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Treosulfan-induced myalgia in pediatric hematopoietic stem cell transplantation identified by an electronic health record text mining tool.

ABSTRACT: Treosulfan is increasingly used as myeloablative agent in conditioning regimen prior to allogeneic hematopoietic stem cell transplantation (HSCT). In our pediatric HSCT program, myalgia was regularly observed after treosulfan-based conditioning, which is a relatively unknown side effect. Using a natural language processing and text-mining tool (CDC), we investigated whether treosulfan compared with busulfan was associated with an increased risk of myalgia. Furthermore, among treosulfan users, we studied the characteristics of given treatment of myalgia, and studied prognostic factors for developing myalgia during treosulfan use. Electronic Health Records (EHRs) until 28 days after HSCT were screened using the CDC for myalgia and 22 synonyms. Time to myalgia, location of pain, duration, severity and drug treatment were collected. Pain severity was classified according to the WHO pain relief ladder. Logistic regression was performed to assess prognostic factors. 114 patients received treosulfan and 92 busulfan. Myalgia was reported in 37 patients; 34 patients in the treosulfan group and 3 patients in the busulfan group (p = 0.01). In the treosulfan group, median time to myalgia was 7 days (0-12) and median duration of pain was 19 days (4-73). 44% of patients needed strong acting opiates and adjuvant medicines (e.g. ketamine). Hemoglobinopathy was a significant risk factor, as compared to other underlying diseases (OR 7.16 95% CI 2.09-30.03, p = 0.003). Myalgia appears to be a common adverse effect of treosulfan in pediatric HSCT, especially in hemoglobinopathy. Using the CDC, EHRs were easily screened to detect this previously unknown side effect, proving the effectiveness of the tool. Recognition of treosulfan-induced myalgia is important for adequate pain management strategies and thereby for improving the quality of hospital stay.

SUBMITTER: van der Stoep MYEC

PROVIDER: S-EPMC8476488 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents' data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents' care linking tool. The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents' care and lives.Meaningful words were extracted through Standard Query Language (SQL) with the LIKE function and wildcards to perform pattern matching, followed by text mining and a word cloud using R® packages. Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents' sample as well as on other health data using a health model measuring the residents' care level needs.By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents' health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents' data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives.This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents' health and quality of care by adding new, simple, useable data to the electronic health record (EHR). When used with a normalized physiotherapy problem list, text mining through information extraction (IE), named entity recognition (NER) and data mining (DM) can provide a real advantage to describe health care, adding new medical material and helping to integrate the EHR system into the health staff work environment.

Project description:ImportanceHealth care-associated infections (HAIs) are preventable, harmful, and costly; however, few resources are dedicated to infection surveillance of nonsurgical procedures, particularly cardiovascular implantable electronic device (CIED) procedures.ObjectiveTo develop a method that includes text mining of electronic clinical notes to reliably and efficiently measure HAIs for CIED procedures.Design, setting, and participantsIn this multicenter, national cohort study using electronic medical record data for patients undergoing CIED procedures in Veterans Health Administration (VA) facilities for fiscal years (FYs) 2016 and 2017, an algorithm to flag cases with a true CIED-related infection based on structured (eg, microbiology orders, vital signs) and free text diagnostic and therapeutic data (eg, procedure notes, discharge summaries, microbiology results) was developed and validated. Procedure data were divided into development and validation data sets. Criterion validity (ie, positive predictive validity [PPV], sensitivity, and specificity) was assessed via criterion-standard manual medical record review.ExposuresCIED procedure.Main outcomes and measuresThe concordance between medical record review and the study algorithm with respect to the presence or absence of a CIED infection. CIED infection in the algorithm included 90-day mortality, congestive heart failure and nonmetastatic tumor comorbidities, CIED or surgical site infection International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes, antibiotic treatment of Staphylococci, a microbiology test of a cardiac specimen, and text documentation of infection in specific clinical notes (eg, cardiology, infectious diseases, inpatient discharge summaries).ResultsThe algorithm sample consisted of 19 212 CIED procedures; 15 077 patients (78.5%) were White individuals, 1487 (15.5%) were African American; 18 766 (97.7%) were men. The mean (SD) age in our sample was 71.8 (10.6) years. The infection detection threshold of predicted probability was set to greater than 0.10 and the algorithm flagged 276 of 9606 (2.9%) cases in the development data set (9606 procedures); PPV in this group was 41.4% (95% CI, 31.6%-51.8%). In the validation set (9606 procedures), at predicted probability 0.10 or more the algorithm PPV was 43.5% (95% CI, 37.1%-50.2%), and overall sensitivity and specificity were 94.4% (95% CI, 88.2%-97.9%) and 48.8% (95% CI, 42.6%-55.1%), respectively.Conclusions and relevanceThe findings of this study suggest that the method of combining structured and text data in VA electronic medical records can be used to expand infection surveillance beyond traditional boundaries to include outpatient and procedural areas.

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Treosulfan-induced myalgia in pediatric hematopoietic stem cell transplantation identified by an electronic health record text mining tool.

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