Dataset Information

Effective combination of isolated symptom variables to help stratifying acute undifferentiated chest pain in the emergency department.

ABSTRACT:

Background

Symptom is still indispensable for the stratification of chest pain in the emergency department. However, it is a sophisticated aggregation of several aspects of characteristics and effective combination of those variables remains deficient. We aimed to develop and validate a chest pain symptom score (CPSS) to address this issue.

Hypothesis

The CPSS may help stratifying acute undifferentiated chest pain in ED.

Methods

Patients with non-ST segment elevation chest pain and negative cardiac troponin (cTn) over 3 hours after symptom onset were consecutively recruited as the derivation cohort. Logistic regression analyses identified statistical predictors from all symptom aspects for 30-day acute myocardial infarction (AMI) or death. The performance of CPSS was compared with the symptom classification methods of the history variable in the history, electrocardiograph, age, risk factors, troponin (HEART) score. This new model was validated in a separated cohort of patients with negative cTn within 3?hours.

Results

Seven predictors in four aspects of chest pain symptom were identified. The CPSS was an independent predictor for 30-day AMI or death (P < 0.001). In the derivation (n?=?1434) and validation (n?=?976) cohorts, the expected and observed event rates were well calibrated (Hosmer-Lemeshow test P > 0.30), and the c-statistics of CPSS were 0.72 and 0.73, separately, significantly better than the previous history classifications in HEART score (P < 0.001). Replacing the history variable with the CPSS improved the discrimination and risk classification of HEART score significantly (P < 0.001).

Conclusions

The effective combination of isolated variables was meaningful to make the most stratification value of symptoms. This model should be considered as part of a comprehensive strategy for chest pain triage.

SUBMITTER: Zheng W

PROVIDER: S-EPMC6712332 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Effective combination of isolated symptom variables to help stratifying acute undifferentiated chest pain in the emergency department.

Zheng Wen W Ma Jingjing J Wu Shuo S Wang Guangmei G Zhang He H Zheng Jiaqi J Xu Feng F Wang Jiali J Chen Yuguo Y

Clinical cardiology 20190319 4

<h4>Background</h4>Symptom is still indispensable for the stratification of chest pain in the emergency department. However, it is a sophisticated aggregation of several aspects of characteristics and effective combination of those variables remains deficient. We aimed to develop and validate a chest pain symptom score (CPSS) to address this issue.<h4>Hypothesis</h4>The CPSS may help stratifying acute undifferentiated chest pain in ED.<h4>Methods</h4>Patients with non-ST segment elevation chest ...[more]

PMID: 30834545

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Project description:BackgroundChest pain is amongst the most common reason for presentation to the emergency department (ED). There are many causes of chest pain, and it is important for the emergency physician to quickly and accurately diagnose life threatening causes such as acute myocardial infarction (AMI). Multiple clinical decision tools have been developed to assist clinicians in risk stratifying patients with chest. There is growing recognition that machine learning (ML) will have a significant impact on the practice of medicine in the near future and may assist with diagnosis and risk stratification. This systematic review aims to evaluate how ML has been applied to adults presenting to the ED with undifferentiated chest pain and assess if ML models show improved performance when compared to physicians or current risk stratification techniques.Methods and findingsWe conducted a systematic review of journal articles that applied a ML technique to an adult patient presenting to an emergency department with undifferentiated chest pain. Multiple databases were searched from inception through to November 2020. In total, 3361 articles were screened, and 23 articles were included. We did not conduct a metanalysis due to a high level of heterogeneity between studies in both their methods, and reporting. The most common primary outcomes assessed were diagnosis of acute myocardial infarction (AMI) (12 studies), and prognosis of major adverse cardiovascular event (MACE) (6 studies). There were 14 retrospective studies and 5 prospective studies. Four studies reported the development of a machine learning model retrospectively then tested it prospectively. The most common machine learning methods used were artificial neural networks (14 studies), random forest (6 studies), support vector machine (5 studies), and gradient boosting (2 studies). Multiple studies achieved high accuracy in both the diagnosis of AMI in the ED setting, and in predicting mortality and composite outcomes over various timeframes. ML outperformed existing risk stratification scores in all cases, and physicians in three out of four cases. The majority of studies were single centre, retrospective, and without prospective or external validation. There were only 3 studies that were considered low risk of bias and had low applicability concerns. Two studies reported integrating the ML model into clinical practice.ConclusionsResearch on applications of ML for undifferentiated chest pain in the ED has been ongoing for decades. ML has been reported to outperform emergency physicians and current risk stratification tools to diagnose AMI and predict MACE but has rarely been integrated into practice. Many studies assessing the use of ML in undifferentiated chest pain in the ED have a high risk of bias. It is important that future studies make use of recently developed standardised ML reporting guidelines, register their protocols, and share their datasets and code. Future work is required to assess the impact of ML model implementation on clinical decision making, patient orientated outcomes, and patient and physician acceptability.Trial registrationInternational Prospective Register of Systematic Reviews registration number: CRD42020184977.

Project description:BackgroundThe Global Registry of Acute Coronary Events (GRACE) score is recommended for stratifying chest pain. However, there are six formulas used to calculate the GRACE score for different outcomes of acute coronary syndrome (ACS), including death (Dth) or composite of death and myocardial infarction (MI), while in hospital (IH), within 6 months after discharge (OH6m) or from admission to 6 months later (IH6m). We aimed to perform the first comprehensive evaluation and comparison of six GRACE models to predict 30-day major adverse cardiac events (MACEs) in patients with acute chest pain in the emergency department (ED).MethodsPatients with acute chest pain were consecutively recruited from August 24, 2015 to September 30, 2017 from the EDs of two public hospitals in China. The 30-day MACEs included death, acute myocardial infarction (AMI), emergency revascularization, cardiac arrest and cardiogenic shock. The correlation, calibration, discrimination, reclassification and diagnostic accuracy at certain cutoff values of six GRACE models were evaluated. Comparisons with the History, ECG, Age, Risk Factors, and Troponin (HEART) and Thrombolysis in Myocardial Infarction (TIMI) scores were conducted.ResultsA total of 2886 patients were analyzed, with 590 (20.4%) patients experiencing outcomes. The GRACE (IHDthMI), GRACE (IH6mDthMI), GRACE (IHDth), GRACE (IH6mDth), GRACE (OH6mDth) and GRACE (OH6mDthMI) showed positive linear correlations with the actual MACE rates (r ≥ 0.568, P < 0.001). All these models had good calibration (Hosmer-Lemeshow test, P ≥ 0.073) except GRACE (IHDthMI) (P < 0.001). The corresponding C-statistics were 0.83(0.81,0.84), 0.82(0.81,0.83), 0.75(0.73,0.76), 0.73(0.72,0.75), 0.72(0.70,0.73) and 0.70(0.68,0.71), respectively, first two of which were comparable to HEART (0.82, 0.80-0.83) and superior to TIMI (0.71, 0.69-0.73). With a sensitivity ≥95%, GRACE (IHDthMI) ≤81 and GRACE (IH6mDthMI) ≤79 identified 868(30%) and 821(28%) patients as low risk, respectively, which were significantly better than other GRACEs and HEART ≤3(22%). With a specificity ≥95%, GRACE (IHDthMI) > 186 and GRACE (IH6mDthMI) > 161 could recognize 12% and 11% patients as high risk, which were greater than other GRACEs, HEART ≥8(9%) and TIMI ≥5(8%).ConclusionsIn this Chinese setting, certain strengths of GRACE models beyond HEART and TIMI scores were still noteworthy for stratifying chest pain patients. The validation and reasonable application of appropriate GRACE models in the evaluation of undifferentiated chest pain should be recommended.

Project description:BackgroundEarly screening and accurately identifying Acute Appendicitis (AA) among patients with undifferentiated symptoms associated with appendicitis during their emergency visit will improve patient safety and health care quality. The aim of the study was to compare models that predict AA among patients with undifferentiated symptoms at emergency visits using both structured data and free-text data from a national survey.MethodsWe performed a secondary data analysis on the 2005-2017 United States National Hospital Ambulatory Medical Care Survey (NHAMCS) data to estimate the association between emergency department (ED) patients with the diagnosis of AA, and the demographic and clinical factors present at ED visits during a patient's ED stay. We used binary logistic regression (LR) and random forest (RF) models incorporating natural language processing (NLP) to predict AA diagnosis among patients with undifferentiated symptoms.ResultsAmong the 40,441 ED patients with assigned International Classification of Diseases (ICD) codes of AA and appendicitis-related symptoms between 2005 and 2017, 655 adults (2.3%) and 256 children (2.2%) had AA. For the LR model identifying AA diagnosis among adult ED patients, the c-statistic was 0.72 (95% CI: 0.69-0.75) for structured variables only, 0.72 (95% CI: 0.69-0.75) for unstructured variables only, and 0.78 (95% CI: 0.76-0.80) when including both structured and unstructured variables. For the LR model identifying AA diagnosis among pediatric ED patients, the c-statistic was 0.84 (95% CI: 0.79-0.89) for including structured variables only, 0.78 (95% CI: 0.72-0.84) for unstructured variables, and 0.87 (95% CI: 0.83-0.91) when including both structured and unstructured variables. The RF method showed similar c-statistic to the corresponding LR model.ConclusionsWe developed predictive models that can predict the AA diagnosis for adult and pediatric ED patients, and the predictive accuracy was improved with the inclusion of NLP elements and approaches.

Project description:ImportanceMultimodal generative artificial intelligence (AI) methodologies have the potential to optimize emergency department care by producing draft radiology reports from input images.ObjectiveTo evaluate the accuracy and quality of AI-generated chest radiograph interpretations in the emergency department setting.Design, setting, and participantsThis was a retrospective diagnostic study of 500 randomly sampled emergency department encounters at a tertiary care institution including chest radiographs interpreted by both a teleradiology service and on-site attending radiologist from January 2022 to January 2023. An AI interpretation was generated for each radiograph. The 3 radiograph interpretations were each rated in duplicate by 6 emergency department physicians using a 5-point Likert scale.Main outcomes and measuresThe primary outcome was any difference in Likert scores between radiologist, AI, and teleradiology reports, using a cumulative link mixed model. Secondary analyses compared the probability of each report type containing no clinically significant discrepancy with further stratification by finding presence, using a logistic mixed-effects model. Physician comments on discrepancies were recorded.ResultsA total of 500 ED studies were included from 500 unique patients with a mean (SD) age of 53.3 (21.6) years; 282 patients (56.4%) were female. There was a significant association of report type with ratings, with post hoc tests revealing significantly greater scores for AI (mean [SE] score, 3.22 [0.34]; P < .001) and radiologist (mean [SE] score, 3.34 [0.34]; P < .001) reports compared with teleradiology (mean [SE] score, 2.74 [0.34]) reports. AI and radiologist reports were not significantly different. On secondary analysis, there was no difference in the probability of no clinically significant discrepancy between the 3 report types. Further stratification of reports by presence of cardiomegaly, pulmonary edema, pleural effusion, infiltrate, pneumothorax, and support devices also yielded no difference in the probability of containing no clinically significant discrepancy between the report types.Conclusions and relevanceIn a representative sample of emergency department chest radiographs, results suggest that the generative AI model produced reports of similar clinical accuracy and textual quality to radiologist reports while providing higher textual quality than teleradiologist reports. Implementation of the model in the clinical workflow could enable timely alerts to life-threatening pathology while aiding imaging interpretation and documentation.

Project description:This first Guideline for Reasonable and Appropriate Care in the Emergency Department (GRACE-1) from the Society for Academic Emergency Medicine is on the topic: Recurrent, Low-risk Chest Pain in the Emergency Department. The multidisciplinary guideline panel used The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess the certainty of evidence and strength of recommendations regarding eight priority questions for adult patients with recurrent, low-risk chest pain and have derived the following evidence based recommendations: (1) for those >3 h chest pain duration we suggest a single, high-sensitivity troponin below a validated threshold to reasonably exclude acute coronary syndrome (ACS) within 30 days; (2) for those with a normal stress test within the previous 12 months, we do not recommend repeat routine stress testing as a means to decrease rates of major adverse cardiac events at 30 days; (3) insufficient evidence to recommend hospitalization (either standard inpatient admission or observation stay) versus discharge as a strategy to mitigate major adverse cardiac events within 30 days; (4) for those with non-obstructive (<50% stenosis) coronary artery disease (CAD) on prior angiography within 5 years, we suggest referral for expedited outpatient testing as warranted rather than admission for inpatient evaluation; (5) for those with no occlusive CAD (0% stenosis) on prior angiography within 5 years, we recommend referral for expedited outpatient testing as warranted rather than admission for inpatient evaluation; (6) for those with a prior coronary computed tomographic angiography within the past 2 years with no coronary stenosis, we suggest no further diagnostic testing other than a single, normal high-sensitivity troponin below a validated threshold to exclude ACS within that 2 year time frame; (7) we suggest the use of depression and anxiety screening tools as these might have an effect on healthcare use and return emergency department (ED) visits; and (8) we suggest referral for anxiety or depression management, as this might have an impact on healthcare use and return ED visits.

Dataset Information

Effective combination of isolated symptom variables to help stratifying acute undifferentiated chest pain in the emergency department.

Background

Hypothesis

Methods

Results

Conclusions

Publications

Effective combination of isolated symptom variables to help stratifying acute undifferentiated chest pain in the emergency department.

Similar Datasets

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