ABSTRACT
Emergency department (ED) nurses need to identify patients with potential acute coronary syndrome (ACS) rapidly because treatment delay could impact patient outcomes. Aims of this secondary analysis were to identify key patient factors that could be available at initial ED nurse triage that predict ACS. Consecutive patients with chest pain who called 9-1-1, received a 12-lead electrocardiogram in the prehospital setting, and were transported via emergency medical service were included in the study. A total of 750 patients were recruited. The sample had an average age of 59 years old, was 57% male, and 40% Black. One hundred and fifteen patients were diagnosed with ACS. Older age, non-Caucasian race, and faster respiratory rate were independent predictors of ACS. There was an interaction between heart rate by Type II diabetes receiving insulin in the context of ACS. Type II diabetics requiring insulin for better glycemic control manifested a faster heart rate. By identifying patient factors at ED nurse triage that could be predictive of ACS, accuracy rates of triage may improve, thus impacting patient outcomes.
Subject(s)
Acute Coronary Syndrome/diagnosis , Acute Coronary Syndrome/nursing , Chest Pain/diagnosis , Chest Pain/nursing , Diagnostic Techniques and Procedures/standards , Early Diagnosis , Emergency Nursing/standards , Triage/standards , Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Practice Guidelines as TopicABSTRACT
OBJECTIVE: Evaluation of diagnostic accuracy of an oscillometry-based device (VascAssist) combining fully automated ankle-brachial index (ABI) and pulse-wave velocity (PWV) assessment for detection of peripheral arterial disease (PAD). SUBJECTS AND METHODS: 110 consecutive subjects including symptomatic PAD patients (n = 41) and healthy PAD-free participants (n = 69) were recruited. All subjects underwent standard manual Doppler-based ABI (sABI) and oscillometry-based automated ABI (aABI) measurements (VascAssist). Oscillometry by the VascAssist included central and peripheral PWV assessment. Additionally, arterial stiffness (AS) was evaluated by flow-mediated vasodilation (FMD) of the brachial artery in all patients. All symptomatic PAD patients underwent catheter angiography for endovascular intervention and post-interventional acquisition of sABI, aABI, PWV and FMD. RESULTS: Sensitivity, specificity, PPV and NPV of aABI for detecting PAD was 73%, 100%, 100%, and 86% as compared to 80%, 96%, 92%, and 89% for sABI. Pearson-correlation for diabetics was r = 0.81; (P < .001) and for non-diabetics r = 0.77; (P < .001). Bland-Altman-analysis revealed a difference (95% CI) for diabetics of 0.09 (-0.22-0.4] and non-diabetics 0.022 [-0.25-0.295]. Weak correlation exists for FMD/AS analysis (pre-interventional R = 0.386, P = .043; post-interventional R = -0.06; P = .76) and significant increase of pre-/post-interventional PWV analysis (P < .001). CONCLUSION: Combined automatic ABI and PWV acquisition with the VascAssist device showed excellent diagnostic accuracy for detection of PAD. Compared to FMD, AS analysis may serve as an investigator-independent (screening) tool for determination of functional vascular damage in atherosclerosis.
