Cost Analysis Comparing Computed Tomographic Pulmonary Angiography (CTPA) and Ventilation-Perfusion (V/Q) Scanning for the Diagnosis of Pulmonary Embolism
Bibliographic record
Abstract
Abstract Abstract 567 Background: Accurate diagnosis of a pulmonary embolism remains problematic due to the nonspecific findings of the clinical presentation and the limitations of radiographic imaging. Computed tomography pulmonary angiography (CTPA) has surpassed ventilation-perfusion (V/Q) scanning as the primary imaging modality in the investigation of patients with suspected pulmonary emboli due to its superior diagnostic accuracy. Data from a large randomized controlled trial has indicated that while strategies using both CTPA and V/Q are equally safe at excluding the diagnosis, CTPA detects significantly more pulmonary emboli (1). The purpose of this study was to perform a cost analysis comparing CTPA and V/Q scanning for the investigation of patients with suspected pulmonary emboli based upon this large trial. Methods: A cost analysis was performed using a decision-analysis model. The costs and outcomes of CTPA and V/Q scanning in detecting or ruling out pulmonary embolism over a 90 day analysis horizon were incorporated into a decision tree where the probabilities for each outcome were taken from (1) and a systematic literature review. The decision tree incorporated the thromboembolic and major bleeding complications associated with the diagnosis and treatment of pulmonary embolism in 100,000 patients. Outcomes in the model were measured in terms of quality-adjusted life years (QALYs). The economic model took a direct-payer perspective, considering direct costs to the health care system and patients. All costs were based on 2009 Canadian dollars. The primary economic evaluation was conducted within a deterministic cost-effectiveness analysis framework in terms of incremental cost per QALY. If the clinical benefits were found to be statistically insignificant the primary evaluation would be collapsed to a cost-minimization analysis and a secondary analysis would be performed to using probabilistic methods to estimate the expected incremental costs and outcomes based upon probability distributions around mean point estimates. Sensitivity analyses around key parameters were also performed. Results: The primary deterministic analysis collapsed to a cost-minimization analysis on the grounds that no statistically differences in the proportion of false negative results or mortality were observed in the randomized trial comparing CTPA with VQ scanning for the diagnosis of pulmonary embolism (1). The cost minimization analysis demonstrated a strategy using V/Q scanning as the primary imaging modality was less costly. CTPA was associated with an incremental cost of $11.3 million per 100,000 patients compared to V/Q scanning. CTPA was associated with an additional 3760 additional diagnoses of pulmonary embolism and 111 major bleeding episodes compared to V/Q scanning. The secondary probabilistic cost-effectiveness analysis revealed that CTPA was associated with an incremental cost of $4.8 million per 100,000-person cohort and 3134 QALYs gained relative to V/Q scanning for a cost-effectiveness of $1543 per QALY gained. Furthermore, the cost-effectiveness acceptability curve demonstrated CTPA had an 89% likelihood of being cost-effective relative to the threshold of $50,000 per QALY gained. Sensitivity analyses demonstrated cost-effectiveness ratio was most impacted by varying prevalence of pulmonary embolism and the relative and absolute differences in the proportions of fatal pulmonary embolism from falsely negative scans between the two tests. Conclusions: The results of the cost-minimization analysis imply that V/Q scanning is a less costly alternative to CTPA resulting from less pulmonary emboli diagnosed while maintaining similar 3-month rate of thromboembolic complications relative to CTPA. Secondary analysis, however, concluded that diagnostic algorithms incorporating CTPA may be cost-effective under defined circumstances. (1) Anderson et al. JAMA 2007; 298:2743-2753 Disclosures: No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".