POS1436 EPIDEMIOLOGY OF LATENT TUBERCULOSIS INFECTION IN PATIENTS WITH RHEUMATIC IMMUNE-MEDIATED DISEASES. SINGLE UNIVERSITY STUDY OF 1117 PATIENTS

Background:Patients with rheumatologic immune-mediated diseases (R-IMID) with Latent tuberculosis infection (LTBI) requiring biologic therapy (BT) are at an increased risk of active tuberculosis (TB). Screening of LTBI with tuberculin skin test (TST) and/or Interferon (IFN)-γ release assays (IGRA) is recommended before starting of BT.Objectives:In patients with R-IMID previously to BT our aim was to assess a) prevalence of LTBI, b) importance of using a booster test in negative TST and c) to compare TST with the IGRA test.Methods:Cross-sectional single University Hospital study including all patients diagnosed with R-IMID who underwent a TST and/or IGRA in the last five years (2016-2020).TST was performed by a subcutaneous injection of 0.1 ml of purified protein derivative (PPD) with a reading after 72 hours. TST was considered positive with an induration of more than 5 mm of diameter. If the first TST was negative, a new TST (Booster) was performed between 1 and 2 weeks after the first TST.LTBI was diagnosed by a positive IGRA and/or TST and absence of active TB (Chest radiograph). Diagnosis with IGRA vs TST was compared (Cohen’s kappa coefficient).Results:We included 1117 patients (741 women/376 men), mean age 53±15 years with LTBI. Chest radiograph was normal in most of the patients, only 39 patients (3.5%) presented signs of previous TB infection, mostly granuloma. Total LTBI prevalence was 31.7% (354/1117). LTBI prevalence in different underlying R-IMID ranges from 35% in vasculitis up to 26.5% in conectivopathies (Figure 1).Booster was positive in 66 patients (7.7%) out of 859 patients with a negative simple TST. Results of TST (+booster) and IGRA tests are shown in Table 1. TST (+booster) was positive in 187 patients (22.9%) out of 817 with a negative or indeterminate IGRA test. IGRA test was positive in 30 (3.8%) out of 793 patients with a negative TST (+booster). Cohen’s Kappa coefficient between TST (+booster) and IGRA (QFT-plus), was 0.381.Conclusion:LTBI is frequent between patients with R-IMID. Booster after negative simple TST may be useful, since it can detect false negatives for LTBI. IGRA and TST(+booster) show a low grade of agreement. Therefore, performing both tests before BT may be recommendable.Table 1.Results of TST (+booster) and IGRA testIGRA (QFT-Plus)PositiveNegativeIndeterminateUnavailableTotalTST(+Booster)Positive891424548324Negative30500130133793Total1196421751811117* Cohen’s kappa coefficient: 0.381Figure 1.Prevalence of LTBI in different underlying R-IMIDLTBI: Latent tuberculosis infection, PsA: Psoriatic arthritis, RA: Rheumatoid arthritis, SpA: Axial spondyloarthritis.Diagnosis of LTBI: Positive TST(+booster) and/or IGRA test.Disclosure of Interests:David Martínez-López: None declared, Joy Osorio-Chavez: None declared, Carmen Álvarez-Reguera: None declared, Virginia Portilla: None declared, Miguel A González-Gay Speakers bureau: Abbvie, Pfizer, Roche, Sanofi and MSD, Consultant of: Abbvie, Pfizer, Roche, Sanofi and MSD, Grant/research support from: Abbvie, MSD, Jansen and Roche, Ricardo Blanco Speakers bureau: Abbvie, Pfizer, Roche, Bristol-Myers, Janssen, Lilly and MSD, Consultant of: Abbvie, Pfizer, Roche, Bristol-Myers, Janssen, Lilly and MSD, Grant/research support from: Abbvie, MSD, and Roche

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Bayesian network-based methodology for selecting a cost-effective sewer asset management model

Water Science & Technology ◽

10.2166/wst.2020.299 ◽

2020 ◽

Author(s):

Julián Guzmán-Fierro ◽

Sharel Charry ◽

Ivan González ◽

Felipe Peña-Heredia ◽

Nathalie Hernández ◽

...

Keyword(s):

Asset Management ◽

Kappa Coefficient ◽

Wilcoxon Test ◽

Cohen’S Kappa ◽

Validation Data ◽

Statistical Measures ◽

Proactive Management ◽

Cohen's Kappa ◽

Cohen’S Kappa Coefficient

Abstract This paper presents a methodology based on Bayesian Networks (BN) to prioritise and select the minimal number of variables that allows predicting the structural condition of sewer assets to support the strategies in proactive management. The integration of BN models, statistical measures of agreement (Cohen's Kappa coefficient) and a statistical test (Wilcoxon test) were useful for a robust and straightforward selection of a minimum number of variables (qualitative and quantitative) that ensure a suitable prediction level of the structural conditions of sewer pipes. According to the application of the methodology to a specific case study (Bogotás sewer network, Colombia), it found that with only two variables (age and diameter) the model could achieve the same capacity of prediction (Cohen's Kappa coefficient = 0.43) as a model considering several variables. Furthermore, the methodology allows finding the calibration and validation percentage subsets that best fit (80% for calibration and 20% for validation data in the case study) in the model to increase the capacity of prediction with low variations. Furthermore, it found that a model, considering only pipes in critical and excellent conditions, increases the capacity of successful predictions (Cohen's Kappa coefficient from 0.2 to 0.43) for the proposed case study.

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Duration of Exposure Among Close Contacts of Patients With Infectious Tuberculosis and Risk of Latent Tuberculosis Infection

Clinical Infectious Diseases ◽

10.1093/cid/ciz1044 ◽

2020 ◽

Vol 71 (7) ◽

pp. 1627-1634

Author(s):

Mary R Reichler ◽

Awal Khan ◽

Yan Yuan ◽

Bin Chen ◽

James McAuley ◽

...

Keyword(s):

Latent Tuberculosis Infection ◽

Multivariable Analysis ◽

Latent Tuberculosis ◽

Index Patient ◽

The United States ◽

Tuberculosis Infection ◽

Ltbi Prevalence ◽

Male Sex ◽

Positive Index ◽

Close Contacts

Abstract Background Predictors of latent tuberculosis infection (LTBI) among close contacts of persons with infectious tuberculosis (TB) are incompletely understood, particularly the number of exposure hours. Methods We prospectively enrolled adult patients with culture-confirmed pulmonary TB and their close contacts at 9 health departments in the United States and Canada. Patients with TB were interviewed and close contacts were interviewed and screened for TB and LTBI during contact investigations. Results LTBI was diagnosed in 1390 (46%) of 3040 contacts, including 624 (31%) of 2027 US/Canadian-born and 766 (76%) of 1013 non-US/Canadian-born contacts. In multivariable analysis, age ≥5 years, male sex, non-US/Canadian birth, smear-positive index patient, and shared bedroom with an index patient (P < .001 for each), as well as exposure to >1 index patient (P < .05), were associated with LTBI diagnosis. LTBI prevalence increased with increasing exposure duration, with an incremental prevalence increase of 8.2% per 250 exposure hours (P < .0001). For contacts with <250 exposure hours, no difference in prevalence was observed per 50 exposure hours (P = .63). Conclusions Hours of exposure to a patient with infectious TB is an important LTBI predictor, with a possible risk threshold of 250 hours. More exposures, closer exposure proximity, and more extensive index patient disease were additional LTBI predictors.

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Approaches to screening for latent tuberculosis infection in patients with immune-mediated disease prior to commencement of biologics

10.12809/hkmj205096 ◽

2020 ◽

Vol 26 (2) ◽

pp. 93-94

Author(s):

LS Tam

Keyword(s):

Latent Tuberculosis Infection ◽

Latent Tuberculosis ◽

Tuberculosis Infection ◽

Immune Mediated

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Automatic Sleep Stage Classification Based on Two-channel EOG and One-channel EMG

10.21203/rs.3.rs-491468/v1 ◽

2021 ◽

Author(s):

Yanjun LI ◽

Xianglin Yang ◽

Zhi Xu ◽

Yu Zhang ◽

Zhongping Cao

Keyword(s):

Sleep Stage ◽

Kappa Coefficient ◽

Sleep Monitoring ◽

Cohen’S Kappa ◽

Cohen's Kappa ◽

Stage Classification ◽

Sleep Stage Classification ◽

Feature Normalization ◽

Leave One Out ◽

Cohen’S Kappa Coefficient

Abstract The sleep monitoring with PSG severely degrades the sleep quality. In order to simplify the hygienic processing and reduce the load of sleep monitoring, an approach to automatic sleep stage classification without electroencephalogram (EEG) was explored. Totally 108 features from two-channel electrooculogram (EOG) and 6 features from one-channel electromyogram (EMG) were extracted. After feature normalization, the random forest (RF) was used to classify five stages, including wakefulness, REM sleep, N1 sleep, N2 sleep and N3 sleep. Using 114 normalized features from the combination of EOG (108 features) and EMG (6 features), the Cohen’s kappa coefficient was 0.749 and the accuracy was 80.8% by leave-one -out cross-validation (LOOCV) for 124 records from ISRUC-Sleep. As a reference for AASM standard, the Cohen’s kappa coefficient was 0.801 and the accuracy was 84.7% for the same dataset based on 438 normalized features from the combination of EEG (324 features), EOG (108 features) and EMG (6 features). In conclusion, the approach by EOG+EMG with the normalization can reduce the load of sleep monitoring, and achieves comparable performances with the "gold standard" EEG+EOG+EMG on sleep classification.

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Safety of latent tuberculosis infection treatment in older patients with immune-mediated inflammatory diseases

Clinical Rheumatology ◽

10.1007/s10067-021-05969-0 ◽

2021 ◽

Author(s):

Chiwook Chung ◽

Yeon Joo Kim ◽

Kyung-Wook Jo ◽

Tae Sun Shim

Keyword(s):

Older Patients ◽

Latent Tuberculosis Infection ◽

Inflammatory Diseases ◽

Latent Tuberculosis ◽

Tuberculosis Infection ◽

Infection Treatment ◽

Immune Mediated

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Patient-Specific Explanations for Predictions of Clinical Outcomes

ACI Open ◽

10.1055/s-0039-1697907 ◽

2019 ◽

Vol 03 (02) ◽

pp. e88-e97

Author(s):

Mohammadamin Tajgardoon ◽

Malarkodi J. Samayamuthu ◽

Luca Calzoni ◽

Shyam Visweswaran

Keyword(s):

Machine Learning ◽

Clinical Outcomes ◽

Characteristic Curve ◽

Community Acquired Pneumonia ◽

Kappa Coefficient ◽

Patient Specific ◽

P Value ◽

Cohen’S Kappa ◽

Cohen's Kappa ◽

Cohen’S Kappa Coefficient

Abstract Background Machine learning models that are used for predicting clinical outcomes can be made more useful by augmenting predictions with simple and reliable patient-specific explanations for each prediction. Objectives This article evaluates the quality of explanations of predictions using physician reviewers. The predictions are obtained from a machine learning model that is developed to predict dire outcomes (severe complications including death) in patients with community acquired pneumonia (CAP). Methods Using a dataset of patients diagnosed with CAP, we developed a predictive model to predict dire outcomes. On a set of 40 patients, who were predicted to be either at very high risk or at very low risk of developing a dire outcome, we applied an explanation method to generate patient-specific explanations. Three physician reviewers independently evaluated each explanatory feature in the context of the patient's data and were instructed to disagree with a feature if they did not agree with the magnitude of support, the direction of support (supportive versus contradictory), or both. Results The model used for generating predictions achieved a F1 score of 0.43 and area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval [CI]: 0.81–0.87). Interreviewer agreement between two reviewers was strong (Cohen's kappa coefficient = 0.87) and fair to moderate between the third reviewer and others (Cohen's kappa coefficient = 0.49 and 0.33). Agreement rates between reviewers and generated explanations—defined as the proportion of explanatory features with which majority of reviewers agreed—were 0.78 for actual explanations and 0.52 for fabricated explanations, and the difference between the two agreement rates was statistically significant (Chi-square = 19.76, p-value < 0.01). Conclusion There was good agreement among physician reviewers on patient-specific explanations that were generated to augment predictions of clinical outcomes. Such explanations can be useful in interpreting predictions of clinical outcomes.

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