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Systematic Review With Meta-Analyses and Critical Appraisal of Clinical Prediction Rules for Pulmonary Tuberculosis in Hospitals

Published online by Cambridge University Press:  18 December 2014

Berenice das Dores Gonçalves ,
Sonia Regina Lambert Passos ,
Maria Angelica Borges dos Santos ,
Carlos Augusto Ferreira de Andrade ,
Maria de Fátima Moreira Martins  and
Fernanda Carvalho de Queiroz Mello
Berenice das Dores Gonçalves*
Affiliation:
Federal University Fluminense, Niterói, RJ, Brazil
Sonia Regina Lambert Passos
Affiliation:
National Institute of Infectious Disease EvandroChagas/Oswaldo Cruz Foundation/Laboratory of Clinical Epidemiology, Rio de Janeiro, RJ, Brazil
Maria Angelica Borges dos Santos
Affiliation:
National School of Public Health, Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil
Carlos Augusto Ferreira de Andrade
Affiliation:
National Institute of Infectious Disease EvandroChagas/Oswaldo Cruz Foundation/Laboratory of Clinical Epidemiology, Rio de Janeiro, RJ, Brazil
Maria de Fátima Moreira Martins
Affiliation:
Oswaldo Cruz Foundation Libraries’ Network/ Institute of Scientific and Technological Information and Communication in Health/ Oswaldo Cruz Foundation, Rio de Janeiro, RJ, Brazil
Fernanda Carvalho de Queiroz Mello
Affiliation:
Thoracic Diseases Institute, Medical School of the Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
*
Address correspondence to Berenice das Dores Gonçalves, MSc, Department of Epidemiology and Biostatistics, Fluminense Federal University, Rua Marques Paraná 303, Prédio Anexo, 3o andar - sala 9 Centro, Niterói, RJ, CEP: 24030-210, Brasil ([email protected]).
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Abstract

Objective

To systematically review studies evaluating clinical prediction rules (CPRs) for adult inpatients suspected to have pulmonary tuberculosis.

Design

Systematic review with meta-analyses.

Setting

Hospitals.

Patients

Inpatients at least 15 years of age admitted to acute care.

Methods

A search was conducted in 5 indexed electronic databases with no language or year of publication restrictions. We performed a meta-analysis for those CPRs with at least 2 validation studies. Results were reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Results

Of the 461 abstracts selected, 36 articles were fully analyzed and 11 articles were included, yielding 8 CPRs derived in 4 countries. Broad validation studies were identified for 2 CPRs. The most frequent clinical predictors were fever and weight loss. All CPRs included chest imaging signs. Most CPRs were derived in countries with a low prevalence of pulmonary tuberculosis and included homeless, immigrants, and those who reacted to the purified protein derivative test. Both of the CPRs derived in countries with a high prevalence of pulmonary tuberculosis strongly relied on chest radiograph predictors. Accuracy of the different CPRs was high (area under receiver operating characteristic curve, 0.79–0.91). Meta-analysis of 4 validation studies for Wisnivesky´s CPR indicates optimistic pooled results: sensitivity, 94.1% (95% CI, 89.7%–96.7%); negative likelihood ratio, 0.22 (95% CI, 0.12–0.40).

Conclusion

On the basis of a critical appraisal of the 2 best validated CPRs, the presence of weight loss and/or fever in inpatients warrants obtaining a chest radiograph, regardless of the presence of productive cough. If the chest radiograph is abnormal, the patient should be placed in isolation until more specific test results are available. Validation in different settings is required to maximize external generalization of existing CPRs.

Infect Control Hosp Epidemiol 2014;00(0): 1–10

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 36 , Issue 2 , February 2015 , pp. 204 - 213
Copyright
© 2014 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

1. World Health Organization. Systematic Screening for Active Tuberculosis: Principles and Recommendations. Geneva: World Health Organization, 2013.Google Scholar
2. Ribeiro, SA, Matsui, TN. Admission for tuberculosis to a university hospital. J Pneumology 2003;29:9–14.CrossRefGoogle Scholar
3. Kobashi, Y, Mouri, K, Fukuda, M, et al. Transitional change in the clinical features of pulmonary tuberculosis. Respiration 2008;75:304–309.CrossRefGoogle ScholarPubMed
4. Davies, PDO. TB in the elderly in industrialised countries. Int J Tuberc Lung Dis 2007;11:1157–1159.Google ScholarPubMed
5. Jensen, PA, Lambert, LA, Iademarco, MF, Ridzon, R; Centers for Disease Control and Prevention. Guidelines for preventing the transmission of Mycobacterium tuberculosis in health-care settings. MMWR Recomm Rep 2005;54:1–141.Google ScholarPubMed
6. Greenway, C, Menzies, D, Fanning, A, et al.; Canadian Collaborative Group in. Nosocomial Transmission of Tuberculosis. Delay in Diagnosis among hospitalized patients with active tuberculosis – predictors and outcomes. Am J Respir Crit Care Med 2002;165:927–933.CrossRefGoogle Scholar
7. Ollenberger, GP, Worsley, DF. Effect of patient location on the performance of clinical models to predict pulmonary embolism. Thromb Res 2006;118:685–690.CrossRefGoogle ScholarPubMed
8. Sokolove, PE, Rossman, L, Cohen, SH. The emergency department presentation of patients with active pulmonary tuberculosis. Acad Emerg Med 2000;7:1056–1060.CrossRefGoogle ScholarPubMed
9. Resende, RM, Sinkoc, VM, Garcia, MT, et al. Indicadores relacionados ao retardo no diagnóstico e na instituição das precauções para aerossóis entre pacientes com tuberculose pulmonar bacilífera em um hospital terciário. J Bras Pneumol 2005;31:225–230.CrossRefGoogle Scholar
10. Behr, MA, Warren, SA, Salamon, H, et al. Transmission of Mycobacterium tuberculosis from patients smear-negative for acid-fast bacilli [published correction appears in Lancet 1999;353:1714]. Lancet 1999;353:444–449.CrossRefGoogle ScholarPubMed
11. Steingart, KR, Schiller, I, Horne, DJ, Pai, M, Boehme, CC, Dendukuri, N. Xpert® MTB/RIF assay for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst Rev 2014;1:CD009593.Google Scholar
12. Catanzaro, A, Perry, S, Clarridge, JE, et al. The role of clinical suspicion in evaluating a new diagnostic test for active tuberculosis: results of a multicenter prospective trial. JAMA 2000;283:639–645.CrossRefGoogle ScholarPubMed
13. McGinn, TG, Guyatt, GH, Wyer, PC, Naylor, CD, Stiell, IG, Richardson, WS. 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.CrossRefGoogle Scholar
14. Wisnivesky, JP, Serebrisky, D, Moore, C, et al. Validity of clinical prediction rules for isolating inpatients with suspected tuberculosis: a systematic review. J Gen Intern Med 2005;20:947–952.CrossRefGoogle ScholarPubMed
15. Solari, L, Acuna-Villaorduna, C, Soto, A, Stuyft, PVD. Evaluation of clinical prediction rules for respiratory isolation of inpatients with suspected pulmonary tuberculosis. Clin Infect Dis 2011;52:595–603.CrossRefGoogle ScholarPubMed
16. Laupacis, A, Sekar, N, Stiel, IG. Clinical prediction rules – a review and suggested modifications of methodological standards. JAMA 1997;277:488–494.CrossRefGoogle ScholarPubMed
17. Toll, DB, Janssen, KJM, Vergouwe, Y, Moons, KGM. Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol 2008;61:1085–1094.CrossRefGoogle ScholarPubMed
18. Moons, KGM, Royston, P, Vergouwe, Y, Grobee, DE, Altman, D. Prognosis and prognostic research: what, why and how? BMJ 2009;338:1317–1320.CrossRefGoogle ScholarPubMed
19. Reilly, MB, Evans, AT. Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med 2006;144:201–209.CrossRefGoogle ScholarPubMed
20. Mello, FCQ, Bastos, LGV, Soares, SLM, et al. Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study. BMC Public Health 2006;6:43.CrossRefGoogle ScholarPubMed
21. Redd, JT, Susser, E. Controlling tuberculosis in an urban emergency department: a rapid decision instrument for patient isolation. Am J Public Health 1997;87:1543–1547.CrossRefGoogle Scholar
22. Solari, L, Acuna-Villaorduna, C, Soto, A, et al. A clinical prediction rule for pulmonary tuberculosis in emergency departments. Int J Tuberc Lung Dis 2008;12:619–624.Google ScholarPubMed
23. Mylotte, JM, Rodgers, J, Fassl, M, Seibel, K, Vacanti, A. Derivation and validation of a pulmonary tuberculosis prediction model. Infect Control Hosp Epidemiol 1997;18:554–560.CrossRefGoogle ScholarPubMed
24. Tattevin, P, Casalino, E, Fleury, L, Egmann, G, Ruel, M, Bouvet, E. The validity of medical history, classic symptoms, and chest radiographs in predicting pulmonary tuberculosis: derivation of a pulmonary tuberculosis prediction model. Chest 1999;115:1248–1253.CrossRefGoogle ScholarPubMed
25. Wisnivesky, JP, Kaplan, J, Henschke, C, McGinn, TG, Crystal, RG. Evaluation of clinical parameters to predict Mycobacterium tuberculosis in inpatients. Arch Intern Med 2000;160:2471–2476.CrossRefGoogle ScholarPubMed
26. Wisnivesky, JP, Henschke, C, Balentine, J, et al. Prospective validation of a prediction model for isolating inpatients with suspected pulmonary tuberculosis. Arch Intern Med 2005;165:453–457.CrossRefGoogle ScholarPubMed
27. Rakoczy Ks, Cohen SH, Nguyen, HH. Derivation and validation of a clinical prediction score for isolation of inpatients with suspected pulmonary tuberculosis. Infect Control Hosp Epidemiol 2008;29:927–932.CrossRefGoogle ScholarPubMed
28. Aguilar, J, Yang, JJ, Brar, I, Markowitz, N. Clinical prediction rule for respiratory isolation of patients with suspected pulmonary tuberculosis. Infectious Disease in Clinical Practice 2009;17:317–322.CrossRefGoogle Scholar
29. Lagrange-Xélot, M, Porcher, R, Gallien, S, et al. Prevalence and clinical predictors of pulmonary tuberculosis among isolated inpatients: a prospective study. Clin Microbiol Infect 2011;17:610–614.CrossRefGoogle ScholarPubMed
30. Aguiar, FS, Almeida, LL, Ruffino-Netto, A, et al.: Classification And Regression Tree (CART) model to predict pulmonary tuberculosis in hospitalized patients. BMC Pulm Med 2012;12:40.CrossRefGoogle ScholarPubMed
31. Liberati, A, Altman, DG, Tetzlaff, J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann Intern Med 2009;151:W65–W94.CrossRefGoogle ScholarPubMed
32. Abramson, JH. WinPepi updated: computer programs for epidemiologists, and their teaching potential. Epidemiol Perspect Innov 2011;8:1.CrossRefGoogle ScholarPubMed
33. Getahun, H, Kittikraisak, W, Heilig, C, et al. Development of a standardized screening rule for tuberculosis in people living with HIV in resource-constrained settings: individual participant data meta-analysis of observational studies. PLOS Med 2011:8.Google ScholarPubMed
34. Ingui, BJ, Rogers, MA. Searching for clinical prediction rules in MEDLINE. JAMIA 2001;8:391–397.Google ScholarPubMed
35. Brehaut, JC, Stiel, IG, Visentin, L, Graham, ID. Clinical decision rules “in the real world”: how a widely disseminated rule is used in everyday practice. Acad Emerg Med 2005;12.Google Scholar