Prediction of Drug-Related Risks Using Clinical Context Information in Longitudinal Claims Data.

OBJECTIVES: To develop and internally validate prediction models for medication-related risks arising from overuse, misuse, and underuse that utilize clinical context information and are suitable for routine risk assessment in claims data (i.e., medication-based models predicting the risk for hospital admission apparent in routine claims data or MEDI-RADAR). METHODS: Based on nationwide claims from health-insured persons in Germany between 2010 and 2012, we drew a random sample of people aged ≥65 years (N = 22,500 randomly allocated to training set, N = 7500 to validation set). Individual duration of drug supply was estimated from prescription patterns to yield time-varying drug exposure windows. Together with concurrent medical conditions (ICD-10 diagnoses), exposure to the STOPP/START (screening tool of older persons' potentially inappropriate prescriptions/screening tool to alert doctors to the right treatment) criteria was derived. These were tested as time-dependent covariates together with time-constant covariates (patient demographics, baseline comorbidities) in regularized Cox regression models. RESULTS: STOPP/START variables were iteratively refined and selected by regularization to include 2 up to 11 START variables and 8 up to 31 STOPP variables in parsimonious and liberal selections in the prediction modeling. The models discriminated well between patients with and without all-cause hospitalizations, potentially drug-induced hospitalizations, and mortality (parsimonious model c-indices with 95% confidence intervals: 0.63 [0.62-0.64], 0.67 [0.65-0.68], and 0.78 [0.76-0.80]). CONCLUSIONS: The STOPP/START criteria proved to efficiently predict medication-related risk in models possessing good performance. Timely detection of such risks by routine monitoring in claims data can support tailored interventions targeting these modifiable risk factors. Their impact on older peoples' medication safety and effectiveness can now be explored in future implementation studies.

Development of a risk assessment tool for osteoporotic fracture prevention: A claims data approach.

BACKGROUND: In aging societies osteoporotic fractures are a major health problem with high economic costs. Targeting prevention at individuals at high risk is important to reduce the future burden of fractures. Available risk assessment tools (e.g., FRAX®, QFracture, the algorithm provided by the German Osteology Society (DVO-Tool)) rely on self-reported patient information to predict fracture risk. Time and resource constraints, limited access to clinical data, and (un)willingness to participate may hamper the use of these tools. To overcome such obstacles, the aim is to develop a fracture risk assessment tool based on claims data that may be directly used on an institutional level. METHODS: Administrative claims data of an elderly (≥65years) population (N=298,530) for the period from 2006 through 2014 was used. Major osteoporotic fractures (MOF) were identified based on hospital diagnoses. We applied Cox proportional hazard regression to determine the association of individual risk factors and fracture risk. Hazard ratios were used to construct a risk score. The discriminative ability of the score was evaluated using C-statistics. RESULTS: We identified 7864 MOF during follow-up. The median time to first fracture during follow-up was 371.5days. Individuals with a MOF during follow-up had a higher mean and median risk score (mean: 4.53; median: 4) than individuals without MOF (mean: 3.07; median: 3). Adding drug-related risk factors slightly improved discrimination compared to a simple model with age, gender, and prior fracture. CONCLUSION: We developed a fracture risk score model based on in-hospital treated subjects to predict MOF that can be used on an institutional level. The score included age, sex and prior fracture as risk factors. Adding other risk factors involved very small improvement in discrimination.

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

BACKGROUND: 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. METHODS: In a cohort of 2558 psychiatric inpatients and outpatients, we modeled whether AZCERT class and number of coprescribed QT-prolonging drugs correlates with observed rate-corrected QT duration (QTc) while also considering age, sex, inpatient status, and other QTc-prolonging risk factors. We concurrently considered administered drug doses and pharmacokinetic interactions modulating drug clearance to calculate individual weights of relative exposure with AZCERT drugs. Because QTc duration is concentration-dependent, we estimated individual drug exposure with these drugs and included this information as weights in weighted regression analyses. RESULTS: Drugs attributing a 'known' risk for clinical consequences were associated with the largest QTc prolongations. However, the presence of at least two versus one QTc-prolonging drug yielded nonsignificant prolongations [exposure-weighted parameter estimates with 95% confidence intervals for 'known' risk drugs + 0.93 ms (-8.88;10.75)]. Estimates for the 'conditional' risk class increased upon refinement with relative drug exposure and co-administration of a 'known' risk drug as a further risk factor. CONCLUSIONS: These observations indicate that indiscriminate combinations of QTc-prolonging drugs do not necessarily result in additive QTc prolongation and suggest that QT prolongation caused by drug combinations strongly depends on the nature of the combination partners and individual drug exposure. Concurrently, it stresses the value of the AZCERT classification also for the risk prediction of combination therapies with QT-prolonging drugs.

Benzodiazepine Use During Hospitalization: Automated Identification of Potential Medication Errors and Systematic Assessment of Preventable Adverse Events.

OBJECTIVE: Benzodiazepines and "Z-drug" GABA-receptor modulators (BDZ) are among the most frequently used drugs in hospitals. Adverse drug events (ADE) associated with BDZ can be the result of preventable medication errors (ME) related to dosing, drug interactions and comorbidities. The present study evaluated inpatient use of BDZ and related ME and ADE. METHODS: We conducted an observational study within a pharmacoepidemiological database derived from the clinical information system of a tertiary care hospital. We developed algorithms that identified dosing errors and interacting comedication for all administered BDZ. Associated ADE and risk factors were validated in medical records. RESULTS: Among 53,081 patients contributing 495,813 patient-days BDZ were administered to 25,626 patients (48.3%) on 115,150 patient-days (23.2%). We identified 3,372 patient-days (2.9%) with comedication that inhibits BDZ metabolism, and 1,197 (1.0%) with lorazepam administration in severe renal impairment. After validation we classified 134, 56, 12, and 3 cases involving lorazepam, zolpidem, midazolam and triazolam, respectively, as clinically relevant ME. Among those there were 23 cases with associated adverse drug events, including severe CNS-depression, falls with subsequent injuries and severe dyspnea. Causality for BDZ was formally assessed as 'possible' or 'probable' in 20 of those cases. Four cases with ME and associated severe ADE required administration of the BDZ antagonist flumazenil. CONCLUSIONS: BDZ use was remarkably high in the studied setting, frequently involved potential ME related to dosing, co-medication and comorbidities, and rarely cases with associated ADE. We propose the implementation of automated ME screening and validation for the prevention of BDZ-related ADE.