Gut microbial RNA and DNA analysis predicts hospitalizations in cirrhosis

Bajaj, Jasmohan S.; Thacker, Leroy R.; Fagan, Andrew; White, Melanie B.; Gavis, Edith; Hylemon, Phillip B.; Brown, Robert E.; Acharya, Chathur; Heuman, Douglas M.; Fuchs, Michael; Dalmet, Swati; Sikaroodi, Masoumeh; Gillevet, Patrick M.

doi:10.1172/jci.insight.98019

Cited by 40 publications

(33 citation statements)

References 35 publications

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“…For this analysis, we considered them equivalent. We also assumed the sample was stool or saliva, both of which have shown to predict hospitalisations and have similar costs for analysis . While fungal profiles have also been shown to predict outcomes in cirrhosis, for this analysis we focused only on bacteria …”

Section: Discussionmentioning

confidence: 99%

“…However, with endpoints as expensive and potentially preventable as admissions in cirrhosis, even this relatively modest increase in prediction is cost‐effective. The studies determining microbial contribution towards prognostication are two‐centre and multi‐centre studies, largely from the US . Therefore, these data are valid for US‐based patients, and may not be generalisable elsewhere.…”

Section: Discussionmentioning

confidence: 99%

“…90‐day transition percentages for staying alive without admission/LT was 50%, 4% for LT, 3% for death and 40% for admissions (range 25%‐60%). The estimate for hospitalisations was made from an admixture of several studies in outpatients and inpatients …”

Section: Methodsmentioning

confidence: 99%

“…Infections and HE are also one of the leading causes of admissions in cirrhosis . Therefore, recent studies in cirrhosis have used microbial composition analysis to predict inpatient and outpatient outcomes . These analyses included testing of faecal and salivary microbiota to predict 90‐day hospitalisations, inpatient deaths as well as admissions and readmissions .…”

Section: Introductionmentioning

confidence: 99%

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Cost‐effectiveness of integrating gut microbiota analysis into hospitalisation prediction in cirrhosis

et al. 2020

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Summary Background Admissions in cirrhosis are expensive and often unpredictable based on purely clinical variables. Admissions could be related to complications associated with gut microbial changes, which can improve prognostication. However, the cost‐effectiveness remains unclear. Aims Determine cost‐effectiveness of adding gut microbiota analysis to clinical parameters in prediction and subsequent reduction of admissions in cirrhosis. Methods Using a Markov model of 1000 cirrhosis patients over 90 days, we modelled microbiota testing using 16srRNA ($250/sample), low‐depth ($350/sample) and high‐depth ($650/sample) metagenomics added to standard‐of‐care (SOC) for prevention of admissions using standard outcome costs and rates of development. We generated quality of life years (QALY) and Incremental cost‐effectiveness ratios (ICER) for the base scenarios and performed sensitivity analyses by varying costs for outcomes (transplant, death, admission) and admission rates (40%, range 25%‐60%). Results Using fixed costs of outcomes and outcome rates, microbiota analysis was cost‐saving ($47K‐$97K) at $250 and $350/sample if admissions were reduced by 5% over SOC and >10% with $650/sample. When costs of liver transplant, death and admissions were varied, these cost‐savings remained robust provided there was >2.1% reduction (range 1.3%‐3.2%) for $250/sample, >2.9% (range 1.8%‐4.4%) for $350/sample and >5.4% (range 3.3%‐8.2%) for $650/sample. These cost‐savings remained robust even when the assumed admission rate was varied for all sample cost values. Conclusions Gut microbiota analysis is cost‐effective for predicting and potentially preventing 90‐day admissions in cirrhosis over current SOC. This cost‐saving remained robust even after sensitivity analyses that varied the background admission rates.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cost‐effectiveness of integrating gut microbiota analysis into hospitalisation prediction in cirrhosis

et al. 2020

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“…More recently the use of the DNA and RNA profile of the microbiota has been applied for morbidity prediction 65 . The reasoning for comparing these two strategies is because the RNA profile may be more reflective of the metabolic function of the species while the DNA profile reflects a combination of living and dead bacteria.…”

Section: Microbiota As a Prognostication Toolmentioning

confidence: 99%

Altered Microbiome in Patients With Cirrhosis and Complications

Acharya

Bajaj

2019

Clinical Gastroenterology and Hepatology

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The microbiome in cirrhosis is affected by multiple processes occurring at the level of the gut and systemically. This dysbiosis, or unfavorable microbiota profile, is present at several body sites and is associated with higher systemic inflammation, bacterial infections and poor outcomes. This increased morbidity in cirrhosis stems from an immune paralysis state that is partly related to the constant stimulation of the immune system by this dysbiosis. Dysbiosis as a dynamic event worsens with decompensation such as with hepatic encephalopathy, infections or acute-on-chronic liver failure (ACLF). These microbial patterns could be applied as diagnostic and prognostic measures in cirrhosis in the outpatient and inpatient setting. Current therapies for cirrhosis have differing impacts on gut microbial composition and functionality. Dietary modifications and the oral cavity have emerged as newer targetable factors to modulate the microbiome, which could affect inflammation and, potentially improve outcomes. Additionally, fecal microbial transplant is being increasingly studied to provide compositional and functional modulation of the microbiome. Ultimately, a combination of targeted therapies may be needed to provide an optimal gut milieu to improve outcomes in cirrhosis.

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