Dataset Information

Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.

ABSTRACT:

Objective

To evaluate a proposed natural language processing (NLP) and machine-learning based automated method to risk stratify abdominal pain patients by analyzing the content of the electronic health record (EHR).

Methods

We analyzed the EHRs of a random sample of 2100 pediatric emergency department (ED) patients with abdominal pain, including all with a final diagnosis of appendicitis. We developed an automated system to extract relevant elements from ED physician notes and lab values and to automatically assign a risk category for acute appendicitis (high, equivocal, or low), based on the Pediatric Appendicitis Score. We evaluated the performance of the system against a manually created gold standard (chart reviews by ED physicians) for recall, specificity, and precision.

Results

The system achieved an average F-measure of 0.867 (0.869 recall and 0.863 precision) for risk classification, which was comparable to physician experts. Recall/precision were 0.897/0.952 in the low-risk category, 0.855/0.886 in the high-risk category, and 0.854/0.766 in the equivocal-risk category. The information that the system required as input to achieve high F-measure was available within the first 4 h of the ED visit.

Conclusions

Automated appendicitis risk categorization based on EHR content, including information from clinical notes, shows comparable performance to physician chart reviewers as measured by their inter-annotator agreement and represents a promising new approach for computerized decision support to promote application of evidence-based medicine at the point of care.

SUBMITTER: Deleger L

PROVIDER: S-EPMC3861926 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Publications

Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.

Deleger Louise L Brodzinski Holly H Zhai Haijun H Li Qi Q Lingren Todd T Kirkendall Eric S ES Alessandrini Evaline E Solti Imre I

Journal of the American Medical Informatics Association : JAMIA 20131015 e2

<h4>Objective</h4>To evaluate a proposed natural language processing (NLP) and machine-learning based automated method to risk stratify abdominal pain patients by analyzing the content of the electronic health record (EHR).<h4>Methods</h4>We analyzed the EHRs of a random sample of 2100 pediatric emergency department (ED) patients with abdominal pain, including all with a final diagnosis of appendicitis. We developed an automated system to extract relevant elements from ED physician notes and lab ...[more]

PMID: 24130231

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Project description:ImportancePatients presenting to the emergency department with chest pain are routinely risk stratified for major adverse cardiac events using the HEART (History, Electrocardiogram, Age, Risk factors, and Troponin) score pathway, which incorporates clinical features, risk factors, electrocardiography findings, and initial serum troponin testing. A new HEART pathway incorporating high-sensitivity troponin level may improve risk stratification among patients with possible acute myocardial infarction (AMI).ObjectiveTo compare health outcomes and resource use among emergency department patients undergoing cardiac risk stratification with a HEART pathway using conventional vs high-sensitivity serum troponin.Design, setting, and participantsThis multicenter pre-post cohort study was conducted between January 1 and September 6, 2021, at 16 Kaiser Permanente Southern California hospitals during uptake of a high-sensitivity serum troponin assay and included 17 384 adult patients who presented to an emergency department with chest pain and were risk stratified with a HEART pathway based on conventional troponin or high-sensitivity troponin.ExposuresA HEART pathway incorporating either conventional or high-sensitivity serum troponin was used to stratify study groups for risk of major adverse cardiac events within 30 days.Main outcomes and measuresThe primary outcome was detection of AMI in the emergency department and within 30 days.ResultsOf the 17 384 patients (median age, 58 years [IQR, 45-69 years]; 9767 women [56.2%]), 12 440 (71.6%) were risk stratified with a HEART pathway based on conventional troponin, and 4944 (28.4%) were risk stratified with a HEART pathway based on high-sensitivity troponin. Detection of AMI within 30 days was higher for the high-sensitivity troponin group than the conventional troponin group (288 [5.8%] vs 545 [4.4%]; P < .001), while the 30-day all-cause mortality rate was unchanged (16 [0.3%] vs 50 [0.4%]; P = .50). In the emergency department, 228 of 4944 patients (4.6%) in the high-sensitivity troponin group received a diagnosis of AMI compared with 251 of 12 440 patients (2.0%) in the conventional troponin group (P < .001). Among those who did not receive a diagnosis of AMI in the emergency department, an additional 60 patients (1.2%) in the high-sensitivity troponin group and 294 (2.4%) in the conventional troponin group (P < .001) received a diagnosis within 30 days. Patients in the high-sensitivity troponin group had lower rates of health care use compared with the conventional troponin group, including admission (605 [12.2%] vs 1862 [15.0%]; P < .001), stress testing within 7 days (506 [10.2%] vs 1591 [12.8%]; P < .001), and coronary revascularization within 30 days (51 [1.0%] vs 244 [2.0%]; P < .001).Conclusions and relevanceThis multicenter pre-post cohort study suggests that a new HEART pathway incorporating high-sensitivity troponin may improve detection of AMI and decrease resource use among emergency department patients with chest pain.

Project description:BACKGROUND: While Canadian ED physicians discharge most syncope patients with no specific further follow-up, approximately 5% will suffer serious outcomes after ED discharge. The goal of this study is to prospectively identify risk factors and to derive a clinical decision tool to accurately predict those at risk for serious outcomes after ED discharge within 30 days. METHODS/DESIGN: We will conduct a prospective cohort study at 6 Canadian EDs to include adults with syncope and exclude patients with loss of consciousness>5 minutes, mental status changes from baseline, obvious witnessed seizure, or head trauma prior to syncope. Emergency physicians will collect standardized clinical variables including historical features, physical findings, and results of immediately available tests (blood, ECG, and ED cardiac monitoring) prior to ED discharge/hospital admission. A second emergency physician will evaluate approximately 10% of study patients for interobserver agreement calculation of predictor variables. The primary outcome will be a composite serious outcome occurring within 30 days of ED discharge and includes three distinct categories: serious adverse events (death, arrhythmia); identification of serious underlying disease (structural heart disease, aortic dissection, pulmonary embolism, severe pulmonary hypertension, subarachnoid hemorrhage, significant hemorrhage, myocardial infarction); or procedures to treat the cause of syncope. The secondary outcome will be any of the above serious outcomes either suspected or those occurring in the ED. A blinded Adjudication Committee will confirm all serious outcomes. Univariate analysis will be performed to compare the predictor variables in patients with and without primary outcome. Variables with p-values <0.2 and kappa values ? 0.60 will be selected for stepwise logistic regression to identify the risk factors and to develop the clinical decision tool. We will enroll 5,000 patients (with 125 positive for primary outcome) for robust identification of risk factors and clinical decision tool development. DISCUSSION: Once successfully developed, this tool will accurately risk-stratify adult syncope patients; however, validation and implementation will still be required. This program of research should lead to standardized care of syncope patients, and improve patient safety.

Dataset Information

Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.

Objective

Methods

Results

Conclusions

Publications

Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.

Similar Datasets

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