Evaluation of the Sgarbossa Algorithm to Detect Acute Myocardial Infarction in the Presence of a Left Bundle Branch Block

2.50
Hdl Handle:
http://hdl.handle.net/10755/162771
Type:
Presentation
Title:
Evaluation of the Sgarbossa Algorithm to Detect Acute Myocardial Infarction in the Presence of a Left Bundle Branch Block
Abstract:
Evaluation of the Sgarbossa Algorithm to Detect Acute Myocardial Infarction in the Presence of a Left Bundle Branch Block
Conference Sponsor:Emergency Nurses Association
Conference Year:2000
Author:Gabany, Jennifer, MSN, FNP, CCRN
P.I. Institution Name:The Heart Group at St. Francis Hospital
Contact Address:1000 Mercer Street, New Castle, PA, 16101, USA
Contact Telephone:(724) 654-3278
Purpose: This research attempted to validate results obtained by Sgarbossa, et al. (1996) in the development of an algorithm to detect acute myocardial infarction in the presence of a left bundle branch block.

Design: This retrospective, descriptional-correlational chart review consisted of evaluating electrocardiograms with a left bundle branch block pattern according to Sgarbossa's Algorithm, then correlating the electrocardiogram criteria with a diagnosis for acute myocardial infarction.

Setting/Sample: The sample population of 75 electrocardiograms was selected from the records in the Cardiac Services Department of one community hospital. Inclusion criteria consisted of patients who had presented to the Emergency Department during 1997 and 1998 with chest pain and a left bundle branch block pattern at the time of the electrocardiogram.

Methodology: Each electrocardiogram was interpreted by the researcher and assigned a percentage of risk for acute myocardial infarction using the electrocardiogram criteria and scoring system set forth by the Sgarbossa Algorithm. Diagnosis of AMI was then obtained from the Medical Record chart and categorized as either positive or negative for acute myocardial infarction. Diagnosis was based on the presence or absence of elevated serum CPK/CK-MB ratio and Troponin I. Using chi-square analysis, the algorithm scores and actual diagnoses were compared for significant relationships. The researcherÆs results were then compared to the results obtained in SgarbossaÆs study, which had found a specificity >90% for all three criteria analyzed in the algorithm.

Results: 3/75 patients positively diagnosed had positive algorithm scores, and 1/75 patients positively diagnosed had negative algorithm scores. 65/75 patients negatively diagnosed had negative algorithm scores, and 6/75 patients negatively diagnosed had positive algorithm scores. These results give a positive predictive value of 33%, a negative predictive value of 98%, a sensitivity of 75%, and a specificity of 92%.

Conclusions: The results demonstrated the algorithm's ability to correctly rule out acute myocardial infarction 98% of the time, however, correctly ruled in only 33% of the time. Further research on a larger scale is needed to establish reliability and validity. [Leadership Challenge - Research Poster Presentation]
Repository Posting Date:
27-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Emergency Nurses Association

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleEvaluation of the Sgarbossa Algorithm to Detect Acute Myocardial Infarction in the Presence of a Left Bundle Branch Blocken_GB
dc.identifier.urihttp://hdl.handle.net/10755/162771-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Evaluation of the Sgarbossa Algorithm to Detect Acute Myocardial Infarction in the Presence of a Left Bundle Branch Block</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Emergency Nurses Association</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2000</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Gabany, Jennifer, MSN, FNP, CCRN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">The Heart Group at St. Francis Hospital</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">1000 Mercer Street, New Castle, PA, 16101, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">(724) 654-3278</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">jgabany@hotmail.com</td></tr><tr><td colspan="2" class="item-abstract">Purpose: This research attempted to validate results obtained by Sgarbossa, et al. (1996) in the development of an algorithm to detect acute myocardial infarction in the presence of a left bundle branch block.<br/><br/>Design: This retrospective, descriptional-correlational chart review consisted of evaluating electrocardiograms with a left bundle branch block pattern according to Sgarbossa's Algorithm, then correlating the electrocardiogram criteria with a diagnosis for acute myocardial infarction.<br/><br/>Setting/Sample: The sample population of 75 electrocardiograms was selected from the records in the Cardiac Services Department of one community hospital. Inclusion criteria consisted of patients who had presented to the Emergency Department during 1997 and 1998 with chest pain and a left bundle branch block pattern at the time of the electrocardiogram.<br/><br/>Methodology: Each electrocardiogram was interpreted by the researcher and assigned a percentage of risk for acute myocardial infarction using the electrocardiogram criteria and scoring system set forth by the Sgarbossa Algorithm. Diagnosis of AMI was then obtained from the Medical Record chart and categorized as either positive or negative for acute myocardial infarction. Diagnosis was based on the presence or absence of elevated serum CPK/CK-MB ratio and Troponin I. Using chi-square analysis, the algorithm scores and actual diagnoses were compared for significant relationships. The researcher&AElig;s results were then compared to the results obtained in Sgarbossa&AElig;s study, which had found a specificity &gt;90% for all three criteria analyzed in the algorithm.<br/><br/>Results: 3/75 patients positively diagnosed had positive algorithm scores, and 1/75 patients positively diagnosed had negative algorithm scores. 65/75 patients negatively diagnosed had negative algorithm scores, and 6/75 patients negatively diagnosed had positive algorithm scores. These results give a positive predictive value of 33%, a negative predictive value of 98%, a sensitivity of 75%, and a specificity of 92%.<br/><br/>Conclusions: The results demonstrated the algorithm's ability to correctly rule out acute myocardial infarction 98% of the time, however, correctly ruled in only 33% of the time. Further research on a larger scale is needed to establish reliability and validity. [Leadership Challenge - Research Poster Presentation]</td></tr></table>en_GB
dc.date.available2011-10-27T10:33:52Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-27T10:33:52Z-
dc.description.sponsorshipEmergency Nurses Associationen_GB
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