PURPOSE Overdosing of the oral antidiabetic metformin in impaired renal function is an important contributory cause to life-threatening lactic acidosis. The presented project aimed to quantify and prevent this avoidable medication error in clinical practice. METHODS We developed and implemented an algorithm into a hospital's clinical information system that prospectively identifies metformin prescriptions if the estimated glomerular filtration rate is below 60 mL/min. Resulting real-time electronic alerts are sent to clinical pharmacologists and pharmacists, who validate cases in electronic medical records and contact prescribing physicians with recommendations if necessary. RESULTS The screening algorithm has been used in routine clinical practice for 3 years and generated 2145 automated alerts (about 2 per day). Validated expert recommendations regarding metformin therapy, i.e., dose reduction or stop, were issued for 381 patients (about 3 per week). Follow-up was available for 257 cases, and prescribers' compliance with recommendations was 79%. Furthermore, during 3 years, we identified eight local cases of lactic acidosis associated with metformin therapy in renal impairment that could not be prevented, e.g., because metformin overdosing had occurred before hospitalization. CONCLUSIONS Automated sensitive screening followed by specific expert evaluation and personal recommendations can prevent metformin overdosing in renal impairment with high efficiency and efficacy. Repeated cases of metformin-associated lactic acidosis in renal impairment underline the clinical relevance of this medication error. Our locally developed and customized alert system is a successful proof of concept for a proactive clinical drug safety program that is now expanded to other clinically and economically relevant medication errors. Conflict of interest statement: This study was supported by unrestricted grants to StefanRussmann from the Swiss National Science Foundation (grant #320030_143867) and ID Suisse AG. The manuscript was made available to ID Suisse before submission, but ID Suisse had no influence on the study design, analysis, or interpretation of the results. All authors declare that they have no disclosures and conflict of interest in relation to the presented study. Key Points• Metformin-associated lactic acidosis is a rare but potentially fatal adverse event.Incident comorbidities and lack of metformin dose-adjustment in renal impairment are important contributing and triggering factors.• We developed and implemented an automated alert with highly sensitive prospective screening for metformin prescriptions in renal impairment into the clinical information system of a tertiary care hospital. Local safety experts validated resulting alerts and issued specific recommendations that effectively prevented inappropriate metformin administrations in renal impairment.• The presented concept of "semi-automated" alerts can be applied to the prevention of further clinically and economically relevant medication errors....

BackgroundRising health care costs are a major public health issue. Thus, accurately predicting future costs and understanding which factors contribute to increases in health care expenditures are important. The objective of this project was to predict patients healthcare costs development in the subsequent year and to identify factors contributing to this prediction, with a particular focus on the role of pharmacotherapy.MethodsWe used 2014–2015 Swiss health insurance claims data on 373′264 adult patients to classify individuals’ changes in health care costs. We performed extensive feature generation and developed predictive models using logistic regression, boosted decision trees and neural networks. Based on the decision tree model, we performed a detailed feature importance analysis and subgroup analysis, with an emphasis on drug classes.ResultsThe boosted decision tree model achieved an overall accuracy of 67.6% and an area under the curve-score of 0.74; the neural network and logistic regression models performed 0.4 and 1.9% worse, respectively. Feature engineering played a key role in capturing temporal patterns in the data. The number of features was reduced from 747 to 36 with only a 0.5% loss in the accuracy. In addition to hospitalisation and outpatient physician visits, 6 drug classes and the mode of drug administration were among the most important features. Patient subgroups with a high probability of increase (up to 88%) and decrease (up to 92%) were identified.ConclusionsPharmacotherapy provides important information for predicting cost increases in the total population. Moreover, its relative importance increases in combination with other features, including health care utilisation.

Background: Potential drug-drug interactions (pDDIs) are described in various case reports, but few studies have evaluated the impact of specific combinations on a population level. Objective: To analyze the type and frequency of multiple contraindicated (X-pDDIs) and major interactions (D-pDDIs) and to subsequently assess the impact of the particular combination of tizanidine and ciprofloxacin on outpatient physician visits and hospitalizations. Methods: Anonymized Swiss claims data from 524 797 patients in 2014-2015 were analyzed. First, frequencies of X- and D-pDDIs were calculated. Next, a retrospective cohort study was conducted among patients prescribed tizanidine and ciprofloxacin (exposed, n = 199) or tizanidine and other antibiotics (unexposed, n = 960). Hospitalizations and outpatient physician visits within 7, 14, and 30 days after initiation of antibiotic therapy were evaluated using multiple binary logistic regression and multiple linear regression. Results: The relative frequencies of X- and D-pDDIs were 0.4% and 6.65%, respectively. In the cohort study, significant associations between exposure to tizanidine and ciprofloxacin and outpatient physician visits were identified for 14 and 30 days (odds ratio [OR] = 1.61 [95% CI = 1.17-2.24], P = 0.004, and OR = 1.59 [95% CI = 1.1-2.34], P = 0.016). A trend for increased risk of hospitalization was found for all evaluated time periods (OR = 1.68 [95% CI = 0.84-3.17], OR = 1.52 [95% CI = 0.63-3.33], and OR = 2.19 [95% CI = 0.88-5.02]). Conclusion and Relevance: The interaction between tizanidine and ciprofloxacin is not only relevant for individual patients, but also at the population level. Further investigation of the impact of other clinically relevant DDIs is necessary to improve patient safety and reduce avoidable health care utilization.