Systematic Review of Risk Factors Assessed in Predictive Scoring Tools for Drug-Related Problems in Inpatients

Drug-related problems (DRP, defined as adverse drug events/reactions and medication errors) are a common threat for patient safety. With the aim to aid improved allocation of specialist resources and to improve detection and prevention of DRP, numerous predictive scoring tools have been proposed. The external validation and evidence for the transferability of these tools still faces limitations. However, the proposed scoring tools include partly overlapping sets of similar factors, which may allow a new approach to estimate the external usability and validity of individual risk factors. Therefore, we conducted this systematic review and analysis. We identified 14 key studies that assessed 844 candidate risk factors for inclusion into predictive scoring tools. After consolidation to account for overlapping terminology and variable definitions, we assessed each risk factor in the number of studies it was assessed, and, if it was found to be a significant predictor of DRP, whether it was included in a final scoring tool. The latter included intake of ≥ 8 drugs, drugs of the Anatomical Therapeutic Chemical (ATC) class N, ≥1 comorbidity, an estimated glomerular filtration rate (eGFR) <30 mL/min and age ≥60 years. The methodological approach and the individual risk factors presented in this review may provide a new starting point for improved risk assessment

Anticholinergic burden and cognitive function in a large German cohort of hospitalized geriatric patients

<div><p>Purpose</p><p>Previous studies suggest an association between use of anticholinergic drugs in elderly patients and cognitive impairment. However, there are still limited data on the association of anticholinergic drug use and cognitive impairment as well as contribution of individual drugs to anticholinergic load using large, well-documented patient cohorts treated in geriatric units from Europe.</p><p>Methods</p><p>We investigated 797,440 prescriptions to 89,579 hospitalized patients treated in geriatric units within the GiB-DAT database. Data of all patients discharged between 1 January 2013 and 30 June 2015 was included. The Anticholinergic Cognitive Burden (ACB) scale was used to classify anticholinergic drugs as definite (score 2 or 3) and possible anticholinergics (score 1). Cognitive function was determined using Mini-Mental State Examination (MMSE) and the standardized scale for dementia (4D+S).</p><p>Results</p><p>In two multivariable logistic regression models age, sex, number of drugs and ACB total scores were identified as variables independently associated with cognitive impairment as measured by MMSE (odds ratio per ACB unit 1.114, 95% CI 1.099–1.130) or the diagnosis dementia (odds ratio 1.159 per ACB unit, 95% CI 1.144–1.173, both p < 0.0001). High anticholinergic load was associated with patients with severe cognitive impairment (p < 0.05 for all pairwise comparisons). ACB score 3 anticholinergic drugs contributed 77.9% to the cumulative amount of ACB points in patients with an anticholinergic load of 3 and higher.</p><p>Conclusions</p><p>Using a cross-sectional study design, a significant positive association between anticholinergic drug load and cognitive impairment in European patients treated in specialised geriatric units was found. The most frequently used definitve anticholinergic drugs were quetiapine, amitriptyline and carbamazepine.</p></div

Multivariable analysis of factors associated with cognitive impairment.

<p>Multivariable analysis of factors associated with cognitive impairment.</p

Most commonly used definite anticholinergic drugs (i.e. ACB score 2 or 3) among the total study population.

<p><i>Left y-axis</i> Absolute number of drugs with definite anticholinergic properties received by patients. <i>Right y-axis</i> Proportion of the respective drug of all definite anticholinergics according to the ACB scale.</p

Documentation of Drug-Related Problems with ICD-11: Application of the New WHO Code-Set to Clinical Routine Data

Drug-related problems (DRPs), i.e., adverse drug reactions (ADRs) and medication errors (MEs), constitute a serious threat to the patient’s safety. DRPs are often insufficiently captured by clinical routine documentation, and thus, they frequently remain unaddressed. The aim of this study was to assess the coverage and usability of the new 11th revision of the WHO International Classification of Diseases (ICD-11) to document DRPs. We refined the ‘Quality and Safety Algorithm’ from the ICD-11 Reference Guide and used it for DRP reporting to code 100 different anonymized DRPs (50 ADRs and 50 MEs) in a German hospital. The ICD-11 three-part model consisting of harm, cause, and mode was used whenever they were applicable. Of 50 ADRs, 15 (30.0%), such as drug-induced osteoporosis, were fully classifiable and codable by the ICD-11, whereas 35 (70.0%), such as drug-induced hypokalaemia, could not be fully classified due to sanctioning rules preventing the postcoordination (i.e., a combination of specific codes, such as drug and diagnosis). However, coding without the loss of information was possible in the 35 of these 35 (100.0%) ADR cases when we were deviating from the cluster code order of the Reference Guide. In all 50 MEs, the mode could be encoded, but for none of the MEs, postcoordination, i.e., the assignment of the ME to a specific drug, was allowed. In conclusion, the ICD-11 three-part model enables us to acquire more detailed documentation of DRPs than the previous ICD versions did. However, the codability, documentation, and reporting of DRPs could be significantly improved by simple modifications of the current ICD-11 sanctioning rules and by the addition of new ICD-11 codes

Cognitive impairment measured by the Mini-Mental State Examination (MMSE) and mean anticholinergic cognitive burden in patients of the GiB-DAT database.

<p>A MMSE score of 0–17 indicates severe, 18–24 moderate and 25–30 no cognitive impairment. Error bar 95% confidence interval, *** p < 0.001 for overall and all pairwise comparisons.</p

Patient characteristics (n = 41,456).

<p>Patient characteristics (n = 41,456).</p