Adoption of antithrombotic stewardship and utilization of clinical decision support systems-A questionnaire-based survey in Dutch hospitals.

Antithrombotics require careful monitoring to prevent adverse events. Safe use can be promoted through so-called antithrombotic stewardship. Clinical decision support systems (CDSSs) can be used to monitor safe use of antithrombotics, supporting antithrombotic stewardship efforts. Yet, previous research shows that despite these interventions, antithrombotics continue to cause harm. Insufficient adoption of antithrombotic stewardship and suboptimal use of CDSSs may provide and explanation. However, it is currently unknown to what extent hospitals adopted antithrombotic stewardship and utilize CDSSs to support safe use of antithrombotics. A semi-structured questionnaire-based survey was disseminated to 12 hospital pharmacists from different hospital types and regions in the Netherlands. The primary outcome was the degree of antithrombotic stewardship adoption, expressed as the number of tasks adopted per hospital and the degree of adoption per task. Secondary outcomes included characteristics of CDSS alerts used to monitor safe use of antithrombotics. All 12 hospital pharmacists completed the survey and report to have adopted antithrombotic stewardship in their hospital to a certain degree. The median adoption of tasks was two of five tasks (range 1-3). The tasks with the highest uptake were: drafting and maintenance of protocols (100%) and professional's education (58%), while care transition optimization (25%), medication reviews (8%) and patient counseling (8%) had the lowest uptake. All hospitals used a CDSS to monitor safe use of antithrombotics, mainly via basic alerts and less frequently via advanced alerts. The most frequently employed alerts were: identification of patients using a direct oral anticoagulant (DOAC) or a vitamin K antagonist (VKA) with one or more other antithrombotics (n = 6) and patients using a VKA to evaluate correct use (n = 6), both reflecting basic CDSS. All participating hospitals adopted antithrombotic stewardship, but the adopted tasks vary. CDSS alerts used are mainly basic in their logic.

The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review.

OBJECTIVE: Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we conducted a scoping review on the current state of the use of AI to optimize medication alerts in a hospital setting. Specifically, we aimed to identify the applied AI methods used together with their performance measures and main outcome measures. MATERIALS AND METHODS: We searched Medline, Embase, and Cochrane Library database on May 25, 2023 for studies of any quantitative design, in which the use of AI-based methods was investigated to optimize medication alerts generated by CDSSs in a hospital setting. The screening process was supported by ASReview software. RESULTS: Out of 5625 citations screened for eligibility, 10 studies were included. Three studies (30%) reported on both statistical performance and clinical outcomes. The most often reported performance measure was positive predictive value ranging from 9% to 100%. Regarding main outcome measures, alerts optimized using AI-based methods resulted in a decreased alert burden, increased identification of inappropriate or atypical prescriptions, and enabled prediction of user responses. In only 2 studies the AI-based alerts were implemented in hospital practice, and none of the studies conducted external validation. DISCUSSION AND CONCLUSION: AI-based methods can be used to optimize medication alerts in a hospital setting. However, reporting on models' development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.

Lean Body Mass and Total Body Weight Versus Body Surface Area as a Determinant of Docetaxel Pharmacokinetics and Toxicity.

AIM: This study examined whether anthropometric and body composition parameters such as body surface area (BSA), lean body mass (LBM), and total body weight (TBW) are correlated with docetaxel clearance and exposure by analyzing area under the curve. In addition, LBM, TBW, and a fixed dose were compared with BSA as dosing parameters for dose individualization of docetaxel. METHODS: Thirty-six patients receiving docetaxel chemotherapy for breast or metastatic castration-resistant prostate carcinoma were included. Before treatment, LBM was measured using a dual-energy X-ray absorptiometry scanner. Blood samples were collected up to 180 minutes after dosing to analyze docetaxel concentrations and determine individual pharmacokinetic parameters. RESULTS: No significant correlations were found between docetaxel clearance and the anthropometric and body composition variables (BSA, LBM, and TBW). The area under the curve was significantly but poorly correlated with BSA [r = 0.452 ( P = 0.016)] and TBW [r = 0.476 ( P = 0.011)]. The mean absolute percentage error and mean error of simulated dosing based on LBM and fixed dosing were not significantly different from those of BSA. For TBW, only mean absolute percentage error was significantly higher compared with dosing based on BSA (24.1 versus 17.1, P = 0.001). CONCLUSIONS: There was no clinically relevant correlation between docetaxel pharmacokinetics and the anthropometric and body composition variables BSA, LBM, and TBW. Therefore, dose individualization of docetaxel based on LBM, TBW, or fixed dosing cannot be recommended over BSA-based dosing.