Correction for potentially inappropriate prescribing can increase specificity when using drug prescriptions as an adjunct to diagnostic codes to assess comorbidities in older patients

Background: Comorbidities are a growing problem in older patients in many clinical settings, but electronic records may give an unsatisfactory picture of this complexity. Analysis of drug prescriptions can add further diagnostic information to that gathered from billing diagnostic codes, but the risk exhists that potentially inappropriate prescriptions may lead to over-estimating comorbidities. Methods: We analysed the administrative records and drug prescriptions of the 304 patients discharged during 2016 from a neurological rehabilitation unit. International Classification of Diseases – 9th revision diagnostic codes were matched with prescriptions at discharge, coded according to the Anatomical Therapeutic Chemical classification. The codes of the prescriptions not explained by the diagnostic codes were recorded, grouped, corrected for potential inappropriate prescribing, and analysed. Results: Of the 304 patients, 295 had at least one prescribed drug not inferable from their diagnostic codes. The mean number of these prescriptions was 3.5 ± 1.9 per patient, and that of prescriptions remaining after correction for potentially inappropriate prescribing was 2.0 ± 1.5. The more frequent groups of potentially inappropriate medications were anti-acids, psychotropic drugs, laxatives, potassium supplements, cardiovascular drugs and lipid modifying agents. Administrative databases underestimate the complexity of older patients in neurological rehabilitation wards. More reliable data can be obtained by adding the analysis of drug prescriptions, but correction for potentially inappropriate prescription seems necessary to avoid an over-estimation of comorbidities

Overlap in drug-disease associations between clinical practice guidelines and drug structured product label indications

BACKGROUND: Clinical practice guidelines (CPGs) recommend pharmacologic treatments for clinical conditions, and drug structured product labels (SPLs) summarize approved treatment indications. Both resources are intended to promote evidence-based medical practices and guide clinicians’ prescribing decisions. However, it is unclear how well CPG recommendations about pharmacologic therapies match SPL indications for recommended drugs. In this study, we perform text mining of CPG summaries to examine drug-disease associations in CPG recommendations and in SPL treatment indications for 15 common chronic conditions. METHODS: We constructed an initial text corpus of guideline summaries from the National Guideline Clearinghouse (NGC) from a set of manually selected ICD-9 codes for each of the 15 conditions. We obtained 377 relevant guideline summaries and their Major Recommendations section, which excludes guidelines for pediatric patients, pregnant or breastfeeding women, or for medical diagnoses not meeting inclusion criteria. A vocabulary of drug terms was derived from five medical taxonomies. We used named entity recognition, in combination with dictionary-based and ontology-based methods, to identify drug term occurrences in the text corpus and construct drug-disease associations. The ATC (Anatomical Therapeutic Chemical Classification) was utilized to perform drug name and drug class matching to construct the drug-disease associations from CPGs. We then obtained drug-disease associations from SPLs using conditions mentioned in their Indications section in SIDER. The primary outcomes were the frequency of drug-disease associations in CPGs and SPLs, and the frequency of overlap between the two sets of drug-disease associations, with and without using taxonomic information from ATC. RESULTS: Without taxonomic information, we identified 1444 drug-disease associations across CPGs and SPLs for 15 common chronic conditions. Of these, 195 drug-disease associations overlapped between CPGs and SPLs, 917 associations occurred in CPGs only and 332 associations occurred in SPLs only. With taxonomic information, 859 unique drug-disease associations were identified, of which 152 of these drug-disease associations overlapped between CPGs and SPLs, 541 associations occurred in CPGs only, and 166 associations occurred in SPLs only. CONCLUSIONS: Our results suggest that CPG-recommended pharmacologic therapies and SPL indications do not overlap frequently when identifying drug-disease associations using named entity recognition, although incorporating taxonomic relationships between drug names and drug classes into the approach improves the overlap. This has important implications in practice because conflicting or inconsistent evidence may complicate clinical decision making and implementation or measurement of best practices