Host Lipidome and Tuberculosis Treatment Failure

2021 ◽  
pp. 2004532
Author(s):  
Rupak Shivakoti ◽  
John W. Newman ◽  
Luke Elizabeth Hanna ◽  
Artur T. L. Queiroz ◽  
Kamil Borkowski ◽  
...  
Keyword(s):  
Treatment Failure ◽  
Plasma Lipids ◽  
Area Under The Curve ◽  
Subsequent Treatment ◽  
Least Square ◽  
Prognostic Accuracy ◽  
Tb Treatment ◽  
Lower Baseline ◽  
Baseline Levels ◽  
Prospective Association

IntroductionHost lipids play important roles in Tuberculosis (TB) pathogenesis. Whether host lipids at TB treatment initiation (baseline) affect subsequent treatment outcomes has not been well-characterised. We utilised unbiased lipidomics to study the prospective association of host lipids with TB treatment failure.MethodsA case-control study (n=192), nested within a prospective cohort study, was used to investigate the association of baseline plasma lipids with TB treatment failure among adults with pulmonary TB. Cases (n=46) were defined as TB treatment failure, while controls (n=146) were those without failure. Complex lipids and inflammatory lipid mediators were measured using liquid chromatography mass spectrometry techniques. Adjusted least square regression was used to assess differences in groups. In addition, machine learning identified lipids with highest area under the curve (AUC) to classify cases and controls.ResultsBaseline levels of 32 lipids differed between controls and those with treatment failure after false discovery rate adjustment. Treatment failure was associated with lower baseline levels of cholesteryl esters (CE) and oxylipin, and higher baseline levels of ceramides and triglycerides compared to controls. Two CE lipids combined in a unique classifier model provided an AUC of 0.79 (0.65–0.93) in the test dataset for prediction of TB treatment failure.ConclusionsWe identified lipids, some with known roles in TB pathogenesis, associated with TB treatment failure. A lipid signature with prognostic accuracy for TB treatment failure was also identified. These lipids could be potential targets for risk-stratification, adjunct therapy and treatment monitoring.

Minimizing Carry-Over Effects After Treatment Failure and Maximizing Therapeutic Outcome

2014 ◽  
Vol 222 (3) ◽  
pp. 171-178 ◽  
Author(s):  
Mareile Hofmann ◽  
Nathalie Wrobel ◽  
Simon Kessner ◽  
Ulrike Bingel
Keyword(s):  
Treatment Failure ◽  
Human Subjects ◽  
Subsequent Treatment ◽  
Clinical Samples ◽  
Administration Route ◽  
Healthy Human ◽  
Negative Experiences ◽  
Analgesic Treatment ◽  
Balanced Design ◽  
Carry Over

According to experimental and clinical evidence, the experiences of previous treatments are carried over to different therapeutic approaches and impair the outcome of subsequent treatments. In this behavioral pilot study we used a change in administration route to investigate whether the effect of prior treatment experience on a subsequent treatment depends on the similarity of both treatments. We experimentally induced positive or negative experiences with a topical analgesic treatment in two groups of healthy human subjects. Subsequently, we compared responses to a second, unrelated and systemic analgesic treatment between both the positive and negative group. We found that there was no difference in the analgesic response to the second treatment between the two groups. Our data indicate that a change in administration route might reduce the influence of treatment history and therefore be a way to reduce negative carry-over effects after treatment failure. Future studies will have to validate these findings in a fully balanced design including larger, clinical samples.

Efficacy of galcanezumab in patients with episodic cluster headaches and a history of preventive treatment failure

2021 ◽  
Vol 4 ◽  
pp. 251581632110156
Author(s):  
Brian Plato ◽  
J Scott Andrews ◽  
Mallikarjuna Rettiganti ◽  
Antje Tockhorn-Heidenreich ◽  
Jennifer Bardos ◽  
...  
Keyword(s):  
Cluster Headache ◽  
Treatment Failure ◽  
Preventive Treatment ◽  
Mean Difference ◽  
Least Square ◽  
Double Blind Study ◽  
Double Blind ◽  
Cluster Headache Attack ◽  
Episodic Cluster Headache ◽  
History Of

Objective: The efficacy of galcanezumab was evaluated in patients with episodic cluster headache and history of preventive treatment failure. Methods: In the randomized, 8-week, double-blind study (CGAL), patients with episodic cluster headache received once-monthly subcutaneous injections of galcanezumab 300 mg or placebo. Patients who completed CGAL and enrolled in an open-label study were queried for preventive treatment history. In a subset of patients with a known history of failure of verapamil or any other prior preventive treatment, a post hoc analysis of least square mean change from baseline in weekly cluster headache attack frequency across Weeks 1 to 3 was assessed. Results: Fifteen patients provided data for known history of prior preventive treatment failure (6 placebo, 9 galcanezumab), of whom 11 failed verapamil. The mean reduction in the weekly frequency of cluster headache attacks was greater with galcanezumab compared to placebo among patients with prior preventive treatment failure (8.2 versus 2.4); mean difference 5.8 (95% confidence interval [CI] 2.0, 13.6) and among patients with verapamil failure (10.1 versus 1.6); mean difference 8.5 (95% CI 0.4, 16.7). Conclusion: In this exploratory analysis of patients with a known history of prior preventive treatment failures, treatment with galcanezumab resulted in greater mean reductions in weekly cluster headache attacks compared with placebo. ClinicalTrials.gov: NCT02397473 (I5Q-MC-CGAL) NCT02797951 (I5Q-MC-CGAR)

scholarly journals Gene expression-based biomarkers designating glioblastomas resistant to multiple treatment strategies

2021 ◽  
Author(s):  
Otília Menyhárt ◽  
János Tibor Fekete ◽  
Balázs Győrffy
Keyword(s):  
Gene Expression ◽  
Treatment Strategies ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
Subsequent Treatment ◽  
Survival Status ◽  
Multiple Treatment ◽  
Post Surgery ◽  
Potential Biomarker ◽  
Elevated Expression

Abstract Despite advances in molecular characterization of glioblastoma multiforme (GBM), only a handful of predictive biomarkers exist with limited clinical relevance. We aimed to identify differentially expressed genes in tumor samples collected at surgery associated with response to subsequent treatment, including temozolomide (TMZ) and nitrosoureas. Gene expression was collected from multiple independent datasets. Patients were categorized as responders/nonresponders based on their survival status at 16 months post-surgery. For each gene, the expression was compared between responders and nonresponders with a Mann-Whitney U test and receiver operating characteristic. The package "roc" was used to calculate the area under the curve (AUC). The integrated database comprises 454 GBM patients from three independent datasets and 10,103 genes. The highest proportion of responders (68%) were among patients treated with TMZ combined with nitrosoureas, where FCGR2B upregulation provided the strongest predictive value (AUC=0.72, p < 0.001). Elevated expression of CSTA and MRPS17 was associated with a lack of response to multiple treatment strategies. DLL3 upregulation was present in subsequent responders to any treatment combination containing TMZ. Three genes (PLSCR1, MX1, and MDM2) upregulated both in the younger cohort and in patients expressing low MGMT delineate a subset of patients with worse prognosis within a population generally associated with a favorable outcome. The identified transcriptomic changes provide biomarkers of responsiveness, offer avenues for preclinical studies, and may enhance future GBM patient stratifications. The described methodology provides a reliable pipeline for the initial testing of potential biomarker candidates for future validation studies.

scholarly journals Diabetes among tuberculosis patients and its impact on tuberculosis treatment in South Asia: a systematic review and meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sanju Gautam ◽  
Nipun Shrestha ◽  
Sweta Mahato ◽  
Tuan P. A. Nguyen ◽  
Shiva Raj Mishra ◽  
...  
Keyword(s):  
Systematic Review ◽  
Sri Lanka ◽  
Treatment Failure ◽  
South Asia ◽  
Treatment Outcomes ◽  
Meta Analysis ◽  
Tb Treatment ◽  
Effect Model ◽  
Pooled Prevalence ◽  
The Impact

AbstractThe escalating burden of diabetes is increasing the risk of contracting tuberculosis (TB) and has a pervasive impact on TB treatment outcomes. Therefore, we conducted this systematic review and meta-analysis to examine the burden of diabetes among TB patients and assess its impact on TB treatment in South Asia (Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, India, Pakistan, and Sri Lanka). PubMed, Excerpta Medica Database (EMBASE), and CINAHL databases were systematically searched for observational (cross-sectional, case–control and cohort) studies that reported prevalence of diabetes in TB patients and published between 1 January 1980 and 30 July 2020. A random-effect model for computing the pooled prevalence of diabetes and a fixed-effect model for assessing its impact on TB treatment were used. The review was registered with PROSPERO number CRD42020167896. Of the 3463 identified studies, a total of 74 studies (47 studies from India, 10 from Pakistan, four from Nepal and two from both Bangladesh and Sri-Lanka) were included in this systematic review: 65 studies for the prevalence of diabetes among TB patients and nine studies for the impact of diabetes on TB treatment outcomes. The pooled prevalence of diabetes in TB patients was 21% (95% CI 18.0, 23.0; I2 98.3%), varying from 11% in Bangladesh to 24% in Sri-Lanka. The prevalence was higher in studies having a sample size less than 300 (23%, 95% CI 18.0, 27.0), studies conducted in adults (21%, 95% CI 18.0, 23.0) and countries with high TB burden (21%, 95% CI 19.0, 24.0). Publication bias was detected based on the graphic asymmetry of the funnel plot and Egger’s test (p < 0.001). Compared with non-diabetic TB patients, patients with TB and diabetes were associated with higher odds of mortality (Odds Ratio (OR) 1.7; 95% CI 1.2, 2.51; I2 19.4%) and treatment failure (OR 1.7; 95% CI 1.1, 2.4; I2 49.6%), but not associated with Multi-drug resistant TB (OR 1.0; 95% CI 0.6, 1.7; I2 40.7%). This study found a high burden of diabetes among TB patients in South Asia. Patients with TB-diabetes were at higher risk of treatment failure and mortality compared to TB alone. Screening for diabetes among TB patients along with planning and implementation of preventive and curative strategies for both TB and diabetes are urgently needed.

scholarly journals Circulatory Inflammatory Mediators in the Prediction of Anti-Tuberculous Drug-Induced Liver Injury Using RUCAM for Causality Assessment

Biomedicines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 891
Author(s):  
Cheng-Maw Ho ◽  
Chi-Ling Chen ◽  
Chia-Hao Chang ◽  
Meng-Rui Lee ◽  
Jann-Yuan Wang ◽  
...  
Keyword(s):  
Liver Injury ◽  
Inflammatory Mediators ◽  
Cox Regression ◽  
Causality Assessment ◽  
Area Under The Curve ◽  
Assessment Method ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Tb Treatment ◽  
Pre Treatment

Background: Anti-tuberculous (TB) medications are common causes of drug-induced liver injury (DILI). Limited data are available on systemic inflammatory mediators as biomarkers for predicting DILI before treatment. We aimed to select predictive markers among potential candidates and to formulate a predictive model of DILI for TB patients. Methods: Adult active TB patients from a prospective cohort were enrolled, and all participants received standard anti-tuberculous treatment. Development of DILI, defined as ≥5× ULN for alanine transaminase or ≥2.6× ULN of total bilirubin with causality assessment (RUCAM, Roussel Uclaf causality assessment method), was regularly monitored. Pre-treatment plasma was assayed for 15 candidates, and a set of risk prediction scores was established using Cox regression and receiver-operating characteristic analyses. Results: A total of 19 (7.9%) in 240 patients developed DILI (including six carriers of hepatitis B virus) following anti-TB treatment. Interleukin (IL)-22 binding protein (BP), interferon gamma-induced protein 1 (IP-10), soluble CD163 (sCD163), IL-6, and CD206 were significant univariable factors associated with DILI development, and the former three were backward selected as multivariable factors, with adjusted hazards of 0.20 (0.07–0.58), 3.71 (1.35–10.21), and 3.28 (1.07–10.06), respectively. A score set composed of IL-22BP, IP-10, and sCD163 had an improved area under the curve of 0.744 (p < 0.001). Conclusions: Pre-treatment IL-22BP was a protective biomarker against DILI development under anti-TB treatment, and a score set by additional risk factors of IP-10 and sCD163 employed an adequate DILI prediction.

scholarly journals Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Xiaoping Chen ◽  
Rujie Chen ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Subsequent Treatment ◽  
Training Dataset ◽  
Discriminative Ability ◽  
Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

scholarly journals Terminal Ileum Thickness During Maintenance Therapy Is a Predictive Marker of the Outcome of Infliximab Therapy in Crohn Disease

2020 ◽  
Vol 26 (10) ◽  
pp. 1619-1625
Author(s):  
Ahmad Albshesh ◽  
Bella Ungar ◽  
Shomron Ben-Horin ◽  
Rami Eliakim ◽  
Uri Kopylov ◽  
...  
Keyword(s):  
Treatment Failure ◽  
Crohn Disease ◽  
Terminal Ileum ◽  
Univariate Analysis ◽  
Multivariable Analysis ◽  
Maintenance Phase ◽  
Predictive Marker ◽  
Failure Criteria ◽  
Subsequent Treatment ◽  
Response To Therapy

Abstract Background Mucosal healing has been associated with long-term response to therapy for Crohn disease (CD). However, little is known about the significance of terminal ileum (TI) transmural thickness in predicting clinical outcomes. Methods In this retrospective observational cohort study, we examined the association of an index ultrasonographic assessment of TI thickness during the maintenance phase and the subsequent clinical outcome of CD in a cohort of patients treated with infliximab (IFX). Treatment failure was defined as treatment discontinuation because of lack of efficacy, a need for dose escalation, or surgery. Clinical response was defined as treatment continuation in the absence of any of the aforementioned failure criteria. Results Sixty patients with CD receiving IFX therapy were included in the study. The patients were followed for a median of 16 months (5-24 months) after an index intestinal ultrasound. Thirty-eight patients (63.3%) maintained response to the therapy and 22 patients (36.6%) failed the treatment, with a mean follow up of 10.5 months (6.5-17 months) vs 9.25 months (1-10.25 months), respectively. On univariate analysis, the only variables differing between treatment response and failure were a TI thickness of 2.8 vs 5 mm (P &lt; 0.0001) and an IFX trough level of 6.6 vs 3.9 µg/mL (P = 0.008). On multivariable analysis, only a small bowel thickness of ≥4 mm was associated with the risk of treatment failure (odds ratio, 2.9; 95% CI, 1.49-5.55; P = 0.002). Conclusions Our findings suggest that transmural thickness of ≥4 mm can predict subsequent treatment failure in patients with CD treated using IFX, indicating transmural thickness &lt;4 mm as a potential novel valuable therapeutic target.

scholarly journals P1162RED BLOOD CELL DISTRIBUTION WIDTH AND PERITONEAL DIALYSIS-ASSOCIATED PERITONITIS PROGNOSIS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Peng He ◽  
Jinping Hu ◽  
Chen Huang
Keyword(s):  
Peritoneal Dialysis ◽  
Blood Cell ◽  
Treatment Failure ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Estimating Equation ◽  
Continuous Variable ◽  
Catheter Removal ◽  
Cell Distribution ◽  
Distribution Width

Abstract Background and Aims The red blood cell distribution width (RDW) is a parameter of the heterogeneity of circulating erythrocyte size. Recent researches have pointed out a link among RDW, chronic kidney disease and inflammation. We sought to investigate the prognostic value of baseline RDW in patients with peritoneal dialysis-associated peritonitis (PDAP), which remains unknown. Method Our study included 337 peritonitis episodes experienced by 202 patients who were undergoing continuous ambulatory peritoneal dialysis (CAPD) at a single center from 2013 to 2018. Episodes were categorized according to the tertiles of baseline RDW levels (T1, &lt;13.2%; T2, 13.2 - 14.3%; T3, &gt;14.3%). Routine logistic regression, generalized estimating equation and restricted cubic spline were used to estimate the association between RDW and treatment failure, which was defined as relapse or recurrent episodes, catheter removal, or death during peritonitis therapy. The predictive performance of multivariate models including RDW and other potential predictors were also evaluated. Results After adjusting for other potential predictors, RDW exhibited an incremental relationship with the risk of treatment failure. The baseline RDW of &gt;14.3% (T3) indicated a 43% and 52% increased venture of treatment failure in logistic and GEE analyses, respectively, compared with &lt; 13.2% (T1). As a continuous variable, the fitting curve based on restricted cubic spiline showed that the relationship was nolinearly but positively correlated. The multivariate model A (combined RDW with age, duration on PD, albumin and ferritin) showed an area under the curve (AUC) of 0.671 (95% CI, 0.592 to 0.749) for the prediction of treatment failure. In sensitive analyses, the predictive performances for other unfavourable outcomes were also reliable (AUC for catheter removal, 0.72, 0.62 to 0.81; for relapse or recurrence, 0.61, 0.48 to 0.74). Conclusion Higher levels of baseline RDW was significantly associated with greater rates of treatment failure among peritonitis episodes independent of other potential predictors.

scholarly journals Melanoma Prognosis: Accuracy of the American Joint Committee on Cancer Staging Manual Eighth Edition

2020 ◽  
Vol 112 (9) ◽  
pp. 921-928 ◽  
Author(s):  
Shirin Bajaj ◽  
Douglas Donnelly ◽  
Melissa Call ◽  
Paul Johannet ◽  
Una Moran ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Statistical Tests ◽  
Area Under The Curve ◽  
Cancer Staging ◽  
Stage I ◽  
Stage Iii ◽  
Prognostic Accuracy ◽  
American Joint Committee ◽  
Melanoma Prognosis ◽  
Eighth Edition

Abstract Background The American Joint Committee on Cancer (AJCC) maintains that the eighth edition of its Staging Manual (AJCC8) has improved accuracy compared with the seventh (AJCC7). However, there are concerns that implementation may disrupt analysis of active clinical trials for stage III patients. We used an independent cohort of melanoma patients to test the extent to which AJCC8 has improved prognostic accuracy compared with AJCC7. Methods We analyzed a cohort of 1315 prospectively enrolled patients. We assessed primary tumor and nodal classification of stage I–III patients using AJCC7 and AJCC8 to assign disease stages at diagnosis. We compared recurrence-free (RFS) and overall survival (OS) using Kaplan-Meier curves and log-rank tests. We then compared concordance indices of discriminatory prognostic ability and area under the curve of 5-year survival to predict RFS and OS. All statistical tests were two-sided. Results Stage IIC patients continued to have worse outcomes than stage IIIA patients, with a 5-year RFS of 26.5% (95% confidence interval [CI] = 12.8% to 55.1%) vs 56.0% (95% CI = 37.0% to 84.7%) by AJCC8 (P = .002). For stage I, removing mitotic index as a T classification factor decreased its prognostic value, although not statistically significantly (RFS concordance index [C-index] = 0.63, 95% CI = 0.56 to 0.69; to 0.56, 95% CI = 0.49 to 0.63, P = .07; OS C-index = 0.48, 95% CI = 0.38 to 0.58; to 0.48, 95% CI = 0.41 to 0.56, P = .90). For stage II, prognostication remained constant (RFS C-index = 0.65, 95% CI = 0.57 to 0.72; OS C-index = 0.61, 95% CI = 0.50 to 0.72), and for stage III, AJCC8 yielded statistically significantly enhanced prognostication for RFS (C-index = 0.65, 95% CI = 0.60 to 0.70; to 0.70, 95% CI = 0.66 to 0.75, P = .01). Conclusions Compared with AJCC7, we demonstrate that AJCC8 enables more accurate prognosis for patients with stage III melanoma. Restaging a large cohort of patients can enhance the analysis of active clinical trials.

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