Influence of nonalcoholic fatty liver disease on inflammatory bowel disease hospitalizations in the United States

Background

The reported prevalence of nonalcoholic fatty liver disease (NAFLD) in patients with inflammatory bowel disease (IBD) is 32%. We assessed the influence of NAFLD on IBD hospitalizations in the United States (US).

Methods

We utilized the National Inpatient Sample database, from 2016-2019, to identify the total IBD hospitalizations in the US and we further subdivided them according to the presence or absence of NAFLD. Hospitalization characteristics, comorbidities and outcomes were compared. Statistical significance was set at P<0.05.

Results

There were 1,272,260 IBD hospitalizations in the US, of which 5.04% involved NAFLD. For IBD hospitalizations with NAFLD, the mean age was 50-64 years, and the proportion of males was 46.97%. IBD hospitalizations with NAFLD had a lower proportion of African Americans (8.7% vs. 11.38%, P<0.001). Comorbidities such as hypertension (50.34% vs. 44.04%, P<0.001) and obesity (18.77% vs. 11.81%, P<0.001) were significantly higher in the NAFLD cohort. Overall, based on the Charlson Comorbidity Index, patients with NAFLD had a higher number of comorbidities (52.77% vs. 20.66%, P<0.001). Mortality was higher in the NAFLD compared to the non-NAFLD cohort (3.14% vs. 1.44%, P<0.001). Patients with NAFLD also incurred significantly higher hospital charges ($69,536 vs. $55,467, p<0.001) and had a longer mean length of stay (6.10 vs. 5.27 days, P<0.001) compared to the cohort without NAFLD. Complications and inpatient procedure requirements were also higher in the NAFLD cohort.

Conclusion

Our study revealed greater mortality, morbidity, and healthcare resource utilization in patients with IBD who were hospitalized with a concomitant diagnosis of NAFLD.

Calculator for detection of colorectal adenomas by using artificial intelligence models in patients with chronic hepatitis C

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Background: Hepatitis C virus (HCV) is known for its oncogenic potential, especially in hepatocellular carcinoma and non-Hodgkin lymphoma. On review, several studies have indicated that patients with chronic hepatitis C (CHC) have an increased risk of developing colorectal cancer (CRC). We developed an artificial intelligence (AI) based tool using machine learning (ML) algorithms to help stratify these patients into a higher risk of CRC/adenomas. Methods: The study was approved by the institutional review board. We developed an AI automated calculator uploaded to a graphical user interface (GUI), and we applied ML to train models to predict the probability and the number of adenomas detected on colonoscopy. Data collected were age, smoking history, significant alcohol consumption, aspirin intake, ethnicity, HCV status, gender, body mass index (BMI), and colonoscopy findings. The models can operate either in the presence or absence of the above parameters. Data sets were split into 70:30 ratios for training and internal validation. Scikit-learn StandardScaler was used to scale values of continuous variables. We used the colonoscopy findings as the gold standard and applied a deep learning architecture to train six ML models for prediction. The ML models used were Support Vector Classifier, Random Forest, Bernoulli Naïve Bayes (BNB), Gradient Boosting Classifier (GBC), Logistic Regression, and Deep Neural Networks. Additional regression models were trained and tested to predict the number of polyps. A Flask (customizable python framework) application programming interface (API) was used to deploy the trained ML model with the highest accuracy as a web application. Finally, Heroku was used for the deployment of the web-based API to https://adenomadetection.herokuapp.com. Results: Data was collected for 415 patients, of which only 206 had colonoscopy results. On internal validation with the remaining patients, BNB predicted the probability of adenoma detection with the highest accuracy of 56%, precision of 55%, recall of 55%, and F1 measure of 54%. Support Vector Regressor (SVR) predicted the number of adenomas with the least mean absolute error (MAE) of 0.905. Conclusions: Our AI-based tool shows an association between CHC and colorectal adenomas. This tool can help providers stratify patients with CHC for early referral for screening colonoscopy. Along with giving a numerical percentage, the calculator can also comment on the number of adenomatous polyps a gastroenterologist can expect while doing a colonoscopy, thus prompting a higher adenoma detection rate.

Influence of non-alcoholic fatty liver disease on non-variceal upper gastrointestinal bleeding: A nationwide analysis

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver disease globally with an estimated prevalence of 25%, with the clinical and economic burden expected to continue to increase. In the United States, non-variceal upper gastrointestinal bleeding (NVUGIB) has an estimated incidence of 61-78 cases per 100000 people with a mortality rate of 2%-15% based on co-morbidity burden.

AIM

To identify the outcomes of NVUGIB in NAFLD hospitalizations in the United States.

METHODS

We utilized the National Inpatient Sample from 2016-2019 to identify all NVUGIB hospitalizations in the United States. This population was divided based on the presence and absence of NAFLD. Hospitalization characteristics, outcomes and complications were compared.

RESULTS

The total number of hospitalizations for NVUGIB was 799785, of which 6% were found to have NAFLD. NAFLD and GIB was, on average, more common in younger patients, females, and Hispanics than GIB without NAFLD. Interestingly, GIB was less common amongst blacks with NAFLD. Multivariate logistic regression analysis was conducted, controlling for the multiple covariates. The primary outcome of interest, mortality, was found to be significantly higher in patients with NAFLD and GIB [adjusted odds ratio (aOR) = 1.018 (1.013-1.022)]. Secondary outcomes of interest, shock [aOR = 1.015 (1.008-1.022)], acute respiratory failure [aOR = 1.01 (1.005-1.015)] and acute liver failure [aOR = 1.016 (1.013-1.019)] were all more likely to occur in this cohort. Patients with NAFLD were also more likely to incur higher total hospital charges (THC) [$2148 ($1677-$2618)]; however, were less likely to have a longer length of stay [0.27 d (0.17-0.38)]. Interestingly, in our study, the patients with NAFLD were less likely to suffer from acute myocardial infarction [aOR = 0.992 (0.989-0.995)]. Patients with NAFLD were not more likely to suffer acute kidney injury, sepsis, blood transfusion, intubation, or dialysis.

CONCLUSION

NVUGIB in NAFLD hospitalizations had higher inpatient mortality, THC, and complications such as shock, acute respiratory failure, and acute liver failure compared to those without NAFLD.

Detection of colorectal adenomas using artificial intelligence models in patients with chronic hepatitis C

BACKGROUND

Hepatitis C virus is known for its oncogenic potential, especially in hepatocellular carcinoma and non-Hodgkin lymphoma. Several studies have shown that chronic hepatitis C (CHC) has an increased risk of the development of colorectal cancer (CRC).

AIM

To analyze this positive relationship and develop an artificial intelligence (AI)-based tool using machine learning (ML) algorithms to stratify these patient populations into risk groups for CRC/adenoma detection.

METHODS

To develop the AI automated calculator, we applied ML to train models to predict the probability and the number of adenomas detected on colonoscopy. Data sets were split into 70:30 ratios for training and internal validation. The Scikit-learn standard scaler was used to scale values of continuous variables. Colonoscopy findings were used as the gold standard and deep learning architecture was used to train six ML models for prediction. A Flask (customizable Python framework) application programming interface (API) was used to deploy the trained ML model with the highest accuracy as a web application. Finally, Heroku was used for the deployment of the web-based API to https://adenomadetection.herokuapp.com.

RESULTS

Of 415 patients, 206 had colonoscopy results. On internal validation, the Bernoulli naive Bayes model predicted the probability of adenoma detection with the highest accuracy of 56%, precision of 55%, recall of 55%, and F1 measure of 54%. Support vector regressor predicted the number of adenomas with the least mean absolute error of 0.905.

CONCLUSION

Our AI-based tool can help providers stratify patients with CHC for early referral for screening colonoscopy. Along with providing a numerical percentage, the calculator can also comment on the number of adenomatous polyps a gastroenterologist can expect, prompting a higher adenoma detection rate.

Utility of bilateral inferior petrosal sinus sampling for diagnosis and lateralization of Cushing's disease in the pediatric population: case series and review of the literature

OBJECTS

Cushing's disease (CD) is the most common cause of ACTH-dependent hypercortisolism in children age ≥ 7. The utility of bilateral inferior petrosal sinus sampling (BIPSS), an important test in adults, is less defined in children. We present a case series of children with ACTH-dependent hypercortisolemia and review the literature to assess the utility of BIPSS in the diagnosis and localization of CD.

METHODS

We performed an IRB-approved chart review of patients aged ≤ 18 with ACTH-dependent hypercortisolism at MGH between 2000 and 2019 and collected clinical, laboratory, radiographic, BIPSS, surgical, and outcomes data.

RESULTS

In our cohort (n = 21), BIPSS had a sensitivity of 93% and specificity of 100% for diagnosis of CD. Compared to surgery, successful BIPSS correctly predicted adenoma laterality in 69% of cases vs. 70% by MRI. Among patients with lesions ≥ 4 mm (n = 9), BIPSS correctly lateralized in 50% vs. 100% by MRI. In patients with subtle lesions (< 4 mm, n = 7), BIPSS correctly lateralized in 80% vs. 71% by MRI. In patients (n = 4) with CD and negative MRIs, BIPSS correctly lateralized in 75% cases. Surgical cure was achieved in 90% of patients and 95% of patients had long-term disease control.

CONCLUSIONS

In our cohort (n = 21; n = 20 CD, n = 1 ectopic ACTH secretion), BIPSS was sensitive and specific for the diagnosis of CD. Compared to MRI, BIPSS was not additionally helpful for lateralization in patients with lesions ≥ 4 mm on MRI. BIPSS was helpful in guiding surgical exploration and achieving immediate postoperative remission among patients with subtle and negative MRI findings.

Extracellular Vesicles in Glioblastoma Tumor Microenvironment

Glioblastomas (GBM) are highly aggressive primary brain tumors. Complex and dynamic tumor microenvironment (TME) plays a crucial role in the sustained growth, proliferation, and invasion of GBM. Several means of intercellular communication have been documented between glioma cells and the TME, including growth factors, cytokines, chemokines as well as extracellular vesicles (EVs). EVs carry functional genomic and proteomic cargo from their parental cells and deliver that information to surrounding and distant recipient cells to modulate their behavior. EVs are emerging as crucial mediators of establishment and maintenance of the tumor by modulating the TME into a tumor promoting system. Herein we review recent literature in the context of GBM TME and the means by which EVs modulate tumor proliferation, reprogram metabolic activity, induce angiogenesis, escape immune surveillance, acquire drug resistance and undergo invasion. Understanding the multifaceted roles of EVs in the niche of GBM TME will provide invaluable insights into understanding the biology of GBM and provide functional insights into the dynamic EV-mediated intercellular communication during gliomagenesis, creating new opportunities for GBM diagnostics and therapeutics.