Comput. Biol. Med. - Informatics can identify systemic sclerosis (SSc) patients at risk for scleroderma renal crisis.

Tópicos

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Resumo

CKGROUND: Electronic medical records (EMR) provide an ideal opportunity for the detection, diagnosis, and management of systemic sclerosis (SSc) patients within the Veterans Health Administration (VHA). The objective of this project was to use informatics to identify potential SSc patients in the VHA that were on prednisone, in order to inform an outreach project to prevent scleroderma renal crisis (SRC).METHODS: The electronic medical data for this study came from Veterans Informatics and Computing Infrastructure (VINCI). For natural language processing (NLP) analysis, a set of retrieval criteria was developed for documents expected to have a high correlation to SSc. The two annotators reviewed the ratings to assemble a single adjudicated set of ratings, from which a support vector machine (SVM) based document classifier was trained. Any patient having at least one document positively classified for SSc was considered positive for SSc and the use of prednisone=10mg in the clinical document was reviewed to determine whether it was an active medication on the prescription list.RESULTS: In the VHA, there were 4272 patients that have a diagnosis of SSc determined by the presence of an ICD-9 code. From these patients, 1118 patients (21%) had the use of prednisone=10mg. Of these patients, 26 had a concurrent diagnosis of hypertension, thus these patients should not be on prednisone. By the use of natural language processing (NLP) an additional 16,522 patients were identified as possible SSc, highlighting that cases of SSc in the VHA may exist that are unidentified by ICD-9. A 10-fold cross validation of the classifier resulted in a precision (positive predictive value) of 0.814, recall (sensitivity) of 0.973, and f-measure of 0.873.CONCLUSIONS: Our study demonstrated that current clinical practice in the VHA includes the potentially dangerous use of prednisone for veterans with SSc. This present study also suggests there may be many undetected cases of SSc and NLP can successfully identify these patients.

Resumo Limpo

ckground electron medic record emr provid ideal opportun detect diagnosi manag system sclerosi ssc patient within veteran health administr vha object project use informat identifi potenti ssc patient vha prednison order inform outreach project prevent scleroderma renal crisi srcmethod electron medic data studi came veteran informat comput infrastructur vinci natur languag process nlp analysi set retriev criteria develop document expect high correl ssc two annot review rate assembl singl adjud set rate support vector machin svm base document classifi train patient least one document posit classifi ssc consid posit ssc use prednisonemg clinic document review determin whether activ medic prescript listresult vha patient diagnosi ssc determin presenc icd code patient patient use prednisonemg patient concurr diagnosi hypertens thus patient prednison use natur languag process nlp addit patient identifi possibl ssc highlight case ssc vha may exist unidentifi icd fold cross valid classifi result precis posit predict valu recal sensit fmeasur conclus studi demonstr current clinic practic vha includ potenti danger use prednison veteran ssc present studi also suggest may mani undetect case ssc nlp can success identifi patient

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