Prof. Annette D. Wagnern is a Senior Physician of the Department of Nephrology / MHH, Hannover Medical School, Germany. She is a specialist of internal medicine and nephrology. She served as head of the department for Clinical Continuing Education in the Center for Internal Medicine / Department Rheumatology of the Hannover Medical School.
Background: Diagnosis in rare disease cases is often delayed by years . The main factor for delayed diagnosis is believed to be the lack of knowledge about rare diseases. Probabilistic diagnostic decision support systems (DDSS) have the potential to accelerate rare disease diagnosis by highlighting differential diagnoses to physicians based on case input and incorporated medical knowledge . We examine a probabilistic DDSS prototype in terms of its potential to provide correct rare disease suggestion early in the course of rare disease cases.
Methods: Retrospectively, information from medical records of 93 patients with confirmed rare inflammatory systemic disease was transferred to the DDSS. Correctness of the DDSS disease suggestions was assessed for all documented visits over time. Time to correct top fit (TF) and top five fit (T5F) disease suggestion was assessed, as was the original time to clinical diagnosis (TD). TF/TD as well as T5F/TD were calculated to allow for comparison of TF respective T5F normalised to TD. Wilcoxon signed-rank test was conducted for TD-TF and TD-T5F.
Results: The DDSS suggested the correct disease at a time earlier than the time of clinical diagnosis among the top five fit disease suggestions in 53.8% of cases (50 of 93), and as the top fit disease suggestion in 37.6% of cases (35 of 93). Median advantage of correct disease suggestions compared to the time point of clinical diagnosis was 3 months or 50% for top five fit respective 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit among the top five fit disease suggestions in 33.3% (top five fit), respective 16.1% of cases (top fit). Wilcoxon signed-rank test shows a significant difference between the time to clinical diagnosis and the time to correct disease suggestion for both top five fit and top fit (z-score -6.68, respective -5.71, ?=0.05, p-value <0.001). The DDSS suggested the correct rare disease at the time of diagnosis in 89% of cases (83 of 93 cases; 95% CI: 82.92% to 95.58%).
Conclusions: The DDSS was capable of providing accurate rare disease suggestions in most of the rare disease cases. In many cases it provided correct rare disease suggestions early in the course of disease, sometimes in the very beginning of a patient journey. The interpretation of these results suggests that DDSS have the potential to highlight the possibility of a rare disease to physicians early in the course of a case. Limitations of this study derive from its retrospective and unblinded design, data input by a single user and the optimisation of the knowledge base during the course of the study. Whether the use of this DDSS leads to a reduced time to rare disease diagnosis in a clinical setting should be validated in prospective studies.