The Role of Adherence Thresholds for Development and Performance Aspects of a Prediction Model for Direct Oral Anticoagulation Adherence

Patients who do not sufficiently adhere to their dosing regimens will, ultimately, do not get the full benefit of their medication. For example, if direct oral anticoagulants (DOAC) are not taken continuously, an intervention to improve adherence or maintain persistence will show direct effects on clinical outcomes. Usually, adherent patients are defined by taking ≥80% of their medication. The resulting binary adherence status from this threshold can as well be used for predictive classification. Thus, the threshold can determine the prediction model’s performance to identify patients at risk for poor adherence by this binary adherence status. In this perspective, we propose a plan for model development and performance considering the threshold’s role. Concerning development demands, we extracted predictors from a systematic literature search on DOAC adherence to be used as a core set of candidate predictors. Independently, we investigated how well a future model would technically have to perform by modeling drug intake and thromboembolic events based on a rivaroxaban pharmacokinetic-pharmacodynamic model. Using this simulation framework for different thresholds, we projected the impact of an imperfectly predicted adherence status on the event risk, and how imperfect sensitivity and specificity affect the cost balance if a supporting intervention was offered to patients classified as non-adherent. Our simulation results suggest applying a rather high threshold (90%) for discrimination between patients at low or high risk for non-adherence by a prediction model in order to assure cost-efficient implementation

Changes in prescribed medicines in older patients with multimorbidity and polypharmacy in general practice

Background: Treatment complexity rises in line with the number of drugs, single doses, and administration methods, thereby threatening patient adherence. Patients with multimorbidity often need flexible, individualised treatment regimens, but alterations during the course of treatment may further increase complexity. The objective of our study was to explore medication changes in older patients with multimorbidity and polypharmacy in general practice. Methods: We retrospectively analysed data from the cluster-randomised PRIMUM trial (PRIoritisation of MUltimedication in Multimorbidity) conducted in 72 general practices. We developed an algorithm for active pharmaceutical ingredients (API), strength, dosage, and administration method to assess changes in physician-reported medication data during two intervals (baseline to six-months: ∆1; six- to nine-months: ∆2), analysed them descriptively at prescription and patient levels, and checked for intervention effects. Results: Of 502 patients (median age 72 years, 52% female), 464 completed the study. Changes occurred in 98.6% of patients (changes were 19% more likely in the intervention group): API changes during ∆1 and ∆2 occurred in 414 (82.5%) and 338 (67.3%) of patients, dosage alterations in 372 (74.1%) and 296 (59.2%), and changes in API strength in 158 (31.5%) and 138 (27.5%) respectively. Administration method changed in 79 (16%) of patients in both ∆1 and ∆2. Simvastatin, metformin and aspirin were most frequently subject to alterations. Conclusion: Medication regimens in older patients with multimorbidity and polypharmacy changed frequently. These are mostly due to discontinuations and dosage alterations, followed by additions and restarts. These findings cast doubt on the effectiveness of cross-sectional assessments of medication and support longitudinal assessments where possible. Trial registration.: 1. Prospective registration: Trial registration number: NCT01171339 ; Name of registry: ClinicalTrials.gov; Date of registration: July 27, 2010; Date of enrolment of the first participant to the trial: August 12, 2010. 2. Peer reviewed trial registration: Trial registration number: ISRCTN99526053 ; Name of registry: Controlled Trials; Date of registration: August 31, 2010; Date of enrolment of the first participant to the trial: August 12, 2010

Quantification of the Time Course of CYP3A Inhibition, Activation, and Induction Using a Population Pharmacokinetic Model of Microdosed Midazolam Continuous Infusion

Background Cytochrome P450 (CYP) 3A contributes to the metabolism of many approved drugs. CYP3A perpetrator drugs can profoundly alter the exposure of CYP3A substrates. However, effects of such drug-drug interactions are usually reported as maximum effects rather than studied as time-dependent processes. Identification of the time course of CYP3A modulation can provide insight into when significant changes to CYP3A activity occurs, help better design drug-drug interaction studies, and manage drug-drug interactions in clinical practice. Objective We aimed to quantify the time course and extent of the in vivo modulation of different CYP3A perpetrator drugs on hepatic CYP3A activity and distinguish different modulatory mechanisms by their time of onset, using pharmacologically inactive intravenous microgram doses of the CYP3A-specific substrate midazolam, as a marker of CYP3A activity. Methods Twenty-four healthy individuals received an intravenous midazolam bolus followed by a continuous infusion for 10 or 36 h. Individuals were randomized into four arms: within each arm, two individuals served as a placebo control and, 2 h after start of the midazolam infusion, four individuals received the CYP3A perpetrator drug: voriconazole (inhibitor, orally or intravenously), rifampicin (inducer, orally), or efavirenz (activator, orally). After midazolam bolus administration, blood samples were taken every hour (rifampicin arm) or every 15 min (remaining study arms) until the end of midazolam infusion. A total of 1858 concentrations were equally divided between midazolam and its metabolite, 1’-hydroxymidazolam. A nonlinear mixed-effects population pharmacokinetic model of both compounds was developed using NONMEM®. CYP3A activity modulation was quantified over time, as the relative change of midazolam clearance encountered by the perpetrator drug, compared to the corresponding clearance value in the placebo arm. Results Time course of CYP3A modulation and magnitude of maximum effect were identified for each perpetrator drug. While efavirenz CYP3A activation was relatively fast and short, reaching a maximum after approximately 2–3 h, the induction effect of rifampicin could only be observed after 22 h, with a maximum after approximately 28–30 h followed by a steep drop to almost baseline within 1–2 h. In contrast, the inhibitory impact of both oral and intravenous voriconazole was prolonged with a steady inhibition of CYP3A activity followed by a gradual increase in the inhibitory effect until the end of sampling at 8 h. Relative maximum clearance changes were +59.1%, +46.7%, −70.6%, and −61.1% for efavirenz, rifampicin, oral voriconazole, and intravenous voriconazole, respectively. Conclusions We could distinguish between different mechanisms of CYP3A modulation by the time of onset. Identification of the time at which clearance significantly changes, per perpetrator drug, can guide the design of an optimal sampling schedule for future drug-drug interaction studies. The impact of a short-term combination of different perpetrator drugs on the paradigm CYP3A substrate midazolam was characterized and can define combination intervals in which no relevant interaction is to be expected. Clinical Trial Registration The trial was registered at the European Union Drug Regulating Authorities for Clinical Trials (EudraCT-No. 2013-004869-14)

Combinations of QTc-prolonging drugs: towards disentangling pharmacokinetic and pharmacodynamic effects in their potentially additive nature

Whether arrhythmia risks will increase if drugs with electrocardiographic (ECG) QT-prolonging properties are combined is generally supposed but not well studied. Based on available evidence, the Arizona Center for Education and Research on Therapeutics (AZCERT) classification defines the risk of QT prolongation for exposure to single drugs. We aimed to investigate how combining AZCERT drug categories impacts QT duration and how relative drug exposure affects the extent of pharmacodynamic drug-drug interactions

Phase I/II intra-patient dose escalation study of vorinostat in children with relapsed solid tumor, lymphoma, or leukemia

Background: Until today, adult and pediatric clinical trials investigating single-agent or combinatorial HDAC inhibitors including vorinostat in solid tumors have largely failed to demonstrate efficacy. These results may in part be explained by data from preclinical models showing significant activity only at higher concentrations compared to those achieved with current dosing regimens. In the current pediatric trial, we applied an intra-patient dose escalation design. The purpose of this trial was to determine a safe dose recommendation (SDR) of single-agent vorinostat for intra-patient dose escalation, pharmacokinetic analyses (PK), and activity evaluation in children (3-18 years) with relapsed or therapy-refractory malignancies. Results: A phase I intra-patient dose (de)escalation was performed until individual maximum tolerated dose (MTD). The starting dose was 180 mg/m(2)/day with weekly dose escalations of 50 mg/m(2) until DLT/maximum dose. After MTD determination, patients seamlessly continued in phase II with disease assessments every 3 months. PK and plasma cytokine profiles were determined. Fifty of 52 patients received treatment. n = 27/50 (54%) completed the intra-patient (de)escalation and entered phase II. An SDR of 130 mg/m(2)/day was determined (maximum, 580 mg/m(2)/day). n = 46/50 (92%) patients experienced treatment-related AEs which were mostly reversible and included thrombocytopenia, fatigue, nausea, diarrhea, anemia, and vomiting. n = 6/50 (12%) had treatment-related SAEs. No treatment-related deaths occurred. Higher dose levels resulted in higher C-max. Five patients achieved prolonged disease control (> 12 months) and showed a higher C-max (> 270 ng/mL) and MTDs. Best overall response (combining PR and SD, no CR observed) rate in phase II was 6/27 (22%) with a median PFS and OS of 5.3 and 22.4 months. Low levels of baseline cytokine expression were significantly correlated with favorable outcome. Conclusion: An SDR of 130 mg/m(2)/day for individual dose escalation was determined. Higher drug exposure was associated with responses and long-term disease stabilization with manageable toxicity. Patients with low expression of plasma cytokine levels at baseline were able to tolerate higher doses of vorinostat and benefited from treatment. Baseline cytokine profile is a promising potential predictive biomarker

A framework to build similarity-based cohorts for personalized treatment advice - a standardized, but flexible workflow with the R package SimBaCo.

Along with increasing amounts of big data sources and increasing computer performance, real-world evidence from such sources likewise gains in importance. While this mostly applies to population averaged results from analyses based on the all available data, it is also possible to conduct so-called personalized analyses based on a data subset whose observations resemble a particular patient for whom a decision is to be made. Claims data from statutory health insurance companies could provide necessary information for such personalized analyses. To derive treatment recommendations from them for a particular patient in everyday care, an automated, reproducible and efficiently programmed workflow would be required. We introduce the R-package SimBaCo (Similarity-Based Cohort generation) offering a simple, but modular, and intuitive framework for this task. With the six built-in R-functions, this framework allows the user to create similarity cohorts tailored to the characteristics of particular patients. An exemplary workflow illustrates the distinct steps beginning with an initial cohort selection according to inclusion and exclusion criteria. A plotting function facilitates investigating a particular patient's characteristics relative to their distribution in a reference cohort, for example the initial cohort or the precision cohort after the data has been trimmed in accordance with chosen variables for similarity finding. Such precision cohorts allow any form of personalized analysis, for example personalized analyses of comparative effectiveness or customized prediction models developed from precision cohorts. In our exemplary workflow, we provide such a treatment comparison whereupon a treatment decision for a particular patient could be made. This is only one field of application where personalized results can directly support the process of clinical reasoning by leveraging information from individual patient data. With this modular package at hand, personalized studies can efficiently weight benefits and risks of treatment options of particular patients

Composite midazolam and 1′-OH midazolam population pharmacokinetic model for constitutive, inhibited and induced CYP3A activity

CYP3A plays an important role in drug metabolism and, thus, can be a considerable liability for drug-drug interactions. Population pharmacokinetics may be an efficient tool for detecting such drug-drug interactions. Multiple models have been developed for midazolam, the typical probe substrate for CYP3A activity, but no population pharmacokinetic models have been developed for use with inhibition or induction. The objective of the current analysis was to develop a composite parent-metabolite model for midazolam which could adequately describe CYP3A drug-drug interactions. As an exploratory objective, parameters were assessed for potential cut-points which may allow for determination of drug-drug interactions when a baseline profile is not available. The final interaction model adequately described midazolam and 1'-OH midazolam concentrations for constitutive, inhibited, and induced CYP3A activity. The model showed good internal and external validity, both with full profiles and limited sampling (2, 2.5, 3, and 4 h), and the model predicted parameters were congruent with values found in clinical studies. Assessment of potential cut-points for model predicted parameters to assess drug-drug interaction liability with a single profile suggested that midazolam clearance may reasonably be used to detect inhibition (4.82-16.4 L/h), induction (41.8-88.9 L/h), and no modulation (16.4-41.8 L/h), with sensitivities for potent inhibition and induction of 87.9% and 83.3%, respectively, and a specificity of 98.2% for no modulation. Thus, the current model and cut-points could provide efficient and accurate tools for drug-drug liability detection, both during drug development and in the clinic, following prospective validation in healthy volunteers and patient populations

Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study

BackgroundPatients with heart failure (HF) are the most commonly readmitted group of adult patients in Germany. Most patients with HF are readmitted for noncardiovascular reasons. Understanding the relevance of HF management outside the hospital setting is critical to understanding HF and factors that lead to readmission. Application of machine learning (ML) on data from statutory health insurance (SHI) allows the evaluation of large longitudinal data sets representative of the general population to support clinical decision-making. ObjectiveThis study aims to evaluate the ability of ML methods to predict 1-year all-cause and HF-specific readmission after initial HF-related admission of patients with HF in outpatient SHI data and identify important predictors. MethodsWe identified individuals with HF using outpatient data from 2012 to 2018 from the AOK Baden-Württemberg SHI in Germany. We then trained and applied regression and ML algorithms to predict the first all-cause and HF-specific readmission in the year after the first admission for HF. We fitted a random forest, an elastic net, a stepwise regression, and a logistic regression to predict readmission by using diagnosis codes, drug exposures, demographics (age, sex, nationality, and type of coverage within SHI), degree of rurality for residence, and participation in disease management programs for common chronic conditions (diabetes mellitus type 1 and 2, breast cancer, chronic obstructive pulmonary disease, and coronary heart disease). We then evaluated the predictors of HF readmission according to their importance and direction to predict readmission. ResultsOur final data set consisted of 97,529 individuals with HF, and 78,044 (80%) were readmitted within the observation period. Of the tested modeling approaches, the random forest approach best predicted 1-year all-cause and HF-specific readmission with a C-statistic of 0.68 and 0.69, respectively. Important predictors for 1-year all-cause readmission included prescription of pantoprazole, chronic obstructive pulmonary disease, atherosclerosis, sex, rurality, and participation in disease management programs for type 2 diabetes mellitus and coronary heart disease. Relevant features for HF-specific readmission included a large number of canonical HF comorbidities. ConclusionsWhile many of the predictors we identified were known to be relevant comorbidities for HF, we also uncovered several novel associations. Disease management programs have widely been shown to be effective at managing chronic disease; however, our results indicate that in the short term they may be useful for targeting patients with HF with comorbidity at increased risk of readmission. Our results also show that living in a more rural location increases the risk of readmission. Overall, factors beyond comorbid disease were relevant for risk of HF readmission. This finding may impact how outpatient physicians identify and monitor patients at risk of HF readmission

Renal Safety of Hydroxyethyl starch 130/0.42 After Cardiac Surgery: A Retrospective Cohort Analysis

Introduction!#!The risk for renal complications from hydroxyethyl starch 130/0.42 (HES) impacts treatment decisions in patients after cardiac surgery.!##!Objective!#!The objective of this study was to determine the impact of postoperatively administered HES on renal function and 90-day mortality compared to sole crystalloid administration in patients after elective cardiac surgery.!##!Methods!#!Using electronic health records from a university hospital, confounding-adjusted models analyzed the associations between postoperative HES administration and the occurrence of postoperative acute kidney injury. In addition, 90-day mortality was evaluated. The impact of HES dosage and timing on renal function on trajectories of estimated glomerular filtration rates over the postoperative period was investigated using linear mixed-effects models.!##!Results!#!Overall 1009 patients (45.0%) experienced acute kidney injury. Less acute kidney injury occurred in patients receiving HES compared with patients receiving only crystalloids for fluid resuscitation (43.7% vs 51.2%, p = 0.008). In multivariate acute kidney injury models, HES had a protective association (odds ratio: 0.89; 95% confidence interval 0.82-0.96). Crystalloids were not as protective as HES (odds ratio: 0.98; 95% confidence interval 0.95-1.00). There was no association between HES and 90-day mortality (odds ratio: 1.05; 95% confidence interval 0.88-1.25). Renal function trajectories were dose dependent and biphasic, HES appeared to slow down the late postoperative decline.!##!Conclusions!#!This study showed no association between HES and the postoperative occurrence of acute kidney injury and thus further closes the evidence gap on HES safety in cardiac surgery patients. Although this was a retrospective cohort study, the results indicated that HES might be safely administered to cardiac surgery patients with regard to renal outcomes, especially if it was administered early and dosed appropriately