Dataset Information

Pretest probability assessment derived from attribute matching.

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 <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%, 1st-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.

SUBMITTER: Kline JA

PROVIDER: S-EPMC1201143 | biostudies-literature | 2005 Aug

REPOSITORIES: biostudies-literature

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Publications

Pretest probability assessment derived from attribute matching.

Kline Jeffrey A JA Johnson Charles L CL Pollack Charles V CV Diercks Deborah B DB Hollander Judd E JE Newgard Craig D CD Garvey J Lee JL

BMC medical informatics and decision making 20050811

<h4>Background</h4>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).<h4>Methods</h4>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 pos ...[more]

PMID: 16095534

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Project description:Probability matching is a classic theory of decision making that was first developed in models of cognition. Posterior probability matching, a variant in which observers match their response probabilities to the posterior probability of each response being correct, is being used increasingly often in models of perception. However, little is known about whether posterior probability matching is consistent with the vast literature on vision and hearing that has developed within signal detection theory. Here we test posterior probability matching models using two tools from detection theory. First, we examine the models' performance in a two-pass experiment, where each block of trials is presented twice, and we measure the proportion of times that the model gives the same response twice to repeated stimuli. We show that at low performance levels, posterior probability matching models give highly inconsistent responses across repeated presentations of identical trials. We find that practised human observers are more consistent across repeated trials than these models predict, and we find some evidence that less practised observers more consistent as well. Second, we compare the performance of posterior probability matching models on a discrimination task to the performance of a theoretical ideal observer that achieves the best possible performance. We find that posterior probability matching is very inefficient at low-to-moderate performance levels, and that human observers can be more efficient than is ever possible according to posterior probability matching models. These findings support classic signal detection models, and rule out a broad class of posterior probability matching models for expert performance on perceptual tasks that range in complexity from contrast discrimination to symmetry detection. However, our findings leave open the possibility that inexperienced observers may show posterior probability matching behaviour, and our methods provide new tools for testing for such a strategy.

Dataset Information

Pretest probability assessment derived from attribute matching.

Background

Methods

Results

Conclusion

Publications

Pretest probability assessment derived from attribute matching.

Similar Datasets

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