Abstract:The physician's estimate of the probability that a patient has a particular disease is a principal factor in the determination of whether to withhold treatment, obtain more data by testing, or treat without subjecting the patient to the risks of further diagnostic tests. Using the concepts of decision analysis, we have derived expressions for two threshold probabilities involved in this choice: a "testing" threshold and a "test-treatment" threshold. Values can be assigned to these thresholds from data on the r… Show more
“…Moreover, the rate of myocardial infarction in group C increased by up to 4%, which was 2-fold higher than that usually identified for patients with low-risk CP (b2%) [2,19]. These observations could lead to a possible implication in decision making [34]. In fact, patients in group C with negative ETT retained a high cardiovascular risk (17.4%) and should be considered for admission or further evaluation to rule out the presence of coronary disease before safe discharge.…”
Objective: To derive and validate a prediction rule in patients with acute chest pain (CP) without existing known coronary disease. Methods: Cohort study including 2233 patients with CP. Based on clinical judgment, 1435 were discharged as very low risk and the remaining 798 underwent exercise tolerance test (ETT). End point: 6-month composite of cardiovascular death, nonfatal myocardial infarction, and revascularization. The prediction rule was derived from a randomly selected test cohort (n = 1106) summing factors of variables selected by multivariate regression analysis: CP score higher than 6 (factor of 3), male gender, age older than 50 years, metabolic syndrome, and diabetes mellitus (factor of 1, for each). The prediction rule was validated in the remaining cohort (n = 1127). All patients with CP were categorized into 3 groups: group A (prediction rule 0-1), B (2-4), or C (5-6). Outcomes and prognostic yield of ETT were compared among each group. Results: In the test cohort, 55 patients (5%) reached the composite end point. Event rate increased as the prediction rule increased: 1% for group A, 6% for B, and 25% for C (P b .001). This pattern was confirmed in the validation cohort (P b .001). A normal ETT did not significantly improve the high (99%) negative predictive value in group A and did not succeed in excluding the composite end point (17%) in group C. Conclusions: In patients with acute CP without existing coronary disease, a prediction rule based on clinical characteristics provided a useful method for prognostication with possible implication in decision making.
“…Moreover, the rate of myocardial infarction in group C increased by up to 4%, which was 2-fold higher than that usually identified for patients with low-risk CP (b2%) [2,19]. These observations could lead to a possible implication in decision making [34]. In fact, patients in group C with negative ETT retained a high cardiovascular risk (17.4%) and should be considered for admission or further evaluation to rule out the presence of coronary disease before safe discharge.…”
Objective: To derive and validate a prediction rule in patients with acute chest pain (CP) without existing known coronary disease. Methods: Cohort study including 2233 patients with CP. Based on clinical judgment, 1435 were discharged as very low risk and the remaining 798 underwent exercise tolerance test (ETT). End point: 6-month composite of cardiovascular death, nonfatal myocardial infarction, and revascularization. The prediction rule was derived from a randomly selected test cohort (n = 1106) summing factors of variables selected by multivariate regression analysis: CP score higher than 6 (factor of 3), male gender, age older than 50 years, metabolic syndrome, and diabetes mellitus (factor of 1, for each). The prediction rule was validated in the remaining cohort (n = 1127). All patients with CP were categorized into 3 groups: group A (prediction rule 0-1), B (2-4), or C (5-6). Outcomes and prognostic yield of ETT were compared among each group. Results: In the test cohort, 55 patients (5%) reached the composite end point. Event rate increased as the prediction rule increased: 1% for group A, 6% for B, and 25% for C (P b .001). This pattern was confirmed in the validation cohort (P b .001). A normal ETT did not significantly improve the high (99%) negative predictive value in group A and did not succeed in excluding the composite end point (17%) in group C. Conclusions: In patients with acute CP without existing coronary disease, a prediction rule based on clinical characteristics provided a useful method for prognostication with possible implication in decision making.
“…In order to facilitate timely general acceptance, collaboration between researchers investigating early arthritis is of paramount importance. An internationally accepted diagnostic model would allow cost-effectiveness studies to find out the levels of probability of persistent (erosive) arthritis above which treatment with the various DMARDs should be started (40,41).…”
Objective. To develop a clinical model for the prediction, at the first visit, of 3 forms of arthritis outcome: self-limiting, persistent nonerosive, and persistent erosive arthritis.Methods. A standardized diagnostic evaluation was performed on 524 consecutive, newly referred patients with early arthritis. Potentially diagnostic determinants obtained at the first visit from the patient's history, physical examination, and blood and imaging testing were entered in a logistic regression analysis. Arthritis outcome was recorded at 2 years' followup. The discriminative ability of the model was expressed as a receiver operating characteristic (ROC) area under the curve (AUC).Results
“…The distance between the mark and the origin represented the value of the given outcome state. From those four values, I calculated treatment thresholds according to the method of Pauker and Kassirer [22,23], as described below.…”
Section: Methodsmentioning
confidence: 99%
“…With those equations, I derived the treatment threshold as defined by Pauker and Kassirer [22,23]. The treatment threshold represented a measure of clinical likelihood above which the surgical option had a greater value, and below it, nonoperative treatment was better.…”
Section: Methodsmentioning
confidence: 99%
“…However, Pauker and Kassirer [22,23] reported that variability in the values attributed to treatment outcomes affects the demanded degree of certainty that a treatment will attain its desired aims. They termed this requisite degree of certainty the ''treatment threshold''.…”
BackgroundWide variation in procedure utilization suggests that surgical indications might not be rigorously defined. An alternative explanation is that surgical outcomes are valued differently across groups. When a patient, using the information provided by the surgeon, places high value on successful results or is indifferent to the costs of ineffective treatment, the treatment threshold is lower and more surgery will be chosen.
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