[Quality of medication documentation in patients discharge summaries after implementing new legal requirements]. / Dokumentationsqualität der Medikation in Entlassbriefen ­ eine Bestandsaufnahme nach Einführung des Rahmenvertrags Entlassmanagement.

INTRODUCTION: Discharge from hospital is a risk to drug continuity and medication safety. In Germany, new legal requirements concerning the management of patient discharge from the hospital came into force in 2017. They set minimum requirements for the documentation of medications in patient discharge summaries, which are the primary means of communication at transitions of care. Six years later, data on their practical implementation in routine care are lacking. METHODS: Within the scope of an explorative retrospective observational study, the minimum requirements were operationalized and a second set of assessment criteria was derived from the recommendation "Good Prescribing Practice in Drug Therapy" published by the Aktionsbündnis Patientensicherheit e.V. as a comparative quality standard. A sample of discharge summaries was drawn from routine care at the University Hospital Heidelberg and assessed according to their fulfilment of the criteria sets. In addition, the potential influence of certain context factors (e. g., involvement of clinical pharmacists or software usage) was evaluated. RESULTS: In total, 11 quality criteria were derived from the minimum requirements. According to the eligibility criteria (i. e., three or more discharge medications) 352 discharge summaries (42 wards; issued in May-July 2021), containing in total 3,051 medications, were included. The practical implementation of the minimum requirements for documenting medications in patient discharge summaries differed considerably depending on the criterion and defined context factors. Core elements (i. e., drug name, strength, and dosage at discharge) were fulfilled in 82.8 %, while further minimum requirements were rarely met or completely lacking (e. g., explanations for special pharmaceutical forms). Involvement of clinical pharmacists and usage of software were shown to be a facilitator of documentation quality, while on-demand medication (compared to long-term medication) as well as newly prescribed medication (compared to home medication or medication changed during hospitalisation) showed poorer documentation quality. In addition, the documentation quality seemed to depend on the department and the day of discharge. CONCLUSION: To date, the wording of the German legal requirements allows for different interpretations without considering the respective clinical setting and the medication actually prescribed. For future clarification of the requirements, implications of the wording for the clinical setting should be considered.

Event Analysis for Automated Estimation of Absent and Persistent Medication Alerts: Novel Methodology.

Background: Event analysis is a promising approach to estimate the acceptance of medication alerts issued by computerized physician order entry (CPOE) systems with an integrated clinical decision support system (CDSS), particularly when alerts cannot be interactively confirmed in the CPOE-CDSS due to its system architecture. Medication documentation is then reviewed for documented evidence of alert acceptance, which can be a time-consuming process, especially when performed manually. Objective: We present a new automated event analysis approach, which was applied to a large data set generated in a CPOE-CDSS with passive, noninterruptive alerts. Methods: Medication and alert data generated over 3.5 months within the CPOE-CDSS at Heidelberg University Hospital were divided into 24-hour time intervals in which the alert display was correlated with associated prescription changes. Alerts were considered "persistent" if they were displayed in every consecutive 24-hour time interval due to a respective active prescription until patient discharge and were considered "absent" if they were no longer displayed during continuous prescriptions in the subsequent interval. Results: Overall, 1670 patient cases with 11,428 alerts were analyzed. Alerts were displayed for a median of 3 (IQR 1-7) consecutive 24-hour time intervals, with the shortest alerts displayed for drug-allergy interactions and the longest alerts displayed for potentially inappropriate medication for the elderly (PIM). Among the total 11,428 alerts, 56.1% (n=6413) became absent, most commonly among alerts for drug-drug interactions (1915/2366, 80.9%) and least commonly among PIM alerts (199/499, 39.9%). Conclusions: This new approach to estimate alert acceptance based on event analysis can be flexibly adapted to the automated evaluation of passive, noninterruptive alerts. This enables large data sets of longitudinal patient cases to be processed, allows for the derivation of the ratios of persistent and absent alerts, and facilitates the comparison and prospective monitoring of these alerts.

Coadministration of fluconazole to boost subtherapeutic sirolimus concentrations: A case report.

Individual sirolimus whole blood concentrations are highly variable, critically influenced by the concomitant use of cytochrome P450 (CYP) 3A inducers or inhibitors, and also modulated by food. Therapeutic drug monitoring is therefore recommended, especially at treatment start or in circumstances that can influence sirolimus exposure. In this case report, we highlight the challenge of achieving therapeutic sirolimus concentrations and present pragmatic solutions with regimen adaptions, pharmacokinetic enhancement (use of a drug­drug interaction), concentration monitoring, and subsequent modeling of population pharmacokinetics to support treatment decisions. In a 69-year-old female patient with allogeneic hematopoietic stem cell transplantation, sirolimus concentrations were stable until she developed cerebral toxoplasmosis with tonic­clonic seizures. During treatment of this acute infection, sirolimus concentrations dropped to subtherapeutic levels and remained largely unaffected by dose increases. [Correction added on 4 May 2024, after first online publication: The word "tacrolimus concentrations" has been changed to "sirolimus concentrations" in the preceding sentence.] Only the simultaneous administration of the CYP3A4 inhibitor fluconazole and a shortening of the sirolimus dosing intervals to a (non-approved) twice-daily administration led to successful control of the concentrations, which ultimately even made a dose reduction possible. This intervention resulted in an increase of sirolimus mean trough concentration to 5.85 ng/mL, i.e., into the desired target range. Additionally, a higher ratio of sirolimus trough levels/daily dose from 26.9 to 109 ng/mL/mg/kg/day was achieved with the initiation of fluconazole. Thus, this case report describes the use of clinical pharmacological concepts and pharmacokinetic modeling to optimize treatment strategies in an individual patient. This strategy could be generalized to other CYP inhibitors and other treatment regimens.

Effect of Tacrolimus Formulation (Prolonged-Release vs Immediate-Release) on Its Susceptibility to Drug-Drug Interactions with St. John's Wort.

Tacrolimus is metabolized by cytochrome P450 3A (CYP3A) and is susceptible to interactions with the CYP3A and P-glycoprotein inducer St. John's Wort (SJW). CYP3A isozymes are predominantly expressed in the small intestine and liver. Prolonged-release tacrolimus (PR-Tac) is largely absorbed in distal intestinal segments and is less susceptible to CYP3A inhibition. The effect of induction by SJW is unknown. In this randomized, crossover trial, 18 healthy volunteers received single oral tacrolimus doses (immediate-release [IR]-Tac or PR-Tac, 5 mg each) alone and during induction by SJW. Concentrations were quantified using ultra-high performance liquid chromatography coupled with tandem mass spectrometry and non-compartmental pharmacokinetics were evaluated. SJW decreased IR-Tac exposure (area under the concentration-time curve) to 73% (95% confidence interval 60%-88%) and maximum concentration (Cmax ) to 61% (52%-73%), and PR-Tac exposure to 67% (55%-81%) and Cmax to 69% (58%-82%), with no statistical difference between the 2 formulations. The extent of interaction appeared to be less pronounced in volunteers with higher baseline CYP3A4 activity and in CYP3A5 expressors. In contrast to CYP3A inhibition, CYP3A induction by SJW showed a similar extent of interaction with both tacrolimus formulations. A higher metabolic baseline capacity appeared to attenuate the extent of induction by SJW.

Feasibility and Safety of Sildenafil to Repair Brain Injury Secondary to Birth Asphyxia (SANE-01): A Randomized, Double-blind, Placebo-controlled Phase Ib Clinical Trial.

OBJECTIVE: To test feasibility and safety of administering sildenafil in neonates with neonatal encephalopathy (NE), developing brain injury despite therapeutic hypothermia (TH). STUDY DESIGN: We performed a randomized, double-blind, placebo-controlled phase Ib clinical trial between 2016 and 2019 in neonates with moderate or severe NE, displaying brain injury on day-2 magnetic resonance imaging (MRI) despite TH. Neonates were randomized (2:1) to 7-day sildenafil or placebo (2 mg/kg/dose enterally every 12 hours, 14 doses). Outcomes included feasibility and safety (primary outcomes), pharmacokinetics (secondary), and day-30 neuroimaging and 18-month neurodevelopment assessments (exploratory). RESULTS: Of the 24 enrolled neonates, 8 were randomized to sildenafil and 3 to placebo. A mild decrease in blood pressure was reported in 2 of the 8 neonates after initial dose, but not with subsequent doses. Sildenafil plasma steady-state concentration was rapidly reached, but decreased after TH discontinuation. Twelve percent of neonates (1/8) neonates died in the sildenafil group and 0% (0/3) in the placebo group. Among surviving neonates, partial recovery of injury, fewer cystic lesions, and less brain volume loss on day-30 magnetic resonance imaging were noted in 71% (5/7) of the sildenafil group and in 0% (0/3) of the placebo group. The rate of death or survival to 18 months with severe neurodevelopmental impairment was 57% (4/7) in the sildenafil group and 100% (3/3) in the placebo group. CONCLUSIONS: Sildenafil was safe and well-absorbed in neonates with NE treated with TH. Optimal dosing needs to be established. Evaluation of a larger number of neonates through subsequent phases II and III trials is required to establish efficacy. CLINICAL TRIAL REGISTRATION: ClinicalTrials.govNCT02812433.

Influence of a Short Course of Ritonavir Used as Booster in Antiviral Therapies Against SARS-CoV-2 on the Exposure of Atorvastatin and Rosuvastatin.

PURPOSE: Early antiviral treatment with nirmatrelvir/ritonavir is recommended for SARS-CoV-2-infected patients at high risk for severe courses. Such patients are usually chronically ill and susceptible to adverse drug interactions caused by ritonavir. We investigated the interactions of short-term low-dose ritonavir therapy with atorvastatin and rosuvastatin, two statins commonly used in this population. METHOD: We assessed exposure changes (area under the concentration-time curve (AUC∞) and maximum concentration (Cmax)) of a single dose of 10 mg atorvastatin and 10 mg rosuvastatin before and on the fifth day of ritonavir treatment (2 × 100 mg/day) in healthy volunteers and developed a semi-mechanistic pharmacokinetic model to estimate dose adjustment of atorvastatin during ritonavir treatment. RESULTS: By the fifth day of ritonavir treatment, the AUC∞ of atorvastatin increased 4.76-fold and Cmax 3.78-fold, and concurrently, the concentration of atorvastatin metabolites decreased to values below the lower limit of quantification. Pharmacokinetic modelling indicated that a stepwise reduction in atorvastatin dose during ritonavir treatment with a stepwise increase up to 4 days after ritonavir discontinuation can keep atorvastatin exposure within safe and effective margins. Rosuvastatin pharmacokinetics were only mildly modified; ritonavir significantly increased the Cmax 1.94-fold, while AUC∞ was unchanged. CONCLUSION: Atorvastatin doses should likely be adjusted during nirmatrelvir/ritonavir treatment. For patients on a 20-mg dose, we recommend half of the original dose. In patients taking 40 mg or more, a quarter of the dose should be taken until 2 days after discontinuation of nirmatrelvir/ritonavir. Patients receiving rosuvastatin do not need to change their treatment regimen. TRIAL REGISTRATION: EudraCT number: 2021-006634-39. DRKS00027838.

CYP3A and CYP2C19 Activity Determined by Microdosed Probe Drugs Accurately Predict Voriconazole Clearance in Healthy Adults.

BACKGROUND AND OBJECTIVE: Voriconazole is an important broad-spectrum anti-fungal drug with nonlinear pharmacokinetics. The aim of this single centre fixed-sequence open-label drug-drug interaction trial in healthy participants (N = 17) was to determine whether microdosed probe drugs for CYP3A and CYP2C19 reliably predict voriconazole clearance (CLVRZ). METHODS: At baseline, a single oral microdose of the paradigm substrates midazolam (CYP3A) and omeprazole (CYP2C19) were given to estimate their clearances (CL). Thereafter, a single oral dose of voriconazole was administered (50, 100, 200 or 400 mg), followed by the microdosed probe drugs. RESULTS: The clearances of midazolam (CLMDZ 790-2790 mL/min at baseline; 248-1316 mL/min during voriconazole) and omeprazole (CLOMZ 66.4-2710 mL/min at baseline; 30.1-1420 mL/min during voriconazole) were highly variable. CLMDZ [geometric mean ratio (GMR) 0.586 at 50 mg voriconazole decreasing to GMR 0.196 at 400 mg voriconazole] and CLOMZ (GMR 0.590 at 50 mg decreasing to GMR 0.166 at 400 mg) were reduced with higher voriconazole doses. CLMDZ was linearly correlated with CLVRZ (slope 1.458; adjusted R2 0.528) as was CLOMZ (slope 0.807; adjusted R2 0.898). Multiple linear regression resulted in an adjusted R2 of 0.997 for the relationship CLVRZ ~ log CLOMZ + log CLMDZ using data during voriconazole treatment and an adjusted R2 of 0.997 for the relationship CLVRZ ~ log CLOMZ + log CLMDZ + voriconazole dose, using baseline data for CLMDZ and CLOMZ. CONCLUSION: Microdosed midazolam and omeprazole accurately described and predicted total CLVRZ TRIAL REGISTRATION: EudraCT No: 2020-001017-20, registered on March 5th, 2020. DRKS: DRKS00022547, registered on August 6th, 2020.

Mortality and Hospitalizations Among Patients Enrolled in an Interprofessional Medication Management Program.

BACKGROUND: Measures for improving medication safety in outpatient care are often complex and involve medication reviews. Over the period 2016-2022 (with a preceeding one-year pilot phase), an interprofessional medication management program- the Medicines Initiative Saxony-Thuringia (Arzneimittelinitiative Sachsen-Thüringen, ARMIN)-was implemented in two German federal states. More than 5000 patients received a medication review by the end of 2019 by a team composed of physicians and pharmacists and were provided with joint, continuous care thereafter. METHODS: In the framework of a retrospectively registered cohort study, the mortality and hospitalizations of this population (5033 patients) were studied using routine data from a statutory health insurer (observation period 2015-2019) and compared with those of a control group (10 039 patients) determined from the routine data by propensity score matching. Mortality was compared by survival analysis (Cox regression), and hospitalization rates were compared in terms of event probabilities within two years of enrollment in the medication management program. Robustness was tested in multiple sensitivity analyses. RESULTS: Over the observation period, 9.3% of the ARMIN participants and 12.9% of persons in the control group died (hazard ratio of the adjusted Cox regression, 0.84; 95% confidence interval [0.76; 0.94], P = 0.001). In the first two years after inclusion, the ARMIN participants were hospitalized just as often as the persons in the control group (52.4% versus 53.4%; odds ratio from the adjusted model, 1.04 [0.96; 1.11], P = 0.347). The effects were consistent in sensitivity analyses. CONCLUSION: In this retrospective cohort study, participation in the ARMIN program was associated with a lower risk of death. Exploratory analyses provide clues to the potential origin of this association.

Effect of Clarithromycin, a Strong CYP3A and P-glycoprotein Inhibitor, on the Pharmacokinetics of Edoxaban in Healthy Volunteers and the Evaluation of the Drug Interaction with Other Oral Factor Xa Inhibitors by a Microdose Cocktail Approach.

PURPOSE: We assessed the differential effect of clarithromycin, a strong inhibitor of cytochrome P450 (CYP) 3A4 and P-glycoprotein, on the pharmacokinetics of a regular dose of edoxaban and on a microdose cocktail of factor Xa inhibitors (FXaI). Concurrently, CYP3A activity was determined with a midazolam microdose. METHODS: In an open-label fixed-sequence trial in 12 healthy volunteers, the pharmacokinetics of a microdosed FXaI cocktail (µ-FXaI; 25 µg apixaban, 50 µg edoxaban, and 25 µg rivaroxaban) and of 60 mg edoxaban before and during clarithromycin (2 x 500 mg/d) dosed to steady-state was evaluated. Plasma concentrations of study drugs were quantified using validated ultra-performance liquid chromatography-tandem mass spectrometry methods. RESULTS: Therapeutic clarithromycin doses increased the exposure of a therapeutic 60 mg dose of edoxaban with a geometric mean ratio (GMR) of the area under the plasma concentration-time curve (AUC) of 1.53 (90 % CI: 1.37-1.70; p < 0.0001). Clarithromycin also increased the GMR (90% CI) of the exposure of microdosed FXaI apixaban to 1.38 (1.26-1.51), edoxaban to 2.03 (1.84-2.24), and rivaroxaban to 1.44 (1.27-1.63). AUC changes observed for the therapeutic edoxaban dose were significantly smaller than those observed with the microdose (p < 0.001). CONCLUSION: Clarithromycin increases FXaI exposure. However, the magnitude of this drug interaction is not expected to be clinically relevant. The edoxaban microdose overestimates the extent of the drug interaction with the therapeutic dose, whereas AUC ratios for apixaban and rivaroxaban were comparable to the interaction with therapeutic doses as reported in the literature. TRIAL REGISTRATION: EudraCT Number: 2018-002490-22.

Anticholinergic burden measures, symptoms, and fall-associated risk in older adults with polypharmacy: Development and validation of a prognostic model.

BACKGROUND: Anticholinergic burden has been associated with adverse outcomes such as falls. To date, no gold standard measure has been identified to assess anticholinergic burden, and no conclusion has been drawn on which of the different measure algorithms best predicts falls in older patients from general practice. This study compared the ability of five measures of anticholinergic burden to predict falls. To account for patients' individual susceptibility to medications, the added predictive value of typical anticholinergic symptoms was further quantified in this context. METHODS AND FINDINGS: To predict falls, models were developed and validated based on logistic regression models created using data from two German cluster-randomized controlled trials. The outcome was defined as "≥ 1 fall" vs. "no fall" within a 6-month follow-up period. Data from the RIME study (n = 1,197) were used in model development, and from PRIMUM (n = 502) for external validation. The models were developed step-wise in order to quantify the predictive ability of anticholinergic burden measures, and anticholinergic symptoms. In the development set, 1,015 patients had complete data and 188 (18.5%) experienced ≥ 1 fall within the 6-month follow-up period. The overall predictive value of the five anticholinergic measures was limited, with neither the employed anticholinergic variable (binary / count / burden), nor dose-dependent or dose-independent measures differing significantly in their ability to predict falls. The highest c-statistic was obtained using the German Anticholinergic Burden Score (0.73), whereby the optimism-corrected c-statistic was 0.71 after interval validation using bootstrapping and 0.63 in the external validation. Previous falls and dizziness / vertigo had the strongest prognostic value in all models. CONCLUSIONS: The ability of anticholinergic burden measures to predict falls does not appear to differ significantly, and the added value they contribute to risk classification in fall-prediction models is limited. Previous falls and dizziness / vertigo contributed most to model performance.

Analysing and improving preoperative medication management in cardiac surgery.

AIMS: The objective of this study was to analyse the preoperative medication management within the cardiac surgery patient population and measure the effectiveness of an interprofessional intervention in routine care. METHODS: A jointly developed preoperative medication management was implemented in routine care on multiple levels (inclusion in admission letter to primary care, hotline for inquiries, pocket cards for physicians and correspondence with referring centres). The effectiveness was evaluated by analysing preoperative management before and after implementation. The primary endpoint was the number of drugs managed correctly according to the guidelines after implementation. Secondary endpoints consisted amongst others of bleeding on the intensive care unit, re-thoracotomy, postoperative infarction and cerebrovascular complications. Additionally, possible associations between the correct management and different variables were investigated by multivariate analysis. RESULTS: After the implementation, the number of drugs managed correctly according to guidelines increased from 54.0 to 73.5% (P < .001). The effect was more prominent for direct oral anticoagulants and prophylactic aspirin where the guideline adherence increased from 29.2 to 74.5% and from 78.6 to 95.1%, respectively. No difference was seen for sodium-glucose transporter-2 inhibitors, metformin, vitamin-K antagonists and dual-antiplatelet therapy. Secondary endpoints showed no safety signals with regard to bleeding or thrombotic events. In multivariate analysis, the intervention was effective (odds ratio 2.17, 95% confidence interval [1.32-3.62]) after adjusting for possible confounders. CONCLUSION: An interprofessional programme was effective to improve preoperative medication management in cardiac surgery patients. Sodium-glucose transporter-2 inhibitors, metformin and direct oral anticoagulants appear to be especially at risk for incorrect management before cardiac surgery with possible adverse events.

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).

A prediction model for nonpersistence or nonadherence to direct oral anticoagulants in hospitalized patients with atrial fibrillation.

BACKGROUND: Medication adherence and persistence is fundamental for drug effectiveness, which is also true for the prevention of strokes in patients with atrial fibrillation (AF). Adherence to direct oral anticoagulants (DOACs) as first-line agents is often high in the early posthospital period. However, adherence often sharply declines (or eventually leads to nonpersistence) in the post-discharge ambulatory period, rendering stroke prevention ineffective. If patients at high risk of nonpersistence or nonadherence could be identified early, they could be offered early intervention measures to improve adherence and/or persistence. OBJECTIVE: To develop and internally validate a predictive model for medication nonadherence and nonpersistence to DOAC treatment in patients with AF after discharge using health insurance claims data. METHODS: We selected health insurance claims data between 2011 and 2016 from 8,055 patients with AF who were newly treated with rivaroxaban or apixaban after a hospital admission for stroke, transient ischemic attack, or AF. In the post-discharge ambulatory period, medication adherence was derived as the proportion of days covered, calculated from drug dispensation data. A maximum permissible 90-day gap between the end of a prescription and the next dispensation was used to estimate persistence. Candidate predictors were either derived from the index hospital admission or summarized from the previous year (eg, comorbidities or medication adherence to long-term treatments, such as ß-blockers, renin-angiotensin system inhibitors, statins, and thyroid hormones). A regularized logistic regression model was fitted using the least absolute shrinkage and selection operator in a split-sample approach (66.7% training data; 33.3% test data) to predict a composite of medication nonadherence/nonpersistence. Discrimination performance was assessed using the area under the receiver operating characteristic curve, the maximum sensitivity/specificity, and the scaled Brier score. A calibration curve fitted by linear regression was used to evaluate model calibration. RESULTS: The average age of the study participants was 79.7 years, 62% were female, and 3,515 patients (44%) were adherent and persistent (median follow-up of 185 days). Medication adherence to previous long-term treatments showed strong predictive properties. The developed model discriminated well (concordance statistic: 0.755), was well calibrated, and showed a scaled Brier score of 0.202 for identification of patients at risk. CONCLUSIONS: The model successfully predicted medication non-adherence/nonpersistence to DOAC treatment after discharge. Such a model could help ensure that targeted interventions are already in place at the time of hospital discharge, potentially preventing strokes and reducing costs. DISCLOSURES: Mr Wirbka is funded by the German Innovation Funds according to § 92a (2) Volume V of the Social Insurance Code (§ 92a Abs. 2, SGBV-Fünftes Buch Sozialgesetzbuch), grant number: 01VSF18019. Dr Haefeli received financial support from Daiichi-Sankyo, app development (https://www.easydoac.de/), and Bayer. He also received personal speaker fees from Bristol Myers-Squibb and Daiichi-Sankyo Online Seminar. Dr Meid is funded by the Physician-Scientist Programme of the Medical Faculty of Heidelberg University.

HIOPP-6 - a pilot study on the evaluation of an electronic tool to assess and reduce the complexity of drug treatment considering patients' views.

BACKGROUND: A complex drug treatment might pose a barrier to safe and reliable drug administration for patients. Therefore, a novel tool automatically analyzes structured medication data for factors possibly contributing to complexity and subsequently personalizes the results by evaluating the relevance of each identified factor for the patient by means of key questions. Hence, tailor-made optimization measures can be proposed. METHODS: In this controlled, prospective, exploratory trial the tool was evaluated with nine general practitioners (GP) in three study groups: In the two intervention groups the tool was applied in a version with (GI_with) and a version without (GI_without) integrated key questions for the personalization of the analysis, while the control group (GC) did not use any tools (routine care). Four to eight weeks after application of the tool, the benefits of the optimization measures to reduce or mitigate complexity of drug treatment were evaluated from the patient perspective. RESULTS: A total of 126 patients regularly using more than five drugs could be included for analysis. GP suggested 117 optimization measures in GI_with, 83 in GI_without, and 2 in GC. Patients in GI_with were more likely to rate an optimization measure as helpful than patients in GI_without (IRR: 3.5; 95% CI: 1.2-10.3). Thereby, the number of optimization measures recommended by the GP had no significant influence (P = 0.167). CONCLUSIONS: The study suggests that an automated analysis considering patient perspectives results in more helpful optimization measures than an automated analysis alone - a result which should be further assessed in confirmatory studies. TRIAL REGISTRATION: The trial was registered retrospectively at the German Clinical Trials register under DRKS-ID DRKS00025257 (17/05/2021).

Prevalence and patient-rated relevance of complexity factors in medication regimens of community-dwelling patients with polypharmacy.

PURPOSE: To describe the prevalence of complexity factors in the medication regimens of community-dwelling patients with more than five drugs and to evaluate the relevance of these factors for individual patients. METHODS: Data were derived from the HIOPP-6 trial, a controlled study conducted in 9 general practices which evaluated an electronic tool to detect and reduce complexity of drug treatment. The prevalence of complexity factors was based on the results of the automated analysis of 139 patients' medication data. The relevance assessment was based on the patients' rating of each factor in an interview (48 patients included for analysis). RESULTS: A median of 5 (range 0-21) complexity factors per medication regimen were detected and at least one factor was observed in 131 of 139 patients. Almost half of these patients found no complexity factor in their medication regimen relevant. CONCLUSION: In most medication regimens, complexity factors could be identified automatically, yet less than 15% of factors were indeed relevant for patients as judged by themselves. When assessing complexity of medication regimens, one should especially consider factors that are both particularly frequent and often challenging for patients, such as use of inhalers or tablet splitting. TRIAL REGISTRATION: The HIOPP-6 trial was registered retrospectively on May 17, 2021, in the German Clinical Trials register under DRKS-ID DRKS00025257.

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing.

BACKGROUND: Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks. OBJECTIVES: To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data. METHODS: In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined ("stacked") to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics. RESULTS: While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66-0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64-0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63-0.66]), HF (c-statistics: 0.61 [95% CI: 0.60-0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56-0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47-0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63-0.64]. CONCLUSION: PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.