Glucagon-like Peptide-1 Receptor Agonists Associated Gastrointestinal Adverse Events: A Cross-Sectional Analysis of the National Institutes of Health All of Us Cohort.

BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are commonly used diabetes and obesity medications but have been associated with gastrointestinal (GI) adverse events. However, real-world evidence on comparative GI adverse reaction profiles is limited. OBJECTIVES: This study aimed to evaluate GI adverse events among GLP-1 RA users and compare semaglutide, dulaglutide, liraglutide, and exenatide safety regarding the GI adverse reaction profile. METHODS: This retrospective cross-sectional analysis utilized real-world data on 10,328 adults with diabetes/obesity in the National Institutes of Health All of Us cohort. New GLP-1 RA users were identified, and GI adverse events were examined. Logistic regression determined factors associated with GI adverse events. RESULTS: The mean age of the study population was 61.4 ± 12.6 years, 65.7% were female, 51.3% were White, and they had a high comorbidity burden. Abdominal pain (57.6%) was the most common GI adverse event, followed by constipation (30.4%), diarrhea (32.7%), nausea and vomiting (23.4%), GI bleeding (15.9%), gastroparesis (5.1%), and pancreatitis (3.4%). Dulaglutide and liraglutide had higher rates of abdominal pain, constipation, diarrhea, and nausea and vomiting than semaglutide and exenatide. Liraglutide and exenatide had the highest pancreatitis (4.0% and 3.8%, respectively). Compared to semaglutide, dulaglutide and liraglutide had higher odds of abdominal pain, and nausea and vomiting. They also had higher odds of gastroparesis than semaglutide. No significant differences existed in GI bleeding or pancreatitis risks between the GLP-1 RAs. CONCLUSIONS: In this real-world cohort, GI adverse events were common with GLP-1 RAs. Differences in GI safety profiles existed between agents, with exenatide appearing safer than other GLP-1 RAs, except for gastroparesis. These findings can inform GLP-1 RA selection considering GI risk factors. Further studies are needed to evaluate the causal relationship and GLP-1 RA safety with concomitant medication use.

Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes.

BACKGROUND: Major Adverse Cardiovascular Events (MACE) are common complications of type 2 diabetes mellitus (T2DM) that include myocardial infarction (MI), stroke, and heart failure (HF). The objective of the current study was to predict MACE among T2DM patients. METHODS: Type 2 diabetes mellitus patients above 18 years old were recruited for the study from the All of Us Research Program. Eligible participants were those who took sodium-glucose cotransporter 2 inhibitors. Different Machine learning algorithms: including RandomForest (RF), XGBoost, logistic regression (LR), and weighted ensemble model (WEM) were employed. Clinical attributes, electrolytes and biomarkers were explored in predicting MACE. The feature importance was determined using mean decrease accuracy. RESULTS: Overall, 9, 059 subjects were included in the analyses, of which 5197 (57.4%) were females. The XGBoost Model demonstrated a prediction accuracy of 0.80 [0.78-0.82], which is higher as compared to the RF 0.78[0.76-0.80], the LR model 0.65 [0.62-0.67], and the WEM 0.75 [0.73-0.76], respectively. The classification accuracy of the models for stroke was more than 95%, which was higher than prediction accuracy for MI (∼85%), and HF (∼80%). Phosphate, blood urea nitrogen and troponin levels were the major predictors of MACE. CONCLUSION: The ML models had shown acceptable performance in predicting MACE in T2DM patients, except the LR model. Phosphate, blood urea nitrogen, and other electrolytes were important predictors of MACE, which is consistent between the individual components of MACE, such as stroke, MI, and HF. These parameters can be calibrated as prognostic parameters of MACE events in T2DM patients.

Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data.

There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were included. Random forest, extreme gradient boost, logistic regression, and weighted ensemble model algorithms were employed. Patients who had a record of uncontrolled diabetes based on the international classification of diseases code were identified as cases. A set of features including basic demographic, biomarkers and hematological indices were included in the model. The random forest model demonstrated high performance in predicting uncontrolled diabetes, yielding an accuracy of 0.80 (95% CI: 0.79-0.81) as compared to the extreme gradient boost 0.74 (95% CI: 0.73-0.75), the logistic regression 0.64 (95% CI: 0.63-0.65) and the weighted ensemble model 0.77 (95% CI: 0.76-0.79). The maximum area under the receiver characteristics curve value was 0.77 (random forest model), while the minimum value was 0.7 (logistic regression model). Potassium levels, body weight, aspartate aminotransferase, height, and heart rate were important predictors of uncontrolled diabetes. The random forest model demonstrated a high performance in predicting uncontrolled diabetes. Serum electrolytes and physical measurements were important features in predicting uncontrolled diabetes. Machine learning techniques may be used to predict uncontrolled diabetes by incorporating these clinical characteristics.

Cost Effectiveness of Dapagliflozin Added to Standard of Care for the Management of Diabetic Nephropathy in the USA.

BACKGROUND: Angiotensin-converting enzyme inhibitors have been used as the standard of care for the treatment of diabetic nephropathy. Recently, dapagliflozin has been shown to reduce diabetic nephropathy when added to the standard of care. OBJECTIVE: The objective of this study was to determine the cost effectiveness of dapagliflozin added to the standard of care in diabetic nephropathy in the United States of America (USA). METHODS: A Markov model was developed to determine the cost-effectiveness outcomes from the Medicare/Medicaid health coverage perspective. Model inputs were derived from the literature. The primary outcomes were total costs, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio. Deterministic and probabilistic sensitivity analyses were performed to determine the robustness of our results. A willingness-to-pay threshold of $100,000 per QALY was applied, which is based on previous studies. RESULTS: Dapagliflozin yielded a lifetime QALY of 2.8. The discounted QALY associated with the standard of care was 2.6. The standard of care was the less costly treatment with a lifetime cost of $106,150.25 as compared with dapagliflozin, which costs $110,689.25. Dapagliflozin demonstrated an incremental cost-effectiveness ratio of $21,141.51 per additional QALY. The most influential parameters of the incremental cost-effectiveness ratio were the adverse drug reaction-related cost of the standard of care and dapagliflozin, the acquisition cost, and the adverse drug reaction-related cost of dapagliflozin. The effects and costs of the interventions were consistent between base-case analyses and the probabilistic model (incremental cost-effectiveness ratio: $19,023.35 [$13,637.8-$27,483.1]). CONCLUSIONS: Dapagliflozin added to the standard of care was cost effective relative to the standard of care alone in the USA for patients with diabetic nephropathy.

Prescribing trend in cardiovascular patients at Ethiopian university hospital: The number of medications and implication on the clinical improvement.

Investigating the prescribing trend is important to improve rational prescribing. This study aimed at assessing the cardiovascular drug use, pattern, and its impact on clinical outcome. A cross-sectional study was employed in the outpatient department of chronic illness clinic of Gondar University specialized hospital, Ethiopia from 15 January 2017 to 15 March 2017. The independent variables were sociodemographic, medication, and other clinical information while cardiovascular disease improvement is the outcome variable. Binary logistic regression was used to test the association between the independent variables and the outcome variable. Kaplan Meier curve was used to analyze the clinical improvement while the Log-rank test was employed to compare the clinical outcome with the number of medications. Eight hundred thirty-three cardiovascular patient medical records were included in the final analysis. The majority (62.5%) of patients were females and more than 61% were above 50 years of age. Diuretics monotherapy accounted for a third (33.6%) of cardiovascular drug use, followed by combination therapy of angiotensin convertase enzyme inhibitors with Diuretics (21.8%) and calcium channel blockers with diuretics (8.3%). Cardiovascular patients followed for 72 months found to have a good level of clinical improvement on combination medication (Log Rank of 28.9, P = 0.000). In this study, diuretics monotherapy or in combination with angiotensin convertase enzyme inhibitors were found to be the frequently prescribed drugs in cardiovascular patients. Combination therapy has an implication for good cardiovascular improvement on long term follow-up. It seems clinicians were restricted to certain cardiovascular medications while plenty of choices are available from the diverse classes of cardiovascular drugs.

Patient Adherence to Novel Oral Anticoagulants (NOACs) for the Treatment of Atrial Fibrillation and Occurrence of Associated Bleeding Events: A Systematic Review and Meta-analysis.

BACKGROUND: Real-world evidence from published observational studies of adherence to Novel Oral Anticoagulants (NOACs) medications and associated clinical outcome events in Atrial Fibrillation (AF) patients, was reviewed systematically. METHODS: Observational studies assessing patient adherence to NOACs conducted on AF patients between September 2010 and June 2016 were identified by systematic searching keywords to locate eligible studies, in accordance with Cochrane guidelines. PubMed, Scopus and Google Scholar databases were searched to identify the studies. Meta-analysis was performed using a random effects model with DerSimonian-Laird weighting to obtain pooled effect sizes. RESULTS: From 185 potentially relevant citations, 6 studies, comprising 1.6 million AF patients, were included. Among these, successful adherence to NOACs occurred in 75.6%. Adherence levels were higher in patients treated with dabigatran (72.7%) compared with those treated with apixaban (59.9%) or rivaroxaban (59.3%). However, adherence was still suboptimal (relative to an expected 80% adherence rate). Bleeding events in non-adherent patients were found to be 7.5%. CONCLUSION: Suboptimal adherence to NOACs among AF patients was highlighted as a significant risk factor that may affect clinical outcomes, with a higher percentage of non-adherent patients having bleeding events. There is an urgent need for research on the effects of specific interventions to improve patient adherence to NOACs and to assess the related outcome factors that may be associated with adherence.

Magnitude and determinants of uncontrolled blood pressure among hypertensive patients in Ethiopia: hospital-based observational study.

BACKGROUND: Hypertension is an important public health problem worldwide. There is lack of data on uncontrolled blood pressure in developing countries. OBJECTIVES: To determine the magnitude and predicting factors of uncontrolled blood pressure in hypertensive patients attending Gondar university hospital, Ethiopia. METHODS: A hospital-based cross-sectional survey was conducted from July 2015 to March 2016. All hypertensive patients were followed and the blood pressure levels were measured. Binary logistic regression analysis was done to determine the predictors of uncontrolled blood pressure. A p-value of <0.05 was set at priori with 95% confidence interval to test the level of significance. RESULTS: Of the total 578 hypertension patients, 543 (93.9%) fulfilled the study criteria and were included in the final analysis. The mean age of the participants was 55.96±14.6 years. Nearly two-third (58.2%) of the participants were females. More than one-tenth (11.4%) of the respondents had uncontrolled blood pressure. High salt intake carried six times more risk of uncontrolled blood pressure. Elderly individuals had lower risk as compared to young age group. However, comorbidities were not related with uncontrolled blood pressure. CONCLUSIONS: Blood pressure control was relatively high in the hospital studied. High salt intake was strongly linked with uncontrolled blood pressure. Individuals with high salt intake should be followed for their medication experience and disease knowledge.