Dual autoencoders modeling of electronic health records for adverse drug event preventability prediction

Background Elderly patients are at increased risk for Adverse Drug Events (ADEs). Proactively screening elderly people visiting the emergency department for the possibility of their hospital admission being drug-related helps to improve patient care as well as prevent potential unnecessary medical costs. Existing routine ADE assessment heavily relies on a rule-based checking process. Recently, machine learning methods have been shown to be effective in automating the detection of ADEs, however, most approaches used only either structured data or free texts for their feature engineering. How to better exploit all available EHRs data for better predictive modeling remains an important question. On the other hand, automated reasoning for the preventability of ADEs is still a nascent line of research. Methods Clinical information of 714 elderly ED-visit patients with ADE preventability labels was provided as ground truth data by Jeroen Bosch Ziekenhuis hospital, the Netherlands. Methods were developed to address the challenges of applying feature engineering to heterogeneous EHRs data. A Dual Autoencoders (2AE) model was proposed to solve the problem of imbalance embedded in the existing training data. Results Experimental results showed that 2AE can capture the patterns of the minority class without incorporating an extra process for class balancing. 2AE yields adequate performance and outperforms other more mainstream approaches, resulting in an AUPRC score of 0.481. Conclusions We have demonstrated how machine learning can be employed to analyze both structured and unstructured data from electronic health records for the purpose of preventable ADE prediction. The developed algorithm 2AE can be used to effectively learn minority group phenotype from imbalanced data

Impact of Drug Recalls on Patients in The Netherlands: A 5-Year Retrospective Data Analysis

Drug recalls occur frequently and have the potential to impact considerable numbers of patients and healthcare providers. However, in the absence of a comprehensive overview the extent of conducted recalls and their impact on patients remains unknown. To address this, we developed a comprehensive overview of drug recalls affecting patients. We compiled this overview based on the drug recall registrations from the Jeroen Bosch Hospital (JBZ), the University Medical Center Utrecht (UMCU), and the Royal Dutch Pharmacists Association (KNMP). A retrospective data analysis was conducted to identify drug recalls that affected patients. Specifically, we defined these as drug recalls that required patients to actively switch their drug to a different batch or brand of the same drug or to switch to a drug within the same or a different class of drugs. To quantify the impact, we used real-world drug dispensing data. Between January 1, 2017, and December 31, 2021, we identified 48 drug recalls that necessitated patients to make active changes to their medications an estimated 855,000 times. Most of the affected patients (292,000) were required to switch to a different brand of the same drug, whereas in 95,000 cases patients had to switch to a drug from another drug class. Our study suggests that a significant number of patients are affected by drug recalls. Future efforts are needed to elucidate patients' experiences and preferences regarding drug recalls, which could provide valuable insights to aid decision-making by relevant (national) authorities concerning drug recalls

Inhibition of CYP2D6 with low dose (5 mg) paroxetine in patients with high 10-hydroxynortriptyline serum levels-A prospective pharmacokinetic study

The antidepressant nortriptyline is metabolized by cytochrome P450 2D6 (CYP2D6) to the less active and more cardiotoxic drug metabolite, 10-hydroxynortriptyline. High serum levels of this metabolite (>200 μg/L) may lead to withdrawal of nortriptyline therapy. Adding CYP2D6 inhibitors reduce the metabolic activity of CYP2D6 (phenoconversion) and so decrease the forming of hydroxynortriptyline. In this study, 5 mg paroxetine is administered to patients with high hydroxynortriptyline concentrations (>200 μg/L). The shift in number of patients to therapeutic nortriptyline (50-150 μg/L) and safe hydroxynortriptyline (<200 μg/L) concentrations, and the degree of phenoconversion, expressed as the change in ratio nortriptyline/hydroxynortriptyline concentrations before and after paroxetine addition, are prospectively observed and described. After paroxetine addition, 12 patients (80%) had therapeutic nortriptyline and safe hydroxynortriptyline concentrations. Hydroxynortriptyline concentrations decreased in all patients. The average nortriptyline/hydroxynortriptyline concentrations ratio increased from 0.32 to 0.59. This study shows that 5 mg paroxetine addition is able to lower high hydroxynortriptyline serum levels to safe ranges

Body weight gain in clozapine-treated patients:Is norclozapine the culprit?

The antipsychotic drug clozapine is associated with weight gain. The proposed mechanisms include blocking of serotonin (5-HT2a/2c ), dopamine (D2 ) and histamine (H1 ) receptors. Clozapine is metabolized by cytochrome P450 1A2 (CYP1A2) to norclozapine, a metabolite with more 5-HT2c -receptor and less H1 blocking capacity. We hypothesized that norclozapine serum levels correlate with body mass index (BMI), waist circumference and other parameters of the metabolic syndrome. We performed a retrospective cross-sectional study in 39 patients (female n = 8 (20.5%), smokers n = 18 (46.2%), average age 45.8 ± 9.9 years) of a clozapine outpatient clinic in the Netherlands between 1 January 2017 and 1 July 2020. Norclozapine concentrations correlated with waist circumference (r = 0.354, P = .03) and hemoglobin A1c (HbA1c) (r = 0.34, P = .03). In smokers (smoking induces CYP1A2), norclozapine concentrations correlated with waist circumference (r = 0.723, P = .001), HbA1c (r = 0.49, P = .04) and BMI (r = 0.63, P = .004). Elucidating the relationship between norclozapine and adverse effects of clozapine use offers perspectives for interventions and treatment options

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.</p

A Systematic Evaluation of Cost-Saving Dosing Regimens for Therapeutic Antibodies and Antibody-Drug Conjugates for the Treatment of Lung Cancer

Background: Expensive novel anticancer drugs put a serious strain on healthcare budgets, and the associated drug expenses limit access to life-saving treatments worldwide. Objective: We aimed to develop alternative dosing regimens to reduce drug expenses. Methods: We developed alternative dosing regimens for the following monoclonal antibodies used for the treatment of lung cancer: amivantamab, atezolizumab, bevacizumab, durvalumab, ipilimumab, nivolumab, pembrolizumab, and ramucirumab; and for the antibody-drug conjugate trastuzumab deruxtecan. The alternative dosing regimens were developed by means of modeling and simulation based on the population pharmacokinetic models developed by the license holders. They were based on weight bands and the administration of complete vials to limit drug wastage. The resulting dosing regimens were developed to comply with criteria used by regulatory authorities for in silico dose development. Results: We found that alternative dosing regimens could result in cost savings that range from 11 to 28%, and lead to equivalent pharmacokinetic exposure with no relevant increases in variability in exposure. Conclusions: Dosing regimens based on weight bands and the use of complete vials to reduce drug wastage result in less expenses while maintaining equivalent exposure. The level of evidence of our proposal is the same as accepted by regulatory authorities for the approval of alternative dosing regimens of other monoclonal antibodies in oncology. The proposed alternative dosing regimens can, therefore, be directly implemented in clinical practice.</p

Dual autoencoders modeling of electronic health records for adverse drug event preventability prediction

Background Elderly patients are at increased risk for Adverse Drug Events (ADEs). Proactively screening elderly people visiting the emergency department for the possibility of their hospital admission being drug-related helps to improve patient care as well as prevent potential unnecessary medical costs. Existing routine ADE assessment heavily relies on a rule-based checking process. Recently, machine learning methods have been shown to be effective in automating the detection of ADEs, however, most approaches used only either structured data or free texts for their feature engineering. How to better exploit all available EHRs data for better predictive modeling remains an important question. On the other hand, automated reasoning for the preventability of ADEs is still a nascent line of research. Methods Clinical information of 714 elderly ED-visit patients with ADE preventability labels was provided as ground truth data by Jeroen Bosch Ziekenhuis hospital, the Netherlands. Methods were developed to address the challenges of applying feature engineering to heterogeneous EHRs data. A Dual Autoencoders (2AE) model was proposed to solve the problem of imbalance embedded in the existing training data. Results Experimental results showed that 2AE can capture the patterns of the minority class without incorporating an extra process for class balancing. 2AE yields adequate performance and outperforms other more mainstream approaches, resulting in an AUPRC score of 0.481. Conclusions We have demonstrated how machine learning can be employed to analyze both structured and unstructured data from electronic health records for the purpose of preventable ADE prediction. The developed algorithm 2AE can be used to effectively learn minority group phenotype from imbalanced data