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Validity of clinical prediction rules for isolating inpatients with suspected tuberculosis

A systematic review

Abstract

OBJECTIVE: Declining rates of tuberculosis (TB) in the United States has resulted in a low prevalence of the disease among patients placed on respiratory isolation. The purpose of this study is to systematically review decision rules to predict the patient’s risk for active pulmonary TB at the time of admission to the hospital.

DATA SOURCES: We searched MEDLINE (1975 to 2003) supplemented by reference tracking. We included studies that reported the sensitivity and specificity of clinical variables for predicting pulmonary TB, used Mycobacterium TB culture as the reference standard, and included at least 50 patients.

REVIEW METHOD: Two reviewers independently assessed study quality and abstracted data regarding the sensitivity and specificity of the prediction rules.

RESULTS: Nine studies met inclusion criteria. These studies included 2,194 participants. Most studies found that the presence of TB risk factors, chronic symptoms, positive tuberculin skin test (TST), fever, and upper lobe abnormalities on chest radiograph were associated with TB. Positive TST and a chest radiograph consistent with TB were the predictors showing the strongest association with TB (odds ratio: 5.7 to 13.2 and 2.9 to 31.7, respectively). The sensitivity of the prediction rules for identifying patients with active pulmonary TB varied from 81% to 100%; specificity ranged from 19% to 84%.

CONCLUSIONS: Our analysis suggests that clinicians can use prediction rules to identify patients with very low risk of infection among those suspected for TB on admission to the hospital, and thus reduce isolation of patients without TB.

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References

  1. Guidelines for preventing the transmission of Mycobacterium tuberculosis in health-care facilities, 1994. Centers for Disease Control and Prevention. MMWR Recomm Rep. 1994;43(RR-13):1–132.

  2. LoBue PA, Catanzaro A. Effectiveness of a nosocomial tuberculosis control program at an urban teaching hospital. Chest. 1998;113:1184–9.

    PubMed  CAS  Google Scholar 

  3. Trends in tuberculosis—United States, 1998–2003. MMWR Morb Mortal Wkly Rep. 2004;53:209–14.

    Google Scholar 

  4. Scott B, Schmid M, Nettleman MD. Early identification and isolation of inpatients at high risk for tuberculosis. Arch Intern Med. 1994;154:326–30.

    PubMed  Article  CAS  Google Scholar 

  5. Greenaway C, Menzies D, Fanning A, et al. Delay in diagnosis among hospitalized patients with active tuberculosis—predictors and outcomes. Am J Respir Crit Care Med. 2002;165:927–33.

    PubMed  Google Scholar 

  6. Greenbaum M, Beyt BE Jr., Murray PR. The accuracy of diagnosing pulmonary tuberculosis at a teaching hospital. Am Rev Respir Dis. 1980;121:477–81.

    PubMed  CAS  Google Scholar 

  7. Rao VK, Iademarco EP, Fraser VJ, et al. Delays in the suspicion and treatment of tuberculosis among hospitalized patients. Ann Intern Med. 1999;130:404–11.

    PubMed  CAS  Google Scholar 

  8. Mathur P, Sacks L, Auten G, et al. Delayed diagnosis of pulmonary tuberculosis in city hospitals. Arch Intern Med. 1994;154:306–10.

    PubMed  Article  CAS  Google Scholar 

  9. Moran GJ, McCabe F, Morgan MT, et al. Delayed recognition and infection control for tuberculosis patients in the emergency department. Ann Emerg Med. 1995;26:290–5.

    PubMed  Article  CAS  Google Scholar 

  10. Chan ED, Heifets L, Iseman MD. Immunologic diagnosis of tuberculosis: a review. Tuberc Lung Dis. 2000;80:131–40.

    Article  CAS  Google Scholar 

  11. Anderson C, Inhaber N, Menzies D. Comparison of sputum induction with fiber-optic bronchoscopy in the diagnosis of tuberculosis. Am J Respir Crit Care Med. 1995;152(Pt 1):1570–4.

    PubMed  CAS  Google Scholar 

  12. Beck-Sague C, et al. Hospital outbreak of multidrug-resistant Mycobacterium tuberculosis infections. Factore in transmission to staff and HIV-infected patients. JAMA. 1992;268:1280–6.

    PubMed  Article  CAS  Google Scholar 

  13. Klein NC, Duncanson FP, Lenox TH 3rd, et al. Use of mycobacterial smears in the diagnosis of pulmonary tuberculosis in AIDS/ARC patients. Chest. 1989;95:1190–2.

    PubMed  CAS  Google Scholar 

  14. Kim TC, Blackman RS, Heatwole KM, et al. Acid-fast bacilli in sputum smears of patients with pulmonary tuberculosis. Prevalence and significance of negative smears pretreatment and positive smears post-treatment. Am Rev Respir Dis. 1984;129:264–8.

    PubMed  CAS  Google Scholar 

  15. Behr MA, Warren SA, Salamon H, et al. Transmission of Mycobacterium tuberculosis from patients smear-negative for acid-fast bacilli. Lancet. 1999;353:444–9.

    PubMed  Article  CAS  Google Scholar 

  16. Laupacis A, Sekar N, Stiell IG. Clinical prediction rules. A review and suggested modifications of methodological standards. JAMA. 1997;277:488–94.

    PubMed  Article  CAS  Google Scholar 

  17. Irwig L, Tosteson AN, Gatsonis C, et al. Guidelines for meta-analyses evaluating diagnostic tests. Ann Intern Med. 1994;120:667–76.

    PubMed  CAS  Google Scholar 

  18. McGinn TG, Guyatt GH, Wyer PC, et al. Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group. JAMA. 2000;284:79–84.

    PubMed  Article  CAS  Google Scholar 

  19. Feinstein A. Diagnostic and spectral markers. ed. Clinical Epidemiology: The Architecture of Clinical Research. Philadelphia: P.W.S.C.; 1985:597–631.

    Google Scholar 

  20. Jaeschke R, Guyatt GH, Sackett DL. Users’ guides to the medical literature. III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for my patients? The Evidence-Based Medicine Working Group. JAMA. 1994;271:703–7.

    PubMed  Article  CAS  Google Scholar 

  21. Wasson JH, Sox HC, Neff RK, Goldman L. Clinical prediction rules. Applications and methodological standards. N Engl J Med. 1985;313:793–9.

    PubMed  CAS  Article  Google Scholar 

  22. Begg CB. Biases in the assessment of diagnostic tests. Stat Med. 1987;6:411–23.

    PubMed  Article  CAS  Google Scholar 

  23. Sutton AJ, Duval SJ, Tweedie RL, Abrams KR, Jones DR. Empirical assessment of effect of publication bias on meta-analyses. BMJ. 2000;320:1574–7.

    PubMed  Article  CAS  Google Scholar 

  24. Begg C. Publication bias. In: Cooper H, Hedges L, eds. The Handbook of Research Synthesis. New York, NY: Russell Sage Foundation; 1994:399–409.

    Google Scholar 

  25. Kesley JL, Evan AS, Thompson WD. Methods in Observational Epidemiology. 2nd ed. New York, NY: Oxford University Press; 1996.

    Google Scholar 

  26. Maclure M, Willett WC. Misinterpretation and misuse of the kappa statistic. Am J Epidemiol. 1987;126:161–9.

    PubMed  CAS  Google Scholar 

  27. Griner PF, Mayewski RJ, Mushlin AI, Greenland P. Selection and interpretation of diagnostic tests and procedure. Principles and applications. Ann Intern Med. 1981;94(Pt 2):557–92.

    PubMed  CAS  Google Scholar 

  28. Bock NN, McGowan JE Jr., Ahn J, et al. Clinical predictors of tuberculosis as a guide for a respiratory isolation policy. Am J Respir Crit Care Med. 1996;154:1468–72.

    PubMed  CAS  Google Scholar 

  29. Cohen R, Muzaffar S, Capellan J, et al. The validity of classic symptoms and chest radiographic configuration in predicting pulmonary tuberculosis. Chest. 1996;109:420–3.

    PubMed  CAS  Google Scholar 

  30. El-Solh A, Mylotte J, Sherif S, et al. Validity of a decision tree for predicting active pulmonary tuberculosis. Am J Respir Crit Care Med. 1997;155:1711–6.

    PubMed  CAS  Google Scholar 

  31. Gaeta TJ, Webheh W, Yazji M, et al. Respiratory isolation of patients with suspected pulmonary tuberculosis in an inner-city hospital. Acad Emerg Med. 1997;4:138–41.

    PubMed  CAS  Google Scholar 

  32. Mylotte JM, Rodgers J, Fassl M, et al. Derivation and validation of a pulmonary tuberculosis prediction model. Infect Control Hosp Epidemiol. 1997;18:554–60.

    PubMed  CAS  Article  Google Scholar 

  33. Redd JT, Susser E. Controlling tuberculosis in an urban emergency department: a rapid decision instrument for patient isolation. Am J Public Health. 1997;87:1543–7.

    PubMed  CAS  Article  Google Scholar 

  34. Tattevin P, et al. The validity of medical history, classic symptoms, and chest radiographs in predicting pulmonary tuberculosis: derivation of a pulmonary tuberculosis prediction model. Chest. 1999;115:1248–53.

    PubMed  Article  CAS  Google Scholar 

  35. Wisnivesky JP, et al. Evaluation of clinical parameters to predict Mycobacterium tuberculosis in inpatients. Arch Intern Med. 2000;160:2471–6.

    PubMed  Article  CAS  Google Scholar 

  36. El-Solh AA, Hsiao CB, Goodnough S, et al. Predicting active pulmonary tuberculosis using an artificial neural network. Chest. 1999;116:968–73.

    PubMed  Article  CAS  Google Scholar 

  37. Wisnivesky JP, Henschke C, Balentine J, Wilner C, Delorie A, McGinn T. Prospective validation of a prediction rule to assess the need for respiratory isolation of inpatients with suspected tuberculosis. Arch Intern Med. 2005;165:453–7.

    PubMed  Article  Google Scholar 

  38. Pearson ML, Jereb JA, Frieden TR, et al. Nosocomial transmission of multidrug-resistant Mycobacterium tuberculosis. A risk to patients and health care workers. Ann Intern Med. 1992;117:191–6.

    PubMed  CAS  Google Scholar 

  39. Frieden TR, Sterling T, Pablos-Mendez A, et al. The emergence of drug-resistant tuberculosis in New York City. N Engl J Med. 1993;328:521–6.

    PubMed  Article  CAS  Google Scholar 

  40. Blumberg HM, Watkins DL, Berschling JD, et al. Preventing the nosocomial transmission of tuberculosis. Ann Intern Med. 1995;122:658–63.

    PubMed  CAS  Google Scholar 

  41. Bachmann LM, Haberzeth S, Steurer J, et al. The accuracy of the Ottawa knee rule to rule out knee fractures: a systematic review. Ann Intern Med. 2004;140:121–4.

    PubMed  Google Scholar 

  42. Irwig L, Macaskill P, Glasziou P, et al. Meta-analytic methods for diagnostic test accuracy. J Clin Epidemiol. 1995;48:119–30. Discussion 131–2.

    PubMed  Article  CAS  Google Scholar 

  43. Lau J, Ioannidis JP, Schmid CH. Quantitative synthesis in systematic reviews. Ann Intern Med. 1997;127:820–6.

    PubMed  CAS  Google Scholar 

  44. Gould MK, Maclean CC, Kuschner WG, et al. Accuracy of positron emission tomography for diagnosis of pulmonary nodules and mass lesions: a meta-analysis. JAMA. 2001;285:914–24.

    PubMed  Article  CAS  Google Scholar 

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Correspondence to Juan P. Wisnivesky MD, MPH.

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The authors have no financial conflicts of interest to report.

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Wisnivesky, J.P., Serebrisky, D., Moore, C. et al. Validity of clinical prediction rules for isolating inpatients with suspected tuberculosis. J GEN INTERN MED 20, 947–952 (2005). https://doi.org/10.1111/j.1525-1497.2005.0185.x

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  • DOI: https://doi.org/10.1111/j.1525-1497.2005.0185.x

Key Words

  • tuberculosis
  • diagnosis
  • clinical prediction rules
  • systematic review