Quantifying Variation in Treatment Utilization for Type 2 Diabetes Across Five Major University of California Health Systems

Predicting post-liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning

Liver transplantation (LT) is a treatment for acute-on-chronic liver failure (ACLF), but high post-LT mortality has been reported. Existing post-LT models in ACLF have been limited. We developed an Expert-Augmented Machine Learning (EAML) model to predict post-LT outcomes. We identified ACLF patients who underwent LT in the University of California Health Data Warehouse. We applied the RuleFit machine learning (ML) algorithm to extract rules from decision trees and create intermediate models. We asked human experts to rate the rules generated by RuleFit and incorporated these ratings to generate final EAML models. We identified 1384 ACLF patients. For death at 1 year, areas under the receiver-operating characteristic curve were 0.707 (confidence interval [CI] 0.625-0.793) for EAML and 0.719 (CI 0.640-0.800) for RuleFit. For death at 90 days, areas under the receiver-operating characteristic curve were 0.678 (CI 0.581-0.776) for EAML and 0.707 (CI 0.615-0.800) for RuleFit. In pairwise comparisons, both EAML and RuleFit models outperformed cross-sectional models. Significant discrepancies between experts and ML occurred in rankings of biomarkers used in clinical practice. EAML may serve as a method for ML-guided hypothesis generation in further ACLF research.

Second-Line Pharmaceutical Treatments for Patients with Type 2 Diabetes

Importance

Assessing the relative effectiveness and safety of additional treatments when metformin monotherapy is insufficient remains a limiting factor in improving treatment choices in type 2 diabetes.

Objective

To determine whether data from electronic health records across the University of California Health system could be used to assess the comparative effectiveness and safety associated with 4 treatments in diabetes when added to metformin monotherapy.

Design, Setting, and Participants

This multicenter, new user, multidimensional propensity score-matched retrospective cohort study with leave-one-medical-center-out (LOMCO) sensitivity analysis used principles of emulating target trial. Participants included patients with diabetes receiving metformin who were then additionally prescribed either a sulfonylurea, dipeptidyl peptidase-4 inhibitor (DPP4I), sodium-glucose cotransporter-2 inhibitor (SGLT2I), or glucagon-like peptide-1 receptor agonist (GLP1RA) for the first time and followed-up over a 5-year monitoring period. Data were analyzed between January 2022 and April 2023.

Exposure

Treatment with sulfonylurea, DPP4I, SGLT2I, or GLP1RA added to metformin monotherapy.

Main Outcomes and Measures

The main effectiveness outcome was the ability of patients to maintain glycemic control, represented as time to metabolic failure (hemoglobin A1c [HbA1c] ≥7.0%). A secondary effectiveness outcome was assessed by monitoring time to new incidence of any of 28 adverse outcomes, including diabetes-related complications while treated with the assigned drug. Sensitivity analysis included LOMCO.

Results

This cohort study included 31 852 patients (16 635 [52.2%] male; mean [SD] age, 61.4 [12.6] years) who were new users of diabetes treatments added on to metformin monotherapy. Compared with sulfonylurea in random-effect meta-analysis, treatment with SGLT2I (summary hazard ratio [sHR], 0.75 [95% CI, 0.69-0.83]; I2 = 37.5%), DPP4I (sHR, 0.79 [95% CI, 0.75-0.84]; I2 = 0%), GLP1RA (sHR, 0.62 [95% CI, 0.57-0.68]; I2 = 23.6%) were effective in glycemic control; findings from LOMCO sensitivity analysis were similar. Treatment with SGLT2I showed no significant difference in effectiveness compared with GLP1RA (sHR, 1.26 [95% CI, 1.12-1.42]; I2 = 47.3%; no LOMCO) or DPP4I (sHR, 0.97 [95% CI, 0.90-1.04]; I2 = 0%). Patients treated with DPP4I and SGLT2I had fewer cardiovascular events compared with those treated with sulfonylurea (DPP4I: sHR, 0.84 [95% CI, 0.74-0.96]; I2 = 0%; SGLT2I: sHR, 0.78 [95% CI, 0.62-0.98]; I2 = 0%). Patients treated with a GLP1RA or SGLT2I were less likely to develop chronic kidney disease (GLP1RA: sHR, 0.75 [95% CI 0.6-0.94]; I2 = 0%; SGLT2I: sHR, 0.77 [95% CI, 0.61-0.97]; I2 = 0%), kidney failure (GLP1RA: sHR, 0.69 [95% CI, 0.56-0.86]; I2 = 9.1%; SGLT2I: sHR, 0.72 [95% CI, 0.59-0.88]; I2 = 0%), or hypertension (GLP1RA: sHR, 0.82 [95% CI, 0.68-0.97]; I2 = 0%; SGLT2I: sHR, 0.73 [95% CI, 0.58-0.92]; I2 = 38.5%) compared with those treated with a sulfonylurea. Patients treated with an SGLT2I, vs a DPP4I, GLP1RA, or sulfonylurea, were less likely to develop indicators of chronic hepatic dysfunction (sHR vs DPP4I, 0.68 [95% CI, 0.49-0.95]; I2 = 0%; sHR vs GLP1RA, 0.66 [95% CI, 0.48-0.91]; I2 = 0%; sHR vs sulfonylurea, 0.60 [95% CI, 0.44-0.81]; I2 = 0%), and those treated with a DPP4I were less likely to develop new incidence of hypoglycemia (sHR, 0.48 [95% CI, 0.36-0.65]; I2 = 22.7%) compared with those treated with a sulfonylurea.

Conclusions and Relevance

These findings highlight familiar medication patterns, including those mirroring randomized clinical trials, as well as providing new insights underscoring the value of robust clinical data analytics in swiftly generating evidence to help guide treatment choices in diabetes.

Translational Bioinformatics to Enable Precision Medicine for All: Elevating Equity across Molecular, Clinical, and Digital Realms

OBJECTIVES

Over the past few years, challenges from the pandemic have led to an explosion of data sharing and algorithmic development efforts in the areas of molecular measurements, clinical data, and digital health. We aim to characterize and describe recent advanced computational approaches in translational bioinformatics across these domains in the context of issues or progress related to equity and inclusion.

METHODS

We conducted a literature assessment of the trends and approaches in translational bioinformatics in the past few years.

RESULTS

We present a review of recent computational approaches across molecular, clinical, and digital realms. We discuss applications of phenotyping, disease subtype characterization, predictive modeling, biomarker discovery, and treatment selection. We consider these methods and applications through the lens of equity and inclusion in biomedicine.

CONCLUSION

Equity and inclusion should be incorporated at every step of translational bioinformatics projects, including project design, data collection, model creation, and clinical implementation. These considerations, coupled with the exciting breakthroughs in big data and machine learning, are pivotal to reach the goals of precision medicine for all.

"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.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.