Patients

Identifying Pulmonary Tuberculosis in
Patients With Negative Sputum Smear
Results*
Alka M. Kanaya, MD; David V. Glidden, PhD; and Henry F. Chambers, MD
Background: Clinicians need to decide whether to begin empiric therapy for patients who are
suspected of having tuberculosis (TB) but have negative sputum smear results. Culture results
may take weeks, and delaying treatment may allow further transmission of disease.
Study objective: To identify the clinical, demographic, and radiographic characteristics that
identify smear-negative patients who have TB, and to create a TB prediction rule.
Design: Retrospective chart review.
Setting: University-affiliated public hospital in San Francisco, CA, between 1993 and 1998.
Patients: Forty-seven patients with TB and 141 control patients who were hospitalized with a
suspicion of pulmonary TB; all had negative sputum smear results.
Measurements and results: Demographic, clinical, and radiographic variables were determined by
chart review. In multivariate analysis, a positive tuberculin skin test result (odds ratio [OR], 4.8;
95% confidence interval [CI], 2.0 to 11.9) was independently associated with an increased risk of
a positive TB culture finding. A radiographic pattern not typical of pulmonary tuberculosis (OR,
0.3; 95% CI, 0.1 to 0.7) and expectoration with cough (OR, 0.3; 95% CI, 0.1 to 0.6) were predictive
of a decreased risk. An interaction between HIV seropositivity and mediastinal lymphadenopathy
on the chest radiograph was also associated with a positive TB culture result (OR, 7.2; 95% CI, 1.4
to 36.0). The TB prediction score (TPS) was created with widely ranging likelihood ratios that
could affect the posterior probability of TB by 30-fold.
Conclusion: The TPS put into context with the overall prevalence of TB in a given area may help
clinicians decide if a patient with negative sputum smear results should start empiric antituberculous therapy or wait for culture results. These results need prospective validation.
(CHEST 2001; 120:349 –355)
Key words: prediction rule; smear negative; tuberculosis
Abbreviations: AFB ⫽ acid-fast bacilli; CI ⫽ confidence interval; LR ⫽ likelihood ratio; OR ⫽ odds ratio;
TB ⫽ tuberculosis; TPS ⫽ tuberculosis prediction score; TST ⫽ tuberculin skin test
ince the resurgence of pulmonary tuberculosis
S (TB)
in the United States in the late 1980s, much
attention has been focused on early case identification and treatment.1 Several studies2–9 have identified the clinical characteristics of persons with the
most infectious form of TB, those harboring the
largest number of organisms, with acid-fast bacilli
(AFB) found by microscopic examination of stained
sputum (AFB smear positive). However, patients
with active TB who have negative sputum smear
results are also capable of transmitting the infection.10,11 The relative transmission rate of smearnegative TB patients compared to smear-positive TB
For editorial comment see page 330
*From the Divisions of General Internal Medicine (Dr. Kanaya)
and Infectious Diseases (Dr. Chambers), Department of Medicine, and Department of Epidemiology and Biostatistics (Dr.
Glidden), University of California, San Francisco, San Francisco,
CA.
Work performed at San Francisco General Hospital, University
of California, San Francisco, CA.
Dr. Kanaya is supported by a grant from the Department of
Health and Human Services: Faculty Development in General
Internal Medicine grant No. 1D08PE50109 – 01.
Correspondence to: Alka M. Kanaya, MD, 1701 Davisadero St,
Suite 554, San Francisco, CA 94143-1732; e-mail: alkak@
itsa.ucsf.edu
patients has been calculated at 22% using a molecular epidemiologic technique.12 Although persons
with smear-negative TB are less infectious than the
smear-positive patients, their overall contribution to
disease transmission is considerable because half of
all patients with TB can present with negative sputum smear findings.13
Given the smaller mycobacterial burden present
with smear-negative disease, these patients may have
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349
different clinical and radiographic findings than
those with smear-positive disease. We are aware of
only one previous study14 from West Africa that
identified predictors of smear-negative patients with
TB. They found that an absence of cavitation, lack of
cough, presence of HIV seropositivity, CD4 cell
count ⬎ 200/␮L, and age ⬎ 40 years predicted
patients with smear-negative TB.14 Likewise, multiple reports15–18 from sub-Saharan countries have
found an increased prevalence of smear-negative TB
in their HIV-infected patients. However, this association between HIV and AFB smear-negative disease
has not been observed in the United States.19,20 We
investigated the predictors of TB in smear-negative
patients in an urban population in the United States,
since these predictors may be quite different from
those reported from Africa.
Early identification of persons who have TB,
whether smear positive or smear negative, is desirable both to enable appropriate isolation procedures
and to provide a basis for early institution of therapy.
Conversely, correct prediction of persons who are
unlikely to have TB is important as well to limit the
expense and potential toxicity of empiric therapy.
We aimed to identify the clinical, demographic, and
radiographic predictors for smear-negative TB to aid
clinicians in predicting the likelihood of TB in
persons with negative sputum smear results. We
developed a clinical prediction rule that may help
physicians identify those patients with negative
smear results who are likely to have TB and should
be started on a regimen of empiric antituberculous
therapy.
Materials and Methods
Study Setting and Patients
The study was conducted at San Francisco General Hospital, a
university-affiliated public hospital. Patients included in the study
were adults admitted to the hospital between 1993 and 1998 with
the suspicion of TB, who had at least two negative sputum smear
samples and corresponding sputum culture results available.
Sputum samples were obtained by spontaneous morning expectoration, saline solution induction, tracheal aspiration, or bronchoscopy with BAL. All sputum smears were concentrated and
examined by trained microbiology technicians. Each sputum
smear was cultured by both BACTEC 12B broth (BectonDickenson; Cockeysville, MD) and Middlebrook 7H11 selective
agar and maintained for at least 6 weeks to detect the presence of
growing organisms.
All patients had a medical chart with a hospital admission note,
microbiology results, and chest radiograph interpretation by a
board-certified radiologist. Physician, nurse, social worker, and
medical student notes during the hospitalization were reviewed
for complete data on each patient. Patients with a history of TB
(pulmonary or extrapulmonary) or those currently receiving
antituberculous therapies were excluded. Control patients could
not have a diagnosis of TB made in the year following their index
hospitalization.
Study Design and Data Collection
We used a case-control design to evaluate potential predictors
of TB in smear-negative patients. We sampled all consecutive
patients with smear-negative culture-positive TB from the San
Francisco General Hospital Clinical Laboratory records. Three
control patients per study patient were sampled at random from
patients who were admitted to the hospital for suspicion of TB
during the same week and had negative smear and culture
results. Approximately five potential control patients existed for
each study patient (case).
We recorded standard risk factors for TB infection and disease
as defined by the American Thoracic Society and Centers for
Disease Control and Prevention,21 including sex, age, ethnicity,
country of birth, current or prior homelessness, and current or
prior history of incarceration. Clinical information recorded
included the presence of cough, expectoration, hemoptysis,
temperature ⬎ 38.5°C, night sweats, weight loss, symptom chronicity, HIV seropositivity, current or prior tuberculin skin test
(TST) result, known exposure to TB, prior isoniazid prophylaxis,
alcoholism, tobacco use, and other comorbidities associated with
TB, such as diabetes, end-stage renal disease, hematologic
cancer, or chronic steroid use. The results of the chest radiograph
were categorized as normal, upper/apical lobe disease (either
infiltrate or cavity), other pattern of infiltration not typically
associated with reactivation TB (lobar or diffuse pattern), pleural
effusion, mediastinal lymphadenopathy, or miliary pattern. We
recorded the number of sputum smears analyzed for each patient
as well. The chart reviewer was blinded to the sputum-culture
status of the patient. Culture results and initial smear interpretation of each sputum sample were verified.
Statistical Analysis and Missing Data Management
Univariate comparisons between study patients and control
patients were performed using Fisher’s Exact Test for categorical
variables and the Student’s t test or the Wilcoxon rank-sum test
for continuous variables where appropriate. All tests of significance were two sided; p ⱕ 0.05 was considered statistically
significant. Odds ratios (ORs) and 95% confidence intervals (CIs)
were calculated.
Multiple logistic regression analysis was conducted using software (Stata 6.0; Stata Corporation; College Station, TX) to
identify independent characteristics of positive culture results for
Mycobacterium tuberculosis. We identified potential predictor
variables for smear-negative TB using univariate analysis, in
which p ⱕ 0.10 determined entry into the multivariate models.
We checked for interactions between HIV and chest radiograph
findings and TST results. We used a backward selection technique to choose our final model.
Missing data were managed using three different analytical
methods. We ran our logistic regression (1) by excluding subjects
who were missing variables necessary to enter the model, (2) by
eliminating the variable with the largest amount of missing data,
and (3) by creating an indicator variable to represent the missing
data.22,23 We found that all three techniques yielded similar
multivariate results. The results obtained using indicator variables are presented.
Using the ␤-coefficients derived from the independent predictors in our logistic regression model, we created a scoring system
to clinically apply these predictors. To simplify the predictive
model we rounded the ␤-coefficient to the nearest integer. We
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Clinical Investigations
calculated the area under a receiver operating characteristic
curve for the prediction rule. We calculated likelihood ratios
(LRs) for each potential score. LRs were determined by dividing
the proportion of study patients with the score by the proportion
of control patients with the score.
Results
The study included 47 patients with smear-negative TB (study patients) and 141 control patients.
Demographic characteristics, clinical findings, and
radiographic results of study patients and control
patients are shown in Table 1. Most study patients
and control patients were men with a median age of
approximately 40 years. Study patients were more
likely to be Latino, born in Central America, and
have histories of incarceration and a positive TST
result. Control patients were more likely to be white,
born in the United States, and have expectoration
with cough and a fever. We found no difference in
the rates of HIV seropositivity between the study
patients and the control patients. There was no clear
relationship with symptom duration for either group,
but control patients had a trend toward longer
symptom duration. A total of 85 patients (45%) did
not have a TST placed during their hospitalization
Table 1—Characteristics of the Study Population*
Characteristics
Demographic
Median age (SD)
Male sex
Race/ethnicity:
White
African American
Latino
Asian/Pacific Islander
Country of Birth
United States
Central America
Asia/Pacific Islands
Incarceration
Homelessness
Clinical
Any cough
Expectoration
Hemoptysis
Temperature ⬎ 38.5°C
Night sweats
Weight loss ⬎ 10 lb
Symptom chronicity, wk
⬍1
1–2
2–4
4–8
⬎8
HIV positive
TST result positive†
Exposure to TB
Prior isoniazid use
Alcoholism
Tobacco use
Other comorbidities‡
Radiographic findings
Abnormal
Apical/upper lobe disease
Atypical infiltrate for TB
Mediastinal lymphadenopathy
Pleural effusion
Miliary infiltrate
Study Patients
(n ⫽ 47)
Control Patients
(n ⫽ 141)
OR (95% CI)
p Value
38.4 (13.0)
40 (85)
42.0 (13.2)
123 (87)
0.8 (0.6–1.0)
1.2 (0.5–3.0)
0.08
0.71
13 (28)
13 (28)
14 (30)
7 (15)
64 (45)
44 (31)
23 (16)
9 (6)
1.0
3.0 (1.2–7.3)
1.5 (0.6–3.4)
3.8 (1.2–12.1)
0.02
0.39
0.02
30 (64)
12 (26)
5 (11)
14 (30)
14 (30)
117 (83)
14 (10)
8 (5)
19 (14)
49 (35)
1.0
3.3 (1.4–8.0)
2.4 (0.7–8.0)
2.7 (1.2–5.9)
0.8 (0.4–1.6)
0.006
0.14
0.01
0.53
27 (58)
17 (36)
6 (13)
23 (49)
22 (47)
20 (44)
125 (89)
98 (70)
10 (7)
96 (68)
63 (45)
55 (41)
0.2 (0.1–0.4)
0.3 (0.1–0.5)
1.9 (0.7–5.4)
0.4 (0.2–0.9)
1.1 (0.6–2.1)
1.1 (0.6–2.3)
⬍ 0.001
⬍ 0.001
0.23
0.02
0.80
0.69
9 (21)
4 (10)
14 (33)
3 (7)
12 (29)
19 (46)
29 (81)
5 (11)
8 (17)
20 (43)
30 (64)
1 (2)
8 (6)
55 (40)
33 (24)
18 (13)
24 (17)
74 (54)
33 (50)
9 (6)
14 (10)
56 (40)
96 (68)
6 (4)
1.0
0.1 (0.0–0.3)
0.4 (0.1–1.2)
0.2 (0.0–0.7)
0.4 (0.1–1.4)
0.6 (0.3–1.2)
4.3 (1.7–10.8)
1.7 (0.6–5.3)
1.7 (0.7–4.7)
1.1 (0.6–2.2)
0.8 (0.4–1.6)
0.5 (0–3.2)
⬍ 0.001
0.10
0.02
0.18
0.15
0.002
0.34
0.19
0.73
0.59
0.50
45 (96)
20 (43)
13 (28)
10 (21)
7 (15)
3 (6)
131 (93)
35 (25)
94 (67)
10 (7)
15 (11)
8 (6)
1.7 (0.4–8.1)
2.2 (1.1–4.5)
0.2 (0.1–0.4)
3.5 (1.4–9.0)
1.5 (0.6–3.8)
1.1 (0.3–4.1)
0.49
0.02
⬍ 0.001
0.006
0.43
0.87
*Data are presented as No. (%) unless otherwise indicated.
†Based on 36 study patients and 67 control patients for whom data were available.
‡Patients with diabetes, end-stage renal disease, hematologic cancer, or long-term steroid use.
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351
and had no documentation of a prior known result.
The proportion of patients with missing TST varied
between the study patients and control patients (23%
vs 52%, respectively). Of note, 19 patients (40%)
with smear-negative TB were not started on any
antituberculous therapy during their hospitalization
or at the time of hospital discharge. There was no
difference between the groups in the number of
sputum smears analyzed; a majority of patients had
two to three sputum smears (79% of study patients vs
74% of control patients) during their hospitalization.
In the multivariate analysis, a positive TST result
(OR, 4.8; 95% CI, 2.0 to 11.9) was independently
associated with an increased risk of a positive TB
culture result, whereas a radiographic pattern not
typical of reactivation pulmonary TB (OR, 0.3; 95%
CI, 0.1 to 0.7) and expectoration with cough (OR,
0.3; 95% CI, 0.1 to 0.6) were predictive of a decreased risk. We detected an interaction between
HIV-positive status and the finding of mediastinal
lymphadenopathy on a chest radiograph that was
associated with an increased risk of a positive TB
culture result as well (OR, 7.2; 95% CI, 1.4 to 36.0;
Table 2).
Using the four variables that were associated with
the risk of TB in multivariate analysis, we created a
TB prediction score (TPS) to help distinguish patients who were culture positive from patients likely
to be culture negative. Based on the magnitude of
the ␤-coefficient, a positive TST result was given a
point score of ⫹ 1, the negative predictors (expectoration and an infiltrate not typical of TB) received
⫺ 1 point each, and HIV positivity and mediastinal
lymphadenopathy together received ⫹ 2 points.
Table 3 displays the range and frequency of each
score for the study patients and control subjects.
Since LRs offer more valuable information than
sensitivity and specificity when a test has more than
two possible results, we calculated separate LRs for
each possible score. Since no cases had a score of
Table 2—Multivariate Predictors of TB in SmearNegative Patients
Predictors
Clinical
Expectoration
TST result positive
Radiographic
Infiltrate not typical of TB
interaction
HIV-positive status and
mediastinal
lymphadenopathy on
chest radiograph
Multivariate OR
(95% CI)
p Value
Points
0.3 (0.1–0.6)
4.8 (2.0–11.9)
0.002
0.001
⫺1
⫹1
0.3 (0.1–0.7)
0.006
⫺1
7.2 (1.4–36.0)
0.02
⫹2
Table 3—TPS Using Multivariate Predictors*
Score of Points
Study Patients
⫺2
⫺1
0
⫹1
⫹2
⫹3
Total
3
9
16
14
1
4
47
Control
Patients
52
55
26
8
0
0
141
LR
0.2
0.5
1.8
7.1
*⫹ 1 point ⫽ positive TST result; ⫺ 1 point ⫽ expectoration or
infiltrate observed on chest radiograph not typical of TB; ⫹ 2
points ⫽ both HIV-positive status and mediastinal lymphadenopathy observed on chest radiograph.
⫹ 2 or ⫹ 3, we combined all of the positive scores in
order to ascertain a meaningful LR. The LRs ranged
widely, from 0.2 for a score of ⫺2, to 7.1 for positive
scores. A receiver operating characteristic curve,
which plots the false-positive rate against the truepositive rate for each possible cutoff for a diagnostic
test, had an area under the curve of 0.89 for our
prediction model. A perfect diagnostic test with
100% sensitivity and specificity would have an area
under the curve of 1.0.
To demonstrate the utility of the TPS for clinical
decision making, we estimated the probability of TB
in a hypothetical patient with negative sputum smear
results in three different areas with varying prevalence rates of TB (Table 4). The TPS could affect the
posterior likelihood of TB by approximately 30-fold
in each of the scenarios described. If the threshold
for empiric treatment is 5%, in cities with high TB
prevalence, a score of ⱖ 0 would predict the risk of
TB to exceed the threshold for empiric treatment. In
a city with low prevalence of TB, even a positive
score would not significantly affect the likelihood
that the patient has TB, and withholding treatment
pending final culture results for these patients may
be prudent.
Discussion
Despite the initial clinical suspicion of TB, when a
patient’s sputum smear results are negative for AFB,
the diagnosis of TB may by missed. For those
patients with a high clinical suspicion, clinicians must
face the dilemma of empirically treating or waiting
for up to 8 weeks for the final culture results. Though
newer rapid diagnostic tests are available in most
large medical centers, they are expensive, have poor
sensitivity and specificity for smear-negative sputum
samples, and are not yet considered standard of
practice.24 –28 Clinicians may use criteria for smear-
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Table 4 —Posttest Probability of TB Given the Prevalence of Smear-Negative TB
in Three Different Settings Using the TPS*
Geographic Area
Prevalence of SmearNegative TB, %
High prevalence
5
Moderate-to-low prevalence
3
Very low prevalence
⬍ 0.1
Hypothetical Score
LR
Posttest Probability
of TB, %
⫺2
0
⫹2
⫺2
0
⫹2
⫺2
0
⫹2
0.2
1.8
7.1
0.2
1.8
7.1
0.2
1.8
7.1
1
8
26
0.6
5
18
0.02
0.2
0.7
*The risk of TB with a given score (posterior probability) was derived by multiplying the patient’s risk of TB based on the estimated prevalence
of TB (prior probability) with the LR, and converting the result (posterior odds) to posterior probability.
positive disease (ie, fever, productive cough, weight
loss) to predict risk in the patient with smearnegative disease. But given the smaller mycobacterial burden present with smear-negative disease,
these patients may have different clinical and radiographic findings. In our study, we attempted to
ascertain the predictors of this narrowly defined
population with smear-negative pulmonary TB. We
derived a prediction rule based on our findings, but
future studies need to validate this rule in different
populations of patients.
Our clinical findings demonstrate that the presence of expectoration in a patient with negative
sputum smear results is not associated with TB, a
clear distinction from the smear-positive patient.
Another more common pulmonary process, such as
bacterial pneumonia or bronchitis, is more likely
than TB possibly because the major cough receptors
in the airways may not be stimulated by the relatively
small amount of bacteria in smear-negative disease.
This finding supports the results14 from Senegal,
where patients with smear-negative disease had an
absence of cough. Similar to smear-positive TB, we
found that a positive TST result was highly predictive
of TB in smear-negative patients as well. In support
of our findings, a recent case-control study29 that
examined predictors of culture-positive TB (regardless of smear status) found a positive purified protein
derivative tuberculin test result to be their strongest
predictor of TB (OR, 13.2; 95% CI, 4.4 to 40.7). A
likely explanation is that the immunologic response
to the TST is determined by a much lower burden of
TB organisms than what exists even in smear-negative TB.
Our radiographic findings are interesting from a
biological perspective, and are different from those
classically found in smear-positive TB. The typical
radiographic pattern of reactivation TB in smearpositive patients, apical or upper lobe infiltrates or
cavities, was not likely in patients with smear-negative disease. Having an infiltrate that is not typical for
reactivation TB, such as a lobar consolidation or a
diffuse pattern, was a negative predictor in our study.
This radiographic finding would make another pulmonary disease process such as a bacterial pneumonia, pulmonary edema, or interstitial disease more
likely, and TB less likely.
We found a significant interaction between the
radiographic finding of mediastinal lymphadenopathy and HIV seropositivity in patients who were
culture positive for TB. The association of mediastinal lymphadenopathy in HIV-infected patients with
TB is well-known.30,31 Our findings confirm this
association and show a striking association of this
interaction with smear-negative TB.
The TPS was derived using the ␤-coefficients of
the four multivariate predictors in our study to help
clinicians apply our findings. Future studies will
need to validate our TPS using a prospective cohort
of inpatients. Using the prediction score with a
simple cutoff of ⱖ 0 to identify persons with TB, we
would have missed 12 of 47 study patients (25%) in
this study. Using ⱖ⫺ 1 as the cutoff point for
identifying patients with TB would increase our
sensitivity to 94%, but lower the specificity. In the
case of any infectious disease with public health
implications, it is important to have a test or rule that
has a high sensitivity and negative predictive value,
so that patients with true disease are treated and
those with a low possibility of disease can be discharged from the hospital safely without treatment.
However, using an arbitrary cut point to identify
patients with TB dichotomizes the TPS and wastes
valuable information from each possible result.
LRs have the advantage of giving more precise
information when a test has more than two outcomes. For the TPS, the range of outcomes extended
from ⫺2 to ⫹ 3, with LRs ranging from 0.2 to 7.1.
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Using LRs, a clinician has the advantage of calculating a specific patient’s probability of having TB based
on the estimated prevalence of TB in his or her
specific setting and the LR result of a given score.
We demonstrated the application of LRs in areas
with varying prevalence of TB. Areas of higher TB
prevalence should have a more conservative threshold for treating TB, perhaps with a TPS of ⱖ 0. In
contrast, areas with very low prevalence of TB can
afford to wait until final culture results are available,
since it is likely that a person with negative smear
results has another more common disease. It is
difficult to know how low the posterior probability of
TB needs to be to withhold empiric treatment for
TB. Given the limited resources and the potential for
drug toxicity, we believe it is reasonable to choose a
threshold for empiric treatment at 5%. However, this
decision should be made together by the physician
and patient on a case-by-case basis.
Although we were limited to chart review to assess
all of the clinical and demographic variables, most of
these were routinely assessed in patients in whom
TB was suspected. However, we did encounter
missing data on an important predictor of smearnegative TB, the TST. We ran our multivariate
analysis using the three different techniques outlined
in the “Materials and Methods” section and found
similar results. The proportion of patients who had
documented TST results was significantly different
among the study patients and control patients (77%
vs 48%, respectively), suggesting a differential suspicion of TB between the two groups of patients.
Perhaps the control patients were less likely to have
a TST placed because the alternative, more common, diagnosis such as a bacterial pneumonia was
responding appropriately to therapy. The study patients may not have responded as well to these
therapies, causing the practitioners to pursue the
diagnosis of TB more aggressively. However, then
we would have expected more bronchoscopic evaluations in the study patients, which was not seen. Due
to limitations in our retrospective study design, we
cannot eliminate the possibility of bias playing a role
in which patients had a TST applied. Nevertheless, a
positive TST result was an important predictor of TB
in smear-negative patients, and was a statistically
significant finding in our study despite the missing
data; we encourage the practice of placing a TST on
every patient who is suspected of having TB.
Since we were limited to chart review, we were
unable to collect information on other factors that
clinicians may use in deciding whether to begin a
patient on empiric antituberculous therapy. These
“unmeasured variables” may be more predictive of
culture-positive TB. For example, it is difficult to
quantify the conviction of an alternate diagnosis or
the response to nontuberculous therapies from medical notes alone, but these factors may be strongly
associated with TB. We evaluated clinical, demographic, and radiographic characteristics that would
be easily available early in the diagnostic workup
of TB, but may have missed other more subtle
determinants that clinicians use that could better
predict TB.
The findings of our study can guide clinical practice in settings similar to that of San Francisco
General Hospital, a large public hospital in a city that
has moderate-to-high levels of TB and HIV prevalence compared to other US cities. It is unclear
whether these findings may be generalizable to
private hospitals, academic settings with different
high-risk groups of patients, and in developing countries. Future studies will need to validate our findings in cohorts of patients in different clinical settings.
Our study provides potentially important information to help clinicians refine decision making in the
case of patients with negative sputum smear results.
Using our simple scoring method, a practitioner
could apply easily available clinical, demographic,
and radiographic characteristics to assess the patient’s risk of pulmonary TB. If the sum of the points
of the TPS is ⬍ 0, the patient has a low probability of
having TB, even in areas of higher TB prevalence,
and may not need to be started on empiric antituberculous therapy pending final culture results.
However, patients with positive scores with our
prediction rule in cities that have moderate levels of
TB prevalence or greater may benefit by early
institution of therapy.
ACKNOWLEDGMENT: The authors thank Philip C. Hopewell,
MD; Deborah Grady, MD; Warren Browner, MD; Michael
Shlipak, MD; Peter M. Small, MD; and Eliseo J. Pérez-Stable,
MD, for their assistance in reviewing and revising the article.
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