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Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study
BACKGROUND: Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential dia...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427854/ https://www.ncbi.nlm.nih.gov/pubmed/30898118 http://dx.doi.org/10.1186/s13023-019-1040-6 |
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author | Ronicke, Simon Hirsch, Martin C. Türk, Ewelina Larionov, Katharina Tientcheu, Daphne Wagner, Annette D. |
author_facet | Ronicke, Simon Hirsch, Martin C. Türk, Ewelina Larionov, Katharina Tientcheu, Daphne Wagner, Annette D. |
author_sort | Ronicke, Simon |
collection | PubMed |
description | BACKGROUND: Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases. RESULTS: Ada DX suggested the correct disease 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). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. 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, α=0.05, p-value <0.001). CONCLUSION: Ada DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases 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 optimization of the knowledge base during the course of the study. Results pertaining to the system’s accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13023-019-1040-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6427854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64278542019-04-01 Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study Ronicke, Simon Hirsch, Martin C. Türk, Ewelina Larionov, Katharina Tientcheu, Daphne Wagner, Annette D. Orphanet J Rare Dis Research BACKGROUND: Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases. RESULTS: Ada DX suggested the correct disease 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). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. 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, α=0.05, p-value <0.001). CONCLUSION: Ada DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases 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 optimization of the knowledge base during the course of the study. Results pertaining to the system’s accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13023-019-1040-6) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-21 /pmc/articles/PMC6427854/ /pubmed/30898118 http://dx.doi.org/10.1186/s13023-019-1040-6 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Ronicke, Simon Hirsch, Martin C. Türk, Ewelina Larionov, Katharina Tientcheu, Daphne Wagner, Annette D. Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study |
title | Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study |
title_full | Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study |
title_fullStr | Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study |
title_full_unstemmed | Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study |
title_short | Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study |
title_sort | can a decision support system accelerate rare disease diagnosis? evaluating the potential impact of ada dx in a retrospective study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427854/ https://www.ncbi.nlm.nih.gov/pubmed/30898118 http://dx.doi.org/10.1186/s13023-019-1040-6 |
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