Secondary analysis of large databases for hepatology research

"Beyond MELD" - Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation

In this review article, we discuss the model for end-stage liver disease (MELD) score and its dual purpose in general and transplant hepatology. As the landscape of liver disease and transplantation has evolved considerably since the advent of the MELD score, we summarise emerging concepts, methodologies, and technologies that may improve mortality prognostication in the future. Finally, we explore how these novel concepts and technologies may be incorporated into clinical practice.

Caracterização epidemiológica da hepatite B em idosos

Resumo Objetivo Caracterizar o perfil e identificar os fatores associados à Hepatite B na população com 60 anos ou mais de idade da região Sudoeste do Paraná, Brasil. Método Trata-se de um estudo epidemiológico descritivo e inferencial que analisou quantitativamente dados secundários obtidos através do Sistema Nacional de Agravos de Notificação (SINAN), entre 2007 a 2017. Resultados A Hepatite B foi mais frequente em homens, com baixa escolaridade e em indivíduos que exerciam atividade ligada à agricultura. A maioria dos casos foi notificada entre 2013 a 2017 e em sujeitos que reportaram exposição a material biológico. No modelo multivariado de regressão logística, as infecções pelos vírus da hepatite B foram associadas à raça/cor não branca (OR: 2,89; IC95% 1,07 – 7,87), ao histórico de realização de transfusão sanguínea (OR: 14,51; IC95% 5,44 – 38,74), em residentes de municípios de 10 a 20 mil habitantes (OR: 4,57; IC95% 1,59 – 13,12) e de municípios entre 20 a 50 mil habitantes (OR: 4,33; IC95% 1,61 – 11,56). Conclusão A caracterização dos possíveis fatores de risco para hepatite B nessa população podem subsidiar ações mais eficazes de prevenção e promoção de saúde, bem como fomentar estudos específicos que possam guiar políticas de atenção integral ao idoso.

Higher odds of irritable bowel syndrome among hospitalized patients using cannabis: a propensity-matched analysis

BACKGROUND

The endogenous cannabinoid system modulates many brain-gut and gut-brain physiologic pathways, which are postulated to be dysfunctional in irritable bowel syndrome (IBS). Herein, we examine the relationship between cannabis use disorder (CUD) and having IBS.

PATIENTS AND METHODS

After selecting patients aged 18 years and above from the 2014 Nationwide Inpatient Survey, we used the International Classification of Diseases, 9th ed. codes to identify individuals with CUD, IBS, and the established risk factors for IBS. We then estimated the crude and adjusted odds ratios of having a diagnosis of IBS with CUD and assessed for the interactions of CUD with other risk factors (SAS 9.4). We confirmed our findings in two ways: conducting a similar analysis on a previous Nationwide Inpatient Survey data (2012); and using a greedy algorithm to design a propensity-scored case-control (1 : 10) study, approximating a pseudorandomized clinical trial.

RESULTS

Out of 4 709 043 patients evaluated, 0.03% had a primary admission for IBS and 1.32% had CUD. CUD was associated with increased odds of IBS [adjusted odds ratio: 2.03; 95% confidence interval (CI): 1.53-2.71]. CUD was related to higher odds for IBS among males compared with females (3.48; 1.98-6.12 vs. 1.48; 0.88-2.50), and Hispanics and Caucasians compared with Blacks (5.28; 1.77-15.76, 1.80; 1.02-3.18 vs. 1.80; 0.65-5.03). On propensity-matching, CUD was associated with 80% increased odds for IBS (1.82; 1.27-2.60).

CONCLUSION

Our findings suggest that CUD is significantly associated with IBS among the general population. Males, Caucasians, and Hispanics might be more impacted by CUD associated IBS. Additional biomedical studies are required to elucidate this relationship.

Secondary data analysis to answer questions in human biology

Despite a growing number of publicly available datasets, the use of these datasets for secondary analyses in human biology is less common compared with other fields. Secondary analysis of existing data offers an opportunity for human biologists to ask unique questions through an evolutionary and biocultural lens, allowing for an analysis of cultural and structural nuances that affect health. Leveraging publicly available datasets for human biology research is a way for students and established researchers to complement their data collection, use existing data for master's and doctoral theses, pilot test questions, and use existing data to answer interesting new questions or explore questions at the population level. Here we describe where publicly available data are stored, highlighting some data repositories and how to access them. We then discuss how to decide which dataset is right, depending on the research question. Next, we describe steps to construct datasets, analytical considerations and methodological challenges, best practices, and limitations depending on the structure of the study. We close by highlighting a number of publicly available datasets that have been used by human biologists and other datasets that may be of interest to the community, including research that has been conducted on some example datasets.

Cannabis use is associated with reduced prevalence of non-alcoholic fatty liver disease: A cross-sectional study

Cannabis use is associated with reduced prevalence of obesity and diabetes mellitus (DM) in humans and mouse disease models. Obesity and DM are a well-established independent risk factor for non-alcoholic fatty liver disease (NAFLD), the most prevalent liver disease globally. The effects of cannabis use on NAFLD prevalence in humans remains ill-defined. Our objective is to determine the relationship between cannabis use and the prevalence of NAFLD in humans. We conducted a population-based case-control study of 5,950,391 patients using the 2014 Healthcare Cost and Utilization Project (HCUP), Nationwide Inpatient Survey (NIS) discharge records of patients 18 years and older. After identifying patients with NAFLD (1% of all patients), we next identified three exposure groups: non-cannabis users (98.04%), non-dependent cannabis users (1.74%), and dependent cannabis users (0.22%). We adjusted for potential demographics and patient related confounders and used multivariate logistic regression (SAS 9.4) to determine the odds of developing NAFLD with respects to cannabis use. Our findings revealed that cannabis users (dependent and non-dependent) showed significantly lower NAFLD prevalence compared to non-users (AOR: 0.82[0.76-0.88]; p<0.0001). The prevalence of NAFLD was 15% lower in non-dependent users (AOR: 0.85[0.79-0.92]; p<0.0001) and 52% lower in dependent users (AOR: 0.49[0.36-0.65]; p<0.0001). Among cannabis users, dependent patients had 43% significantly lower prevalence of NAFLD compared to non-dependent patients (AOR: 0.57[0.42-0.77]; p<0.0001). Our observations suggest that cannabis use is associated with lower prevalence of NAFLD in patients. These novel findings suggest additional molecular mechanistic studies to explore the potential role of cannabis use in NAFLD development.