Elevated serum uric acid levels are a frequent finding in persons with obesity, hypertension, cardiovascular and kidney disease as well as in those with the cardiorenal metabolic syndrome (CRS). The increased consumption of a fructose-rich Western diet has contributed to the increasing incidence of the CRS, obesity and diabetes especially in industrialized populations. There is also increasing evidence that supports a causal role of high dietary fructose driving elevations in uric acid in association with the CRS. Animal and epidemiological studies support the notion that elevated serum uric acid levels play an important role in promoting insulin resistance and hypertension and suggest potential pathophysiological mechanisms that contribute to the development of the CRS and associated cardiovascular disease and chronic kidney disease. To this point, elevated serum levels of uric acid appear to contribute to impaired nitric oxide production/endothelial dysfunction, increased vascular stiffness, inappropriate activation of the renin-angiotensin-aldosterone system, enhanced oxidative stress, and maladaptive immune and inflammatory responses. These abnormalities, in turn, promote vascular, cardiac and renal fibrosis as well as associated functional abnormalities. Small clinical trials have suggested that uric acid-lowering therapies may be beneficial in such patients; however, a consensus on the treatment of asymptomatic hyperuricemia is lacking. Larger randomized controlled trials need to be performed in order to critically evaluate the beneficial effect of lowering serum uric acid in patients with the CRS and those with diabetes and/or hypertension.

Objective: To determine whether information in medical and pharmacy claims data can predict, at the time of prescribing the first antiepileptic drug (AED), which patients with epilepsy will become resistant to AEDs. Method: We analyzed longitudinal claims data from 1,376,756 patients with epilepsy from 2006 to 2015. Of these, 582,258 satisfied all inclusion criteria; 49,916 were ultimately AED resistant, operationally defined as a patient with claims filed for at least 4 distinct AEDs. We constructed 1,270 candidate predictors (“features”) reflecting demographics, comorbidities, medications, procedures, epilepsy status, and payer status to characterize the cohort. On the training dataset (528,640 patients) we performed ANOVA F-value tests to select predictive features and trained several prediction algorithms, including logistic regression, support vector machines (SVM), and random forests. A model with only age and gender was used as a benchmark model. Results: On a held-out test set (53,618 patients), the best model achieves an area under the receiver operating characteristic (ROC) curve (AUC) [95% CI] of 0.753 [0.747, 0.759], compared to 0.664 [0.658, 0.671] for the benchmark model. Moreover, predicted probabilities for drug resistance closely match the observed frequencies. Compared to waiting for 2 AED failures, our model predicts drug resistance on average 2.25 years earlier. Conclusion: Predictive models created from large claims data using machine learning methods can accurately predict which patients with epilepsy will prove drug resistant at the time of prescribing the first AED. The ability to predict refractoriness may help patients consider alternative therapies earlier in the course of their epilepsy.

Background and Purpose: We determined the rates and predictors of acute kidney injury (AKI) and renal adverse events (AEs), and effects of AKI and renal AEs on death or disability in patients with intracerebral hemorrhage. Methods: We analyzed data from a multicenter trial which randomized 1000 intracerebral hemorrhage patients with initial systolic blood pressure ≥180 mm Hg to intensive (goal 110–139 mm Hg) over standard (goal 140–179 mm Hg) systolic blood pressure reduction within 4.5 hours of symptom onset. AKI was identified by serial assessment of daily serum creatinine for 3 days post randomization. Results: AKI and renal AEs were observed in 149 patients (14.9%) and 65 patients (6.5%) among 1000 patients, respectively. In multivariate analysis, the higher baseline serum creatinine (≥110 μmol/L) was associated with AKI (odds ratio 2.4 [95% CI, 1.2–4.5]) and renal AEs (odds ratio 3.1 [95% CI, 1.2–8.1]). Higher area under the curve for intravenous nicardipine dose was associated with AKI (odds ratio 1.003 [95% CI, 1.001–1.005]) and renal AEs (odds ratio 1.003 [95% CI, 1.001–1.006]). There was a higher risk to death (relative risk 2.6 [95% CI, 1.6–4.2]) and death or disability (relative risk 1.5 [95% CI, 1.3–1.8]) at 90 days in patients with AKI but not in those with renal AEs. Conclusions: Intracerebral hemorrhage patients with higher baseline serum creatinine and those receiving higher doses of nicardipine were at higher risk for AKI and renal AEs. Occurrence of AKI was associated higher rates of death or disability at 3 months. Registration: URL: https://clinicaltrials.gov . Unique identifier: NCT01176565.

Objectives: Acute ischemic stroke patients are at risk of acute kidney injury due to volume depletion, contrast exposure, and preexisting comorbid diseases. We determined the occurrence rate and identified predictors associated with acute kidney injury in acute ischemic stroke patients. Setting: Multiple specialized ICUs within academic medical centers. Design: Post hoc analysis of pooled data from prospective randomized clinical trials. Patients: Acute ischemic stroke patients recruited within 3 hours or within 5 hours of symptom onset. Interventions: IV recombinant tissue plasminogen activator, endovascular treatment, IV albumin, or placebo. Measurements and Main Results: Serum creatinine levels from baseline and within day 5 or discharge were used to classify acute kidney injury classification into stages. Any increase in serum creatinine was seen in 697 (36.1%) and acute kidney injury was seen in 68 (3.5%) of 1,931 patients with acute ischemic stroke. Severity of acute kidney injury was grade I, II, and III in 3.1%, 0.4%, and 0.05% patients, respectively. Patients with albumin (5.5% compared with 2.6%; p = 0.001), preexisting hypertension (4.3% compared with 1.5%; p = 0.0041), and preexisting renal disease (9.1% compared with 3.0%; p < 0.0001) had higher risk of acute kidney injury. The risk of acute kidney injury was lower between those who either underwent CT angiography (2.0% compared with 4.7%; p = 0.0017) or endovascular treatment (1.6% compared with 4.2%; p = 0.0071). In the multivariate analysis, hypertension (odds ratio, 2.6; 95% CI, 1.2–5.6) and renal disease (odds ratio, 3.5; 95% CI, 1.9–6.5) were associated with acute kidney injury. The risk of death was significantly higher among patients with acute kidney injury (odds ratio, 2.7; 95% CI, 1.4–4.9) after adjusting for age and National Institutes of Health Stroke Scale score strata. Conclusions: The occurrence rate of acute kidney injury in acute ischemic stroke patients was low and was not higher in patients who underwent CT angiogram or those who received endovascular treatment. Occurrence of acute kidney injury increased the risk of death within 3 months among acute ischemic stroke patients.

Background and objectives Dialysis disequilibrium syndrome (DDS) is a rare neurological complication, most commonly affecting patients undergoing new initiation of hemodialysis (HD), but can also be seen in patients receiving chronic dialysis who miss regular treatments, patients having acute kidney injury (AKI), and in those treated with continuous kidney replacement therapy (CKRT) or peritoneal dialysis (PD). Although the pathogenesis is not well understood, DDS is likely a result of multiple physiological abnormalities. In this systematic review, we provide a synopsis of the data available on DDS that allow for a clear picture of its pathogenesis, preventive measures, and focus on effective management strategies. Methods We conducted a literature search on PubMed/Medline and Embase from January 1960 to January 2021. Studies were included if the patient developed DDS irrespective of age and gender. A summary table was used to summarize the data from individual studies and included study type, population group, age group, sample size, patient characteristics, blood and dialysate flow rate, and overall outcome. A descriptive analysis calculating the frequency of population size, symptoms, and various treatments was performed using R software version 3.1.0. Results A total of 49 studies (321 samples) were identified and analyzed. Out of the included 49 studies, a total of 48 studies reported the presence of DSS among patients (1 study reported based on number of dialysis and therefore was not considered for analysis). Among these 48 studies, 74.3% (226/304) patients were reported to have DSS. The most common symptoms were nausea (25.2%), headache (24.8%), vomiting (23.9%), muscle cramps (18.1%), affected level of consciousness (8.8%), confusion (4.4%), and seizure (4.9%) among the 226 DDS patients. Furthermore, 12 studies decided to switch from HD to alternative dialysis modalities including continuous venovenous hemofiltration/hemodiafiltration (CVVH/CVVHDF) or PD which reported no DDS symptoms. Conclusion Early recognition and timely prevention are crucial for DDS patients. We have provided comprehensive clinical practice points for pediatric, adolescent, and young adult populations. However, it is essential to recognize that DDS was reported more frequently in the early dialysis era, as there was a lack of advanced dialysis technology and limited resources.

We demonstrated the importance of diverse sources of features in predicting GBM patient survival outcome. The predictive model presented in this study is a preliminary step in a long-term plan of developing personalized treatment plans for GBM patients that can later be extended to other types of cancers.

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To manage and integrate information gathered from heterogeneous databases, an ontology is often used. Like all systems, ontology-driven systems evolve over time and must be regression tested to gain confidence in the behavior of the modified system. Because rerunning all existing tests can be extremely expensive, researchers have developed regressiontest-selection (RTS) techniques that select a subset of the available tests that are affected by the changes, and use this subset to test the modified system. Existing RTS techniques have been shown to be effective, but they operate on the code and are unable to handle changes that involve ontologies. To address this limitation, we developed and present in this paper a novel RTS technique that targets ontologydriven systems. Our technique creates representations of the old and new ontologies, compares them to identify entities affected by the changes, and uses this information to select the subset of tests to rerun. We also describe in this paper OntoReTest, a tool that implements our technique and that we used to empirically evaluate our approach on two biomedical ontology-driven database systems. The results of our evaluation show that our technique is both efficient and effective in selecting tests to rerun and in reducing the overall time required to perform regression testing.