Patients with suspected acute coronary syndrome in a university hospital emergency department: an observational study

BACKGROUND: It is widely considered that improved diagnostics in suspected acute coronary syndrome (ACS) are needed. To help clarify the current situation and the improvement potential, we analyzed characteristics, disposition and outcome among patients with suspected ACS at a university hospital emergency department (ED). METHODS: 157 consecutive patients with symptoms of ACS were included at the ED during 10 days. Risk of ACS was estimated in the ED for each patient based on history, physical examination and ECG by assigning them to one of four risk categories; I (obvious myocardial infarction, MI), II (strong suspicion of ACS), III (vague suspicion of ACS), and IV (no suspicion of ACS). RESULTS: 4, 17, 29 and 50% of the patients were allocated to risk categories I-IV respectively. 74 patients (47%) were hospitalized but only 19 (26%) had ACS as the discharge diagnose. In risk categories I-IV, ACS rates were 100, 37, 12 and 0%, respectively. Of those admitted without ACS, at least 37% could probably, given perfect ED diagnostics, have been immediately discharged. 83 patients were discharged from the ED, and among them there were no hospitalizations for ACS or cardiac mortality at 6 months. Only about three patients per 24 h were considered eligible for a potential ED chest pain unit. CONCLUSIONS: Almost 75% of the patients hospitalized with suspected ACS did not have it, and some 40% of these patients could probably, given perfect immediate diagnostics, have been managed as outpatients. The potential for diagnostic improvement in the ED seems large

Pretest probability assessment derived from attribute matching

BACKGROUND: Pretest probability (PTP) assessment plays a central role in diagnosis. This report compares a novel attribute-matching method to generate a PTP for acute coronary syndrome (ACS). We compare the new method with a validated logistic regression equation (LRE). METHODS: Eight clinical variables (attributes) were chosen by classification and regression tree analysis of a prospectively collected reference database of 14,796 emergency department (ED) patients evaluated for possible ACS. For attribute matching, a computer program identifies patients within the database who have the exact profile defined by clinician input of the eight attributes. The novel method was compared with the LRE for ability to produce PTP estimation <2% in a validation set of 8,120 patients evaluated for possible ACS and did not have ST segment elevation on ECG. 1,061 patients were excluded prior to validation analysis because of ST-segment elevation (713), missing data (77) or being lost to follow-up (271). RESULTS: In the validation set, attribute matching produced 267 unique PTP estimates [median PTP value 6%, 1(st)–3(rd )quartile 1–10%] compared with the LRE, which produced 96 unique PTP estimates [median 24%, 1(st)–3(rd )quartile 10–30%]. The areas under the receiver operating characteristic curves were 0.74 (95% CI 0.65 to 0.82) for the attribute matching curve and 0.68 (95% CI 0.62 to 0.77) for LRE. The attribute matching system categorized 1,670 (24%, 95% CI = 23–25%) patients as having a PTP < 2.0%; 28 developed ACS (1.7% 95% CI = 1.1–2.4%). The LRE categorized 244 (4%, 95% CI = 3–4%) with PTP < 2.0%; four developed ACS (1.6%, 95% CI = 0.4–4.1%). CONCLUSION: Attribute matching estimated a very low PTP for ACS in a significantly larger proportion of ED patients compared with a validated LRE

What is the evidence for the management of patients along the pathway from the emergency department to acute admission to reduce unplanned attendance and admission? An evidence synthesis

Background Globally, the rate of emergency hospital admissions is increasing. However, little evidence exists to inform the development of interventions to reduce unplanned Emergency Department (ED) attendances and hospital admissions. The objective of this evidence synthesis was to review the evidence for interventions, conducted during the patient’s journey through the ED or acute care setting, to manage people with an exacerbation of a medical condition to reduce unplanned emergency hospital attendance and admissions. Methods A rapid evidence synthesis, using a systematic literature search, was undertaken in the electronic data bases of MEDLINE, EMBASE, CINAHL, the Cochrane Library and Web of Science, for the years 2000–2014. Evidence included in this review was restricted to Randomised Controlled Trials (RCTs) and observational studies (with a control arm) reported in peer-reviewed journals. Studies evaluating interventions for patients with an acute exacerbation of a medical condition in the ED or acute care setting which reported at least one outcome related to ED attendance or unplanned admission were included. Results Thirty papers met our inclusion criteria: 19 intervention studies (14 RCTs) and 11 controlled observational studies. Sixteen studies were set in the ED and 14 were conducted in an acute setting. Two studies (one RCT), set in the ED were effective in reducing ED attendance and hospital admission. Both of these interventions were initiated in the ED and included a post-discharge community component. Paradoxically 3 ED initiated interventions showed an increase in ED re-attendance. Six studies (1 RCT) set in acute care settings were effective in reducing: hospital admission, ED re-attendance or re-admission (two in an observation ward, one in an ED assessment unit and three in which the intervention was conducted within 72 h of admission). Conclusions There is no clear evidence that specific interventions along the patient journey from ED arrival to 72 h after admission benefit ED re-attendance or readmission. Interventions targeted at high-risk patients, particularly the elderly, may reduce ED utilization and warrant future research. Some interventions showing effectiveness in reducing unplanned ED attendances and admissions are delivered by appropriately trained personnel in an environment that allows sufficient time to assess and manage patients