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Risk stratification of emergency department patients with Crohn's disease could reduce computed tomography use by nearly half.

ABSTRACT: Computed tomography (CT) is a useful tool for assessing disease activity and excluding complications in patients with Crohn's disease (CD). However, excessive radiation increases risk for malignancy. We aimed to identify automatable algorithms with high negative predictive values for significant CT findings in patients with CD who present at the emergency department.We conducted a retrospective review of a tertiary center's medical records to identify adults diagnosed with CD who presented from 2000 through 2011. Logistic regression was used to model complications (perforations, abscesses, or other serious findings) and inflammation.There were 1095 visits made by 613 individuals that included a CT scan within 24 hours of arrival. The average number of CT scans was 1.8 (range, 1-31). Complications of CD were observed in 16.8% of CT scans, inflammation in 54.5%, and new/worse findings in 67.2%. On the basis of 10-fold cross-validation, the area under the receiver operating characteristic curve value for the complications model was 0.80 (95% confidence interval, 0.74-0.86) and for the inflammation model was 0.71 (95% confidence interval, 0.68-0.74). Scanning only patients with model-predicted complications would reduce scans by 43.0%, with a miss rate of 0.8% (4 of 491).Patients presenting to the emergency department with CD are frequently assessed by CT. However, no significant findings are observed in 32.8%, and only 17% have complications from CD. We created models to identify patients not likely to have significant findings from CT with high negative predictive values; these could aid physicians in avoiding CT scans for many patients. Studies are needed to validate these models beyond a single center.

SUBMITTER: Govani SM

PROVIDER: S-EPMC4164599 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Risk stratification of emergency department patients with Crohn's disease could reduce computed tomography use by nearly half.

Govani Shail M SM Guentner Amanda S AS Waljee Akbar K AK Higgins Peter D R PD

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association 20140315 10

<h4>Background & aims</h4>Computed tomography (CT) is a useful tool for assessing disease activity and excluding complications in patients with Crohn's disease (CD). However, excessive radiation increases risk for malignancy. We aimed to identify automatable algorithms with high negative predictive values for significant CT findings in patients with CD who present at the emergency department.<h4>Methods</h4>We conducted a retrospective review of a tertiary center's medical records to identify ad ...[more]

PMID: 24642110

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Project description:BackgroundRapid magnetic resonance imaging (MRI) protocols may be effective in the emergency department (ED) to evaluate nontraumatic neurologic complaints. We evaluate neuroimaging (rapid MRI [rMRI]), head computerized tomography [HCT], and full MRI) use following widespread implementation of rMRI protocols in a pediatric emergency department (ED).MethodsWe conducted a retrospective study in a tertiary care pediatric ED of encounters with neuroimaging during two 9-month periods: one prior to (control period) and one after generalized availability of 4 rMRI protocols (rMRI period). The primary outcome was differences in neuroimaging rates between the two periods. Secondary outcomes included ED process measures, unsuccessful imaging, and undetected pathology, with full MRI within 14?days as the reference standard.ResultsThere were 1052 encounters with neuroimaging during the control and 1308 during the rMRI periods. Differences in neuroimaging between periods were 27.7% for rMRI (95% CI, 24.4, 31.0), -?21.5% for HCT (95% CI, -?25.5, -?17.5), and?-?6.2% for full MRI (95% CI, -?9.3, -?3.1%.) Time to imaging (182 [IQR 138-255] versus 86 [IQR 52-137] minutes) as well as ED length of stay (396 [IQR 304-484] versus 257 [IQR 196-334] minutes) was longer for rMRI versus HCT (p?<?0.01). Between the control and rMRI periods, there were differences in types of neuroimaging performed for patients with altered mental status, headache, seizure, shunt dysfunction, stroke, syncope, trauma, vomiting, infection, and other neurologic complaints (p?<?0.05). rMRI studies were unsuccessful in 3.6% of studies versus 0.0% of HCTs (p?<?0.01). The 22 unsuccessful rMRI studies were unsuccessful due to artifacts from dental hardware (n?=?2) and patient motion (n?=?20). None of the rMRI studies with full MRI follow-up imaging had undetected pathology; the false negative rate for the HCT exams was as high as 25%.ConclusionsAfter routine ED use of 4 rMRI protocols, there was a more than 20% decrease in HCT use without missed diagnoses. Time to neuroimaging and length of stay were longer for rMRI than HCT, with higher rates of unsuccessful imaging. Despite these limitations, rMRI may be an alternative to HCT for nontraumatic complaints in the ED.

Project description:BACKGROUND:Reliably abstracting outcomes from free-text electronic health records remains a challenge. While automated classification of free text has been a popular medical informatics topic, performance validation using real-world clinical data has been limited. The two main approaches are linguistic (natural language processing [NLP]) and statistical (machine learning). The authors have developed a hybrid system for abstracting computed tomography (CT) reports for specified outcomes. OBJECTIVES:The objective was to measure performance of a hybrid NLP and machine learning system for automated outcome classification of emergency department (ED) CT imaging reports. The hypothesis was that such a system is comparable to medical personnel doing the data abstraction. METHODS:A secondary analysis was performed on a prior diagnostic imaging study on 3,710 blunt facial trauma victims. Staff radiologists dictated CT reports as free text, which were then deidentified. A trained data abstractor manually coded the reference standard outcome of acute orbital fracture, with a random subset double-coded for reliability. The data set was randomly split evenly into training and testing sets. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for certainty and temporal status. Findings were filtered for low certainty and past/future modifiers and then combined with the manual reference standard to generate decision tree classifiers using data mining tools Waikato Environment for Knowledge Analysis (WEKA) 3.7.5 and Salford Predictive Miner 6.6. Performance of decision tree classifiers was evaluated on the testing set with or without NLP processing. RESULTS:The performance of machine learning alone was comparable to prior NLP studies (sensitivity = 0.92, specificity = 0.93, precision = 0.95, recall = 0.93, f-score = 0.94), and the combined use of NLP and machine learning showed further improvement (sensitivity = 0.93, specificity = 0.97, precision = 0.97, recall = 0.96, f-score = 0.97). This performance is similar to, or better than, that of medical personnel in previous studies. CONCLUSIONS:A hybrid NLP and machine learning automated classification system shows promise in coding free-text electronic clinical data.

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Risk stratification of emergency department patients with Crohn's disease could reduce computed tomography use by nearly half.

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Risk stratification of emergency department patients with Crohn's disease could reduce computed tomography use by nearly half.

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