Stimulant Drugs and Stimulant Use Disorder.

The authors aim to summarize several key points of stimulant drugs and stimulant use disorder, including their indications, short-term and long-term adverse effects, current treatment strategies, and association with opioid medications. The global prevalence of stimulant use has seen annual increase in the last decade. Multiple studies have shown that stimulant use and stimulant use disorder are associated with a range of individual and public health issues. Stimulant misuse has led to a significant increase of overdose deaths in the United States.

Polygenic risk scores in pharmacogenomics: opportunities and challenges-a mini review.

Pharmacogenomics (PGx) aims at tailoring drug therapy by considering patient genetic makeup. While drug dosage guidelines have been extensively based on single gene mutations (single nucleotide polymorphisms) over the last decade, polygenic risk scores (PRS) have emerged in the past years as a promising tool to account for the complex interplay and polygenic nature of patients' genetic predisposition affecting drug response. Even though PRS research has demonstrated convincing evidence in disease risk prediction, the clinical utility and its implementation in daily care has yet to be demonstrated, and pharmacogenomics is no exception; usual endpoints include drug efficacy or toxicity. Here, we review the general pipeline in PRS calculation, and we discuss some of the remaining barriers and challenges that must be undertaken to bring PRS research in PGx closer to patient care. Besides the need in following reporting guidelines and larger PGx patient cohorts, PRS integration will require close collaboration between bioinformatician, treating physicians and genetic consultants to ensure a transparent, generalizable, and trustful implementation of PRS results in real-world medical decisions.

Intelligent Telehealth in Pharmacovigilance: A Future Perspective.

Pharmacovigilance improves patient safety by detecting and preventing adverse drug events. However, challenges exist that limit adverse drug event detection, resulting in many adverse drug events being underreported or inaccurately reported. One challenge includes having access to large data sets from various sources including electronic health records and wearable medical devices. Artificial intelligence, including machine learning methods, such as natural language processing and deep learning, can detect and extract information about adverse drug events, thus automating the pharmacovigilance process and improving the surveillance of known and documented adverse drug events. In addition, with the increased demand for telehealth services, for managing both acute and chronic diseases, artificial intelligence methods can play a role in detecting and preventing adverse drug events. In this review, we discuss two use cases of how artificial intelligence methods may be useful to improve the quality of pharmacovigilance and the role of artificial intelligence in telehealth practices.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review.

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.

High-sensitive cardiac troponin T as a marker of hemorrhagic complications in elderly patients anticoagulated for non-massive pulmonary embolism.

BACKGROUND: Recent data have raised concerns about the risk/benefit ratio of thrombolysis in non-high risk pulmonary embolism patients due to increased serious bleeding events. Whether cardiac biomarkers could be of help for bleeding risk stratification in this setting remains elusive. OBJECTIVES: To determine the prognostic accuracy of hs-cTnT, NT-proBNP, RIETE and PESI score for the occurrence of clinically relevant bleeding (CRB) in elderly patients under conventional anticoagulation therapy for non-massive pulmonary embolism (NMPE). METHODS: We evaluated 230 elderly patients with available blood sample taken within one day from diagnosis. The primary study endpoint was CRB at 1, 3 and 24 months. Prognostic accuracies and associations were determined using C-statistics and subhazard ratios (SHR), respectively. RESULTS: hs-cTnT displayed the highest discriminatory power at 1 month (C-statistics: 0.77, 95% CI: 0.68-0.88) which remained stable over time. Although C-statistics comparison indicated that hs-cTnT was not statistically superior to RIETE score (0.77 vs 0.67, p = 0.11), adding hs-cTnT to RIETE score significantly improved the C-statistics from 0.67 to 0.78 (p = 0.02). SHRs indicated that for each hs-cTnT log-unit increase, there was a 58% increase in the risk of CRB independently of the RIETE score (adjusted SHR: 1.58, 95% CI: 1.31-1.92). At the pre-specified cut-off of 14 ng/l, the negative predictive value of hs-cTnT was 96.9% (95% CI: 91.4-99.0) and 94.9 (95%CI: 88.6-97.8) at 1 and 3 months, respectively. CONCLUSION: In elderly, hs-cTnT provides incremental prognostic information over the RIETE score and could represent a valuable tool to identify NMPE patients at low risk of bleeding.

Comparison of Cardiac and Non-Cardiac Biomarkers for Risk Stratification in Elderly Patients with Non-Massive Pulmonary Embolism.

UNLABELLED: Biomarkers unrelated to myocardial necrosis, such as cystatin C, copeptin, and mid-regional pro-adrenomedullin (MR-proADM), showed promise for cardiovascular risk prediction. Knowing whether they are comparable to cardiac biomarkers such as high-sensitive cardiac-troponin T (hs-cTnT) or N-terminal pro-Brain natriuretic peptide (NT-proBNP) in elderly patients with acute non-massive pulmonary embolism (NMPE) remains elusive. This study aims at comparing the prognostic accuracy of cardiac and non-cardiac biomarkers in patients with NMPE aged ≥65 years over time. In the context of the SWITCO65+ cohort, we evaluated 227 elderly patients with an available blood sample taken within one day from diagnosis. The primary study endpoint was defined as PE-related mortality and the secondary endpoint as PE-related complications. The biomarkers' predictive ability at 1, 3, 12 and 24 months was determined using C-statistics and Cox regression. For both study endpoints, C-statistics (95% confidence interval) were stable over time for all biomarkers, with the highest value for hs-cTnT, ranging between 0.84 (0.68-1.00) and 0.80 (0.70-0.90) for the primary endpoint, and between 0.74 (0.63-0.86) and 0.65 (0.57-0.73) for the secondary endpoint. For both study endpoints, cardiac biomarkers were found to be independently associated with risk, NT-proBNP displaying a negative predictive value of 100%. Among non-cardiac biomarkers, only copeptin and MR-proADM were independent predictors of PE-related mortality but they were not independent predictors of PE-related complications, and displayed lower negative predictive values. In elderly NMPE patients, cardiac biomarkers appear to be valuable prognostic to identify very low-risk individuals. TRIAL REGISTRATION: ClinicalTrials.gov NCT00973596.