Does Introduction of Thresholds in Decision Aids Benefit the Patient?

Purpose: To assess how different diagnostic decision aids perform in terms of sensitivity, specificity, and harm. Methods: Four diagnostic decision aids were compared, as applied to a simulated patient population: a findings-based algorithm following a linear or branched pathway, a serial threshold–based strategy, and a parallel threshold–based strategy. Headache in immune-compromised HIV patients in a developing country was used as an example. Diagnoses included cryptococcal meningitis, cerebral toxoplasmosis, tuberculous meningitis, bacterial meningitis, and malaria. Data were derived from literature and expert opinion. Diagnostic strategies’ validity was assessed in terms of sensitivity, specificity, and harm related to mortality and morbidity. Sensitivity analyses and Monte Carlo simulation were performed. Results: The parallel threshold–based approach led to a sensitivity of 92% and a specificity of 65%. Sensitivities of the serial threshold–based approach and the branched and linear algorithms were 47%, 47%, and 74%, respectively, and the specificities were 85%, 95%, and 96%. The parallel threshold–based approach resulted in the least harm, with the serial threshold–based approach, the branched algorithm, and the linear algorithm being associated with 1.56-, 1.44-, and 1.17-times higher harm, respectively. Findings were corroborated by sensitivity and Monte Carlo analyses. Conclusion: A threshold-based diagnostic approach is designed to find the optimal trade-off that minimizes expected harm, enhancing sensitivity and lowering specificity when appropriate, as in the given example of a symptom pointing to several life-threatening diseases. Findings-based algorithms, in contrast, solely consider clinical observations. A parallel workup, as opposed to a serial workup, additionally allows for all potential diseases to be reviewed, further reducing false negatives. The parallel threshold–based approach might, however, not be as good in other disease settings.