Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data.

OBJECTIVE: Observational studies using electronic health record (EHR) databases often face challenges due to unspecific clinical codes that can obscure detailed medical information, hindering precise data analysis. In this study, we aimed to assess the feasibility of refining these unspecific condition codes into more specific codes in a Dutch general practitioner (GP) EHR database by leveraging the available clinical free text. METHODS: We utilized three approaches for text classification-search queries, semi-supervised learning, and supervised learning-to improve the specificity of ten unspecific International Classification of Primary Care (ICPC-1) codes. Two text representations and three machine learning algorithms were evaluated for the (semi-)supervised models. Additionally, we measured the improvement achieved by the refinement process on all code occurrences in the database. RESULTS: The classification models performed well for most codes. In general, no single classification approach consistently outperformed the others. However, there were variations in the relative performance of the classification approaches within each code and in the use of different text representations and machine learning algorithms. Class imbalance and limited training data affected the performance of the (semi-)supervised models, yet the simple search queries remained particularly effective. Ultimately, the developed models improved the specificity of over half of all the unspecific code occurrences in the database. CONCLUSIONS: Our findings show the feasibility of using information from clinical text to improve the specificity of unspecific condition codes in observational healthcare databases, even with a limited range of machine-learning techniques and modest annotated training sets. Future work could investigate transfer learning, integration of structured data, alternative semi-supervised methods, and validation of models across healthcare settings. The improved level of detail enriches the interpretation of medical information and can benefit observational research and patient care.

Real-world treatment trajectories of adults with newly diagnosed asthma or COPD.

BACKGROUND: There is a lack of knowledge on how patients with asthma or chronic obstructive pulmonary disease (COPD) are globally treated in the real world, especially with regard to the initial pharmacological treatment of newly diagnosed patients and the different treatment trajectories. This knowledge is important to monitor and improve clinical practice. METHODS: This retrospective cohort study aims to characterise treatments using data from four claims (drug dispensing) and four electronic health record (EHR; drug prescriptions) databases across six countries and three continents, encompassing 1.3 million patients with asthma or COPD. We analysed treatment trajectories at drug class level from first diagnosis and visualised these in sunburst plots. RESULTS: In four countries (USA, UK, Spain and the Netherlands), most adults with asthma initiate treatment with short-acting ß2 agonists monotherapy (20.8%-47.4% of first-line treatments). For COPD, the most frequent first-line treatment varies by country. The largest percentages of untreated patients (for asthma and COPD) were found in claims databases (14.5%-33.2% for asthma and 27.0%-52.2% for COPD) from the USA as compared with EHR databases (6.9%-15.2% for asthma and 4.4%-17.5% for COPD) from European countries. The treatment trajectories showed step-up as well as step-down in treatments. CONCLUSION: Real-world data from claims and EHRs indicate that first-line treatments of asthma and COPD vary widely across countries. We found evidence of a stepwise approach in the pharmacological treatment of asthma and COPD, suggesting that treatments may be tailored to patients' needs.

Multinational cohort study of mortality in patients with asthma and severe asthma.

BACKGROUND: Data on the risk of death following an asthma exacerbation are scarce. With this multinational cohort study, we assessed all-cause mortality rates, mortality rates following an exacerbation, and patient characteristics associated with all-cause mortality in asthma. METHODS: Asthma patients aged ≥18 years and with ≥1 year of follow-up were identified in 5 European electronic databases from the Netherlands, Italy, UK, Denmark and Spain during the study period January 1, 2008-December 31, 2013. Patients with asthma-COPD overlap were excluded. Severe asthma was defined as use of high dose ICS + use of a second controller. Severe asthma exacerbations were defined as emergency department visits, hospitalizations or systemic corticosteroid use, all for reason of asthma. RESULTS: The cohort consisted of 586,436 asthma patients of which 42,611 patients (7.3%) had severe asthma. The age and sex standardized all-cause mortality rates ranged between databases from 5.2 to 9.5/1000 person-years (PY) in asthma, and between 11.3 and 14.8/1000 PY in severe asthma. The all-cause mortality rate in the first week following a severe asthma exacerbation ranged between 14.1 and 59.9/1000 PY. Mortality rates remained high in the first month following a severe asthma exacerbation and decreased thereafter. Higher age, male gender, comorbidity, smoking, and previous severe asthma exacerbations were associated with mortality. CONCLUSION: All-cause mortality following a severe exacerbation is high, especially in the first month following the event. Smoking cessation, comorbidity-management and asthma-treatment focusing on the prevention of exacerbations might reduce associated mortality.