Validity of clinical prediction rules for isolating inpatients with suspected tuberculosis. A systematic review

Division of General Internal Medicine, Mount Sinai Medical Center, New York, NY. USA.
Journal of General Internal Medicine (Impact Factor: 3.42). 11/2005; 20(10):947-52. DOI: 10.1111/j.1525-1497.2005.0185.x
Source: PubMed


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.
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.
Two reviewers independently assessed study quality and abstracted data regarding the sensitivity and specificity of the prediction rules.
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%.
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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Available from: Denise Serebrisky, Jan 16, 2014
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    • "The decision to isolate patients is largely based in physician experience and intuition but this can be misleading [26]. Clinical prediction rules have been developed to assist the clinician in decision making of isolation, with utilization of many statistical techniques such as logistic regression and neural networks, for example [27]. "
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    ABSTRACT: Background Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. Methods Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. Results We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. Conclusions The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in countries with limited resources.
    BMC Pulmonary Medicine 08/2012; 12(1):40. DOI:10.1186/1471-2466-12-40 · 2.40 Impact Factor
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    • "The operational definition of a CPR for this study was that defined by McGinn et al. (2008). Although it has been suggested that there should be a minimum of three variables in a CPR (Laupacis et al., 1997; Stiell and Wells, 1999), previous systematic reviews (Tamariz et al., 2004; Wisnivesky et al., 2005) have included studies with two or more predictor variables. To ensure all relevant studies were identified, this review used the more liberal definition of a CPR as that containing two or more predictor variables. "
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    ABSTRACT: To identify, appraise and determine the clinical readiness of diagnostic, prescriptive and prognostic Clinical Prediction Rules (CPRs) in the physiotherapy management of Low Back Pain (LBP). MEDLINE, EMBASE, CINAHL, AMED and the Cochrane Database of Systematic Reviews were searched from 1990 to January 2010 using sensitive search strategies for identifying CPR and LBP studies. Citation tracking and hand-searching of relevant journals were used as supplemental strategies. Two independent reviewers used a two-phase selection procedure to identify studies that explicitly aimed to develop one or more CPRs involving the physiotherapy management of LBP. Diagnostic, prescriptive and prognostic studies investigating CPRs at any stage of their development, derivation, validation, or impact-analysis, were considered for inclusion using a priori criteria. 7453 unique records were screened with 23 studies composing the final included sample. Two reviewers independently extracted relevant data into evidence tables using a standardised instrument. Identified studies were qualitatively synthesized. No attempt was made to statistically pool the results of individual studies. The 23 scientifically admissible studies described the development of 25 unique CPRs, including 15 diagnostic, 7 prescriptive and 3 prognostic rules. The majority (65%) of studies described the initial derivation of one or more CPRs. No studies investigating the impact phase of rule development were identified. The current body of evidence does not enable confident direct clinical application of any of the identified CPRs. Further validation studies utilizing appropriate research designs and rigorous methodology are required to determine the performance and generalizability of the derived CPRs to other patient populations, clinicians and clinical settings.
    Manual therapy 06/2011; 17(1):9-21. DOI:10.1016/j.math.2011.05.001 · 1.71 Impact Factor
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    ABSTRACT: The wise integration of evidence from health care research into diagnostic decisions could influence patient outcomes by improving clinical diagnosis, reducing unnecessary testing, and minimizing diagnostic error. Yet for many, this promise does not match reality. Here, we collect and categorize barriers to the use of health care research evidence in diagnostic decisions, examine their potential consequences, and propose potential ways to overcome these impediments. Barriers were derived from observations over years of trying to inform clinical diagnoses with research evidence, and from interpretations of the literature. Barriers are categorized into those related to the evidence itself, those related to diagnosticians, and those related to health care systems. Tentative solutions are proffered. Data are lacking on the frequency and impact of the identified barriers, as well as on the effectiveness of the proposed solutions. Barriers to the sensible use of evidence from health care research in clinical diagnosis can be identified and categorized, and possible solutions can be imagined. We could, and should, muster the will to overcome these barriers.
    Journal of Clinical Epidemiology 04/2007; 60(3):217-27. DOI:10.1016/j.jclinepi.2006.06.002 · 3.42 Impact Factor
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