Evaluation of Natural Language Processing for the Identification of Crohn Disease-Related Variables in Spanish Electronic Health Records: A Validation Study for the PREMONITION-CD Project.

BACKGROUND: The exploration of clinically relevant information in the free text of electronic health records (EHRs) holds the potential to positively impact clinical practice as well as knowledge regarding Crohn disease (CD), an inflammatory bowel disease that may affect any segment of the gastrointestinal tract. The EHRead technology, a clinical natural language processing (cNLP) system, was designed to detect and extract clinical information from narratives in the clinical notes contained in EHRs. OBJECTIVE: The aim of this study is to validate the performance of the EHRead technology in identifying information of patients with CD. METHODS: We used the EHRead technology to explore and extract CD-related clinical information from EHRs. To validate this tool, we compared the output of the EHRead technology with a manually curated gold standard to assess the quality of our cNLP system in detecting records containing any reference to CD and its related variables. RESULTS: The validation metrics for the main variable (CD) were a precision of 0.88, a recall of 0.98, and an F1 score of 0.93. Regarding the secondary variables, we obtained a precision of 0.91, a recall of 0.71, and an F1 score of 0.80 for CD flare, while for the variable vedolizumab (treatment), a precision, recall, and F1 score of 0.86, 0.94, and 0.90 were obtained, respectively. CONCLUSIONS: This evaluation demonstrates the ability of the EHRead technology to identify patients with CD and their related variables from the free text of EHRs. To the best of our knowledge, this study is the first to use a cNLP system for the identification of CD in EHRs written in Spanish.

Clinical characteristics and prognostic factors for Crohn's disease relapses using natural language processing and machine learning: a pilot study.

BACKGROUND: The impact of relapses on disease burden in Crohn's disease (CD) warrants searching for predictive factors to anticipate relapses. This requires analysis of large datasets, including elusive free-text annotations from electronic health records. This study aims to describe clinical characteristics and treatment with biologics of CD patients and generate a data-driven predictive model for relapse using natural language processing (NLP) and machine learning (ML). METHODS: We performed a multicenter, retrospective study using a previously validated corpus of CD patient data from eight hospitals of the Spanish National Healthcare Network from 1 January 2014 to 31 December 2018 using NLP. Predictive models were created with ML algorithms, namely, logistic regression, decision trees, and random forests. RESULTS: CD phenotype, analyzed in 5938 CD patients, was predominantly inflammatory, and tobacco smoking appeared as a risk factor, confirming previous clinical studies. We also documented treatments, treatment switches, and time to discontinuation in biologics-treated CD patients. We found correlations between CD and patient family history of gastrointestinal neoplasms. Our predictive model ranked 25 000 variables for their potential as risk factors for CD relapse. Of highest relative importance were past relapses and patients' age, as well as leukocyte, hemoglobin, and fibrinogen levels. CONCLUSION: Through NLP, we identified variables such as smoking as a risk factor and described treatment patterns with biologics in CD patients. CD relapse prediction highlighted the importance of patients' age and some biochemistry values, though it proved highly challenging and merits the assessment of risk factors for relapse in a clinical setting.

Usefulness of oral beclometasone dipropionate in the treatment of active ulcerative colitis in clinical practice: the RECLICU Study.

BACKGROUND: Beclometasone dipropionate (BDP) is a relatively new topically acting oral steroid to treat mild to moderately active ulcerative colitis (UC). We estimate that 20,000 patients have received oral BDP in Spain in the last two years. Our aim was to evaluate the efficacy and safety of oral BDP in clinical practice. METHODS: Retrospective and multicenter study that included 434 patients with active UC treated with BDP. The partial Mayo Clinic score (pMS, 0-9) was used to measure disease activity. Remission was defined as post-treatment pMS of 0 or 1; response as a decrease in pMS of 3 points or 2 points and >30%, and failure as lack of remission or response. RESULTS: BDP dose was 5 mg/day in 88% of patients and mean treatment duration was 6.2 weeks. BDP achieved remission in 44.4%, response in 22.3% and failed in 33.2% of patients. Mean pMS decreased from 4.9 ± 1.3 to 2.4 ± 2.3 (p<0.0001). Remission rate was higher in mild and moderate than in severe UC (p<0.043) and tended to be higher in left-sided and extensive UC than in proctitis (p<0.06). Failure was less frequent in patients treated for >4 weeks (p<0.02). Mild adverse events were reported in 7.6% of patients. CONCLUSION: BDP induces response or remission in two thirds of active UC patients, with a good safety profile. Patients with mild to moderate, left-sided or extensive UC, receiving BDP for more than 4 weeks are most likely to benefit from this treatment.