A computer protocol to evaluate subjects with chest pain in the emergency department: a multicenter study

Diagnostic performance of electronic syndromic surveillance systems in acute care: a systematic review

CONTEXT

Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making.

OBJECTIVE

To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems.

DATA SOURCES

All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012.

STUDY SELECTION

Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values.

DATA EXTRACTION

Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created.

RESULTS

The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% -100% and specificities ranging from 5%-100%.

CONCLUSIONS

There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.

Diagnostic accuracy of prehospital clinical prediction models to identify short-term outcomes in patients with acute coronary syndromes: a systematic review

BACKGROUND

Although cardiac risk prediction is widely used in various clinical settings, its potential role in enhancing prehospital triage is yet to be understood.

OBJECTIVE

To systematically review the diagnostic accuracy of short-term clinical prediction models for potential use in a prehospital population with suspected acute coronary syndrome.

METHODS

Eleven electronic medical databases were searched from 1990 to the end of August 2010 for all English-language observational and interventional studies. An online search strategy tool was used to identify grey-literature studies. Eligibility criteria were: 1) an unselected population of adult acute coronary syndrome patients; 2) recruited within the Emergency Department or Emergency Medical Services; 3) reported multivariate analysis encompassing patient history or physical examination; 4) reported short-term outcome measures; 5) were not solely computer protocols; and 6) were not reliant on tests unavailable out of the hospital. Data extraction was conducted by a single reviewer and verified by a second reviewer. Study quality was assessed independently by two reviewers using a validated quality assessment tool.

RESULTS

A total of seven clinical prediction models were identified. Only two models reported were derived from a prehospital study population. Six clinical prediction models described good discriminate abilities (c-statistic) of 0.72 to 0.87. Among the range of independent predictors identified, electrocardiogram abnormalities, age, heart rate, and systolic blood pressure provided the strongest prognostic information.

CONCLUSION

The models identified provided reasonable diagnostic accuracy for determining short-term outcomes. Methodological weaknesses and variability in the populations investigated limit their use in clinical practice.

Diagnostic accuracy of clinical prediction rules to exclude acute coronary syndrome in the emergency department setting: a systematic review

OBJECTIVE

We sought to determine the diagnostic accuracy of clinical prediction rules to exclude acute coronary syndrome (ACS) in the emergency department (ED) setting.

METHODS

We searched MEDLINE, EMBASE, Web of Science and the Cochrane Database of Systematic Reviews. We contacted content experts to identify additional articles for review. Reference lists of included studies were hand searched. We selected articles for review based on the following criteria: 1) enrolled consecutive ED patients; 2) incorporated variables from the history or physical examination, electrocardiogram and cardiac biomarkers; 3) did not incorporate cardiac stress testing or coronary angiography into prediction rule; 4) based on original research; 5) prospectively derived or validated; 6) did not require use of a computer; and 7) reported sufficient data to construct a 2 x 2 contingency table. We assessed study quality and extracted data independently and in duplicate using a standardized data extraction form.

RESULTS

Eight studies met inclusion criteria, encompassing 7937 patients. None of the studies verified the prediction rule with a reference standard on all or a random sample of patients. Six studies did not report blinding prediction rule assessors to reference standard results, and vice versa. Three prediction rules were prospectively validated. Sensitivities and specificities ranged from 94% to 100% and 13% to 57%, and positive and negative likelihood ratios from 1.1 to 2.2 and 0.01 to 0.17, respectively.

CONCLUSION

Current prediction rules for ACS have substantial methodological limitations and have not been successfully implemented in the clinical setting. Future methodologically sound studies are needed to guide clinical practice.

A study to derive a clinical decision rule for triage of emergency department patients with chest pain: design and methodology

BACKGROUND

Chest pain is the second most common chief complaint in North American emergency departments. Data from the U.S. suggest that 2.1% of patients with acute myocardial infarction and 2.3% of patients with unstable angina are misdiagnosed, with slightly higher rates reported in a recent Canadian study (4.6% and 6.4%, respectively). Information obtained from the history, 12-lead ECG, and a single set of cardiac enzymes is unable to identify patients who are safe for early discharge with sufficient sensitivity. The 2007 ACC/AHA guidelines for UA/NSTEMI do not identify patients at low risk for adverse cardiac events who can be safely discharged without provocative testing. As a result large numbers of low risk patients are triaged to chest pain observation units and undergo provocative testing, at significant cost to the healthcare system. Clinical decision rules use clinical findings (history, physical exam, test results) to suggest a diagnostic or therapeutic course of action. Currently no methodologically robust clinical decision rule identifies patients safe for early discharge.

METHODS/DESIGN

The goal of this study is to derive a clinical decision rule which will allow emergency physicians to accurately identify patients with chest pain who are safe for early discharge. The study will utilize a prospective cohort design. Standardized clinical variables will be collected on all patients at least 25 years of age complaining of chest pain prior to provocative testing. Variables strongly associated with the composite outcome acute myocardial infarction, revascularization, or death will be further analyzed with multivariable analysis to derive the clinical rule. Specific aims are to: i) apply standardized clinical assessments to patients with chest pain, incorporating results of early cardiac testing; ii) determine the inter-observer reliability of the clinical information; iii) determine the statistical association between the clinical findings and the composite outcome; and iv) use multivariable analysis to derive a highly sensitive clinical decision rule to guide triage decisions.

DISCUSSION

The study will derive a highly sensitive clinical decision rule to identify low risk patients safe for early discharge. This will improve patient care, lower healthcare costs, and enhance flow in our busy and overcrowded emergency departments.