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:BACKGROUND:The HEART Pathway (history, ECG, age, risk factors, and initial troponin) is an accelerated diagnostic protocol designed to identify low-risk emergency department patients with chest pain for early discharge without stress testing or angiography. The objective of this study was to determine whether implementation of the HEART Pathway is safe (30-day death and myocardial infarction rate <1% in low-risk patients) and effective (reduces 30-day hospitalizations) in emergency department patients with possible acute coronary syndrome. METHODS:A prospective pre-post study was conducted at 3 US sites among 8474 adult emergency department patients with possible acute coronary syndrome. Patients included were ?21 years old, investigated for possible acute coronary syndrome, and had no evidence of ST-segment-elevation myocardial infarction on ECG. Accrual occurred for 12 months before and after HEART Pathway implementation from November 2013 to January 2016. The HEART Pathway accelerated diagnostic protocol was integrated into the electronic health record at each site as an interactive clinical decision support tool. After accelerated diagnostic protocol integration, ED providers prospectively used the HEART Pathway to identify patients with possible acute coronary syndrome as low risk (appropriate for early discharge without stress testing or angiography) or non-low risk (appropriate for further in-hospital evaluation). The primary safety and effectiveness outcomes, death, and myocardial infarction (MI) and hospitalization rates at 30 days were determined from health records, insurance claims, and death index data. RESULTS:Preimplementation and postimplementation cohorts included 3713 and 4761 patients, respectively. The HEART Pathway identified 30.7% as low risk; 0.4% of these patients experienced death or MI within 30 days. Hospitalization at 30 days was reduced by 6% in the postimplementation versus preimplementation cohort (55.6% versus 61.6%; adjusted odds ratio, 0.79; 95% CI, 0.71-0.87). During the index visit, more MIs were detected in the postimplementation cohort (6.6% versus 5.7%; adjusted odds ratio, 1.36; 95% CI, 1.12-1.65). Rates of death or MI during follow-up were similar (1.1% versus 1.3%; adjusted odds ratio, 0.88; 95% CI, 0.58-1.33). CONCLUSIONS:HEART Pathway implementation was associated with decreased hospitalizations, increased identification of index visit MIs, and a very low death and MI rate among low-risk patients. These findings support use of the HEART Pathway to identify low-risk patients who can be safely discharged without stress testing or angiography. CLINICAL TRIAL REGISTRATION:URL: http://www.clinicaltrials.gov . Unique identifier: NCT02056964.

Project description:ImportanceNoninvasive testing and coronary angiography are used to evaluate patients who present to the emergency department (ED) with chest pain, but their effects on outcomes are uncertain.ObjectiveTo determine whether cardiovascular testing-noninvasive imaging or coronary angiography-is associated with changes in the rates of coronary revascularization or acute myocardial infarction (AMI) admission in patients who present to the ED with chest pain without initial findings of ischemia.Design, setting, and participantsThis retrospective cohort analysis used weekday (Monday-Thursday) vs weekend (Friday-Sunday) presentation as an instrument to adjust for unobserved case-mix variation (selection bias) between 2011 and 2012. National claims data (Truven MarketScan) was used. The data included a total of 926 633 privately insured patients ages 18 to 64 years who presented to the ED with chest pain without initial diagnosis consistent with acute ischemia.ExposuresNoninvasive testing or coronary angiography within 2 days or 30 days of presentation.Main outcomes and measuresThe primary end points were coronary revascularization (percutaneous coronary intervention or coronary artery bypass graft surgery) and AMI admission at 7, 30, 180, and 365 days. The secondary end points were coronary angiography and coronary artery bypass grafting in those who underwent angiography.ResultsThe patients were ages 18 to 64 years with an average age of 44.4 years. A total of 536 197 patients (57.9%) were women. Patients who received testing (224 973) had increased risk at baseline and had greater risk of AMI admission than those who did not receive testing (701 660) (0.35% vs 0.14% at 30 days). Weekday patients (571 988) had similar baseline comorbidities to weekend patients (354 645) but were more likely to receive testing. After risk factor adjustment, testing within 30 days was associated with a significant increase in coronary angiography (36.5 per 1000 patients tested; 95% CI, 21.0-52.0) and revascularization (22.8 per 1000 patients tested; 95% CI, 10.6-35.0) at 1 year but no significant change in AMI admissions (7.8 per 1000 patients tested; 95% CI, -1.4 to 17.0). Testing within 2 days was also associated with a significant increase in coronary revascularization but no difference in AMI admissions.Conclusions and relevanceCardiac testing in patients with chest pain was associated with increased downstream testing and treatment without a reduction in AMI admissions, suggesting that routine testing may not be warranted. Further research into whether specific high-risk subgroups benefit from testing is needed.

Project description:Emergency department (ED) crowding adversely affects multiple facets of high-quality care. The Commonwealth of Massachusetts mandates specific, hospital action plans to reduce ED boarding via a mechanism termed "Code Help." Because implementation appears inconsistent even when hospital conditions should have triggered its activation, we hypothesized that compliance with the Code Help policy would be associated with reduction in ED boarding time and total ED length of stay (LOS) for admitted patients, compared to patients seen when the Code Help policy was not followed.This was a retrospective analysis of data collected from electronic, patient-care, timestamp events and from a prospective Code Help registry for consecutive adult patients admitted from the ED at a single academic center during a 15-month period. For each patient, we determined whether the concurrent hospital status complied with the Code Help policy or violated it at the time of admission decision. We then compared ED boarding time and overall ED LOS for patients cared for during periods of Code Help policy compliance and during periods of Code Help policy violation, both with reference to patients cared for during normal operations.Of 89,587 adult patients who presented to the ED during the study period, 24,017 (26.8%) were admitted to an acute care or critical care bed. Boarding time ranged from zero to 67 hours 30 minutes (median 4 hours 31 minutes). Total ED LOS for admitted patients ranged from 11 minutes to 85 hours 25 minutes (median nine hours). Patients admitted during periods of Code Help policy violation experienced significantly longer boarding times (median 20 minutes longer) and total ED LOS (median 46 minutes longer), compared to patients admitted under normal operations. However, patients admitted during Code Help policy compliance did not experience a significant increase in either metric, compared to normal operations.In this single-center experience, implementation of the Massachusetts Code Help regulation was associated with reduced ED boarding time and ED LOS when the policy was consistently followed, but there were adverse effects on both metrics during violations of the policy.

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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