Abstract
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 st-3rd quartile 1-10%] compared with the LRE, which produced 96 unique PTP estimates [median 24%, 1st-3rd 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.
| Original language | English (US) |
|---|---|
| Journal | BMC Medical Informatics and Decision Making |
| Volume | 5 |
| DOIs | |
| State | Published - Aug 11 2005 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Pretest probability assessment derived from attribute matching. / Kline, Jeffrey; Johnson, Charles L.; Pollack, Charles V.; Diercks, Deborah B.; Hollander, Judd E.; Newgard, Craig D.; Garvey, J. Lee.
In: BMC Medical Informatics and Decision Making, Vol. 5, 11.08.2005.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Pretest probability assessment derived from attribute matching
AU - Kline, Jeffrey
AU - Johnson, Charles L.
AU - Pollack, Charles V.
AU - Diercks, Deborah B.
AU - Hollander, Judd E.
AU - Newgard, Craig D.
AU - Garvey, J. Lee
PY - 2005/8/11
Y1 - 2005/8/11
N2 - 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 st-3rd quartile 1-10%] compared with the LRE, which produced 96 unique PTP estimates [median 24%, 1st-3rd 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.
AB - 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 st-3rd quartile 1-10%] compared with the LRE, which produced 96 unique PTP estimates [median 24%, 1st-3rd 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=24944553020&partnerID=8YFLogxK
U2 - 10.1186/1472-6947-5-26
DO - 10.1186/1472-6947-5-26
M3 - Article
C2 - 16095534
AN - SCOPUS:24944553020
VL - 5
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
SN - 1472-6947
ER -