Background: Hospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can lead to cardiovascular-related unplanned readmissions. Objective: We aimed to develop a machine learning (ML) model to predict 90-day unplanned readmissions for major adverse cardiovascular events (MACE) in hospitalized patients with blood cancers. Design: A retrospective population-based cohort study. Methods: We analyzed patients aged ≥18 with blood cancers (leukemia, lymphoma, myeloma) using the Nationwide Readmissions Database. MACE included acute myocardial infarction, ischemic heart disease, stroke, heart failure, revascularization, malignant arrhythmias, and cardiovascular-related death. Six ML algorithms (L2-Logistic regression, Support Vector Machine, Complement Naïve Bayes, Random Forest, XGBoost, and CatBoost) were trained on 2017-2018 data and tested on 2019 data. The SuperLearner algorithm was used for stacking models. Cost-sensitive learning addressed data imbalance, and hyperparameters were tuned using 5-fold cross-validation with Optuna framework. Performance metrics included the Area Under the Receiver Operating Characteristics Curve (ROCAUC), Precision-Recall AUC (PRAUC), balanced Brier score, and F2 score. SHapley Additive exPlanations (SHAP) values assessed feature importance, and clustering analysis identified high-risk subpopulations. Results: Among 76 957 patients, 1031 (1.34%) experienced unplanned 90-day MACE-related readmissions. CatBoost achieved the highest ROCAUC (0.737, 95% CI: 0.712-0.763) and PRAUC (0.040, 95% CI: 0.033-0.050). The SuperLearner algorithm achieved slight improvements in most performance metrics. Four leading predictive features were consistently identified across algorithms, including older age, heart failure, coronary atherosclerosis, and cardiac dysrhythmias. Twenty-three clusters were determined with the highest-risk cluster (mean log odds of 1.41) identified by nonrheumatic/unspecified valve disorders, coronary atherosclerosis, and heart failure. Conclusions: Our ML model effectively predicts MACE-related readmissions in hospitalized patients with blood cancers, highlighting key predictors. Targeted discharge strategies may help reduce readmissions and alleviate the associated healthcare burden.

Background: Novel antidiabetic medications (SGLT-2 inhibitors, DPP-4 inhibitors, and GLP-1 agonists) are commonly used worldwide; however, the available research lacks definitive conclusions on their protective effects or potential risks on cancer. Objectives: Compared to other antidiabetics, our systematic review and network meta-analysis (NMA) aims to use real-world studies to assess the potential cancer risks or protective effects of these novel antidiabetics. Methods: We comprehensively searched PubMed, CINAHL, and Web of Science from their inception until November 30, 2023. We included observational studies examining at least one novel antidiabetics in the systematic review. The novel antidiabetics include sodium-glucose cotransporter-2 inhibitors (SGLT-2i), dipeptidyl peptidase-4 inhibitors (DPP-4i), and glucagon-like peptide-1 agonists (GLP-1a). Design: We focused on cohort studies that provided data on cancer incidence and sample size in the NMA. Using NetMetaXL ^® , the random effects model with informative priors was used in the NMA to estimate the pooled odds ratio (OR) with 95% credible intervals (CrI). Results: The systematic review included 62 studies, of which 22 met the inclusion criteria for the NMA. SGLT-2i users had lower overall cancer risk compared to sulfonylureas (OR: 0.54; 95% CrI: 0.40–0.74, low certainty), GLP-1a (OR: 0.70; 95% CrI: 0.53–0.92, low certainty), and DPP-4i users (OR: 0.72; 95% CrI: 0.57–0.92, very low certainty). DPP-4i users also had a lower cancer risk than sulfonylureas users (OR: 0.76; 95% CrI: 0.60–0.96, low certainty). No other statistically significant ORs were found in other direct comparisons. Conclusion: SGLT-2i users have a lower risk of developing cancers than sulfonylureas, GLP-1a, and DPP-4i users. These results may improve patient safety by guiding future clinical practice and medication choices. Future studies should investigate the mechanisms behind these observed associations. Trial registration: This NMA was registered in PROSPERO (CRD42023469941).

Background: The absorption rates of mycophenolate mofetil (MMF) and enteric-coated mycophenolate sodium (ECMPS) may be influenced by the concomitant use of omeprazole. Methods: One hundred kidney transplant patients were recruited during their outpatient visits, including 50 on MMF and 50 on EC-MPS. At the clinic, a predose mycophenolic acid (MPA) sample (C-0) was collected; subsequently, the participants received the proton-pump inhibitor omeprazole along with either MMF or EC-MPS. Two more blood samples were collected at 1.5 and 3.5 hours and used to estimate an area under the curve (AUC) from zero to 12 hours [AUC (0-12)]. Results: The mean number of months after transplant was 92 months. The median AUC (0-12) and C o results were 62.2 mgh/L and 2.0 mg/L for the MMF group and 71.9 mgh/L and 1.8 mg/L for the EC-MPS group (P = 0.160 and 0.225, respectively). Interestingly, 54% of the MMF group and 62% of the EC-MPS group showed AUCs above the target values. The correlation between MPA C-0 and the predicted AUC was poor in both groups. Conclusion: Omeprazole can be safely co-administered with either MMF or EC-MPS, as it did not compromise the MPA exposure. Unexpectedly, however, a high percentage of patients presented MPA AUCs exceeding the target value, highlighting the importance of periodically assessing MPA level.

Background Cancer diagnoses impact adherence to antidiabetic medications, but limited research has focused on patients with prostate cancer and type 2 diabetes (T2DM). We investigated adherence trajectories to oral antidiabetic medications one year before and after a prostate cancer diagnosis and identified risk factors. Methods This retrospective cohort study used the 2011–2021 MarketScan Commercial and Medicare Supplemental databases. We included newly diagnosed prostate cancer patients with T2DM with continuous insurance enrollment. We applied group-based trajectory modeling with a beta distribution to evaluate adherence patterns before and after prostate cancer diagnosis. Model covariates included age, total number of medications, number of antidiabetic medications, the Charlson Comorbidity Index (CCI), cost, insurance type, and complicated diabetes from the year before diagnosis. Metastasis and cancer treatments were included in the model after diagnosis. Results The study included 7864 patients (mean age = 74.5 ± 7.1). Three adherence trajectories were identified before diagnosis: consistently high adherence, steady decliners, and consistently low adherence. After diagnosis, a fourth trajectory revealing a moderate decline emerged. Over half (61.2%) changed adherence patterns after diagnosis. Among those with consistently high adherence before diagnosis, 57.8% transitioned to a lower adherence trajectory. In contrast, 58.5% of steady decliners and 57.6% of consistently low adherents transitioned to a higher adherence trajectory after diagnosis. Predictors of high adherence included older age, fewer antidiabetic medications, lower CCI, and complicated diabetes before diagnosis. After diagnosis, fewer antidiabetic medications and complicated diabetes remained predictive of high adherence. Conclusion Patterns of adherence to oral antidiabetic medications undergo substantial changes after a prostate cancer diagnosis. Targeted interventions are needed to support and facilitate effective diabetes management in this population.

Background: Lipid management is a key aspect of secondary atherosclerotic cardiovascular disease (ASCVD) prevention. However, real-world studies show that ~72–88% of patients with ASCVD fail to meet their low-density lipoprotein cholesterol (LDL-C) target. Nonstatin agents are available as add-on therapies that can be utilized when maximally tolerated statins are insufficient to achieve LDL-C goals. This retrospective study aimed to evaluate the current prescribing habits of nonstatins as add-on therapy to statins for secondary ASCVD prevention at a federally qualified health center (FQHC). Methods: Patients were included if they had a history of clinical ASCVD, ≥1 lipid panel obtained during the study period, and were prescribed any intensity statin. Results: Among 398 included participants, 11.1% were prescribed nonstatin therapy and 35.9% were meeting a LDL-C target of <70 mg/dL. There was a significant association between being prescribed ezetimibe based on the type of healthcare coverage (p = 0.04) and a higher number of ASCVD qualifying indications (p < 0.01). Conclusions: Overall, nonstatins were found to be underutilized for LDL-C management in this underserved population. Future initiatives should target ways to optimize nonstatin therapy to optimize secondary ASCVD prevention.