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Lauren S Peetluk

Bio: Lauren S Peetluk is an academic researcher from Vanderbilt University. The author has contributed to research in topics: Tuberculosis & Medicine. The author has an hindex of 2, co-authored 7 publications receiving 12 citations. Previous affiliations of Lauren S Peetluk include Vanderbilt University Medical Center.

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Journal ArticleDOI
02 Mar 2021-BMJ Open
TL;DR: A systematic review and evaluation of prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB was conducted in this article, where the most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV.
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).

24 citations

Journal ArticleDOI
TL;DR: High proportions of this representative Brazilian population had knowledge of LTBI and were willing to seek treatment for it, however, such knowledge was associated with TB-specific stigma, which must include efforts to decrease TB stigma.
Abstract: Tuberculosis (TB) elimination requires treatment of millions of persons with latent M. tuberculosis infection (LTBI). LTBI treatment acceptance depends on population-wide TB knowledge and low stigma, but limited data are available on the relationship between stigma and knowledge. We assessed knowledge of TB disease and LTBI throughout Brazil and examined their association with TB stigma and incidence. We performed a nationwide survey with multi-stage probability design through AmericasBarometer from April–May 2017; the sample was representative of Brazil at regional and national levels. Knowledge of and stigma toward TB were assessed by validated survey questions. Survey-weighted responses of 1532 individuals suggest that 57% of the population knew LTBI can occur, and 90% would seek treatment for it. Regarding active TB, 85% knew TB symptoms, 70% reported they should avoid contact with someone with active TB, and 24% had stigma toward persons with TB (i.e., thought persons with tuberculosis should feel ashamed, or deserved their illness). In regression models adjusting for clinical and demographic variables, knowledge of LTBI was associated with increased stigma toward persons with TB (adjusted odds ratio [OR] = 2.13, 95% confidence interval [CI]: 1·25–3.63, for “should feel ashamed”; OR = 1·82, 95% CI: 1·15–2·89, for “deserve illness”). Adjusting for regional TB incidence did not affect this association. High proportions of this representative Brazilian population had knowledge of LTBI and were willing to seek treatment for it. However, such knowledge was associated with TB-specific stigma. Strategies to educate and implement treatment of latent tuberculosis must include efforts to decrease TB stigma.

10 citations

Journal ArticleDOI
TL;DR: Blood neutrophil metrics could potentially be exploited to develop a simple and rapid test to help determine TB disease severity, monitor drug treatment response, and identify subjects at diagnosis who may respond poorly to treatment.
Abstract: Tuberculosis remains a leading cause of death globally despite curative treatment, partly due to the difficulty of identifying patients who will not respond to therapy. Simple host biomarkers that correlate with response to drug treatment would facilitate improvement in outcomes and the evaluation of novel therapies. In a prospective longitudinal cohort study, we evaluated neutrophil count and phenotype at baseline, as well as during TB treatment in 79 patients [50 (63%) HIV-positive] with microbiologically confirmed drug susceptible TB undergoing standard treatment. At time of diagnosis, blood neutrophils were highly expanded and surface expression of the neutrophil marker CD15 greatly reduced compared to controls. Both measures changed rapidly with the commencement of drug treatment and returned to levels seen in healthy control by treatment completion. Additionally, at the time of diagnosis, high neutrophil count, and low CD15 expression was associated with higher sputum bacterial load and more severe lung damage on chest x-ray, two clinically relevant markers of disease severity. Furthermore, CD15 expression level at diagnosis was associated with TB culture conversion after 2 months of therapy (OR: 0.14, 95% CI: 0.02, 0.89), a standard measure of early TB treatment success. Importantly, our data was not significantly impacted by HIV co-infection. These data suggest that blood neutrophil metrics could potentially be exploited to develop a simple and rapid test to help determine TB disease severity, monitor drug treatment response, and identify subjects at diagnosis who may respond poorly to treatment.

7 citations

Journal ArticleDOI
TL;DR: In this article, the authors developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of HIV-related severity and isoniazid acetylator status via bootstrapped backward selection.
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.

6 citations

Journal ArticleDOI
TL;DR: PLWH gained less weight during the first two months of TB treatment, and lack of weight gain and HIV independently predicted unsuccessful TB treatment outcomes, suggesting weight, an easily-collected biomarker, may identify patients who would benefit from alternative treatment strategies.
Abstract: Background Weight change may inform tuberculosis treatment response, but its predictive power may be confounded by human immunodeficiency virus (HIV). Methods We prospectively followed up adults with culture-confirmed, drug-susceptible, pulmonary tuberculosis receiving standard 4-drug therapy (isoniazid, rifampin, pyrazinamide, and ethambutol) in Brazil. We examined median weight change 2 months after treatment initiation by HIV status, using quantile regression, and unsuccessful tuberculosis treatment outcome (treatment failure, tuberculosis recurrence, or death) by HIV and weight change status, using Cox regression. Results Among 547 participants, 102 (19%) were HIV positive, and 35 (6%) had an unsuccessful outcome. After adjustment for confounders, persons living with HIV (PLWH) gained a median of 1.3 kg (95% confidence interval [CI], -2.8 to .1) less than HIV-negative individuals during the first 2 months of tuberculosis treatment. PLWH were at increased risk of an unsuccessful outcome (adjusted hazard ratio, 4.8; 95% CI, 2.1-10.9). Weight change was independently associated with outcome, with risk of unsuccessful outcome decreasing by 12% (95% CI, .81%-.95%) per 1-kg increase. Conclusions PLWH gained less weight during the first 2 months of tuberculosis treatment, and lack of weight gain and HIV independently predicted unsuccessful tuberculosis treatment outcomes. Weight, an easily collected biomarker, may identify patients who would benefit from alternative treatment strategies.

5 citations


Cited by
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01 Jan 2010
TL;DR: Neutrophils are the predominant cell types infected with Mtb in patients with TB and that these intracellular bacteria appear to replicate rapidly, consistent with a role for neutrophils in providing a permissive site for a final burst of active replication of the bacilli prior to transmission.
Abstract: Background The exact role of neutrophils in the pathogenesis of TB is poorly understood. Recent evidence suggests that neutrophils are not simply scavenging phagocytes in Mycobacterium tuberculosis ( Mtb ) infection. Methods Three different types of clinical specimens from patients with active pulmonary TB who underwent lung surgery were examined: sputum, BAL fluid, and cavity contents. Differential cell separation and quantification were performed for intracellular and extracellular bacteria, and bacterial length was measured using microscopy. Results Neutrophils were more abundant than macrophages in sputum (86.6% ± 2.2% vs 8.4% ± 1.3%) and in BAL fluid (78.8% ± 5.8% vs 11.8% ± 4.1%). Inside the cavity, lymphocytes (41.3% ± 11.2%) were the most abundant cell type, followed by neutrophils (38.8% ± 9.4%) and macrophages (19.5% ± 7.5%). More intracellular bacilli were found in neutrophils than macrophages in sputum (67.6% ± 5.6% vs 25.2% ± 6.5%), in BAL fluid (65.1% ± 14.4% vs 28.3% ± 11.6%), and in cavities (61.8% ± 13.3% vs 23.9% ± 9.3%). The lengths of Mtb were shortest in cavities (1.9± 0.1 ± m), followed by in sputum (2.9 ± 0.1 μm) and in BAL fluid (3.6 ± 0.2 μm). Conclusions Our results show that neutrophils are the predominant cell types infected with Mtb in patients with TB and that these intracellular bacteria appear to replicate rapidly. These results are consistent with a role for neutrophils in providing a permissive site for a final burst of active replication of the bacilli prior to transmission.

388 citations

Journal ArticleDOI
TL;DR: This work proposes an optimized machine learning-based model that extracts optimal texture features from TB-related images and selects the hyper-parameters of the classifiers and highlights the efficiency of modified SVM classifier compared with other standard ones.
Abstract: Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technology provides important services to healthcare professionals and organizations, as well as to patients and their families, researchers, and decision-makers. Thus, any innovations that improve the diagnostic process while maintaining quality and safety are crucial to the development of the healthcare field. Many diseases can be tentatively diagnosed during their initial stages. In this study, all developed techniques were applied to tuberculosis (TB). Thus, we propose an optimized machine learning-based model that extracts optimal texture features from TB-related images and selects the hyper-parameters of the classifiers. Increasing the accuracy rate and minimizing the number of characteristics extracted are our goals. In other words, this is a multitask optimization issue. A genetic algorithm (GA) is used to choose the best features, which are then fed into a support vector machine (SVM) classifier. Using the ImageCLEF 2020 data set, we conducted experiments using the proposed approach and achieved significantly higher accuracy and better outcomes in comparison with the state-of-the-art works. The obtained experimental results highlight the efficiency of modified SVM classifier compared with other standard ones.

20 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated social and health factors associated with unfavourable treatment outcomes in young people with tuberculosis in Brazil and found that young people who received government cash transfers were less likely to have an unfavourability outcome.

18 citations

Journal ArticleDOI
TL;DR: In this article, the authors conducted a retrospective study to assess the impact of the COVID-19 pandemic on TB case detection and treatment outcomes at the Chest Clinic at Connaught Hospital in Freetown, Sierra Leone.
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.

15 citations

Journal ArticleDOI
15 Apr 2021
TL;DR: 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 to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process.
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.

13 citations