Sumeet K. Asrani M.D.

Lago-Hernandez, C., Nguyen, N.H., Khera, R., Loomba, R., Asrani, S.K. and Singh, S. (2021). “Cost-Related Nonadherence to Medications Among US Adults With Chronic Liver Diseases.” Mayo Clin Proc Jun 10;S0025-6196(21)00255-X. [Epub ahead of print].

Full text of this article.

OBJECTIVE: To estimate the prevalence, risk factors, and consequences of cost-related medication nonadherence (CRN) in individuals with chronic liver diseases (CLDs) in the United States. PATIENTS AND METHODS: Using the National Health Interview Survey from January 1, 2014, to December 31, 2018, we identified individuals with CLDs. Using complex weighted survey analysis, we obtained national estimates and risk factors for CRN and its association with cost-reducing behaviors and measures of financial toxicity. We evaluated the association of CRN with unplanned health care use, adjusting for age, sex, race/ethnicity, insurance, income, education, and comorbid conditions. RESULTS: Of 3237 respondents (representing 4.6 million) US adults with CLDs, 813 (representing 1.2 million adults, or 25%; 95% CI, 23% to 27%) reported CRN, of whom 68% (n=554/813) reported maladaptive cost-reducing behaviors. Younger age, female sex, low income, and multimorbidity were associated with a higher prevalence of CRN. Compared with patients without CRN, patients experiencing CRN had 5.1 times higher odds of financial hardship from medical bills (adjusted odds ratio [aOR], 5.05; 95% CI, 3.73 to 6.83) and 2.9 times higher odds of food insecurity (aOR, 2.85; 95% CI, 2.02 to 4.01). The CRN was also associated with 1.5 times higher odds of emergency department visits (aOR, 1.46; 95% CI, 1.11 to 1.94). CONCLUSION: We observed a high prevalence of CRN and associated consequences such as high financial distress, financial hardship from medical bills, food insecurity, engagement in maladaptive cost-reducing strategies, increased health care use, and work absenteeism among patients with CLD. These financial determinants of health have important implications in the context of value-based care.

Pinchot, J.W., Kalva, S.P., Majdalany, B.S., Kim, C.Y., Ahmed, O., Asrani, S.K., Cash, B.D., Eldrup-Jorgensen, J., Kendi, A.T., Scheidt, M.J., Sella, D.M., Dill, K.E. and Hohenwalter, E.J. (2021). “ACR Appropriateness Criteria® Radiologic Management of Portal Hypertension.” J Am Coll Radiol 18(5s): S153-s173.

Full text of this article.

Cirrhosis is a heterogeneous disease that cannot be studied as a single entity and is classified in two main prognostic stages: compensated and decompensated cirrhosis. Portal hypertension, characterized by a pathological increase of the portal pressure and by the formation of portal-systemic collaterals that bypass the liver, is the initial and main consequence of cirrhosis and is responsible for the majority of its complications. A myriad of treatment options exists for appropriately managing the most common complications of portal hypertension, including acute variceal bleeding and refractory ascites. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Nitski, O., Azhie, A., Qazi-Arisar, F.A., Wang, X., Ma, S., Lilly, L., Watt, K.D., Levitsky, J., Asrani, S.K., Lee, D.S., Rubin, B.B., Bhat, M. and Wang, B. (2021). “Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data.” Lancet Digit Health 3(5): e295-e305.

Full text of this article.

BACKGROUND: Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. Few clinical tools are available to identify patients at risk of these complications, which would flag them for screening tests and potentially life-saving interventions. In this retrospective analysis, we aimed to assess the ability of deep learning algorithms of longitudinal data from two prospective cohorts to predict complications resulting in death after liver transplantation over multiple timeframes, compared with logistic regression models. METHODS: In this machine learning analysis, model development was done on a set of 42 146 liver transplant recipients (mean age 48·6 years [SD 17·3]; 17 196 [40·8%] women) from the Scientific Registry of Transplant Recipients (SRTR) in the USA. Transferability of the model was further evaluated by fine-tuning on a dataset from the University Health Network (UHN) in Canada (n=3269; mean age 52·5 years [11·1]; 1079 [33·0%] women). The primary outcome was cause of death, as recorded in the databases, due to cardiovascular causes, infection, graft failure, or cancer, within 1 year and 5 years of each follow-up examination after transplantation. We compared the performance of four deep learning models against logistic regression, assessing performance using the area under the receiver operating characteristic curve (AUROC). FINDINGS: In both datasets, deep learning models outperformed logistic regression, with the Transformer model achieving the highest AUROCs in both datasets (p<0·0001). The AUROC for the Transformer model across all outcomes in the SRTR dataset was 0·804 (99% CI 0·795-0·854) for 1-year predictions and 0·733 (0·729-0·769) for 5-year predictions. In the UHN dataset, the AUROC for the top-performing deep learning model was 0·807 (0·795-0·842) for 1-year predictions and 0·722 (0·705-0·764) for 5-year predictions. AUROCs ranged from 0·695 (0·680-0·713) for prediction of death from infection within 5 years to 0·859 (0·847-0·871) for prediction of death by graft failure within 1 year. INTERPRETATION: Deep learning algorithms can incorporate longitudinal information to continuously predict long-term outcomes after liver transplantation, outperforming logistic regression models. Physicians could use these algorithms at routine follow-up visits to identify liver transplant recipients at risk for adverse outcomes and prevent these complications by modifying management based on ranked features. FUNDING: Canadian Donation and Transplant Research Program, CIFAR AI Chairs Program.

Bhakta, D., Patel, M., Ma, T.W., Boutté, J., Sarmast, N. and Asrani, S.K. (2021). “Model for End Stage Liver Disease-Lactate score and prediction of inpatient mortality in critically ill patients with cirrhosis.” Liver Transpl

Full text of this article.

The burden of decompensated liver disease is high with a significant proportion of cirrhosis patients requiring inpatient hospitalization. Patients with cirrhosis have high inpatient mortality in the intensive care unit (ICU) setting, especially as compared to other chronic conditions. Objective models that predict short-term mortality at the time of ICU admission are needed for assessing response to therapy, transplant candidacy as well as futility of care.

Asrani, S.K. and Nadim, M.K. (2021). “Chronic Kidney Disease after Simultaneous Liver Kidney Transplantation: Refining Patient Selection.” Liver Transpl.

Full text of this article.

The number of simultaneous liver kidney transplantations (SLKT) performed in the United States have steadily increased over the last 15 years: approximately 1 out of 10 liver transplantations is a dual organ transplant.(1) Given the disparity between availability of donor organs and recipients awaiting transplant and the increasing numbers of patients on the waitlist with MELD ≥ 40 with the majority having acute kidney injury (AKI), the number of patients who will qualify for SLKT will most likely continue to increase.(2) Over the years, the transplant community has refined SLKT listing criteria in order to ensure appropriate utilization of kidney organs given the increasing numbers of SLKT.(2, 3) In 2017, new criteria were formalized to set up a “sustained AKI” and “chronic kidney disease” (CKD) criteria for listing for SLKT.