Clinical Characterization of Inpatients with Acute Conjunctivitis: A Retrospective Analysis by Natural Language Processing and Machine Learning

Abstract

Background Acute bacterial conjunctivitis (ABC) is a relatively common medical condition caused by different pathogens. Although it rarely threatens vision, it is one of the most common conditions that cause red eyes and may be accompanied by discomfort and discharge. The study aimed to identify and characterize inpatients with ABC treated with topical antibiotics. Methods The EHRead® technology, based on natural language processing (NLP) and machine learning, was used to extract and analyze the clinical information in the electronic health records (EHRs) of antibiotic-treated patients with conjunctivitis and admitted to five hospitals in Spain between January 2014 and December 2018. Categorical variables were described by frequency, whereas numerical variables included the mean, standard deviation, median, and quartiles. Results From a source population of 2,071,812 adult patients who attended the participating hospitals in the study period, 11,110 patients diagnosed with acute conjunctivitis were identified. Six thousand five hundred eighty-three patients were treated with antibiotics, comprising the final study population. Microbiology was tested only on 12.1% of patients. Antibiotics, mainly tobramycin, and corticosteroids, mainly dexamethasone, were usually prescribed. NSAIDs were also used in about 50% of patients, always combined with antibiotics. Conclusions The present study provided a realistic representation of the hospital practice concerning managing patients with acute antibiotic-treated conjunctivitis. The diagnosis is usually based on the clinical ground, microbiology is rarely tested, few bacteria species are involved, and local antibiotics are frequently associated with corticosteroids and/or NSAIDs. Moreover, this study provided clinically relevant outcomes, based on new technology, that could be applied in clinical practice.

Keywords

• acute conjunctivitis
• bacterial infection
• in-hospital patients
• natural language processing
• machine learning
• electronic medical reports