Implementation and evaluation of an algorithm for isolation of patients with suspected pulmonary tuberculosis

Nomogram to predict multidrug-resistant tuberculosis

Background

Multidrug-resistant tuberculosis (MDR-TB) is burgeoning globally, and has been a serious challenge in TB management. Clinically, the ability to identify MDR-TB is still limited, especially in smear-negative TB. The aim of this study was to develop a nomogram for predicting MDR-TB.

Methods

Demographics and clinical characteristics of both MDR-TB and drug-susceptible TB patients were utilized to develop a nomogram for predicting MDR-TB. The LASSO regression method was applied to filter variables and select predictors, and multivariate logistic regression was used to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). Moreover, calibration analysis and decision curve analysis (DCA) of the model were performed. This study involved a second analysis of a completed prospective cohort study conducted in a country with a high TB burden.

Results

Five variables of TB patients were selected through the LASSO regression method, and a nomogram was built based on these variables. The predictive model yielded an AUC of 0.759 (95% CI, 0.719–0.799), and in the internal validation, the AUC was 0.757 (95% CI, 0.715–0.793). The predictive model was well-calibrated, and DCA showed that if the threshold probability of MDR-TB was between 70 and 90%, using the proposed nomogram to predict MDR-TB would obtain a net benefit.

Conclusions

In this study, a nomogram was constructed that incorporated five demographic and clinical characteristics of TB patients. The nomogram may be of great value for the prediction of MDR-TB in patients with sputum-free or smear-negative TB.

Development of a Predictive Model of Tuberculosis Transmission among Household Contacts

Background

Household contacts of patients with tuberculosis (TB) are at great risk of TB infection. The aim of this study was to develop a predictive model of TB transmission among household contacts.

Method

This was a secondary analysis of data from a prospective cohort study, in which a total of 700 TB patients and 3417 household contacts were enrolled between 2010 and 2013 at two study sites in Peru. The incidence of secondary TB cases among household contacts of index cases was recorded. The LASSO regression method was used to reduce the data dimension and to filter variables. Multivariate logistic regression analysis was applied to develop the predictive model, and internal validation was performed. A nomogram was constructed to display the model, and the AUC was calculated. The calibration curve and decision curve analysis (DCA) were also evaluated.

Results

The incidence of TB disease among the contacts of index cases was 4.4% (149/3417). Ten variables (gender, age, TB history, diabetes, HIV, index patient's drug resistance, socioeconomic status, spoligotypes, and the index-contact share sleeping room status) filtered through the LASSO regression technique were finally included in the predictive model. The model showed good discriminatory ability, with an AUC value of 0.761 (95% CI, 0.723–0.800) for the derivation and 0.759 (95% CI, 0.717–0.796) for the internal validation. The predictive model showed good calibration, and the DCA demonstrated that the model was clinically useful.

Conclusion

A predictive model was developed that incorporates characteristics of both the index patients and the contacts, which may be of great value for the individualized prediction of TB transmission among household contacts.

Validation of a clinical-radiographic score to assess the probability of pulmonary tuberculosis in suspect patients with negative sputum smears

Background

Clinical suspects of pulmonary tuberculosis in which the sputum smears are negative for acid fast bacilli represent a diagnostic challenge in resource constrained settings. Our objective was to validate an existing clinical-radiographic score that assessed the probability of smear-negative pulmonary tuberculosis (SNPT) in high incidence settings in Peru.

Methodology/Principal Findings

We included in two referral hospitals in Lima patients with clinical suspicion of pulmonary tuberculosis and two or more negative sputum smears. Using a published but not externally validated score, patients were classified as having low, intermediate or high probability of pulmonary tuberculosis. The reference standard for the diagnosis of tuberculosis was a positive sputum culture in at least one of 2 liquid (MGIT or Middlebrook 7H9) and 1 solid (Ogawa) media. Prevalence of tuberculosis was calculated in each of the three probability groups.

684 patients were included. 184 (27.8%) had a diagnosis of pulmonary tuberculosis. The score did not perform well in patients with a previous history of pulmonary tuberculosis. In patients without, the prevalence of tuberculosis was 5.1%, 31.7% and 72% in the low, intermediate and high probability group respectively. The area under de ROC curve was 0.76 (95% CI 0.72–0.80) and scores ≥6 had a positive LR of 10.9.

Conclusions/Significance

In smear negative suspects without previous history of tuberculosis, the clinical-radiographic score can be used as a tool to assess the probability of pulmonary tuberculosis and to guide the decision to initiate or defer treatment or to requesting additional tests.

Health care workers' perceptions of respiratory and gastrointestinal algorithms for patient management in emergency care settings

Background

Patients with respiratory or gastrointestinal illness in emergency care settings are often not yet diagnosed but are at risk of transmitting disease. Infection control algorithms delineating a standard approach to patient management decrease risk of secondary exposure, but few articles document health care workers' (HCWs) perceptions as to their effectiveness and ease of implementation.

Methods

A cross-sectional survey approach was used to explore HCWs perceptions in 2 emergency departments of the current algorithms for management of potentially infectious respiratory and gastrointestinal illnesses.

Results

Surveys from 96 HCWs revealed that algorithms were perceived as invaluable in protecting staff, patients, and colleagues. Differences in self-reported compliance, clarity, and ease of implementation of the respiratory algorithm were noted between facilities, likely reflecting variation in the physical plant. Physicians scored significantly lower for compliance with the respiratory algorithm.

Conclusion

Algorithms were perceived to offer a clear and consistent approach to patient management and protect HCWs in spite of environmental and resource limitations.

Tuberculosis and human immunodeficiency virus infection

Mycobacterium tuberculosis infects one third of the world's population, and tuberculosis remains one of the most common infectious diseases of humans. From a global perspective, tuberculosis may be one of the most common HIV-related opportunistic infections. HIV immunosuppression has had a dramatic influence on the epidemiology, natural history and clinical presentation of tuberculosis. Treatment is highly effective for drug susceptible tuberculosis and has been shown to have a significant impact on resistant, especially multidrug-resistant, tuberculosis if started promptly. Directly observed therapy and rigorous adherence to infection control principles have helped control the tuberculosis epidemic in the United States.