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Open accessJournal ArticleDOI: 10.1136/BMJOPEN-2020-044687

Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults.

02 Mar 2021-BMJ Open (BMJ)-Vol. 11, Iss: 3
Abstract: Objective To systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB. Design Systematic review. Data sources PubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020. Study selection and data extraction Studies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures. Results 14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68-0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis. Conclusions TB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models. Trial registration The study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782).

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Topics: Systematic review (53%)
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5 results found


Open accessJournal ArticleDOI: 10.1016/S2214-109X(21)00300-4
Abstract: Summary Background Tuberculosis elimination strategies in Brazil might neglect adolescents and young adults aged 10–24 years, hampering tuberculosis control. However, little is known about factors associated with tuberculosis treatment outcomes in this underserved group. In this study, we aimed to investigate social and health factors associated with unfavourable treatment outcomes in young people with tuberculosis in Brazil. Methods A national retrospective cohort study was done using data from Sistema de Informacao de Agravos de Notificacao (SINAN), the national tuberculosis registry in Brazil. People aged 10–24 years (young people) with tuberculosis registered in SINAN between Jan 1, 2015, and Dec 31, 2018, were included. Unfavourable outcomes were defined as loss to follow-up, treatment failure, and death. Favourable outcome was defined as treatment success. Multiple logistic regression models estimated the association between social and health factors and tuberculosis treatment outcomes. Findings 67 360 young people with tuberculosis were notified to SINAN, and we included 41 870 young people in our study. 7024 (17%) of the 41 870 included individuals had unfavourable treatment outcomes. Young people who received government cash transfers were less likely to have an unfavourable outcome (adjusted odds ratio 0·83, 95% CI 0·70–0·99). Homelessness (3·03, 2·07–4·42), HIV (2·89, 2·45–3·40), and illicit drug use (2·22, 1·93–2·55) were the main factors associated with unfavourable treatment outcome. Interpretation In this national cohort of young people with tuberculosis in Brazil, tuberculosis treatment success rates were lower than WHO End TB Strategy targets, with almost a fifth of participants experiencing unfavourable treatment outcomes. Homelessness, HIV, and illicit drug use were the main factors associated with unfavourable outcome. In Brazil, strategies are required to support this underserved group to ensure favourable tuberculosis treatment outcomes. Funding Wellcome Trust, UK Medical Research Council, and UK Foreign Commonwealth and Development Office.

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1 Citations


Open accessJournal ArticleDOI: 10.3390/TROPICALMED6030154
Abstract: The COVID-19 pandemic has adversely affected tuberculosis (TB) care delivery in high burden countries. We therefore conducted a retrospective study to assess the impact of COVID-19 on TB case detection and treatment outcomes at the Chest Clinic at Connaught Hospital in Freetown, Sierra Leone. Overall, 2300 presumptive cases were tested during the first three quarters of 2020 (intra-COVID-19) versus 2636 in 2019 (baseline), representing a 12.7% decline. Testing declined by 25% in women, 20% in children and 81% in community-initiated referrals. Notwithstanding, laboratory-confirmed TB cases increased by 37.0% and treatment success rate was higher in 2020 (55.6% vs. 46.7%, p = 0.002). Multivariate logistic regression analysis found that age < 55 years (aOR 1.74, 95% CI (1.80, 2.56); p = 0.005), new diagnosis (aOR 1.69, 95% CI (1.16, 2.47); p = 0.007), pulmonary TB (aOR 3.17, 95% CI (1.67, 6.04); p < 0.001), HIV negative status (aOR 1.60, 95%CI (1.24, 2.06); p < 0.001) and self-administration of anti-TB drugs through monthly dispensing versus directly observed therapy (DOT) (aOR 1.56, 95% CI (1.21, 2.03); p = 0.001) independently predicted treatment success. These findings may have policy implications for DOTS in this setting and suggest that more resources are needed to reverse the negative impact of the COVID-19 pandemic on TB program activities in Sierra Leone.

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Topics: Sierra leone (64%)

1 Citations


Open accessJournal ArticleDOI: 10.3390/INFORMATICS8020027
15 Apr 2021-
Abstract: Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class.

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Topics: Tuberculosis (51%)

1 Citations


Open accessJournal ArticleDOI: 10.1177/20499361211034066
Saibin Wang1Institutions (1)
29 Jul 2021-
Abstract: Background:Poor adherence to tuberculosis (TB) treatment is a substantial barrier to global TB control. The aim of this study was to construct a nomogram for predicting the probability of TB treatm...

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Topics: Tuberculosis (53%), Nomogram (51%), Directly Observed Therapy (51%)

Journal ArticleDOI: 10.1093/CID/CIAB598
Abstract: BACKGROUND Despite widespread availability of curative therapy, tuberculosis treatment outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to improve outcomes. Using baseline clinical data, we developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of HIV-related severity and isoniazid acetylator status. METHODS Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly-diagnosed tuberculosis patients in Brazil from 2015-2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary tuberculosis who started first-line anti-tuberculosis therapy and had ≥12 months of follow-up. The endpoint was unsuccessful tuberculosis treatment: composite of death, treatment failure, regimen switch, incomplete treatment, or not evaluated. Missing predictors were imputed. Predictors were chosen via bootstrapped backward selection. Discrimination and calibration were evaluated with c-statistics and calibration plots, respectively. Bootstrap internal validation estimated overfitting, and a shrinkage factor was applied to improve out-of-sample prediction. Incremental value was evaluated with likelihood ratio-based measures. RESULTS Of 944 participants, 191 (20%) had unsuccessful treatment outcomes. The final model included seven baseline predictors: hemoglobin, HIV-infection, drug use, diabetes, age, education, and tobacco use. The model demonstrated good discrimination (c-statistic=0.77; 95% confidence interval: 0.73-0.80) and was well-calibrated (optimism-corrected intercept and slope: -0.12 and 0.89, respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model. CONCLUSIONS The prediction model, using information readily available at treatment initiation, performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients.

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Topics: Population (51%), Regimen (50%)
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69 results found


Open accessJournal ArticleDOI: 10.1016/J.JBI.2008.08.010
Abstract: Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.

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Topics: Translational research informatics (55%), Metadata (52%), Workflow (52%) ... read more

20,023 Citations


Open accessJournal ArticleDOI: 10.1016/J.JBI.2019.103208
Paul A. Harris1, Paul A. Harris2, Robert Taylor2, Brenda L. Minor2  +8 moreInstitutions (2)
Abstract: The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.

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3,234 Citations


Open accessBook
16 Mar 2009-
Abstract: Introduction.- Applications of prediction models.- Study design for prediction models.- Statistical models for prediction.- Overfitting and optimism in prediction models.- Choosing between alternative statistical models.- Dealing with missing values.- Case study on dealing with missing values.- Coding of categorical and continuous predictors.- Restrictions on candidate predictors.- Selection of main effects.- Assumptions in regression models: Additivity and linearity.- Modern estimation methods.- Estimation with external methods.- Evaluation of performance.- Clinical usefulness.- Validation of prediction models.- Presentation formats.- Patterns of external validity.- Updating for a new setting.- Updating for a multiple settings.- Prediction of a binary outcome: 30-day mortality after acute myocardial infarction.- Case study on survival analysis: Prediction of secondary cardiovascular events.- Lessons from case studies.

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Topics: Missing data (52%), Overfitting (50%), Categorical variable (50%)

2,442 Citations


Open accessJournal ArticleDOI: 10.7326/M14-0698
Abstract: The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

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Topics: Checklist (51%)

1,957 Citations


Journal ArticleDOI: 10.1136/BMJ.B604
31 Mar 2009-BMJ
Abstract: In the second article in their series, Patrick Royston and colleagues describe different approaches to building clinical prognostic models

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901 Citations