Evidence of Incremental Diagnostic Quality Gain in The Assessment of Pulmonary Embolism With Computed Tomography Angiography versus Ventilation Perfusion Scan Using Wells Score and Bayesian Statistical Modeling

HEART Score and Stress Test Emergency Department Bayesian Decision Scheme: Results from the Acute Care Diagnostic Collaboration

BACKGROUND

Accurate identification of patients at risk of major adverse cardiac events (MACE) places a substantial burden on emergency physicians (EPs). Bayesian nomogram for risk stratification in low- to intermediate-risk cardiovascular patients has not been investigated previously.

OBJECTIVE

The objective of this study was to develop a comparative diagnostic model using Bayesian statistics for exercise treadmill test (ETT) and stress echocardiogram (ECHO) to calculate post-test diagnostic risk of MACE using HEART (history, electrocardiogram, age, risk factors, and troponin) risk score as predictor of pretest probability.

METHODS

Stratification was made by applying HEART scores for the prediction of MACE. Likelihood ratios (LR) were calculated using pooled sensitivity and specificity of ETT and ECHO from the American College of Cardiology Foundation/American Heart Association systematic review. Post-test probabilities were obtained after inserting HEART score and LR into Bayesian nomogram. Analysis of variance was used to assess statistical association.

RESULTS

Positive LR (LR+) for ETT was 4.56 and negative LR (LR-) was 0.27; for ECHO, LR+ 5.65 and LR- 0.15. Bayesian statistical modeling post-test probabilities for LR+ and low HEART risk yielded a post-test probability for ETT of 7.75% and 9.09% for ECHO; intermediate risk gave 47.62% and 52.63%, respectively. For LR-, low HEART risk post-test probability for ETT was 0.46% and for ECHO 0.26%; intermediate risk probabilities were 4.48% and 2.49%, respectively. LR- was statistically significant in ruling out MACE with ECHO (p < 0.001), but no significant differences were seen for LR+ (p = 0.64).

CONCLUSIONS

This Bayesian analysis demonstrated slight superiority of stress ECHO over ETT in low- and intermediate-risk patients in ruling out MACE.

Bayesian comparative assessment of diagnostic accuracy of low-dose CT scan and ultrasonography in the diagnosis of urolithiasis after the application of the STONE score

OBJECTIVE

The objective of our study was to assess the diagnostic quality of low-dose computed tomography (CT) when compared to ultrasound (US) in diagnosis of urolithiasis using STONE score as a predictor of pre-test probability and the Bayesian statistical model to calculate post-test probabilities (POST) for both diagnostic tests.

METHODS

STONE score was used to form risk groups to obtain pre-test probabilities. Likelihood ratios (LR) were calculated from external data for low-dose CT and US. POST were obtained using pre-test probabilities and likelihood ratios with Bayesian nomogram. Absolute (ADG) and relative (RDG) gains in diagnostic value were calculated.

RESULTS

Calculated +LR for US was 12 and -LR was 0.32; for CT, +LR was 19 and -LR 0.04. +LR and low STONE for US yielded POST 57% and RDG 470%; intermediate STONE POST 92% and RDG 84%; and high STONE POST 99% and RDG 10%. -LR and low STONE for US POST 3% and RDG -70%; intermediate POST 24% and RDG -52%; and high STONE POST 74% and RDG -17.7%. +LR and low STONE for CT POST 68% and RDG 580%; moderate STONE POST 95% and RDG 90%; and high STONE POST 99% and RDG 10%. -LR and low STONE for CT POST 0% and RDG -100%; intermediate POST 4% and RDG -92%; and high STONE POST 26% and RDG -71.1%. ANOVA calculations comparing CT vs US for +LR showed no statistical significance (P value = 0.9893; LR- P value = 0.5488).

CONCLUSION

Bayesian statistical analysis demonstrated slight superiority of CT scan over US on STONE score low- and moderate-risk stratified subtypes, whereas no significant advantage was seen when evaluating high-probability patients.

Bayesian comparative model of CT scan and ultrasonography in the assessment of acute appendicitis: results from the Acute Care Diagnostic Collaboration project

The objective of this study was to develop a comparative diagnostic model for computed tomography (CT) and ultrasound (US) in the assessment of acute appendicitis using Alvarado risk score as a predictor of pretest probability and Bayesian statistical model as a tool to calculate posttest probabilities for both diagnostic test. Stratification was made by applying the Alvarado score for the prediction of acute appendicitis. Likelihood ratios were calculated using sensitivity and specificity of both CT and US from a Meta-analysis. Posttest probabilities were obtained after inserting Alvarado score and likelihood ratios into Bayesian nomogram. Absolute and relative gains were calculated. ANOVA was used to assess statistical association. 4341 patients from 31 studies yielded a pooled sensitivity and specificity US of 83% (95% CI, 78%-87%) and 93% (95% CI, 90%-96%) and 94% (95% CI, 92%-95%) and 94% (95% CI, 94%-96%), respectively, for CT studies. Positive likelihood ratios (LR) for US were 12 and negative LR was 0.18; for CT +LR was 16 and -LR 0.06. Bayesian statistical modeling posttest probabilities for +LR and low Alvarado risk results yielded a posttest probability for US of 83.72% and 87.27% for CT, intermediate risk gave 95.88% and 96.88%, high risk 99.37% and 99.53 respectively. No statistical differences were found between Ultrasound and CT. This Bayesian analysis demonstrated slight superiority of CT scan over US low-risk patients, whereas no significant advantage was seen when evaluating intermediate and high risk patients. This study also favored elevated accuracy of the Alvarado score.