Dataset Information

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

ABSTRACT: 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. 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. 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). 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.

SUBMITTER: Ho CM

PROVIDER: S-EPMC8389605 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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

Ho Cheng-Maw CM Chen Chi-Ling CL Chang Chia-Hao CH Lee Meng-Rui MR Wang Jann-Yuan JY Hu Rey-Heng RH Lee Po-Huang PH

Biomedicines 20210725 8

<h4>Background</h4>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.<h4>Methods</h4>Adult active TB patients from a prospective cohort were enrolled, and all participants received standard anti-tuberculous treatment. Deve ...[more]

PMID: 34440095

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Project description:BackgroundAnti-tuberculosis drug-induced liver injury (ATB-DILI) is one of the most common adverse reactions that brings great difficulties to the treatment of tuberculosis. Thus, early identification of individuals at risk for ATB-DILI is urgent. We conducted a prospective cohort study to analyze the urinary metabolic and microbial profiles of patients with ATB-DILI before drug administration. And machine learning method was used to perform prediction model for ATB-DILI based on metabolomics, microbiome and clinical data.MethodsA total of 74 new TB patients treated with standard first-line anti-TB treatment regimens were enrolled from West China Hospital of Sichuan University. Only patients with an updated RUCAM score of 6 or more were accepted in this study. Nontargeted metabolomics and microbiome analyses were performed on urine samples prior to anti-tuberculosis drug ingestion to screen the differential metabolites and microbes between the ATB-DILI group and the non-ATB-DILI group. Integrating electronic medical records, metabolomics, and microbiome data, four machine learning methods was used, including random forest algorithm, artificial neural network, support vector machine with the linear kernel and radial basis function kernel.ResultsOf all included patients, 69 patients completed follow-up, with 16 (23.19%) patients developing ATB-DILI after antituberculosis treatment. Finally, 14 ATB-DILI patients and 30 age- and sex-matched non-ATB-DILI patients were subjected to urinary metabolomic and microbiome analysis. A total of 28 major differential metabolites were screened out, involving bile secretion, nicotinate and nicotinamide metabolism, tryptophan metabolism, ABC transporters, etc. Negativicoccus and Actinotignum were upregulated in the ATB-DILI group. Multivariate analysis also showed significant metabolic and microbial differences between the non-ATB-DILI and severe ATB-DILI groups. Finally, the four models showed high accuracy in predicting ATB-DILI, with the area under the curve of more than 0.85 for the training set and 1 for the validation set.ConclusionThis study characterized the metabolic and microbial profile of ATB-DILI risk individuals before drug ingestion for the first time. Metabolomic and microbiome characteristics in patient urine before anti-tuberculosis drug ingestion may predict the risk of liver injury after ingesting anti-tuberculosis drugs. Machine learning algorithms provides a new way to predict the occurrence of ATB-DILI among tuberculosis patients.

Project description:BackgroundDrug-induced liver injury (DILI) represents an increasing morbidity in the general population, but more so in the elderly cohort of patients. Despite this, the concept of its prevention through prospective analysis has largely remained unexamined. We evaluated the utility of recently validated adverse drug reactions (ADR) avoidability tool in a cohort of elderly patients with DILI.MethodsWe examined 38 DILI-drug pairs from n=38 patients in a prospective cohort of patients presenting with adverse drug reactions to a Weill Cornell-affiliated tertiary hospital between February 2019 and January 2020. DILI outcomes were adjudicated by the updated Roussel Uclaf Causality Assessment Method (RUCAM). Two clinical pharmacologists and two general physicians utilized the Liverpool adverse drug reactions avoidability tool (LAAT) and the modified Hallas tools to rate the preventability of DILI-drug pairs. Inter-rater, exact agreement proportions, as well as intraclass correlation coefficients were generated and expressed as ordinal outcomes.ResultsThe cases examined for the determination of DILI avoidability had probability likelihood of "probable" or "highly probable" by the updated RUCAM scale. Examination of the 38 DILI-drug pairs (n= 38 patients) resulted in a total of 152 ordinal outcome decisions. We found about 32.3% (50/152) and 34.2% (52/152) of DILI-drug pairs were rated as "avoidable" ("probable" or "definite") by the LAAT and the modified Hallas tools respectively. The overall median Krippendorf's kappa with the LAAT was 0.61 (SE 0.12, CI 0.36, 0.85) and for modified Hallas tool was 0.53 (SE 0.18; CI 0.16, 0.89). The inter-rater correlation coefficient (ICC) for the LAAT and modified Hallas were 0.50 [0.32, 0.65] and 0.63 [0.48, 0.76] respectively. Exact pairwise agreement was present in 30/38 (IQR 29.5, 34.5), and 28/38 (IQR 27.5-35.5) of DILI-ADR pairs using the LAAT and modified Hallas tools respectively.ConclusionWe found a significant proportion of drug-induced liver injury adjudicated by the updated RUCAM scale in elderly hospitalized cohort of patients were avoidable with significant implication for therapeutic commissioning as well as cost effectiveness interventions in this cohort of patients.

Dataset Information

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

Publications

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

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